Hydrothermal sandstone reservoirs in the Songliao Basin's key oil-producing area are typified by medium–thin beds, medium–low porosity, and low permeability. These characteri-stics are traditionally labelled as "poor." Using these reservoirs as a case study, we recalibrate the classification criteria for low-quality hydrothermal systems and develop parsimonious, rapid-assessment protocols that minimize data requirements.

First, the fundamental reservoir characteristics (thickness, porosity, and permeability) were statistically analyzed. The Golden Section Method was applied to classify parameter levels and establish evaluation criteria. Second, given the limited temperature variation among the studied low-temperature geothermal reservoirs, single-well daily production rate emerged as a critical evaluation metric. Consequently, sand body thickness, porosity, permeability, and single-well daily production rate within the study area were selected as input variables. Multivariate linear regression analysis was employed to derive a geothermal reservoir evaluation formula. This formula was then used to calculate evaluation unit scores, with final grading established using the Golden Section Method.

Pairwise comparison of the score rankings from this method against those of two established evaluation methods demonstrated the validity of incorporating sand body thickness while excluding temperature. Furthermore, the relatively high standard deviation of the scores obtained by this method enhances its ability to delineate variations among the evaluation units. Consequently, this approach demonstrates greater feasibility, and the formulated evaluation criteria are particularly well-suited for hydrothermal reservoirs with limited storage capacity.

The evaluation criteria for low-storage hydrothermal systems developed in this study provide a valuable reference for geothermal-reservoir assessment in other basins. While basin-specific heterogeneity limits the direct transferability of the concise, few-parameter rapid-assessment model, the methodological framework used to derive the evaluation equation offers a robust, replicable template for analogous studies.

Earthquake disaster observation data is multi-source and heterogeneous, with scattered and poorly correlated knowledge, making it difficult to efficiently utilize the data for information integration and efficient querying, and thus providing support for risk assessment and rescue decision-making.

Knowledge graphs are an effective means of data association and fusion. Firstly, based on a top-down approach, the concepts in the earthquake disaster domain are sorted out, and the ontologies of earthquake disaster data, geological/geographical environment, earthquake disaster events, earthquake disaster emergency tasks, and earthquake disaster models are constructed to form the earthquake disaster ontology layer. Combined with a bottom-up approach, a high-quality data layer is constructed. Through convolutional neural networks, changes before and after disasters in remote sensing images are identified, achieving intelligent structured conversion from image information to text knowledge. The fine-tuned UIE (universal information extraction) pre-training model is used to extract named entities and relationship attribute knowledge from text data, with precision rates of 82.04% and 70.66% respectively. Data fusion and unified expression are achieved by calculating the semantic similarity of word vectors.

Taking the earthquake in Jishishan County, Linxia Prefecture, Gansu Province on December 18, 2023 as an example, a high-quality earthquake disaster knowledge graph is formed through ontology construction, data extraction, and unified expression, achieving the transformation from multi-source heterogeneous earthquake data to unified knowledge expression.

Based on the constructed earthquake disaster knowledge graph, queries and displays of disaster losses and emergency chain decision support are realized, and potential secondary disasters are inferred and queried in combination with relevant geological data. This method combines deep learning and pre-training techniques, integrates multi-modal data, and constructs an earthquake disaster knowledge graph, providing auxiliary support for rapid and accurate earthquake disaster information queries and the occurrence of secondary disasters.

The Yaha area on the southern slope of the Kuqa sub-basin is a significant hydrocarbon-rich region in the Tarim Basin; however, the origin of its crude oil remains unclear.

This study analyzed 13 crude oil samples from the Yaha area using organic geochemical techniques and employed an end-member oil mixing model to quantify the contributions of marine and terrestrial sources in the mixed oils.

Biomarker characteristics indicate that the crude oils in the Yaha area can be broadly classified into three types: Terrestrial oils, marine oils, and mixed oils. Terrestrial oils primarily derive from lacustrine source rocks, with pristane/phytane (Pr/Ph) values ranging from 1.20 to 2.35 (averaging 1.74). The tricyclic terpanes (TT) are dominated by C₂₀TT and C₂₁TT, with a high abundance of rearranged compounds such as C₃₀ early-eluting rearranged hopanes (compound X). The n-alkane carbon isotope values range from −28‰ to 32‰. Marine oils are a mixture of multi-stage marine source rock contributions, exhibiting Pr/Ph values between 0.74 and 0.92 (average of 0.81), with peak C₂₃TT and C₂₄TT compounds and a high abundance of long-chain tricyclic terpanes. Their n-alkane carbon isotope values are generally lighter than −32‰. Additionally, the presence of 25-norhopane compounds in the marine oils indicates significant early-stage biodegradation.. Mixed oils display biomarker and isotopic features characteristic of both marine and terrestrial oils. Except for the lower absolute maturity values observed in wells Yaha5, Yaha 401, and Yaha 3 (Well Yaha 3: 0.57% Rc; Well Yaha 401: 0.54% R_c), the overall maturity distribution across the study area is relatively uniform, ranging from 0.86 to 1.11% R_c, with an average of 0.97% R_c. Two-end-member mixing calculation suggests that marine oils account for over 60% of the mixed oils in the study area, with marine the contribution in Well Qiaogu 1 exceeding 90%. Source analysis indicates that the terrestrial oils primarily originate from the Triassic Huangshanjie Formation, while the marine oils are derived from the Cambrian Yuertusi Formation.

These findings demonstrate that the continental oil prospects on the southern slope of the Kuqa sub-basin remain promising for discovering marine oil and gas resources. This has significant implications for expanding future oil and gas exploration in the southern slope of the Kuqa Depression.

Three-dimensional reservoir modeling technology can automatically characterize the spatial heterogeneity structure of the reservoir. However, oilfield exploitation is difficult and the development cost is high, resulting in large well spacing and scarce drilling data. How to realize the three-dimensional modeling of oil and gas reservoirs based on the sparse and limited available data has always been a challenge in oil and gas development.

Therefore, this paper proposes a three-dimensional reservoir modeling method based on the self-attention mechanism and generative adversarial network. Geological profiles or boreholes with rich spatial structure information are introduced as conditional constraint data. The U-Net network structure combined with the self-attention mechanism is used to extract key structural features. A spatial context conditional loss function is designed to further constrain the reconstruction process, so that the conditional distribution of the reconstructed result is closer to that of the real data.

The results of multiple sets of three-dimensional reservoir structure and complex sandstone pore modeling experiments show that the three-dimensional reservoir modeling method proposed in this study can reproduce the geological spatial structure features and is in line with the conditional data distribution of the reference model. The reconstruction accuracy rate is 90%.

The method proposed in this study successfully captures those long-distance dependent features that are difficult to identify for traditional convolutional layers. It overcomes the potential problems caused by the sparse condition data and the simulation results can reflect geological randomness. It can be applied to the efficient and accurate reconstruction of various reservoir geological structures.

Deep tunnel construction is impacted by various geological processes, with geothermal activity significantly affecting construction safety and efficiency. However, case studies specifically revealing geothermal genesis remain scarce.

An integrated approach employing geophysical exploration, drilling, hydrogeochemical analysis, thermal infrared remote sensing, and UAV surveys was utilized to characterize the engineering geological and hydrogeological conditions of a planned expressway tunnel in Xinjiang (maximum burial depth: 1348 m), with specific focus on adjacent hot springs and high-temperature phenomena.

Results show that hot springs are exclusively distributed within the valley area northeast of the intersection between the F36 and F37 faults. Low-resistance fractured rock masses are presented within the study area, particularly concentrated near fault zones and hot spring locations. Additionally, hot spring water samples exhibit significantly higher mineralization compared to the Arxian River water, along with slightly elevated temperatures relative to the ambient strata.

Based on aformentioned findings, the geothermal water formation mechanism within the study area is proposed. Recharge occurs not from the proximal Arxian River, but from atmospheric precipitation and meltwater sourced from distal glaciers/snowfields approximately 3 km away. These recharged groundwaters converge near the F35-F36-F37 fault intersection, where it is heated at about 200 m depth by a deep-seated heat source before ascending along fractures in the fault zone towards the surface. Furthermore, employing the Analytic Hierarchy Process (AHP) for thermal hazard assessment of present planned tunnel alignments-considering faults, geothermal gradient, water inflow potential, and surrounding rock lithology as key factors-identified alignment B8 as optimal, evidenced by its lowest AHP score indicating minimal thermal hazard impact. This study elucidates the formation mechanisms and distribution patterns of geothermal activity in deep-buried tunnels, providing a scientific basis for thermal hazard mitigation in high-altitude permafrost regions.

The Kawu area is a typical high-temperature hydrothermal system in southern Tibet, with high potential for exploitation. However, the current understanding of its genesis mechanisms is still insufficient, which limits the further development and utilization of geothermal resources. This study aims to investigate the genesis and thermal sources of the Kawu geothermal region.

Hydrogeochemical characteristics and hydrogen-oxygen isotope data of geothermal and shallow cold waters were analyzed to assess the thermal reservoir temperatures of the geothermal system. The hydrogeochemical processes involved in the formation of geothermal waters, including water-rock interactions, cold water mixing, and water-steam separation, were explored. Additionally, the deep thermal sources of the geothermal system were identified, shedding light on the genesis mechanisms of the system.

