Sensor selection for seismic wavefield reconstruction based on sparse observation (Part II: Preliminary study on method based on data-driven low-dimensional model)

Takayuki Nagata, Kumi Nakai, Yuji Saito, Taku Nonomura, Masayuki Kano, Shin-ichi Ito, Hiromichi Nagao

Presentation information

[J] Poster

S (Solid Earth Sciences ) » S-TT Technology & Techniques

[S-TT37] Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics

Thu. Jun 3, 2021 5:15 PM - 6:30 PM Ch.14

convener:Hiromichi Nagao(Earthquake Research Institute, The University of Tokyo), Aitaro Kato(Earthquake Research Institute, the University of Tokyo), Keisuke Yano(The Institute of Statistical Mathematics), Takahiro Shiina(National Institute of Advanced Industrial Science and Technology)

5:15 PM - 6:30 PM

[STT37-P07] Sensor selection for seismic wavefield reconstruction based on sparse observation (Part II: Preliminary study on method based on data-driven low-dimensional model)

*Takayuki Nagata¹, Kumi Nakai¹, Yuji Saito¹, Taku Nonomura¹, Masayuki Kano¹, Shin-ichi Ito², Hiromichi Nagao² (1.Tohoku University, 2.Earthquake Research Institute, The University of Tokyo)

Keywords:data-driven science, low-dimensional model, sparse sensing

In the present study, we conducted a preliminary study on data-driven sensor selection for seismic wavefield reconstruction based on sparse observation. Simulations of the seismic wavefield with the horizontally layered subsurface structure model were performed under various conditions and were extracted the modes of the seismic wavefield. Sensors suitable for accurate seismic wavefield reconstruction were selected based on the modes of the extracted seismic wave.