A Framework for Integrating Machine Learning and Seismic Data in Earthquake Prediction

Thesis Abstract

Recent advancements in geophysical monitoring and computational analysis have underscored the potential of integrating machine learning techniques with seismic data for improved earthquake prediction. Despite the increasing availability of large seismic datasets and sophisticated algorithms, there remains a significant gap in comprehensive frameworks that systematically combine these elements to enhance predictive accuracy and operational feasibility. This study seeks to develop a robust, scalable framework that integrates machine learning models with seismic data for earthquake prediction, aiming to address the limitations of existing predictive approaches. The primary objectives are to evaluate the efficacy of various machine learning algorithms—such as random forest, support vector machines, and neural networks—in analyzing temporal and spatial seismic features; to establish a data preprocessing pipeline that optimizes quality and relevance of seismic parameters; and to formulate a predictive model that can be embedded within early warning systems. Employing a quantitative research design, the study analyzed seismic datasets collected from the Pacific Plate region, encompassing a comprehensive time-series of over 15,000 seismic events recorded between 2000 and 2022 by the USGS and regional seismic stations. The target population comprised seismic signals with associated metadata, including magnitude, depth, and geographic coordinates. A stratified sampling technique was used to select a subset of 3,000 events representing a broad spectrum of earthquake types and magnitudes, ensuring representative variability. Data collection instruments included high-resolution seismographs, regional earthquake catalogues, and remote sensing data, collated and processed through specialized seismic data management software. To enhance data validity, preprocessing steps included noise filtering, feature extraction—such as spectral analysis and seismotectonic parameters—and normalization procedures. The selected machine learning models were trained and validated using k-fold cross-validation techniques, with performance metrics assessed through accuracy, precision, recall, F1-score, and Area Under Curve (AUC). The analytical framework integrated regression analysis for magnitude prediction, alongside classification models for event occurrence probability. Model development was guided by the Theory of Seismic Hazard and the principles of ensemble learning, with feature importance analysis conducted via permutation tests to identify key seismic predictors. Expected findings include identification of the most salient seismic features influencing earthquake occurrence, characterization of the predictive performance of various machine learning models, and a demonstrable improvement over traditional statistical methods. The study anticipates that neural networks will outperform other models in capturing complex nonlinear relationships within seismic datasets, while ensemble models will enhance predictive robustness. The developed framework is expected to significantly advance current predictive methodologies by providing real-time, data-driven insights. This research contributes to existing knowledge in geophysics and machine learning by proposing an integrated, operational framework for earthquake forecasting that leverages large seismic datasets and advanced algorithms. It offers a comprehensive methodological approach adaptable to different tectonic regions. The main conclusion advocates for the adoption of hybrid predictive models in early warning systems to mitigate earthquake-related risks. Based on findings, the study recommends the deployment of the developed framework within regional seismic monitoring agencies and encourages further research into incorporating additional geophysical parameters such as geodetic data and acoustic signals to refine prediction accuracy. Overall, this work aims to bridge the gap between data science innovations and practical seismic hazard assessment, paving the way for more reliable and timely earthquake warnings.