AI-Enhanced Seismic Data Analysis for Earthquake Prediction Accuracy

Thesis Abstract

Earthquake prediction remains a critical challenge in geophysics due to the limitations of conventional seismic data analysis techniques in accurately forecasting seismic events and providing timely warnings. This study aims to develop an advanced AI-driven model to enhance seismic data analysis and improve earthquake prediction accuracy, thereby contributing to disaster preparedness and risk mitigation efforts. The specific objectives are to (1) evaluate the effectiveness of machine learning algorithms in seismic pattern recognition, (2) develop an integrated AI framework for real-time seismic data processing, and (3) assess the predictive performance of the model against existing traditional methods. A mixed-methods research design was employed, combining quantitative and qualitative approaches. The quantitative component involved the collection of seismic datasets from a network of 50 seismic monitoring stations across a seismically active region over a five-year period, producing a dataset comprising approximately 10 million raw seismic signals. The qualitative component included expert interviews with seismologists and disaster management practitioners to contextualize the technical findings. Data collection instruments consisted of high-resolution accelerometers, spectral analyzers, and custom-developed AI software utilizing deep learning techniques, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The sampling technique was stratified sampling to ensure diverse seismic activity levels and geospatial distribution. Data analysis employed a combination of machine learning model training, validation, and testing, with model performance evaluated through metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves. Several analytical techniques were applied, including hyperparameter tuning via grid search, feature extraction through wavelet transform analysis, and model interpretability assessments using SHAP (SHapley Additive exPlanations). The analytical framework also integrated geographic information system (GIS) mapping to spatially visualize seismic patterns and model predictions. The study adhered to rigorous ethical standards, ensuring data privacy and obtaining necessary permissions from relevant authorities. The anticipated findings suggest that AI-enhanced seismic data analysis significantly outperforms traditional statistical approaches, with the developed CNN-RNN hybrid model achieving prediction accuracies exceeding 85% in identifying precursor seismic signatures. The integration of spectral feature extraction and spatial analysis is expected to improve early warning capabilities and reduce false positive rates. The study also aims to reveal critical seismic features—such as microseismic activities and foreshock patterns—that are most predictive of major earthquakes, aligning with the theoretical framework established by the Self-Organized Criticality (SOC) theory and the Earthquake Nucleation Theory. This research advances knowledge by demonstrating the effectiveness of deep learning techniques in capturing complex seismic phenomena and translating them into actionable predictions. It provides a scalable AI framework adaptable to diverse seismic regions, addressing a significant gap in operational earthquake forecasting systems. The contribution extends to the development of a real-time seismic monitoring dashboard that integrates AI predictions with GIS mapping for disaster management agencies. The main conclusion underscores that AI-driven seismic analysis markedly enhances earthquake prediction accuracy, offering a valuable tool for early warning systems. Recommendations include adopting the developed AI framework for operational deployment, integrating it with existing seismic networks, and conducting further longitudinal studies to improve model robustness. Future research should explore the incorporation of multi-modal data sources, such as geodetic and atmospheric data, to refine predictive capabilities further. Overall, this study provides a scientifically grounded, technologically innovative approach to mitigating earthquake-related hazards through intelligent seismic data analysis.