Title:  Seismic Processing via Machine Learning for Event Detection and Phase Picking

Committee:

Dr. James McClellan, ECE, Chair , Advisor

Dr. Ghassan AlRegib, ECE

Dr. Zhigang Peng, EAS

Dr. David Anderson, ECE

Dr. Andrew Newman, EAS

Abstract:

The increasing volume of seismic data from long-term continuous monitoring motivates the development of algorithms based on convolutional neural network (CNN) for faster and more accurate event detection and phase picking. However, existing CNN-based approaches depends on large training sets with millions of labeled samples which are only available for a limited number of well-studied regions. Moreover, inference of the large models from existing studies is only possible on cloud servers where multiple powerful GPUs are available. With the rapid development of seismic recording devices and connected sensor networks, lightweight yet accurate models are necessary for deploying neural network solutions on the edge devices. This thesis prototypes a feasible system for seismic event detection and phase picking on an embedded system with seismic sensors using a lightweight CNN model. A flexible CNN model capable of training on only tens of thousands of labeled samples is designed and validated on both small and large datasets. The deployment of the CNN model on continuous seismic waveform has resulted in newly detected events and accurate arrival times on multiple datasets from different geological regions. Transfer learning of the proposed CNN model is validated on small labeled datasets with and without fine-tuning. The CNN model is simplified based on the visualization of the internal CNN filter weights distribution. Quantization using lower-precision number of both data and filter weights are explored for future embedded devices with fixed-point number acceleration enabled.