Deep learning seismic damage assessment with embedded signal denoising considering three-dimensional time–frequency feature correlation

Abstract

Fast and accurate seismic damage assessment is crucial for timely post-earthquake evaluation and rescue. In seismic damage assessment, the correlation between ground motions in three directions during the time–frequency transform and the appropriate denoising thresholds during signal data preprocessing pose challenges for traditional methods. We propose a novel deep learning framework, DRSNet (deep residual shrinkage network), which incorporates the CNN structure of ResNet and a soft-threshold denoising module for simultaneous seismic signal denoising and damage assessment. To consider the feature correlation in time–frequency transform of seismic signals, they are first decomposed synchronously with noise-assisted multivariate empirical mode decomposition (NAMEMD) into multi-dimensional intrinsic mode functions (MIMF), and MIMFs are then transformed into seismic time–frequency maps using a non-parametric time–frequency analysis with scalable time windows and the Duhamel integral form. The subsidence and the subsidence rate of the dam are computed with finite element analysis and considered to assess seismic damage. The time–frequency maps and the finite element analysis results are used as input and output data to train DRSNet with no need for preprocessing seismic signal noise. The proposed model has been applied to a large-scale hydropower dam and achieved an average precision of 94.52% for predicting the seismic damage levels, which outperforms ResNet and ResNeSt by 3.09% and 2.62%, respectively. Thorough comparison and analysis of the model demonstrate its potential for accurate and efficient seismic damage assessment.