Convolutional Neural Network-Based Moving Ground Target Classification Using Raw Seismic Waveforms as Input

Seismic vibration signatures are strong criteria to recognize moving ground targets in unattended ground sensor (UGS) systems. However, it is a challenging task because of the complexity of seismic waves and their high dependency on the underlying geology. In order to approach this problem, this paper proposes a novel method called VibCNN based on convolutional neural networks (CNNs). Instead of preprocessing signals to extract features, the proposed model takes raw waveforms as input. Another characteristic of the model is that it can handle very short input, which only contains 1024 sample points. The experimental results show that the model yields performance much better than benchmarks and generalizes quite well across different geological types. To further improve the performance of VibCNN, we introduce two auxiliary input channels based on seismic signals and add each auxiliary channel to the input layer of VibCNN separately. Furthermore, we explore different fusion rules of the auxiliary channels at three levels: sample level, feature level, and decision level. The best result achieves relative improvement of 2.05%. In addition, data augmentation for seismic data has not been deeply investigated yet. Thus, we conduce a data augmentation experiment to explore the influence of different augmentation techniques on the performance of the model. The appropriate augmentation improves the accuracy of the model from 93.44% to 95.20%.