Deep learning model for co-seismic response detection of fixed-point deformation data
Peng Zhao
Shao Yongqian
Li Yingnan
Liu Wenbing
Zhao Liming
Abstract：Co-seismic response identification of fixed-point deformation data currently relies on manual selection,and no automatic co-seismic response detection method has yet been applied.This study proposes the first deep learning model for the co-seismic response detection of fixed-point deformation data in China,which is used to detect co-seismic response signals quickly and accurately on the second dataset of a single vertical pendulum broadband tiltmeter.The model was constructed using the transfer learning technique;it introduces three representative pre-trained models for earthquake detection in seismic data as feature extractors,migrates their knowledge and capabilities of earthquake detection in seismic data to fixed-point deformation data,and then designs and adapts supporting data converters and classifiers.Tests on real observational data showed that the model provided a good detection performance.The application of continuous data from Jixian station proved that the model was not only capable of detecting all the co-seismic response events recorded manually,but it also found events that were not recognized manually,with an accuracy rate of no less than 75%.Compared with traditional manual processing,the detection efficiency,detection capability,and consistency are greatly improved using this model.
Keywords：fixed-point deformationco-seismic response detectiondeep learningtransfer learningvertical pendulum broadband tiltmeter
Publication Date：2025-09-25
Online Publishing Date：2025-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 521-528 )