Seismic Signal Processing and Aftershock Analysis using Machine Learning

The anticipated method aims to develop a robust machine learning-based approach for aftershock classification using seismic sensing signals. Accurate classification of after- shocks plays a crucial role in understanding seismic processes and assessing risks associated with earthquakes. By distinguishing aftershocks from mainshocks and background noise, we enhance earthquake hazard prediction and mitigation. proposed methodology combines feature extraction techniques, machine learning algorithms, and rigorous evaluation measures for reliable aftershock classification. The study begins with an introduction to aftershock classification’s significance in earthquake research and disaster management, followed by a literature survey exploring existing methodologies and their limitations. Machine learning algorithms are identified as promising solutions for these challenges, motivating the choice of methodology. We preprocess seismic sensing signals through data cleaning and noise reduction to ensure high-quality input. Feature extraction methods capture signal characteristics, including statistical, time- domain, and spectral features. These extracted features serve as input to the primary classification algorithm, the SVM, which is best suited to handle complex data and non linear relationships with ease and effectiveness. SVM is trained on a labeled dataset comprising manually classified aftershocks and other seismic events, with hyperparameter tuning for optimization Extensive experiments on a specific region’s seismic dataset demonstrate outstanding accuracy, with an F1-score of 0.99974 and 99.98% accuracy. High precision and recall values indicate reliable aftershock classification, as confirmed by negligible false positives and false negatives from the confusion matrix analysis. In conclusion, this research presents a robust machine learning- based approach for aftershock classification using seismic sensing signals. The methodology offers high accuracy and reliability, contributing to earthquake research and disaster management by enabling precise aftershock identification. It is extendable to various seismic datasets and applicable in real-time earthquake monitoring systems. Future work involves exploring additional machine learning algorithms, incorporating domain knowledge, and integrating the models into operational systems to support seismic event decision-making