The results indicated that the geothermal waters primarily exhibited a neutral to weakly alkaline HCO₃-Cl-Na chemical type. The Na-K geothermometer estimated the uniform deep thermal reservoir temperature at 280°C, while the K-Mg and quartz geothermometers estimated the shallow thermal reservoir temperature at approximately 175°C. The cold-water mixing ratio ranged from 50% to 76%. The deuterium and oxygen isotopic values of the reduced deep thermal reservoir ranged from −207.20‰ to −185.25‰ and −22.26‰ to −17.74‰, respectively. Based on these findings, a conceptual model for the Kawu geothermal system is proposed. It is suggested that Kawu area is a geothermal system with a magmatic heat source, where a uniform deep geothermal fluid rises along various regional fractures and undergoes different hydrogeochemical processes, resulting in the formation of four distinct shallow thermal reservoirs with varying distributions. This ultimately leads to the emergence of Kawu geothermal water.

The study provides important guidance for the rational development and efficient utilization of geothermal resources in the Kawu geothermal region and offers valuable insights for studying the genesis mechanisms of similar geothermal systems in southern Tibet.

Therefore, this study focuses on the Lower Carboniferous Dawuba Formation shale (interval 1457–2466 m) from the Well Qian Shui Di-1 in Southwest Guizhou.

Utilizing the natural gamma ray (GR) log as a proxy indicator, methods including time series analysis, INPEFA (integrated noise-enhanced population evolutionary frequency analysis), and wavelet analysis were applied to conduct cyclostratigraphic and sequence stratigraphic investigations. The aim is to achieve a "quantitative" division of the sequence stratigraphy for this shale unit from an astronomical forcing perspective.

The results demonstrate that the Dawuba Formation shale records clear astronomical periodic signals. COCO (correlation coefficient) analysis estimates an optimal average sedimentation rate of 16.4 cm/ka, corresponding to a sediment thickness of 66.42 m for the 405 ka long eccentricity cycle. Spectral analysis and astronomical tuning were performed on segmented GR data from the Well Qian Shui Di-1 (upper segment: 1457-1932 m; lower segment: 1932-2466 m). The optimal sedimentation rates for the upper and lower segments are 16.5 cm/ka and 11.2 cm/ka, respectively. The entire Dawuba Formation recorded 19 long eccentricity cycles, enabling the establishment of a "floating" astronomical time scale, which estimates a total duration of approximately 7.86 Ma for the formation. Furthermore, relative sea-level change curves were reconstructed using sedimentary noise modeling (DYNOT and ρ1 methods).Building upon the temporal framework established by cyclostratigraphy, and integrating relative sea-level extrema, INPEFA, and wavelet analysis results, six third-order sequence boundaries were identified, dividing the formation into five third-order sequences. The development of these third-order sequences is interpreted to be controlled by a stable approximately 1.2 Ma obliquity amplitude modulation cycle.

By applying cyclostratigraphy to the sequence division of the Dawuba Formation shale, this study explores the relationship between astronomical orbital parameters and relative sea-level change at different temporal scales, achieving the division of both third-order and fourth-order sequences. This methodology enables potential high-resolution (10000-year scale) chronostratigraphic correlation of marine shales. It provides a refined temporal framework for predicting intervals of high-quality source rock development within shale sequences, thereby offering crucial theoretical guidance for shale oil and gas exploration.

Coal structure directly affects the pore and fracture system of coal reservoirs. Therefore, the accurate identification of coal structure is crucial for guiding coal seam fracturing and coal bed methane extraction. Taking No. 8 coal of the Benxi Formation in the Yulin area of the Ordos Basin as an example, the complex coal structure necessitates the introduction of machine learning methods to address the nonlinear challenges in logging data interpretation.

In this study, Back Propagation(BP) neural network, Random Forest, and XGBoost algorithms are used to train on pre-processed core well data from the study area to invert coal structure across this region. By integrating the top and bottom plates of the coal seams and the coal thickness, we explore the development of coal structure under tectonic control.

The results indicate that: (1) Compared to the BP neural network, Random Forest and XGBoost algorithms provide more accurate inversion results, aligning more closely with real core observations. (2) The degree of coal structure fragmentation in No. 8 coal in the Yulin area increases progressively from northwest to southeast. (3) Tectonic zones, developed from the central to southeastern part of the study area, cause a decrease in coal thickness, with the coal structure transitioning from primary coal to mylonitic coal under tectonic influences.

The three machine learning algorithms employed in this study successfully inverted the complex coal structure, with Random Forest and XGBoost achieving higher inversion accuracy. Additionally, the relationship between coal structural variations and the development of tectonic zones was analyzed, providing valuable insights for identifying coal structures and evaluating tectonic zones in coalbed methane production.

Geothermal energy, owing to its stability and economic advantages, has become a crucial direction in the construction of new clean energy systems.

Electromagnetic methods are core geophysical techniques for resolving the electrical structure of geothermal systems and exhibit distinct complementary features. The magnetotelluric (MT) method uses natural alternating electromagnetic fields with the advantages of high detection depth and sensitivity to deep low resistance bodies, but it is susceptible to the influence of electromagnetic environmental noise. Wide-field electromagnetic method(WFEM) utilizes artificial field sources, exhibiting strong anti-interference capabilities and showing higher resolution for shallow anomalous bodies and small fractures. This study explores the complementarity and effectiveness of MT and WFEM in geothermal exploration through a two-dimensional joint inversion method.

Theoretical models and synthetic data tests have shown that two-dimensional joint inversion combines the respective advantages of MT and WFEM, and can more clearly characterize the cap rock and thermal reservoir structure of geothermal systems. This paper conducted a joint inversion study on the measured data of MT and WFEM in a geothermal area in Datong, Shanxi, China. The results showed that the resistivity distribution obtained from the joint inversion clearly characterized shallow fault channels and deep thermal reservoirs.

Based on the resistivity structure model obtained from joint inversion and other geological data, a conceptual model of the geothermal system in the region was constructued, providing a reference basis for the precise exploration of geothermal resources in the future.

Hydrogeochemical characteristics play a pivotal role in site selection and long-term safety assessment for high-level radioactive waste (HLW) disposal repositories.

This study employs integrated hydrogeochemical analysis and modeling to investigate the basic hydrogeochemical characteristics, horizontal zoning, and formation mechanisms in different hydrogeological zones of the Beishan preselected site for HLW disposal in Gansu Province, China.

The results show that the predominant hydrogeochemical types were Cl·SO₄-Na and SO₄·Cl-Na, Province with pH values generally ranging from 7.5 to 8.3. Fractured bedrock groundwater is typically undersaturated with respect to halite, gypsum, fluorite, glauconite, and feldspar, and oversaturated with respect to clay minerals. A distinct horizontal zonation is observed in the hydrogeochemical composition from the recharge area to the discharge area. The Mazongshan region serves as the primary recharge zone, characterized by low mineralization, where hydrogeochemical composition is mainly controlled by leaching processes. The sedimentary basins act as the main discharge areas with high mineralization, where evaporation processes dominate. The water-rock interaction processes along the flow path are primarily driven by the dissolution of halite and gypsum, while the effect of carbonate and silicate dissolution or precipitation remains relatively weak.

Overall, the hydrogeochemical formation of fractured bedrock groundwater in the Beishan area is predominantly governed by evaporation and water-rock interaction processes. This study provides fundamental hydrogeochemical data and insights to support the site selection of the HLW disposal repository.

Marine-continental transitional shale gas is a vital alternative resource for unconventional oil and gas exploration. Accurately characterizing the spatial distribution of total organic carbon content (TOC) is vital for predicting shale gas “sweet spots”.

Due to the absence of an effective relationship between TOC content and rock elastic parameters in marine-continental transitional shale, conventional TOC content prediction methods based on elastic parameters, commonly used in marine shale gas evaluations, are difficult to apply. To overcome this limitation, we propose a waveform indication simulation seismic prediction method driven by facies control, which is performed after TOC content evaluation through a logging parameter fitting technique. This approach enables the spatial prediction of TOC content within transitional shale strata.

The marine-continental transitional shale exhibits features such as thin monolayer thickness, high organic matter content, and irregular longitudinal distribution. High-frequency waveform simulation based on reservoir characteristic parameters effectively predicts TOC content in the transitional shale. The inversion results demonstrate relatively high vertical and horizontal resolutions and show a strong correlation with the TOC content curves obtained from logging parameter modeling. The coincidence rate reaches 84.4%, effectively reflecting the spatial variation of TOC content within the shale reservoir.

This method effectively addresses the challenge that the key evaluation parameter (TOC content) for identifying shale gas “sweet spots” in transitional facies cannot be quantitatively predicted solely from rock elastic parameters. It provides technical support for the exploration and development of transitional shale gas.

In order to reconstruct the climatic and oceanic environments during the Early Silurian, and reveal the influence of continental weathering and upwelling on the organic matter accumulation,

this study presents the geochemical compositions of the Lower Silurian succession in the BD 1 Well of the Upper Yangtze Platform.

The results show that despite persistently restricted watermass conditions, the Longmaxi period experienced a transition from weak chemical weathering intensity in the early period to intense chemical weathering in the late period. During the early stage of Longmaxi Formation deposition, coinciding with the termination of the Hirnantian glaciation, global climatic warming and associated deglaciation induced a significant increase in fluvial runoff. This amplified runoff intensified continental weathering and erosion rates, concurrently elevating primary productivity. The deposition of organic-rich black shale within the Longmaxi Formation exhibits distinct spatiotemporal heterogeneity. Temporally, the evolution of continental weathering was modulated by pronounced climatic fluctuations. Only episodes of intense weathering facilitated the enrichment of organic matter within the Lower Longmaxi Formation. Spatially, across the Yangtze Sea paleoshelf, organic matter and biogenic silica enrichment was primarily driven by strong upwelling dynamics specific to the outer shelf setting, contrasting with the inner shelf regions.

This study reveals the mechanisms of organic matter enrichment in the study area during the Early Silurian. It summarizes the diverse spatial development patterns of organic-rich shale in the Longmaxi Formation of the Yangtze Block of South China, providing new perspectives for understanding organic matter enrichment mechanisms within the Longmaxi Formation.

