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

Committee: 

Dr. McClellan, Advisor 

Dr. AlRegib, Chair

Dr. Peng

Abstract:

The objective of the proposed research is prototyping a feasible solution for seismic event detection and phase picking on an embedded system with seismic sensors using a lightweight convolutional neural network (CNN). Accurate offline training of the CNN is demonstrated for small and large datasets, while observing the trade-off between accuracy and computation cost which gives an efficient online deployment of the neural network. When trained for a small dataset from one area, the generality of the CNN model is validated for processing in other regions. Simplification of the model and quantization of its parameters is explored to develop a prototype that is needed for embedded devices. The product of this research is a universal seismic event detection and phase picking tool that is accurate and efficient for processing large volumes of data and lightweight for deployment on an embedded system.