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Recent advances in earthquake monitoring II: Emergence of next-generation intelligent systems

期刊

EARTHQUAKE SCIENCE
卷 34, 期 6, 页码 531-540

出版社

KEAI PUBLISHING LTD
DOI: 10.29382/eqs-2021-0054

关键词

earthquake monitoring; phase picking; machine learning; template matching

资金

  1. USTC Research Funds of the Double First-Class Initiative [YD2080002006]
  2. Special Fund of the Institute of Geophysics, China Earthquake Administration [DQJB21Z05]

向作者/读者索取更多资源

Seismic data processing techniques have significantly improved earthquake monitoring capabilities in the past two decades, thanks to advancements in computational power, signal processing, and machine learning. Techniques such as template matching and deep learning have enhanced the ability to extract small earthquake signals from noisy data. Future research needs to focus on developing highly generalizable detection algorithms and real-time monitoring technologies for ongoing seismic sequences.
Seismic data processing techniques, together with seismic instrumentation, determine our earthquake monitoring capability and the quality of resulting earthquake catalogs. This paper is intended to review the improvement of earthquake monitoring capability from the perspective of data processing. Over the past two decades, seismologists have made considerable advancements in seismic data processing, partly thanks to the significant development of computational power, signal processing, and machine learning techniques. In particular, wide application of template matching and increasing use of deep learning significantly enhance our capability to extract signals of small earthquakes from noisy data. Relative location techniques provide a critical tool to elucidate fault geometries and seismicity migration patterns at unprecedented resolution. These techniques are becoming standard, leading to emerging intelligent software systems for next-generation earthquake monitoring. Prospective improvements in future research must consider the urgent needs in highly generalizable detection algorithms (for both permanent and temporary deployments) and in emergency real-time monitoring of ongoing sequences (e.g., aftershock and induced seismicity sequences). We believe that the maturing of intelligent and high-resolution processing systems could transform traditional earthquake monitoring workflows and eventually liberate seismologists from laborious catalog construction tasks.

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