EWR-Net: Earthquake Waveform Regularization Network for Irregular Station Data Based on Deep Generative Model and ResNet

Gan, Haodong1,2,3; Pan, Xiao4; Tang, Ke5; Hu, Nan6; Zhang, Wei1,2,3

2022-10-01

10.1029/2022JB024122

JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH   影响因子和分区

2169-9313

2169-9356

Owing to the limitations of surface conditions, the distribution of earthquake station arrays, even dense arrays, is uneven and spatially irregular. The station intervals are too large with respect to migration algorithm requirements. Therefore, the regularization of irregular station data is an important preprocessing step before imaging. Several methods, such as the curvelet transform method based on the sparse transform, have been developed for regularizing teleseismic data. In this study, we present a novel deep learning (DL) approach for teleseismic waveform regularization in 2D surveys. We designed an earthquake waveform regularization network (EWR-Net) based on a deep generative model and residual network, consisting of a transposed convolution block, convolution block, and full connection block. The convolution block was variable and adjusted according to different data complexities to improve adaptability. The network was able to capture complex mapping between station locations and waveforms, and could be used to regularize both randomly and regularly sampled data without spatial smoothing. Unlike other DL methods, the EWR-Net was trained and used for each event. It was trained using recorded teleseismic waveforms at irregular stations, and was then used to predict waveforms at regular stations. To avoid overfitting, L2 regularization, dropout in the full connection block, and early stopping were employed. The test results on both synthetic and field data showed that EWR-Net could generate more accurate earthquake waveforms at virtual stations than the curvelet method. Reverse-time migration imaging tests using regularized data demonstrated the feasibility of the proposed method.

earthquake waveform regularization irregular station data deep generative model residual network

SCI

英语

第一 ; 通讯

National Natural Science Foundation of China["42074056","U1901602"] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0203] ; Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology[ZDSYS20190902093007855] ; Shenzhen Science and Technology Program[KQTD20170810111725321]

Geochemistry & Geophysics

Geochemistry & Geophysics

WOS:000863583400001

AMER GEOPHYSICAL UNION

GEOSCIENCES

Web of Science

1.Southern Univ Sci & Technol, Shenzhen Key Lab Deep Offshore Oil & Gas Explorat, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China
3.Southern Marine Sci & Engn Guangdong Lab Guangzho, Guangzhou, Peoples R China
4.Chengdu Univ Technol, Coll Geophys, Chengdu, Peoples R China
5.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China
6.Harbin Inst Technol, Dept Mech & Aerosp Engn, Harbin, Peoples R China

Gan, Haodong,Pan, Xiao,Tang, Ke,et al. EWR-Net: Earthquake Waveform Regularization Network for Irregular Station Data Based on Deep Generative Model and ResNet[J]. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,2022,127(10).

Gan, Haodong,Pan, Xiao,Tang, Ke,Hu, Nan,&Zhang, Wei.(2022).EWR-Net: Earthquake Waveform Regularization Network for Irregular Station Data Based on Deep Generative Model and ResNet.JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,127(10).

Gan, Haodong,et al."EWR-Net: Earthquake Waveform Regularization Network for Irregular Station Data Based on Deep Generative Model and ResNet".JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 127.10(2022).