Development of AI-based Seismic Data Interpretation for Earthquake Risk Assessment

Thesis Abstract

Earthquake risk assessment relies heavily on the accurate interpretation of seismic data, which traditionally involves manual and semi-automated processes prone to subjectivity and inefficiency. This study addresses the critical need for enhanced interpretative capabilities through the development of an artificial intelligence (AI)-based framework aimed at improving the accuracy, speed, and predictive power of seismic data analysis. The primary aim is to create a robust AI-driven model that automates seismic data interpretation workflows, thereby facilitating more reliable earthquake risk assessments. Specific objectives include evaluating current seismic interpretation methodologies, designing and training machine learning models—particularly convolutional neural networks (CNNs) and support vector machines (SVMs)—on a comprehensive dataset of 10,000 seismic records from a tectonically active region, and validating the models’ performance against existing interpretative benchmarks. The study employs a quantitative research design, integrating supervised machine learning techniques to develop predictive models. The population comprises seismic datasets obtained from well-established regional seismic monitoring agencies, with a targeted sample size of 10,000 seismic records spanning a period of 15 years. Stratified random sampling ensures representativeness across different seismic activity levels, fault types, and geological settings. Data collection instruments include digital seismic databases, enhanced with metadata and geological information. Data preprocessing involves noise reduction, feature extraction, and normalization, with the dataset split into 70% training, 15% validation, and 15% testing subsets. Analytical methods encompass feature selection through principal component analysis (PCA), model training via CNN and SVM algorithms, and model performance evaluation using metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The models are further optimized through hyperparameter tuning employing grid search and cross-validation procedures. To interpret the models' outputs and assess their consistency with geophysical phenomena, the study incorporates the Theory of Seismic Signal Processing and the Theory of Machine Learning Explainability. These frameworks guide the interpretation of model decision-making processes and ensure the scientific validity of the AI-driven interpretations. Anticipated findings suggest that the AI models will outperform traditional interpretative methods, achieving accuracy levels above 85% in identifying seismic anomalies associated with earthquake precursors. The models are expected to reduce interpretative timeframes from hours to minutes while providing consistent results across varying seismic contexts. Additionally, the integration of AI-based interpretative tools is projected to enhance the spatial and temporal resolution of risk models, thus enabling more precise earthquake hazard zoning and early warning systems. This research contributes novel insights into the application of advanced machine learning techniques within seismic data interpretation, bridging gaps highlighted in previous literature concerning the automation and scalability of seismic risk assessment methods. It offers a comprehensive framework for deploying AI models that are scientifically rigorous, operationally feasible, and adaptable to different tectonic settings. The study underscores the potential of AI to transform seismic monitoring and hazard assessment paradigms, supporting policymakers and disaster management agencies in formulating data-driven mitigation strategies. In conclusion, the study recommends the integration of AI-powered seismic interpretation frameworks into existing seismic monitoring infrastructures, advocating for further research into hybrid models combining geophysical knowledge with deep learning techniques. Future work should explore real-time data integration, the development of interpretability dashboards for end-users, and the application of this framework to regions with limited seismic monitoring resources. Overall, this research lays a foundational step toward deploying intelligent systems that significantly enhance earthquake risk assessment and disaster preparedness efforts globally.