基于微震技术的河流过程监测及水文参数反演的初步探索

冯亮; 张世哲; 谢斌; 周晓悦; 续伟; 王艳梅

doi:10.19509/j.cnki.dzkq.tb20240293

基于微震技术的河流过程监测及水文参数反演的初步探索

doi: 10.19509/j.cnki.dzkq.tb20240293

冯亮^{1a, 1b, ,},
张世哲^{1a, 2, 3},
谢斌⁴,
周晓悦^1a,
续伟^1a,
王艳梅^1a

1a.
江西理工大学：资源与环境工程学院
1b.
江西理工大学：河流源头水生态保护江西省重点实验室，江西赣州 341000
2.
中国科学院大学，北京 100049
3.
中国科学院、水利部成都山地灾害与环境研究所，成都 610041
4.
江西省地震局赣州地震监测中心站，江西赣州 341000

基金项目: 国家自然科学基金青年科学基金项目（4210071309）；江西省自然科学基金面上项目（20224BAB203040）；中国地震局“监测、预报、科研”三结合课题基金项目（3JH-202401050）；江西省地震局“2111”孵化基金项目（FD2111-202404）

详细信息

通讯作者:
E-mail：liang.feng@jxust.edu.cn

中图分类号: X43；X84；TM93
计量
- 文章访问数: 142
- PDF下载量: 112
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-03
- 录用日期: 2024-07-26
- 修回日期: 2024-07-25
- 网络出版日期: 2025-12-18

Preliminary exploration of river process monitoring and hydrological parameter inversion based on microseismic technology

FENG Liang^{1a, 1b
, ,},
ZHANG Shizhe^{1a, 2, 3},
XIE Bin⁴,
ZHOU Xiaoyue^1a,
XU Wei^1a,
WANG Yanmei^1a

1a.
School of Resources and Environmental Engineering, Jiangxi University of Science and Technology
1b.
Jiangxi Provincial Key Laboratory of Water Ecology Protection of River Headwaters, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Chengdu Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Ministry of Water Resources, Chengdu 610041, China
4.
Ganzhou Earthquake Monitoring Center Station, Jiangxi Provincial Seismological Bureau, Ganzhou Jiangxi 341000, China

More Information

Corresponding author: E-mail：liang.feng@jxust.edu.cn

摘要

摘要:
河流监测手段趋向遥感、智能化，传统河流监测技术费时费力，且在洪水期间面临仪器损坏和缺失数据的风险。国家地震台网分布广泛密集，微震技术具有远程无接触、低成本和24 h无间断监测的特点，正逐渐被用于河流监测中。通过野外河流微震监测试验，监测与分析河流过程的动态振动信号，获取河流湍流过程微震信号的物理特征。在此基础上，利用带通滤波的方法保留2～7 Hz频带的信号，采用Welch法计算出微震信号时频分析图上在2～7 Hz频带内1 min平均地震功率，将其转换为能量的形式，并与现场测得的河流水文数据相匹配，从而评估微震监测河流的潜力。为获取河流的水文参数，提出一个简单的线性回归模型量化平均功率谱密度（PSD）与河流湍流过程之间的关系，由此推导出反演计算河流流量的线性近似模型，模型反演结果在实测值附近波动，相对误差在10.29%以内，反演结果较为准确。本研究为野外河流微震监测试验的初步探索，研究成果可为依托国家高密度地震台站的河流洪水及常态水文遥感、智能化监测提供参考和理论依据。
- 微震监测 /
- 河流过程监测 /
- 时频分析 /
- 功率谱密度（PSD） /
- 流量反演 /
- 水文参数反演
Abstract:
River monitoring tends to be adopt remote sensing and intelligent technologies. Traditional river monitoring techniques are time-consuming and labor-intensive, and face the risks of instrument damage and missing data during floods. Benefiting from the advantages of remote non-contact operation, low cost and 24-hour continuous monitoring, microseismic technology is increasingly applied in river monitoring. In this paper, field microseismic monitoring experiments on rivers were conducted to monitor and analyze the dynamic vibration signals of river processes. Thereby obtaining the physical characteristics of microseismic signals generated by river turbulence. On this basis, a band-pass filtering method was used to retain signals in the 2-7 Hz frequency band. The Welch method, was employed to calculate the 1-minute average seismic power within the 2-7 Hz frequency band from the time-frequency analysis diagram of microseismic signals, which was then converted energy form and matched with the field measured river hydrological data to evaluate the potential of microseismic technology in river monitoring. To acquire the hydrological parameters of the rivers, a simple linear regression model was proposed to quantify the relationship between the average power spectral density (PSD) and the river turbulence process. A linear approximation model for inverting river discharge was derived accordingly. The inversion results of the model fluctuated around the measured values with a relative errors within 10.29%, indicating high accuracy. This study is a preliminary exploration of field microseismic monitoring experiments on rivers. The research results can provide a reference and theoretical basis for remote sensing and intelligent monitoring of river floods and normal hydrology relying on the national high-density seismic network.
- microseismic monitoring /
- river process monitoring /
- time-frequency analysis /
- power spectral density (PSD) /
- discharge inversion /
- hydrological parameter inversion

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图 1 试验仪器

Figure 1. Experimental instruments

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图 2 显示试验及台站位置的场地地形图

Figure 2. Topographic map of the study area showing the locations of the experiments and stations

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图 3 河道仪器布设图（SZ2等均为实验仪器编号）

Figure 3. Layout of seismic instruments

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图 4 速度面积法估算河流流量示意图

p₀. 河岸起点；p_i. 第i个测量点；p_ip₀. 测量点至河岸距离；h_i. 测量点p_i所测水位；v_i. 测量点p_i所测表面流速；u_i. 切片流速；d_i. 切片深度；b_i. 切片宽度

Figure 4. Schematic diagram of river discharge estimation by the velocity-area method

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图 5 不同断面位置处河流信号和环境噪音中的车辆信号波形（通道E为微震设备EW方向接收到的信号；通道N为微震设备NS方向接收到的信号；通道Z为微震设备垂直方向接收到的信号；下同）

Figure 5. Waveforms of river signals and vehicle signals at different section locations

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图 6 第1次试验位置SZ4微震基站通道E记录的河流产生的微震信号(a～b)及其频谱(c)特征

Figure 6. Microseismic signals generated by the river (a-b) and their spectral characteristics (c) recorded by Chamel E of the SZ4 microseismic station at the 1st experiment location

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图 7 第3次试验位置处SZ3微震基站通道E记录波形图

Figure 7. Waveform diagram recorded by Channel E of the SZ3 microseismic station at the 3rd experiment station

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图 8 车辆产生的微震信号(a～b)及其频谱特征(c)（SZ3微震基站通道E 15:55记录的数据）

Figure 8. Microseismic signals generated by vehicles (a-b) and their spectral characteristics (c)

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图 9 试验期间河流平均流速变化(a)与SZ4微震基站通道E记录的2～7 Hz频带上的波形图(b)及时频分析图(c)

Figure 9. Correlation between the average flow velocity changes of the river during the experiment (a), waveform diagram (b) and time-frequency analysis diagram (c) of signals in the 2-7 Hz frequency band recorded by Channel E of the SZ4 microseismic station

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图 10 平均流速和平均地震功率变化对比图

Figure 10. Comparison between average flow velocity and average seismic power

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图 11 湍流过程在通道E、N、Z上的流量回归系数及95%置信区间

Figure 11. Flow regression coefficients and 95% confidence intervals of the turbulent flow process in the E, N, and Z Channels

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图 12 河流流量预测值与实际观测值对比图

Figure 12. Comparison chart of predicted river discharge and observed discharge

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