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Machine Learning in detecting Low-frequency Earthquakes in Shikoku, Japan

Huiyun Guo, Hui Huang, Tian Feng, & Lingsen Meng

Published August 15, 2018, SCEC Contribution #8713, 2018 SCEC Annual Meeting Poster #061

Low-frequency earthquakes (LFEs) occur largely during slow slip events at subduction interfaces. Their actions affect the stress state of the seismogenic zone and potentially link to larger ordinary earthquakes. Detecting and analyzing low-frequency earthquakes is crucial for a better understanding of the subduction process. As LFEs lack distinct, impulsive body waves, it is challenging to detect them using classical techniques. The most common method for identifying low-frequency earthquakes involves autocorrelation or template matching techniques that often have low computational efficiencies and therefore difficult to be applied to large dataset. Fortunately, a rapid transformation has occurred in the field of computer vision in recent years due to the emergence of convolutional neural network (CNN). These CNNs are powerful variants of supervised machine learning which significantly improve the computational detecting similar waves in seismology. In this paper, we attempt to detecting low-frequency earthquakes 2018 in Shikoku, Japan using a highly scalable CNN developed by Perol et.al, 2018. We adopt the continuous waveforms from April, 2004 to March, 2011 recorded by five Hi-net stations, KWBH, YNDH, TBEH, OOZH and HIYH in central Shikoku. Based on the LFE catalog from Ohta et.al, 2017, we obtain 1222 events for the training set and 195 events for the testing set. Our preliminary results show that we detect 7251 of tremors in the testing period. These newly detected LFEs are confirmed with visual inspections and anticorrelation tests. Our initial attempt demonstrate that CNN is effective in detecting LFEs. We plan to expand our effort to detect the LFEs across western Japan and Cascadia.

Guo, H., Huang, H., Feng, T., & Meng, L. (2018, 08). Machine Learning in detecting Low-frequency Earthquakes in Shikoku, Japan. Poster Presentation at 2018 SCEC Annual Meeting.

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