日本地球惑星科学連合2019年大会

講演情報

[J] ポスター発表

セッション記号 S (固体地球科学) » S-CG 固体地球科学複合領域・一般

[S-CG62] 固体地球科学における機械学習の可能性

2019年5月26日(日) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 8ホール)

コンビーナ:内出 崇彦(産業技術総合研究所 地質調査総合センター 活断層・火山研究部門)、小田 啓邦(産業技術総合研究所地質情報研究部門)

[SCG62-P03] Detection of Gas Bubble signals recorded at the OBS Stations by Machine-Learning

*emmy TY CHANG1 (1.Institute of Oceanography, National Taiwan University)

キーワード:gas emission, bubble, machine learning, Ocean bottom seismometer

Along the OBS seismograms, the waveforms of gas emission signals exhibit a high-frequency resonant vibration alike the bubble bursting at the free surface of a non-Newtonian fluid. Every single signal is a short-duration event (hereafter termed as “SDE”, <1.0 second). In this study, we shall develop the computer algorithm to detect the SDE signals along the OBS seismograms. Our work carried out in 2018 has been laid on the mathematical matching of the bubble signals. We conclude that even though the bubbles exhibit a specific waveform, the mathematical equation cannot perfectly describe the bubble with all ambient conditions at the seafloor. Our strategy is to adopt the Machine Learning (ML) to identify bubbles by images. In the following years, two phases of our project are designed as (i) Establishing the method to quantify the SDE signals by means of ML modeling: we shall train a neural network model with varied parameters to gain a complete estimation of the bubble signals within our OBS records. (ii) The experiments with OBS instruments will also be used for tests of generating different waveforms in water tanks of the ifremer as well as a constrained offshore area (e.g. lake), to provide referenced waveforms for the SDE or bubble signals.