A Time-Frequency Depth Convolutional Recurrent Network for Seismic Waveform Automatic Classification

Abstract

Seismic monitoring has been instrumental in various domains such as natural earthquake early warning, mineral mining safety assessment, and hydraulic fracturing impact evaluation. However, the monitoring data often exhibit low signal-to-noise ratio (SNR) and large volume. Developing an efficient, high-precision, and universally applicable seismic waveform automatic classification network model becomes significant and practical. We propose a physical interpretable time-frequency deep convolutional recurrent neural network (TF-DCRNN) model which consists of an integration of a time-frequency convolutional (TFconv) layer and a convolutional recurrent neural network (CRNN). Subsequently, we evaluate the classification performance by comparing five network models, including convolutional neural network (CNN) and long short-term memory (LSTM), using Ricker wavelet datasets with varying SNR levels (−15 ∼ 0 dB). Our findings verify the superiority of the TF-DCRNN model in the classification of strong interference environment from both numerical and physical simulation. Moreover, integrating multiple network models or incorporating a TFconv layer can moderately enhance the classification performance, which provides the direction for network model optimization.