Design and Implementation of a Seismic Data Processing Workflow for Earthquake Detection

Thesis Abstract

Seismic monitoring and earthquake detection are critical components of disaster risk reduction and public safety management, yet existing data processing workflows often suffer from issues related to processing efficiency, detection accuracy, and real-time applicability. This study aims to design and implement an optimized seismic data processing workflow that enhances the timely and accurate detection of earthquakes. The specific objectives include analyzing existing seismic data processing methods, developing an integrated workflow framework incorporating automated signal detection algorithms, and evaluating the workflow’s performance against real-world seismic data. The research adopts a mixed-methods approach, combining qualitative assessment of current practices with quantitative evaluation using a sample of 150 seismic events recorded over a five-year period at a regional seismic monitoring station. The population encompasses all seismic signals captured within the region, with a stratified random sampling technique employed to select representative seismic events across different magnitudes and depths. Data collection relies on digital seismogram recordings obtained from the regional seismic network, processed through custom-developed software modules integrated within the workflow, which include signal filtering, noise suppression, and event classification algorithms. For analytical rigor, the study employs a combination of statistical and machine learning techniques, including receiver operating characteristic (ROC) analysis to determine detection accuracy, and ensemble methods such as Random Forest classifiers to improve event discrimination. Key performance metrics include detection rate, false positive/negative rates, processing latency, and computational efficiency. Validation involves cross-comparison with manually verified seismic event catalogs and benchmarking against existing workflows to measure improvements in detection precision and speed. The study also explores the application of the Theory of Human-Computer Interaction to optimize user interface design for operational monitoring and the Seismic Signal Processing Theory to underpin algorithm robustness. Expected findings indicate that the integrated workflow will achieve a significant increase in earthquake detection accuracy—improving the true positive rate by an estimated 25%—and reduce average processing time per event by approximately 40%, compared to conventional workflows. The application of machine learning classifiers within the workflow is anticipated to enhance noise discrimination, thereby minimizing false alarms and improving reliability. These improvements are expected to facilitate real-time earthquake monitoring, particularly in regions with high seismic activity. The contribution to knowledge lies in providing a systematic framework for seismic data processing that combines state-of-the-art automated techniques with established theoretical models, thereby advancing the operational capabilities of seismic networks. It offers a replicable and scalable model applicable to regional and national seismic monitoring systems, emphasizing efficiency and accuracy. The study concludes that an integrated, automated seismic data processing workflow significantly enhances earthquake detection capabilities, emphasizing the importance of tailored algorithm development and user-centered design in operational settings. Recommendations include integrating the workflow within existing seismic monitoring infrastructure and investing in continuous algorithm refinement through machine learning-based adaptive models. Additionally, it advocates for further research into multi-sensor data fusion techniques and the deployment of deep learning architectures to further improve detection efficacy in diverse seismic environments. The findings underscore the potential for such workflows to contribute substantially to early warning systems and disaster preparedness initiatives globally, marking a significant step forward in seismological operational research.