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.

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 disaster 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.

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 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.

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.

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.

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.

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.

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.

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℃, while the K-Mg and quartz geothermometers estimated the shallow thermal reservoir temperature at approximately 175℃. 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.

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 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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.