Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies
Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies
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DOI：
10.1109/tgrs.2020.3032664
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发表时间：
2021-09-01
影响因子：
8.2
通讯作者：
Stankovic, Vladimir
中科院分区：
文献类型：
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作者：
Li, Jiangfeng;Stankovic, Lina;Stankovic, Vladimir
在过去的二十年中，微地震监测越来越多地用于照亮（下）表面过程，如滑坡，由于其能够记录土壤运动和/或岩石的脆性行为产生的小地震波。了解滑坡过程的演变对于预测甚至避免即将发生的故障至关重要。微震监测记录通常是连续的、有噪声的，并且由各种源发出的信号组成。人工检测和区分不稳定斜坡发出的信号具有挑战性。自动端到端去噪，检测和分类的微震事件，作为一个早期预警系统的研究，仍处于起步阶段。为此，我们的工作重点是共同评估和开发合适的方法，信号去噪，准确的事件检测，非现场特定的功能建设，功能选择和事件分类。我们提出了一个自动化的端到端系统，可以快速处理连续地震记录的大数据集，并证明对各种事件（远程和本地地震，滑动地震，人为噪声等）的适用性和鲁棒性。数学贡献在于新的信号处理和分析方法，具有比现有技术更少的可调参数，在两个现场数据集上进行评估，并以现有技术为基准。
Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes, such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behavior of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding an imminent failure. Microseismic monitoring recordings are often continuous, noisy, and consist of signals emitted by various sources. Manually detecting and distinguishing the signals emitted by an unstable slope is challenging. Research on automated end-to-end denoising, detection, and classification of microseismic events, as an early warning system, is still in its infancy. To this effect, our work is focused on jointly evaluating and developing suitable approaches for signal denoising, accurate event detection, nonsite-specific feature construction, feature selection, and event classification. We propose an automated end-to-end system that can process big data sets of continuous seismic recordings fast and demonstrate applicability and robustness to a wide range of events (distant and local earthquakes, slidequakes, anthropogenic noise, etc.). Algorithmic contributions lie in novel signal processing and analysis methods with fewer tunable parameters than the state of the art, evaluated on two field data sets and benchmarked against the state of the art.