Land resource assessment and specialty industry cultivation are pivotal for China's rural revitalization and poverty alleviation initiaives. To optimize regional agricultural planning and specialty sector development, this study implemented a selenium-rich land resource survey in Tunliu District, Changzhi City, Shanxi Province.

A comprehensive land quality evaluation framework was established, integrating three dimensions: Selenium-rich industrial potential, ecological integrity, and arable land productivity. The fuzzy mathematical method, entropy weighting, and composite index modeling were synergistically applied for land classification, with spatial patterns visualized through ArcGIS.

The results show that: ①54.47% of the study area contains medium-to-high selenium concentrations, predominantly clustered in eastern regions. Arable land productivity demonstrates spatial heterogeneity, with lower values in western zones versus higher eastern values. ②299.33 km² (26.21% of total) of first-class quality land was identified in eastern plains. ③Wheat, chili peppers, and bell peppers exhibit 100% selenium enrichment compliance with crops in good condition. ④Based on land and crop evaluation results, and considering spatial planning, the land is classified into three types: A, B, and C. Zone A (Premium): Supports selenium-rich wheat/vegetable cultivation and agro-tourism (eastern plains). Zone B (Transitional): Suitable for diversified cropping with selenium supplementation. Zone C (Marginal): Recommended for agricultural product processing and trade hubs due to suboptimal selenium (<0.30 mg/kg) and nutrient levels.

This study provides theoretical support and scientific recommendations for planning the selenium-rich agricultural industry in Tunliu District and for its integrated regional development.

The Jingchong copper-cobalt deposit, located in the ore-concentration area of northeastern Hunan Province, contains medium-scale cobalt resources. However, the cobalt occurrence and the relationship between copper and cobalt mineralization remian unclear.

Based on detailed underground mine investigations, combined with microscopic observation, μ-XRF scanning, backscattered electron imaging (BSE), and electron probe quantitative analysis, the mineralization stages were divided, and the cobalt occurrence and the relationship between copper and cobalt mineralization were studied. We proposed the comprehensive utilization of cobalt .

The results indicate that the cobalt occurrence in the Jingchong copper-cobalt deposit can be classified into two major categories. The first category consists of invisible cobalt hosted in coarse-grained pyrite, where cobalt enters the interior of coarse-grained pyrite through a dissolution-reprecipitation process. The second category is the independent cobaltite mineral, which is further subdivided into four different occurrences: cobaltite occurring as a rim or interstitial texture along the margins or within the interior of coarse-grained pyrite; cobaltite located at the edge of the porous within coarse-grained pyrite; fine-grained cobaltite-pyrite aggregates hosted in quartz adjacent to the coarse-grained pyrite; and fine-grained cobaltite-pyrite aggregates oriented and distributed in the quartz growth bands. In terms of cobalt resource contribution, invisible cobalt in coarse-grained pyrite dominates. Additionally, the mineralization sequence indicates that cobalt mineralization mainly occurs in the intermediate stage of the hydrothermal period, while copper mineralization occurs in the late stage, and lead-zinc mineralization is nearly simultaneous with or slightly later than copper mineralization.

Due to the relatively low content of cobaltite minerals and its grain size predominantly less than 30 μm, conventional mineral processing fineness is difficult to effectively separate them. Therefore, the focus of comprehensive cobalt utilization should be on the coarse-grained cobalt-rich pyrite. The delineation of cobalt-rich ore bodies and the optimization of beneficiation process could potentially enhance the cobalt grade in sulfide concentrates and achieve comprehensive cobalt utilization.

Over the past 20 years, physical simulation experiments for geological hazards have rapidly developed, evolving into an interdisciplinary field with widespread applications and continuous technological advancements. Analyzing the current status and trends of physical simulation experiments for geological hazards helps researchers gain a comprehensive understanding of the field, design experiments, develop equipment, and update technologies aligned with future directions, thereby promoting innovation in key theories related to geological hazards.

This paper reviews a significant body of domestic and international literature on physical simulation experiments of geological hazards, summarizes five key significances of conducting such experiments, and analyzes the current status of six core physical simulation technologies. Model box and flume are the most widely used simulation technologies due to their versatile combinations, low cost, ease of installation, and simple operation. Base friction simulation technology enables coupling between the model and the gravitational field in two-dimensional settings. Shaking table and centrifuge technologies, while expensive to build and operate, provide controlled vibrational and gravitational conditions, playing an indispensable role in physical simulation experiments. In-situ simulation technology, while facing challenges such as long experimental cycles, difficult model fabrication, high personnel input, low automation, and poor repeatability, offers distinct advantages by mitigating issues such as scaling effects, boundary constraints, and gravitational distortion.

Physical simulation experiments for geological hazards are evolving toward greater scenario complexity, larger-scale testing, more scientific material selection, and intelligent data collection. These advancements impose higher demands on experimental technologies and economic costs, highlighting the urgent need to foster a conducive development environment. Doing so will enable physical simulation technology to play an even greater role in geohazard research.

The evaluation of debris flow susceptibility can identify key areas for disaster reduction and prevention in this region.

Taking Bomi and Motuo Counties of Tibet Autonomous Region as the study area, 12 factors with high influence on debris flow, including elevation, slope, stratigraphic lithology and rainfall, were selected by Pearson Chi-square test algorithm as evaluation indexes. Data collected from 282 sits with and without debris flows in the study area were taken as the sample database. Based on ArcGIS platform, four susceptibility evaluation models were established by using Information Value Method and Machine Learning Method. The ROC curve and AUC index were introduced to evaluate the accuracy of debris flow susceptibility obtained from the proposed methods.

A debris flow susceptibility map for the study area was obtained.

The results indicate that: (1) Considering different types of debris flows in different dimensions and controlling factors, the normalization coefficients of latitude and temperature are used as the evaluation index of debris flow susceptibility, which can eliminate the excessive responses of debris flow to temperature in low altitude areas to a certain extent. (2) Air temperature, distance from water system, distance from road, formation lithology and elevation are the main factors of debris flow occurrence in the study area; Factors such as vegetation coverage, terrain humidity, and slope also play an important role. (3) Considering the relationship between the disaster points of debris flows and the classification attributes of the impact factors, the classification attributes of the impact factors are assigned scores and trained as input features. The machine learning model performs well, and its average AUC is 0.980, which was better than the traditional information models. (4) The AUC of SVM model is as high as 0.987, and the FR value of the highly prone region is 41.13. The prediction area of high-risk regions takes up the smallest proportion, demonstrating superior high-precision prediction capability in large-scale regions.

Controlled by complex factors such as basin structure, climate, and water depth, the quantitative reconstruction of source-sink systems, sand transport pathways, and sedimentation processes in rifting lacustrine basins remains a persistent challenge. This study addresses this knowledge gap by investigating the First Member of the Wenchang Formation in the Wenchang Sag of the western Pearl River Mouth Basin.

Key elements of the source-sink system are quantitatively analyzed through methods including the restoration of lithology and geomorphology in the provenance area, characterization of paleo-valleys, recognition of fault boundary patterns, and determination of sedimentary body types and scales. The coupling relationships between these elements are then discussed.

The results show that the deposition of the First Member of the Wenchang Formation was dominated by fan delta, braided river delta, and shore-shallow lacustrine deposits. The bedrock types of the provenance area are primarily magmatic and sedimentary rocks, with seven provenance systems identified. Four sand control mechanisms within the source-sink system are recognized, and a total of 37 paleo-valleys are identified. In the steep slope zone, sedimentary sand bodies show the closest correlation with elevation difference, watershed area, and cross-sectional area of the valley. In contrast, the ramp zone exhibits the strongest correlation with watershed area, valley length, and cross-sectional area of the valley.

These findings provide a basis for describing sedimentary systems in continental rifting lacustrine basins.

To improve the calculation efficiency of slope stabilities for multiple profiles, this study prop oses a method based on spatial slope stability coefficient calculations.

The two-dimensional residual thrust method is incorporated with spatial profiles, which forms a residual thrust method for spatial profiles.

The calculation process of the residual thrust method for spatial profiles is optimized by the automatic generation and management of profiles within a three-dimensional engineering geological mode of a slope. In order to achieve rapid and efficient calculation of slope stability coefficients, a method based on multi-threaded parallel computation is introduced. Combining this approach with the residual thrust method for spatial profiles allows for more accurate and efficient computation of stability coefficients for multiple spatial profiles.

Using an open-pit mine in Xilinhot, Inner Mongolia, as a case study, we establish a three-dimensional slope engineering geological model for the internal dump site slope. On this basis, seven spatial profiles are automatically generated, and stability coefficients are calculated using multi-threaded parallel computation. Finally, the results are presented visually.

The research findings indicate that adopting multi-threaded parallel computation for calculating slope stability coefficients for multiple profiles can fully utilize computing resources and significantly improve computational efficiency. Furthermore, application in engineering projects confirms the feasibility and applicability of the proposed method.

pH is a key parameter for characterizing the chemical properties of a solution. Experimental measurement and model prediction of the pH values of pure and impure CO₂-saturated systems can be used to evaluate the chemical changes under CO₂ geological storage conditions.

In this study, the pH values of pure and impure CO₂-water systems were measured in situ using potentiometric and spectroscopic methods, under temperatures conditions ranging from 35 to 93℃ and pressure condictions raging from 0.38 to 18 MPa. A component chemical equilibrium model calibrated using solubility data, was developed to calculate and predict the pH values of pure and impure CO₂-water systems.

