Developing Deep Learning Tools in Earthquake Detection and Phase Picking

Abstract

With the rapid growth of seismic data volumes, traditional automated processing methods, which have been in use for decades, face increasing challenges in handling these data, especially in noisy environments. Deep learning (DL) methods, due to their ability to handle large datasets and perform well in complex scenarios, offer promising solutions to these challenges. When I started my Ph.D. degree, although a sizeable number of researchers were beginning to explore the application of deep learning in seismology, almost no one was involved in the development of much-needed automated data annotation tools and deep learning training platforms for this field. In other rapidly evolving fields of artificial intelligence, such automated tools and platforms are often a prerequisite and critical to advancing the development of deep learning. Motivated by this gap, my Ph.D. research focuses on creating these essential tools and conducting critical investigations in the field of earthquake detection and phase picking using DL methods. The first research chapter introduces QuakeLabeler, an open-source Python toolbox that facilitates the efficient creation and management of seismic training datasets. This tool aims to address the laborious process of producing training labels in the vast amount of seismic data available today. Building on this foundational tool, the second research chapter presents Blockly Earthquake Transformer (BET), a deep learning platform that provides an interactive dashboard for efficient customization of deep learning phase pickers. BET aims to optimize the performance of seismic event detection and phase picking by allowing easy customization of model parameters and providing extensions for transfer learning and fine-tuning. The third and final research chapter investigates the performance of DL pickers by examining the effect of training data size and deployment settings on phase picking accuracy. This investigation provides insight into the optimal size of training datasets, the suitability of DL pickers for new target regions, and the impact of various factors on training and on model performance. Through the development of these tools and investigations, this thesis contributes to the application of DL in seismology, paving the way for more efficient seismic data processing, customizable model creation, and a better understanding of DL model performance in earthquake detection and phase-picking tasks.