Abstract
Earthquake prediction is an area of interest to researchers around the world as well as anyone who has experienced a major earthquake. Major earthquakes often cause loss of lives and property, as well as injuries and destruction. Large investments of time and money are required to build communities back to near where they were before disasters such as earthquakes. Experiencing the 2019 Ridgecrest earthquake sequence was the main motivation for me to dedicate my thesis research to studying earthquakes. For decades, scientists have considered different methods for earthquake prediction. Machine learning (ML) applications have been used in seismology for at least a decade but ML applications in seismology have increasingly grown during the past few years. In this study, deep learning models will be applied to three different earthquake datasets, with the goal of predicting earthquake magnitude. A specific type of recurrent neural network, Long Short-Term Memory (LSTM), with memory cells that allow for utilizing information form recent past steps, will be applied to earthquake datasets. These datasets vary in size and are in the form of time-series where earthquake magnitudes, in Richter scale, are recorded across the time axis. Dataset II is the largest dataset, containing 5o years of seismic data from 1973/01/02 to 2023/12/31, in a large region that covers the state of California with a minimum longitude of -133, maximum longitude of -107, minimum latitude of 24 and maximum latitude of 50. Dataset I is of medium size, covering 3 years of seismic data from 1970/01/02 to 1973/01/02, in the same region. Dataset III is the smallest dataset which contains seismic data for 30 days from 2024/05/05 to 2024/06/04. Different sizes of datasets have been used to study the effect of different timescales. LSTM architectures will be proposed and tested on three different datasets that are acquired from the United States Geological Survey Website and their performance will be evaluated and compared. Since earthquakes are natural phenomena that happen at arbitrary points in time, these time-series are irregular time-series, meaning the time intervals in between consecutive observations vary in size. To address the irregularity of the time-series, interpolation will be applied to datasets. It is observed that interpolation considerably improves the model performance.