Identification of Earthquake Precursors Origin and AI Framework for Automatic Classification for One of These Precursors - Ghada Ali; Lotfy Samy; Omar M. Saad; Ali G. Hafez; El-Sayed Hasaneen; Kamal AbdElrahman; Ibrahim Salah; Mohammed S. Fnais; Hamed Nofel; Ahmed M. Mohamed (IEEE Access)
Many precursory signal patterns arise before the initial P-wave arrival. The origin of these patterns can be traced to either instrumental or natural ground effects. Identifying these signals’ origin and nature helps to enhance automatic P-wave identification and earthquake source parameter determination. Several investigations have been accomplished to define the origin of these precursory signals, starting from installing a temporary station with 50 and 200 sampling rates for 17 months, in addition to the original station with a sampling rate of 100 samples per second at the New Abu Dabbab station (NADB). From these two stations, 429 earthquakes were recorded at three sampling rates: 50, 100, and 200 samples per second. Then, a spectral analysis was performed, where it was found that there was a clear relationship between the first pattern and the acquisition sampling rate. This proved that the first pattern (the ramping pattern) is an instrumental artifact that occurs from the finite impulse response (FIR) filter in the analog-to-digital conversion stage of the digitizer. The other type of precursory signals was found to have varying spectra independent of the used sampling rate. This led to the differentiation of the natural ground origin and instrumental artifacts-based signals in front of these earthquakes. By automating the classification of these patterns, the true P-wave arrival can be determined in real-time processing, reducing the error in P-wave arrival timing. The current study also introduces this automatic classification by developing various machine learning (ML) and Convolutional Neural Network (CNN) models to highlight the features characterizing each pattern. The classifier topology distinguishes these patterns, and then the automatic P-wave detector can decide the arrival of the P-wave depending on the type of precursory. In instrumental artifacts, the arrival is taken after the precursory and before it in the case of the natural ground-based pattern. The examined classification topologies are Logistic Regression (LR), K-nearest neighbors Classifier (KNN), Support Vector Machine (SVM), Decision Tree Classifier (DT), Random Forest Classifier (RF), XGB Classifier, Naïve Bayes (NB), Voting Classifier and Convolutional Neural Network (CNN). Out of these methodologies, the CNN outperformed with a classification accuracy of 97.67% because of its capability in feature extraction.