[S-TT46] 最先端ベイズ統計学が拓く地震ビッグデータ解析

[STT46-06] Deep-learning-based Earthquake Detection for Continuous Seismic Network Records

*矢野 恵佑¹、椎名 高裕²、倉田 澄人¹、加藤 愛太郎²、駒木 文保¹、酒井 慎一²、平田 直² (1.東京大学情報理工学研究科、2.東京大学地震研究所)

キーワード：地震学、深層学習

Over the last decade, continuous seismic data have been enormously acquired on seismic networks consisting of multiple sensors at distributed locations. Analyzing these data efficiently and thoroughly offers substantial benefits to seismology. The first important step in the analysis is earthquake detection, that is, detecting earthquakes in continuous massive datasets.

In this talk, we present a deep-learning-based scheme for earthquake detection from continuous records in a seismic network. We work with a convolutional neural network (CNN), which is one of the most powerful supervised learning techniques, to capture features discriminating between earthquakes and noises. Our scheme has an advantage of utilizing multiple stations in a seismic network to discriminate between earthquakes and noises.

We apply our scheme to continuous data on Metropolitan Seismic Observation network (MeSO-net) from September 4, 2011 to September 16, 2011. We show our scheme improves on CNNs based on few stations especially in preventing mis-detection. In addition, the trained network in the last fully connected layer has quasi-sparsity, by which we identify features important for CNN to recognize earthquakes.