Development of Machine Learning Algorithms for Real-Time Seismic Event Detection

Thesis Abstract

Seismic events pose significant risks to human safety, infrastructure integrity, and economic stability, necessitating the development of reliable, rapid detection systems capable of operating in real time. Traditional seismic monitoring methods, while effective in post-event analysis, often lack the responsiveness required for immediate hazard mitigation. This study aims to develop and validate advanced machine learning algorithms tailored for real-time seismic event detection, thereby enhancing early warning capabilities. The specific objectives include evaluating the performance of various supervised learning classifiers, such as convolutional neural networks (CNNs), support vector machines (SVMs), and random forests, in distinguishing seismic signals from noise; optimizing feature extraction techniques to improve detection accuracy; and assessing the operational efficiency of these models within a live seismic monitoring environment. A quantitative research design underpins this study, employing a retrospective analysis of seismic data collected from a network of 50 broadband seismometers distributed across a tectonically active region with a historical record of 10,000 seismic events over five years. The dataset comprises both small-magnitude microseismic events and larger, well-documented earthquakes, totaling approximately 2 million raw signal segments. Data collection involved implementing high-fidelity accelerometers and digital recording systems with time synchronization capabilities. To simulate real-time detection scenarios, the dataset was partitioned into training (70%) and testing (30%) subsets, maintaining class balance to prevent bias. Feature extraction was carried out using continuous wavelet transforms (CWT) and principal component analysis (PCA) to distill salient properties from raw signals, such as frequency content, amplitude variations, and temporal patterns. The machine learning models were trained and validated using cross-validation techniques, with hyperparameter tuning performed through grid search optimization. Model performance was evaluated via metrics including precision, recall, F1-score, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Analytical procedures incorporated confusion matrix analysis to assess classification accuracy, and sensitivity-specificity assessments to examine detection robustness against noise interference. Expected findings indicate that CNNs, particularly when combined with wavelet-based feature extraction, will outperform traditional classifiers in accurately discriminating seismic events from background noise within real-time constraints. It is anticipated that the optimized models will achieve detection sensitivities exceeding 95% with false positive rates below 2%, thus demonstrating suitability for operational deployment. These results are projected to contribute novel insights into the applicability of deep learning techniques in seismic monitoring, addressing notable gaps in existing literature, particularly regarding the integration of machine learning algorithms into real-time systems and their performance in noisy environments. This study advances the current understanding of seismic event detection by providing a comprehensive evaluation of machine learning models under operational conditions, emphasizing their scalability and adaptability. The findings will inform the development of an intelligent seismic alert system capable of rapid, reliable detection, ultimately enhancing early warning infrastructures and disaster preparedness strategies. The research concludes with recommendations for deploying these algorithms within existing seismic networks, emphasizing the importance of continuous data updating and model retraining to maintain accuracy over time. Further research suggestions include exploring multimodal data integration, such as incorporating GPS and infrasound signals, and investigating transfer learning approaches to improve detection capabilities across different geographical regions.