The results show that that N₂ and CH₄ influence the CO₂-saturated systems by reducing CO₂ solubility in water and increasing the pH value, with CH₄ having a greater effect than N₂. The model can accurately predict the pH values of pure CO₂-water systems, with a maximum deviation within 0.05 pH units. In impure CO₂-water systems, deviations are mainly observed under the condition of 50℃ and a CO₂-to-impurity gas ratio of 1∶9, with deviations within 0.15 pH units.

The potentiometric and spectroscopic methods are applicable for in situ pH measurements under high-temperature and high-pressure conditions, and the pH prediction model for pure and impure CO₂-water systems shows good accuracy. The findings provide theoretical support for understanding the chemical changes induced by the injection of impure CO₂ into geological formations, which is of great significance for enhancing the safety and effectiveness of carbon storage.

Carbon capture and storage (CCS) is crucial for mitigating global climate change, and deep saline aquifers, with the largest identified storage potential, are considered the preferred storage sites. However, CO₂ injection is prone to escape through interconnected fractures or reactivated faults toward the surface due to buoyancy. Therefore, investigating the impact of CO₂ injection on faults and the feedback effect of fault reactivation on CO₂ leakage is of significant importance.

In this study, we develop a fully coupled two-phase thermo-hydro-mechanical model to simulate the interactions between CO₂ injection, fault failure, and CO₂ plume propagation.

The modeling results reveal that upon fault activation, the permeability distribution exhibits a clear dichotomy. Moreover, the evolution of fault permeability is closely linked to the spatio-temporal changes in the pore pressure field. As the initial failure zone evolves into a high-permeability area, it facilitates the release of pore pressure, thereby suppressing further fault activation and leading to localized fault reactivation. Additionally, the migration range of CO₂ plumes is not directly correlated with the cooled region of the rock mass. The plume spreads rapidly and extensively, reaching a front migration distance of up to 1500 m after just two years of continuous injection. In contrast, the temperature field spreads slowly and is more concentrated, with the cooled zone reaching only 200 m after 20 years of injection. This restricted temperature field is less likely to induce fault reactivation, which enhances the long-term safety of carbon sequestration. Finally, fault configuration significantly affects CO₂ storage safety. Reverse faults provide the best sealing performance, normal faults the worst, and strike-slip faults lie in between. Specifically, the effective CO₂ storage capacity in reverse faults is approximately 25% higher than in normal faults.

In conclusion, the developed two-phase thermo-hydro-mechanical model, incorporating damage behavior, demonstrates robust performance and effectively captures the complex interaction between fault progressive failure and CO₂ plume migration. This model offers both theoretical and technical support for assessing the long-term safety of carbon sequestration projects.

Overpressure in the Triassic interval of the western Central Depression, Junggar Basin, shows high magnitudes and complex genesis. Current understanding of its distribution and origin remains limited.

Integrating data of drilling fluid relative density, measured formation pressure, and logging data, and utilizing methods of logging curve combination, the Bowers method, and acoustic velocity-density crossplots, this study characterizes the log response characteristics of Triassic overpressure in the western Central Depression, and investigates the genesis and main controlling factors of Triassic overpressure.

The mudstones of the Triassic overpressure section exhibit characteristics of elevated sonic time difference and reduced resistivity. The neutron density and neutron porosity of the mudstone deviate from the normal compaction trend line, but the degree of deviation spatially varies across different well areas.

The current Triassic overpressure is mainly caused by the combined effects of disequilibrium compaction and deep-seated pressure transfer. Affected by the lithological assemblage, sedimentary rate, and fault activity intensity, the relative contribution of disequilibrium compaction to overpressure generation varies significantly between well areas. Particularly in the Karamay Formation: the Shawodi area exhibits the largest contribution, followed by the Moxizhuang area, and the Zhengshacun area shows the smallest contribution. The research results provide a deeper understanding of the mechanism of deep to ultra-deep overpressure in the western central depression, Junggar Basin.

The subsidence deformation caused by the "three underground" mining pose a threat to the safety of surface buildings such as transmission lines, and there is an urgent need for an early perception and intelligent warning method for subsidence and deformation in goaf areas.

This paper proposed an intelligent early warning framework for subsidence and deformation in goaf areas based on the frequency of microseismic events. This framework utilized a Distributed Acoustic Sensing (DAS) system to collect microseismic data, extracted microseismic events using the STA/LTA algorithm, and classified the microseismic events using a deep clustering method that combined AutoEncoder (AE) and Gaussian Mixture Models (GMM). Based on the correlation coefficient between microseismic event frequency and subsidence deformation data, microseismic events that induced subsidence deformation were selected. The VGG-16 deep learning model was then used to achieve intelligent recognition of such microseismic events, and real-time warning was carried out by setting warning thresholds.

This paper took a typical coal mine goaf in western China as the research area and applied the framework to field monitoring. The results show that the framework classifies the collected microseismic events in goaf into five categories, extracts one type of microseismic event that induces subsidence deformation, and combines with an intelligent microseismic event recognition model to successfully issue a warning for the sudden increase in tower inclination caused by subsidence deformation.

Therefore, this framework can effectively capture the correlation between microseismic events and subsidence and deformation, to achieve early warning of subsidence and deformation in goaf areas, and has practical feasibility and engineering application value.

The distribution of structural planes plays a significant role in determining the engineering and mechanical properties of rock masses. Accurately obtaining information of structural planes is crucial for analyzing the characteristics and stability of rock masses.

3D point cloud data of a highly steep rock slope were acquired via 3D laser scanning technology. After filtering and preprocessing of the point cloud data, the open-source program Discontinuity Set Extractor (DSE) was then used to semiautomatically recognize and classify the point cloud data, obtaining key parameters and clustering information of the structural planes of rock slopes, such as attitude, trace length, and spacing. By fitting the point cloud clustering information, a probability distribution model was created, and a discrete fracture network (DFN) model was established. Furthermore, a 3D block discrete element model of the steep slope was developed via the "Rhino-Griddle-3DEC" integrated modeling method, which is based on point cloud data. The model investigated the stability of the slope and potential failure areas.

Under gravity conditions, the safety factor of the entire slope is approximately 1.5, and the potentially unstable area is the dangerous rock mass located at the top of the slope.

Therefore, the structural plane parameters identified by this method can better reflect the engineering properties of the rock mass, providing important guidance for the analysis and evaluation of the stability of highly steep rock slopes.

Landslides in reservoir areas represent one of the most prevalent geological hazards in hydropower engineering construction. Landslides in reservoir area, they can generate surge waves, obstruct river channels, and even trigger dam breaches, resulting in significant economic losses and casualties. Therefore, understanding the deformation behavior of reservoir landslides is critical for early identification and monitoring.

This study employs Stacking Interferometry Synthetic Aperture Radar (Stacking-InSAR) and Small Baseline Subset Interferometry Synthetic Aperture Radar (SBAS-InSAR) techniques with Sentinel-1 data to identify active landslides and analyze deformation patterns in the Baihetan Reservoir area before and after impoundment. In addition, Sentinel-2 imagery and the Automated Water Extraction Index (AWEI) were used to derive reservoir water level variations. Representative landslides exhibiting substantial deformation were selected—one each from asceding-track, descending-track, and pre-impoundment datasets—to analyze the influence of water level fluctuations and rainfall on deformation behavior.

The results demonstrate that the AWEI- based water level extraction method using Sentinel-2 imagery achieved robust performance in the study area. The extracted water levels exhibited a mean error of 0.89 m compared to measured values, confirming the method's reliability for data-scarce regions. A total of 103 active landslides were identified in the Baihetan Reservoir area during the monitoring period through analysis of both ascending and descending orbit images. A total of 103 active landslides were detected in the Baihetan reservoir area during the obervation period, 37 exhibited submergence of their front edges, while 23 demonstrated clear deformation responses to water level fluctuations. Reservoir bank landslides showed significantly stronger correlation with water level changes than with rainfall. Notably, drawdown conditions exerted particularly pronounced effects on bank stability, with deformation rates increasing during lowering phases compared to rising water levels.

Previous deep mineral exploration by organic hydrocarbons revealed that the deep-source superimposed anomalies observed in the deep and marginal parts of the Woxi and Wangu gold deposits differ significantly from the syngenetic superimposed anomalies identified in peripheral mineralized bodies. To further investigate the different halo forming mechanisms between Au mineralization and organic matter formation,

this study focused on large to medium sized gold deposits (Woxi and Wangu) in the Xuefeng uplift belt and their peripheral mineralized bodies (characterized by good gold mineralization in shallow but poor deep mineralization). The analytical methods include rock- pyrolysis analysis (Rock-Eval), chloroform asphalt "A" extraction and fractional component separation quantification, saturated hydrocarbon analyzed by gas chromatography-mass spectrometry (GC-MS), fluid inclusion, and C-H-O-S stable isotope analysis. These comparative investigations were conducted on their ore forming geological characteristics, organic geochemical signatures, fluid inclusion and isotopic geochemical features. This approach aims to elucidate the Au-organic matter mineralization and halo-forming mechanisms.

