Automated Event Detection and Denoising Method for Passive Seismic Data Using Residual Deep Convolutional Neural Networks

There has been a recent rise in the uses and applications of passive seismic data, such as tomographic imaging, volcanic monitoring, and hydrocarbon exploration. Consequently, the sharp increase in passive seismic applications requires real-time event detection capabilities with high accuracy. Proper analysis of such events depends largely on the signal-to-noise ratio improvement through noise suppression techniques. Recent advances in the fields of signal processing and deep learning coupled with the available computational resources provide a great opportunity to address this challenge. In this work, a workflow is proposed where a residual deep neural network is customized and employed to detect passive seismic events. The automated detection is followed by a denoising step to extract the signal of interest from background noise using an IIR Wiener filter. The proposed method does not require any prior knowledge of the signal/noise, and therefore, it can work with various types of signals/noises. Another benefit of the proposed detection method is that the deep neural network is trained only on synthetic seismic data without the need to use real data in the training process. Nevertheless, it exhibits high accuracy in detecting and denoising events from real passive seismic data sets. In particular, field seismic data is recorded in northern Saudi Arabia and used to test the complete detection and denoising method. The detection method proved its capability of detecting events automatically in large data sets and in real time (due to off-line training).