日本地球惑星科学連合2018年大会

講演情報

[JJ] Eveningポスター発表

セッション記号 S (固体地球科学) » S-TT 計測技術・研究手法

[S-TT50] 地震観測・処理システム

2018年5月23日(水) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 7ホール)

コンビーナ:吉見 雅行(産業技術総合研究所活断層・火山研究部門)

[STT50-P04] シングルチャンネル記録における地震波初動同定のニューラルネットワークに基づくアプローチ

*亀 伸樹1西條 祥2西田 究1 (1.東京大学地震研究所、2.東京大学理学部地球惑星物理学科)

キーワード:地震波形、初動検知、ニューラルネットワーク

This paper applies a neural-network (NN)-based approach to the identification of initial seismic phase in a single channel record. In this approach, NNs are trained to extract an abrupt increase (or decrease) from the input waveform to identify the arrival of seismic P-wave. Such automated identification will be helpful in the posterior monitoring of off-line records using ocean bottom seismometers and deep mine in-site instruments. Several NN-based classification (or regression) models with fully connected units were developed for the initial phase extraction. Firstly, these models with small degree-of-freedom (input waveforms with 100 points) were trained and tested using 50000 waveforms (artificial model data in the presence of white background noise). The performance of these models was compared in terms of their accuracy, generalization ability, and noise tolerance limits. The results showed the fully connected three-layer NN was best able to determine the presence of initial seismic motion with high accuracy. Secondly, these models with realistic degree-of-freedom were trained and tested using 210000 waveforms (from the Hi-net data with 12000 points, 2-minute long with 100 Hz sampling, including the P-wave arrivals). The results were found to be unsuccessful: training may have required much more waveforms to extract the information necessary for the initial phase identification.