The results indicate that: (1) Large-to-medium gold deposits and peripheral mineralized bodies have different metallogenic geological characteristics. The former underwent regional metamorphic hydrothermal filling and metasomatism followed by a superimposed mineralization involving deep-source fluids, while the latter only experienced regional metamorphic hydrothermal filling and metasomatism. (2) Both systems contain adsorbed organic and, the source of organic matter is similar with their original depositional environments. However, The total organic carbon (TOC) content in large-to-medium gold deposits exceeds that of peripheral mineralized bodies by over 50%, along with oxygen and hydrocarbon indices being 10 and 3-8 times lower, respectively. This indicates higher abundance and maturity of organic matter in large-to-medium gold deposits. (3) C-H-O-S stable isotope results demonstrate that metallogenic materials in large-to-medium gold deposits originates from the mantle, while the ore-forming fluids is a "deep-source fluids" formed by multi-stage evolution and mixing of mantle-derived fluids. In contrast, the metallogenic materials of peripheral mineralized bodies derived solely from ore-bearing strata, and their ore-forming fluids originated from shallow crustal fluids ("shallow-source fluids"). These differences reflect distinct fluid dynamics and mixing mechanisms, leading to divergent geological significances in mineralization. (4) Two type deposits have different organic matter sources in ore-forming fluids. The large-to-medium gold deposits contain not only the "incremental" organic matter introduced by deep-source fluids but also the adsorbed organic matter, whereas peripheral mineralized bodies contain only adsorbed organic matter. This likely explains the significantly higher TOC in the former. (5) The different halo-forming mechanisms between Au and organic matter mineralization. In large-to-medium gold deposits, Au transported by mantle-derived fluids predominantly exists as organic complexes ore chelates (e.g., Au(CH₃)²⁺, [Au(CH₂NH₂COO)]²⁺) and migrates via both liquid and gaseous phases. In contrast, Au in peripheral mineralized bodies associates with organic matter through physical adsorption, lacking geochemical significance of organic complexation/chelation. This distinction manifests as stronger organic hydrocarbon anomalies and strong correlation between Au and organic hydrocarbon by different carriers (ore bodies, overlying strata, or soils) in large-to-medium gold deposit. In peripheral mineralized bodies, hydrocarbon anomalies are weaker, and Au-hydrocarbon are poorer. These conclusions align with early organic hydrocarbon-based deep prospecting observations: "deep-source fluid mineralization—metallogenic materials derived from deep sources -strong Au-organic hydrocarbon correlations—deep-source superimposed anomalies—high deep prospecting potential" versus "shallow-source fluid mineralization—mineralizing material confined to strata—weak Au-organic hydrocarbon correlations-syngenetic superimposed anomalies-limited deep prospecting potential".

This study provides novel insights and directions for evaluating deep prospecting potential in exploration geochemistry.

The primary goal of this study is to establish a graptolite biostratigraphic framework for the Renheqiao Formation. To achieve this, the research focuses on the Banpo and Chadi sections in Shidian County, Baoshan City, Yunnan Province, which are critical outcrops for biostratigraphic analysis in the area.

A combination of high-resolution biostratigraphic and lithologic records was conducted to precisely define the stratigraphy and identify key biozones within the formation.

The Renheqiao Formation in the study area is approximately 71.6 meters thick, with a about 25 cm of paleo-weathering crust at the base. It unconformably overlies the Pupiao Formation, indicating that the study area was subaerially exposed during the Late Ordovician. Based on detailed fieldwork and high-resolution surveys, a total of nine graptolite zones were identified, spanning from the upper Hirnantian in the Ordovician to the Silurian Llandovery Telychian stage. These identified graptolite zones are in ascending order as follows: the Metabolograptus persculptus zone (R1), Akidograptus ascensus zone (R2), Parakidograptus acuminatus zone (R3), Cystograptus vesiculosus zone (R4), Coronograptus cyphus zone (R5), Demirastrites triangulatus zone (R6), Lituigraptus convolutus zone (R7), Stimulograptus sedgwickii zone (R8), and Spirograptus guerichi zone (R9).

The nine graptolite zones of the Renheqiao Formation in the Baoshan region correlate well with those of the Longmaxi Formation (LM1–LM9) in the Yangtze region. This correlation not only strengthens the regional stratigraphic framework but also provides a solid basis for future shale gas exploration and assessment in the Baoshan region.

The simulation of three-dimensional morphology of geological bodies through 3D modeling technology can effectively reveal the spatial distribution characteristics of geological bodies. Compared with implicit modeling methods, explicit modeling methods can depict the small-scale geological structure features more accurately. However, for the large-scale and fine 3D modeling of complex geological bodies such as vein-type ore bodies with complex shapes, there are still problems such as slow modeling speed, high difficulty, and low accuracy that need to be solved.

This study, focusing on some complex vein-type ore bodies, comprehensively applied techniques such as densified constraint points, construction of ore body splitting lines, segmented modeling, and ore body stitching, to systematically conduct research on the explicit 3D modeling methods of four types of vein-type ore bodies: branched and bifurcated composite ore bodies, ore bodies with barren windows, ore bodies with intercalated rocks, and ore bodies cut by faults.

It achieved high-precision and rapid 3D modeling of complex vein-type ore bodies, which is of great significance for the fine 3D modeling of rare and precious metal vein-type ore bodies, estimation of mineral resources, and formulation of mineral resource development and utilization plans.

This study focuses on a reservoir in the South China Sea, taking into account the heterogeneity of the formation and the actual location of the lithological traps. The study establishes a heterogeneous geological model based on seismic reflection characteristics and drilling data, using TOUGHREACT to simulate the effects of different injection locations on CO₂ migration and sequestration in the formation.

The results indicate that the upward migration of CO₂ is hindered by mudstone, while lateral migration is more pronounced. For different injection scenarios, significant variations in reservoir pressure distribution were observed. The reservoir pressure reaches 40.1 MPa when injecting at the top, 39.7 MPa at the bottom, and 40.3 MPa when injecting in a complete well. Therefore, the pressure buildup from long-term implementation of complete well injection and top injection schemes may damage the reservoir cap rock, increasing the risk of CO₂ leakage. The bottom and middle injection schemes are safer. When injected continuously for 100 years, CO₂ is predominantly in the supercritical phase, accounting for more than 77% of the total sequestration, with the dissolved phase making up less than 23%. At the same wellhead injection pressure, the homogeneous model overestimates the storage volume of the reservoir. The heterogeneous model injects 55.1% and 49.3% more CO₂ at the top and middle, respectively, compared to the bottom. Sensitivity analysis results show that porosity and permeability have a more significant impact on the results than capillary pressure.

This study aims to investigate the impact of injection well locations on CO₂ migration mechanisms and storage capacity when considering the layered heterogeneity of the reservoir, with the aim of providing theoretical guidance for the design of well placement for CO₂ storage.

The complex topography of Sichuan Province, characterized by intersecting mountainous terrain, leads to frequent, sudden, and highly susceptible landslides. These events pose significant threats to both people's property and environmental resources. Therefore, conducting landslide identification and charaterization are crucial for effective hazard prevention, monitoring, and post-disaster preparedness.

To overcome the limitations of conventional visual interpretation methods-including high economic costs, time-intensive procedures, labor demands, and challenges in acquiring historical samples, this study incorporates multiple landslide-influencing factors such as elevation, slope gradient, and aspect into the analysis framework. A quantitative information value analysis was conducted to evaluate the predictive capacity of these influencing factors for historical landslide identification, thereby improving the reliability of historical landslide inventories. To solve issues such as inaccurate localization and ambiguous segmentation boundaries in automatic landslide identification results, this paper improves the Mask R-CNN model using a recursive pyramid network and DIoU loss, proposing an improved algorithm for intelligent landslide identification.

Evaluation results demonstrate that the enhanced algorithm significant improvements over the baseline Mask R-CNN, with 3.6% increase in precision and 5.2% increase in recall. The model attains 74.4% identification accuracy in Qingchuan County, Sichuan, showing particular effectiveness in delineating historical landslide boundaries with clear geomorphological fidelity.

Combining satellite remote sensing with deep learning advancements, this improved algorithm enables intelligent landslide identification and supports data-driven risk assessment, offering critical insights for geohazard mitigation.

With continuous advances in high technologies such as remote sensing, the Internet of Things, artificial intelligence, big data, cloud computing, and more recently, large language models (LLMs), the field of earthquake and geological hazard research is shifting from traditional paradigms relying on single data sources and empirical models toward integrated systems driven by multi-source data fusion and intelligent decision support.

This article, based on the themed column “Applications of Advanced Technologies in Earthquake and Geological Hazard Research,” reviews recent progress across five key directions: physical simulation modeling, deep learning-based recognition, remote sensing integration, intelligent early warning techniques, and knowledge graph construction. These studies collectively demonstrate how cutting-edge technologies are being applied to hazard monitoring, mechanism analysis, and emergency response.

On this basis, the article further identifies current technical bottlenecks, including challenges in multimodal data integration, disaster chain modeling, model generalization, and scenario adaptability, and explores the potential role of LLMs in this field, particularly in knowledge extraction, causal inference, and multi-scenario risk assessment.

The bonding performance of the anchor-mortar interface is influenced by multi factors, yet existing studies predominantly focus on single-factor effects, leaving a gap in understanding the combined impact of multiple factors. This study aims to address this gap by systematically investigating the interfacial bonding performance under multifactorial conditions.

The anchor-mortar interface is taken as the research object. Electrochemical impedance spectroscopy is used to characterize the interfacial state and derive electrochemical parameters under different varying conditions. Pullout tests are conducted to quantify the bond strength of the anchor-mortar interface. By correlating electrochemical parameters with pullout load data at the completion of sample curing, the relationship between these parameters and interfacial performance is established. Additionally, the influence of the three factors on the bonding performance of the anchor-mortar interface is analyzed.

Sensitivity analysis of the orthogonal test results reveals that the pullout load is predominantly governed by the anchor rod diameter, whereas the pore solution resistance (R_s) is primarily influenced by the water-cement ratio. No dominant factor is identified for the charge transfer resistance (R_ct). During the early curing stage, two distinct interfacial states are observed under the combined influence of the three factors: A fully developed passivation film and partially developed passivation film. Within the tested parameter range, the pullout load increases with larger fine sand particle sizes and lower water-cement ratios. A positive correlation is observed between the pullout load and both the pore solution resistance (R_s) and charge transfer resistance (R_ct).

