An Optimized Learning Model Augment Analyst Decisions for Seismic Source Discrimination

Efficient handling and planning for the urban regions' sustainable development require a vast range of up-to-date and thematic information. Besides, obtaining an uncontaminated catalog of seismic activity is desirable to study the earthquake (EQ) clusters' spatial allocation, which is a key role in mitigating seismic hazards and alleviating EQ losses by enhancing the assessment of seismic hazards. This article considers the northeastern part of Egypt where the seismicity catalog is contaminated by quarry blasts (QBs) operated throughout the mapped area. Consequently, it is desirable to discriminate these QBs from the EQs for genuine seismicity and hazard analysis. Accordingly, we provide an efficient machine learning (ML) model for decontaminating the seismicity database so that EQ clusters can be properly delineated by relying on 870 events (EQs and QBs) observed by only one seismic station called "GLL," a member of the Egyptian National Seismic Network (ENSN). The model focuses on magnitudes < 3 that have high uncertainty of being EQs or QBs and take a long time for analysis. The approach examines several linear and nonlinear ML models and, finally, selects the best model with only two features leading to the optimal classification between the EQs and QBs. The optimization process is accomplished throughout two stages. The obtained results prove that the proposed scheme achieves 100% discrimination between the EQs and QBs relying on the extreme gradient boosting (XGB) model.