These findings provide critical insights for optimizing the formulation and application of anchored structural mortar.

Landslides are geological disasters that cause significant damage to both natural and social environment. Effective landslide susceptibility assessment is crucial for disaster prevention and mitigation. Existing landslide databases are often used as the primary data source for susceptibility assessments. However, due to delays in updates, these databases suffer from issues such as poor timeliness and incompleteness. Moreover, traditional landslide susceptibility assessment methods primarily rely on static data (e.g., topography, geology, and hydrology) and lack dynamic data (e.g., surface deformation), making it difficult to fully characterize the deforming landslides and reducing assessment reliability.

This study combined optical remote sensing technology and synthetic aperture radar interferometry (Interferometric Synthetic Aperture Radar, InSAR) to identify landslides in the study area and obtain surface deformation as a dynamic evaluation factor. In combination with static evaluation factors, two methods—joint training and weighted superposition—were employed, alongside the Maximum Entropy (MaxEnt) model and the Iterative Self-Organizing (ISO) clustering algorithm to assess and categorize landslide susceptibility in Shimian County.

The findings are as follows: (1) By integrating optical remote sensing and InSAR technologies, 139 landslides are identified in the study area. High-risk landslide zones in Shimian County are predominantly located along riverbanks and roadsides. The distribution of landslide disaster points aligns well with the zoned areas. (2) Incorporating the InSAR deformation factor enhances the susceptibility accuracy by 6.1% (AUC=0.921) and substantially reduces the occurrence of false positives and false negatives, thereby improving overall model accuracy.

This study demonstrates the advantages of incorporating InSAR deformation data into landslide susceptibility models, offering valuable support for landslide disaster prevention in Shimian County.

In tunnel engineering, the prerequisite for tunnel design and construction safety is to accurately assess the amount of deformation of the rock surrounding a tunnel.

In this work, the firefly algorithm (FA), whale optimization algorithm (WOA), and gray wolf optimization algorithm (GWO) are combined with optimized support vector regression (SVR), and three intelligent hybrid swarm optimization prediction models are constructed to predict the deformation of extruding surrounding rock tunnels. A database containing 62 samples was constructed, and seven initial parameters of tunnels and surrounding rock were selected as the input parameters of the prediction models, with the radial deformation of tunnels as the output quantity. The coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE) were selected as the evaluation indices of the model's prediction performance. Finally, the effects of different input parameters on the prediction results of tunnel rock deformation were evaluated via normalized mutual information values.

Compared with the GWO-SVR and WOA-SVR models, the FA-SVR model demonstrated superior predictive performance during both the training and testing phases. The corresponding R² values were 0.9634 and 0.9648, the RMSE values were 18.786 and 14.699, and the MAE values were 9.460 and 11.170 for the training and testing sets, respectively. The ranking of predictive capability was as follows: FA-SVR>WOA-SVR>GWO-SVR. The results show that the firefly algorithm, whale optimization algorithm, and gray wolf optimization algorithm can improve the prediction performance of the support vector regression model. The FA-SVR model has the best prediction effect, and the optimized hybrid prediction model performs significantly better than the classical models. The sensitivity analysis reveals that the joint frequency is the most important parameter affecting the predicted value of deformation of the rock surrounding a tunnel.

The research results can provide an important reference for the safety control of tunnel engineering.

This study investigates the dissolution and mechanical degradation of carbonate rocks under the influence of acidic leachate.

Limestone samples are subjected to acidic leachate under varying flow conditions and durations. The dissolution effects are evaluated through comprehensive analyses of apparent characteristics, mass loss, porosity, uniaxial compressive strength, acoustic emission activity, and other key indicators. These measurements elucidate the impact of the acidic leachate on the physical and mechanical properties of carbonate rocks.

Experimental results demonstrate that the dissolution rate and porosity of the rock samples increase with prolonged exposure time and higher flow rates, whereas the mechanical strength exhibits a corresponding decline. As dissolution progresses, the formation of increasingly thicker fluorite mineral layers on the rock surfaces is observed, which subsequently attenuates the dissolution rate. Furthermore, the failure mode of uniaxial samples shifts from shear-dominated to tensile-dominated damage.

The acidic environment of phosphogypsum leachate induces the dissolution of internal minerals within limestone, resulting in significant changes to macromechanical properties. These findings provide critical theoretical and experimental insights for assessing the stability of karst media under acidic wastewater conditions, as well as for optimizing the safety design of acidic wastewater treatment and tailings management systems.

The spatial distribution analysis of earthquake-induced landslides offers critical insights for identifying potential geological disasters in affected regions, which is fundamental for informing post-disaster reconstruction planning, relocation strategies, site selection processes, and the development of effective geological disaster mitigation measures.

Taking the earthquake that struck Luding County, Ganzi, Sichuan Province, on September 5, 2022, as a case study, earthquake-induced landslides were initially identified using artificial visual 3D remote sensing data. This analysis was based on an optical image (DOM) with a resolution of 0.2 meter and a digital elevation model (DEM) with a resolution of 0.5 meter, both acquired post-earthquake. Field investigations and subsequent corrections were performed to validate and finalize the landslide inventory. Subsequently, the relationships between the distribution of earthquake-induced landslides and various geological factors, including topography, geological structures, and earthquake parameters, were systematically analyzed.

①The Luding earthquake triggered 9248 landslides, covering an area of approximately 680 km², with the majority classified as small- to medium-sized. The highest landslide density was observed at the tectonic intersection of the Xianshuihe fault, Daduhe fault, and Jinping mountain fault. The cumulative landslide area reached approximately 45.57 km², with an average landslide area of 4941 m². ② The spatial distribution of landslide in the earthquake-affected region is predominantly controlled by PGA and fault structures, with the majority of landslides concentrated in areas where PGA exceeds 0.6 g and within 1 km on either side of the seismogenic fault. Furthermore, landslide occurrence exhibits a negative correlation with proximity to water systems and roads. At a local scale, topographic factors significantly influence landslide distribution, with the highest frequency observed at elevations ranging from 1200 to 2400 m, on slopes inclined between 30° and 60°, and predominantly facing east or southeast. Additionally, landslides are more prevalent in areas underlain by hard rock strata. ③ The number and area of landslides exhibit an exponential relationship with the magnitude of the Luding earthquake. Leveraging high-precision data, our analysis identified a significantly higher number of earthquake-induced landslides compared to previous studies, with a reduced minimum landslide area and an increased total affected area.

The findings of this study have been implemented to support post-disaster recovery and reconstruction efforts in the Luding earthquake-affected region, providing a scientific basis for enhancing resilience and reducing future earthquake risks.

Seabed carbon dioxide (CO₂) geological sequestration technology has gained popularity in the fields of carbon sequestration and carbon neutralization. In the northern South China Sea, favorable conditions for the formation of CO₂ hydrates exist in seabed sediments, where hydrates forming in cracks and pores may block the further upward migration of CO₂ and generate self-sealing capacities. However, the effects of CO₂ leakage in fractures and the instability of hydrate plugging remain unclear.

A visualization experimental platform for hydrate growth and the instability process under water injection supercharging at high pressure and low temperature was used to observe CO₂ hydrate formation. The platform simulated the conditions of seafloor sedimentary cover at 2℃ and 3～4 MPa, and evaluated hydrate instability and plugging effects using parameters of breakthrough pressure, breakthrough pressure difference, duration, permeability coefficient at the initial instability stage, and plugging rate.

The experimental results show that hydrate formation proceeds through four stages: nucleation, expansion, formation, and aggregation. Hydrates formed in cracks effectively block the migration of fluids such as water and CO₂; however, instability begins when fluid pressure increases and reaches the critical breakthrough pressure. The instability process can be divided into two stages: particle size degradation and surface friction failure. The hydrate mass core becomes unstable first, while the sealing state persists until the surface of the hydrate fails due to friction. The breakthrough pressure ranges from 6.414 to 6.966 MPa, and the breakthrough pressure difference is between 2.403 and 3.203 MPa. Hydrate instability is primarily influenced by flow rate, with the flow rate affecting the instability rate through interface effects. The breakthrough pressure of hydrates is mainly determined by temperature and pressure, while flow rate influences the exact time when hydrates enter instability. At 3–4 MPa seafloor sedimentary cover, the sealing rate is 99.0%–99.6%, and the permeability coefficient at the initial instability stage ranges from 0.555×10⁻³ to 1.260×10⁻³ μm².

The experimental results offer a reference for risk assessments of overlying layers in similar conditions in the South China Sea for CO₂ seabed geological sequestration. To maintain the sealing effect of CO₂ hydrates, the pressure difference between the capped hydrate layer and the actual seabed pressure should be less than 2 MPa.

Dry rock, a widely distributed and abundant geothermal resource, is of paramount importance in the reduction of fossil energy consumption, alleviating environmental pollution, and ensuring energy security. Enhanced geothermal systems are currently the main method for developing hot dry rock resources and are generally implemented through several methods, such as engineering site selection, geothermal drilling, thermal reservoir stimulation, flow testing, and power generation engineering. Among these, flow tests are a crucial link in undertaking thermal reservoir stimulation and power generation engineering and are used to form injection and production well groups, evaluate cycle circuits, expand heat exchange capacity, and lay the foundation for ultimately achieving power generation goals safely and stably. The implementation process of flow tests is long-term and complex, which can easily lead to problems such as insufficient connectivity, strong microseismic effects, liquid leakage, scaling of the circulating liquid, and insufficient equipment reliability. Therefore, flow tests at hot dry rock development sites internationally are often intertwined with drilling and reservoir stimulation and accompanied by scheme adjustments to gradually achieve the power generation goal.

This article briefly summarizes the flow test experiences and exploration directions of typical hot dry rock development EGS systems at home and abroad, elaborates on the influence of various factors on flow tests, and proposes development suggestions based on the actual situation at the Qinghai Gonghe site. In the past, improving the effectiveness of flow tests was achieved through methods such as adjusting the development layer and well groups, long-term circulation, hydraulic fracturing, and chemical stimulation. However, current technicians have focused on accurately obtaining engineering parameters and improving the design of well groups and reservoir stimulation processes.

In summary, the formulation of a flow test plan needs to fully consider geological factors and adapt to local conditions. Moreover, key technologies in flow tests, such as reservoir evaluation, reservoir stimulation, and engineering implementation, are worthy of in-depth research. The progress of these key technologies requires the establishment of numerical models with greater accuracy, improvements in the accuracy and stability of various monitoring techniques, the application of more diverse hydraulic fracturing and chemical stimulation processes, and the establishment of a more comprehensive risk prevention and control system for induced earthquakes. In addition, the application of new technologies is also a possible breakthrough. Supercritical carbon dioxide and liquid nitrogen fracturing technologies have advantages in thermal reservoir fracturing and energy enhancement in hot dry rock stimulation. Explosive fracturing technology has a certain effect on increasing the complexity of fractures near wellbores and enhancing the injection capacity of well groups. Finally, with the goal of experimental power generation and focusing on key issues in multi-well group flow tests, improving the construction process of flow tests is also an effective means to improve efficiency and reduce costs. With increasingly mature development technology, hot dry rock geothermal resources will become an important part of China's energy structure, playing a significant role in economic development and environmental protection.

To address this challenge, it is imperative to strengthen the research and geological exploration of new types of niobium deposits.

This study focuses on the clay rocks in the lower part of the Upper Permian Longtan Formation (P₃l) in Xingwen area of southern Sichuan. Based on the analysis of Nb contents in the collected samples, we combine various analytical techniques such as X-ray powder diffraction (XRD), scanning electron microscopy with energy dispersive spectrometer (SEM-EDS), and electron probe micro-analyzer (EPMA) to conduct mineral identification and quantitative analysis on Nb-enriched samples.

The results show that the content of Nb₂O₅ in the clay rocks ranges from 41×10⁻⁶ to 437×10⁻⁶, with an average of 187.2×10⁻⁶, reaching the lowest industrial indicator for weathering crust-type deposits. The enrichment of elements like Li, Ga, and others is also significant, making the clay layer rich in multiple critical metals and possessing considerable ore-forming potential. XRD analysis reveals a substantial presence of anatase in the Nb-rich clay rock. EPMA analysis indicates that the content of Nb₂O₅ in anatase ranges from 0.09% to 3.40%, with an average of 1.17%. Based on the Nb content in anatase, SEM-EDS scanning, and the Nb₂O₅ content of whole-rock samples, we conclude that Nb primarily exists in anatase as an isomorphous substitution, and some are adsorbed by clay minerals. Nb is predominantly inherited from the weathering products of minerals such as sphene in the Emeishan basalts, and the weathering degree has a significant impact on the enrichment and mineralization of Nb, with characteristic of weathering-sedimentary deposit.

The research results provide a scientific basis for the geological prospecting, resource evaluation, and comprehensive development and utilization of niobium resources.

To improve the engineering properties of clay and increase the utilization of waste steel slag (SS).

Geogrid reinforcement was employed, followed by direct shear tests, cyclic shear tests, and post-cyclic direct shear tests conducted on steel slag-clay mix, sand-clay mix, and pure clay. The study investigated the strength characteristics, damping ratio, shear stiffness changes, and displacement of the mixed soil reinforcement-soil interface under various conditions, including different steel slag contents, vertical stress, moisture content, and shear amplitudes.

The test results indicate that steel slag significantly enhances the shear strength of the clay-reinforcement interface, with improvement being more effective than conventional sand-modified clay. The steel slag-clay mixed soil exhibited higher damping ratio and shear stiffness, suggesting better vibration damping and energy dissipation properties. Among the various mixtures, the steel slag-clay mix with 40% steel slag content demonstrated the best shear strength, damping ratio, and shear stiffness. Additionally, the shear strength of the steel slag-clay mixed soil increased after cyclic loading compared to pre-cyclic direct shear conditions. The results also show that moisture content has a more significant impact on shear strength, shear stiffness, and damping ratio than vertical stress and shear amplitude.

The steel slag-clay mixed soil exhibits improved damping and energy dissipation properties under cyclic shear loading. The experimental findings provide a theoretical basis for using steel slag as a substitute for sand to improve clay soils.

Lithology identification is a crucial step in the process of oil and gas detection and exploration, providing important guidance for exploration positioning, reservoir evaluation, and the establishment of reservoir models. However, traditional manual lithology identification methods are time-consuming and labor-intensive. Although classical deep learning models achieve high identification accuracy, they often have a large number of parameters. To enhance model accuracy while reducing the number of parameters, the aim of this research is to make the model suitable for real-time lithology identification.

This paper first collected a dataset of 3016 rock images consisting of eight types of rocks, including dolomite and sandstone. Based on the lightweight convolutional neural network ShuffleNetV2, the paper proposes a Rock-ShuffleNetV2 lithology identification model (hereafter referred to as the RSHFNet model). The model incorporates the Convolutional Block Attention Module (CBAM) and Multi-Scale Feature Fusion Module (MSF) into the basic network to enhance feature extraction capabilities and improve identification performance. Additionally, the number of stacked ShuffleNetV2 units is optimized to reduce the model's parameters.

The experimental results show that the RSHFNet model achieved an accuracy of 87.21%, which is a 4.98% improvement over the baseline model. Furthermore, the model's parameters and floating-point operations were reduced to 8.69×10⁶ and 9.3×10⁷, respectively, representing 67% of the model's parameters and 63% of the floating-point operations of the baseline model. This reduction significantly decreases the model's size. Additionally, the RSHFNet model demonstrates superior overall performance compared to existing convolutional neural networks.

The proposed RSHFNet lithology identification model offers high recognition accuracy and strong generalization capabilities while being more lightweight, providing a new approach for real-time lithology identification in the field.

The interpretation results of SBAS-InSAR exhibit multiple solutions, making it uncertain to directly use the interpreted deformation points for identifying potential landslide-prone areas. Therefore, taking the north bank of the Qingjiang River (Changyang Section) as the study area, this study proposed a method to comprehensively optimize SBAS-InSAR interpretation results by incorporating landslide susceptibility evaluation.

First, the deformation points interpreted by SBAS-InSAR were analyzed using clustering and outlier detection (Anselin Local Moran's I), and low-value cluster deformation points were retained. Subsequently, eight factors, including elevation, slope, and slope aspect, were selected to evaluate and generate a landslide susceptibility zoning map using the information value method. The reliability of the landslide susceptibility evaluation was confirmed by an ROC curve, with an AUC value of 0.844.

The optimized SBAS-InSAR results were obtained by filtering low-value cluster deformation points based on a threshold value (v ≤ -10 mm/a) and incorporating the landslide susceptibility zoning map. Field verification in selected areas shows that the number of deformation points was reduced after optimization, and their distribution characteristics were more consistent with the historical landslide development in the study area. Additionally, taking the Yupingcun landslide group and the Pianshan landslide as typical cases, the surface deformation values monitored by SBAS-InSAR and GNSS at the same time were compared. In the case of the Yupingcun landslide, the difference between surface displacement values monitored by SBAS-InSAR and GNSS ranged from 0 to 7.87 mm, with an average difference of approximately 2.23 mm and an RMSE of 3.67.

The proposed optimization method for SBAS-InSAR interpretation was demonstrated to be both practical and reliable, providing valuable insights into the application of InSAR technology in the field of geological disasters.

The seepage in fractured rock masses is non-uniform and anisotropic, with its complexity reflected in the density, orientation, and trace length of individual fractures, as well as the connectivity of fracture networks. The connectivity of fracture networks poses a significant challenge in calculating the seepage parameters of three-dimensional (3D) fractured rock masses. Currently, methods for calculating seepage parameters of 3D fractured rock masses have their own advantages and disadvantages, depending on the model assumptions they used. To analyse the hydraulic anisotropy and permeability coefficients of fractured rock masses, a new method for solving the permeability coefficient of a 3D fractured rock mass based on dense sections is proposed using the dimensionality reduction.

This method involves simulating 3D fracture networks and approximating fractured rock masses using dense sections in different directions. The 3D fracture network is decomposed into multiple continuous two-dimensional (2D) sectional fracture networks, and the graph theory is applied to analysing the hydraulic connectivity and permeability paths. Water head boundary conditions are then set to calculate the permeability coefficient. By leveraging the geometric relationships among lines, surfaces, and volumes in 3D space, the calculated permeability coefficient is expressed as the directional permeability in 3D space, and a permeability tensor for the 3D fractured rock mass is constructed.

By treating the section permeability coefficient as a permeability ellipse, the new method calculates the equivalent permeability coefficient of the section and provides a formula for the equivalent permeability tensor of the rock mass. The scale effect and representation of anisotropy in the rock mass are also discussed. The 3D and 2D fracture networks are constructed, and different sizes of unit cells are intercepted to calculate the permeability coefficient, with a typical unit size determined to be 20 m. The method’s feasibility is verified through field water pressure experiments.

This method provides a important reference for solving the permeability coefficients in different directions of heterogeneous fractured rock masses at various scales.

Karst groundwater resources are vital water sources in China. Due to the combined effects of global climate change and intense human activities, such as coal mining, groundwater quality in the Jinci Spring area has deteriorated. Identifying water environmental issues and understanding the groundwater pollution mechanisms in the karst critical zone of this area are crucial for the protection of karst groundwater resources.

The groundwater quality in the spring area was evaluated using the entropy weight method and water quality index. Based on the water quality assessment, isotopic tracing of sulfate oxygen and nitrate nitrogen in groundwater was further performed to trace pollution sources. Additionally, the pollution pathways in karst groundwater were identified through the analysis of inorganic carbon isotopes, strontium isotopes, and sulfur isotopes.

The results showed that the average sulfate concentration in karst groundwater was 572.07 mg/L, while the nitrate concentration in pore water reached 424.72 mg/L, indicating obvious sulfate and nitrate pollution in the groundwater from the study area. Sulfur isotopes in the polluted karst water exhibited a remarkable negative deviation, and the strontium and carbon isotopic characteristics of nitrate-polluted karst water resemble to those of deep pore water.

The primary source of excessive sulfate in groundwater is sulfide oxidation and gypsum dissolution, while sewage discharge and manure input are the important sources of nitrate contamination. The main pollution pathways in karst aquifers include reverse recharge from pore water and cascade recharge from upper-layer goaf water. This study provides important scientific evidence for the control of karst groundwater pollution and the rational development and utilization of karst water resources in the Jinci Spring area.

Under complex geological conditions, the mechanical properties of the surrounding rock in underground water-sealed storage caverns are weakened due to construction disturbances, accompanied by stress redistribution and deformation accumulation, leading to the higher risk of localized instability. The creep effect further exacerbates the deformation and plastic failure of the surrounding rock, posing a threat to the long-term stability of the cavern. Therefore, the study of surrounding rock stability should fully utilize monitoring data to assess the state of the surrounding rock and guide construction and operation.

Based on the comprehensive analysis of multi-source monitoring data, such as surrounding rock displacement, anchor stress, and borehole wave velocity, numerical experiments using orthogonal design were employed to invert the mechanical parameters of the rock mass. Additionally, pore water pressure, surrounding rock deformation laws, stress variation, and plastic zone distribution characteristics under layered excavation of the cavern during construction were analyzed. Finally, the stability characteristics of the underground water-sealed storage cavern under long-term water-sealing conditions were evaluated using a creep model of the underground cavern group.

The results show that the deformation of the surrounding rock sharply increases when passing through the monitored section during excavation, with a maximum increment of approximately 3 mm, and then tends to converge. The area affected by the J₁ jointed zone exhibits higher displacement. The overall stress of the anchor rod system is relatively low, and the stress of the anchor rod is synchronized with the deformation of the surrounding rock. The depth of the loosening zone of the surrounding rock is approximately 1.0 m. During construction period, the pore pressure in the excavation area approaches 0 MPa, and the seepage flow of cavern and deformation of surrounding rock are densely distributed along the J₁ jointed zone. The excavation of the middle and lower layers causes the displacement at the intersection of J₁ and the arch line to increase by 90.4% and 28.7%, respectively. The plastic zone in the sidewalls deepens layer by layer, with a maximum depth of 9.2 m. The long-term deformation characteristics of the surrounding rock are manifested as sidewall convergence > floor uplift > crown settlement. The cumulative deformation at the intersection of the J₁ and the arch line during the first year accounts for 92% of the total deformation over 30 years, with a maximum deformation of 27.1 mm. Under the creep effect, stress is gradually released, and the stress distribution tends to become more uniform. The plastic zone near the J₁ expands significantly, while the plastic range in the intact granite area is relatively small, indicating higher long-term stability, indicating that the geological structure-affected zone is the main instability risk zone.

This study provides engineering significance and reference value for stability evaluation during both the construction and operational phases of underground water-sealed storage caverns.

Previous thermal structure analyses in the Songliao Basin have predominantly focused on the sedimentary scale investigations within the north-south zone. The lack of a comprehensive basin-wide thermal structure analysis at the lithospheric scale has hindered the genetic interpretation of its geodynamic setting.

Utilizing published datasets of surface heat flow, geothermal gradient, and thermophysical properties, this study enhances the existing framework by incorporating new thermophysical measurements from the Yaojia Formation, Qingshankou Formation and Quantou Formation. Supplementary geothermal filed datasets were integrated to establish a holistic characterization of the basin's geothermal regime. This multi-scale approach enables systematic analysis of the contemporary lithospheric thermal architecture.

The results reveal that the geothermal temperature gradient in the Songliao Basin ranges from 21.10 to 63.45°C/km, with an average of 41.41±7.97°C/km, significantly exceeding the global average of 30°C/km. The distribution of surface heat flow values ranges from 30.38 to 106.58 mW/m², with an average of 71.85±12.87 mW/m², surpassing the global average of 60 mW/m², confirming the basin as a typical "hot" basin. Under the influence of Pacific plate subduction, delamination and thermal erosion, the lithosperic thickness have thinned to 58.59 km. Radiogenic heat production in the thinned crust contributes 16.40 mW/m² (22.83% of total surface heat flow), while mantle-derived heat flow from upwelling melts triggered by stagnant slab dehydration accounts for 55.45 mW/m² (77.17% of total)

Controlled by lithospheric thinning and mantle upwelling, the Songliao Basin exhibits characteristic of "hot" basin attributues with a "hot mantle and cold crust" lithospheric thermal structure.

The development of karst reservoirs in the Maokou Formation of the Lower Permian in southern Sichuan Province is critical for conventional oil and gas exploration in this region. The development level of supergene karst reservoirs is directly controlled by the rich and diverse karst microgeomorphology, leading to strong lateral heterogeneity within these reservoirs.

In this study, 3D seismic data, combined with strata thickness, gradient structure tensor attributes, and geological body carving techniques, were employed to characterize the microgeomorphic features of the Dongwu karst in the Yunjin area. Moreover, favourable zones for surface karst reservoirs were predicted through model forward modelling and amplitude attributes.

The results suggested that significant differences in karst microgeomorphology existed during the Dongwu period in the Yunjin area, where a series of karst caves developed. These caves exhibit a seismic feature of a "pull-down" in the seismic event axis at the top boundary of the Maokou Formation. ② The distribution pattern of karst collapse bodies in the Yunjin syncline area was effectively characterized using gradient structure tensor attributes combined with geological body carving technology. The karst collapse bodies exhibit three distinct distribution patterns: isolated, linear, or contiguous distributions. ③ The seismic amplitude on both sides of the karst collapse bodies is weakened, indicating strong karstification and the development of karst caves, which are favorable zones for reservoir development.

This research provides guidance for the subsequent prediction and exploration of reservoirs of the Maokou Formation.

The traditional temperature-pressure field analysis method relies heavily on the interpolation of existing borehole data, which fails to accurately characterize the coupled seepage-heat transfer processes in geothermal systems. This limitation hinder comprehensive understanding of geothermal resources formation mechanisms.

To address these issues, we constructed a three-dimensional geological model of the Zhangye Basin by integrating multisource datasets (borehole information, geophysical data, and digital elevation model). Our integrated approach enhanced the resolution of inter-well stratigraphic correlation by 50–300 m compared to conventional methods. Numerical simulations of coupled seepage-thermal process revealed that multi-physical-field coupling analysis (incorporating both temperature and pressure fields) outperforms traditional key-node spatial interpolation approach in reliability. The results show hydraulic head gradient decreased from the southeastern to the northwestern discharge area, and northeastward heat depletion patterns are due to reservoir shallowing and caprock thinning. The maximum temperatures of 78℃ occurs at the basin center with peripheral cooling effects. A three-dimensional geothermal conceptual model was subsequently developed, synthesizing structural, hydrogeological, and thermal geological constraints.

Coupling this model with heat-flux simulations demonstrated, our study demonstrate that it could provide more realiable interpretation of the northwestward groundwater migration and the Rhombic-lobate spatial distribution of the geothermal anomalies.These findings provide a theoretical framework for targeting high-enthalpy geothermal reservoirs and optimizing sustainable exploitation strategies.

The Upper Paleozoic sandstone reservoirs in the Xinzhao area, northern Ordos Basin are rich in natural gas and characterized by underpressure. The mechanisms of paleo-pressure evolution and underpressure formation are unclear, constraining the understanding of tight sandstone gas accumulation and the enhancement of natural gas production.

In this study, we comprehensively analyzed the petroleum charging history in the second member of the Shanxi Formation using fluid inclusion petrographic observation, micrometry, and laser Raman analysis. Subsequently, we obtained the paleo-pressure during the key period of reservoir formation. The paleo-pressure evolution history was reconstructed by the basin simulation method, and the coupling relationship between paleo-fluid pressure evolution and petroleum charging was established. The relationship between the causes of underpressure and tight gas accumulation is further discussed.

The results indicate that: (1) CO₂ was captured in the second member of the Shanxi Formation in the Xinzhao area from 170 to 180 Ma, when the source rock was in the middle to low maturity stage, and the methane inclusion was captured in the peak of hydrocarbon generation from 138 to 121 Ma. (2) Overpressure in the second member of the Shanxi Formation began to develop in the Early Jurassic and reached a maximum paleo-pressure and paleo-pressure coefficient of 50 MPa and 1.31, respectively, by the end of the Early Cretaceous. (3) The decrease in formation pressure in the second member of the Shanxi Formation, caused by temperature decrease, pore rebound, and gas diffusion, accounted for 49%, 14.5%, and 36.5% of the total formation pressure decrease, respectively.

The tight gas reservoir of the second member in the Shanxi Formation has undergone a pressure evolution process from normal pressure to medium overpressure to normal pressure and finally to underpressure. Hydrocarbon generation supercharging and pressure conduction are the primary factors contributing to ancient overpressure. Temperature decrease and natural gas diffusion are the primary factors contributing to the formation of underpressure in the second member of the Shanxi Formation.