Enhancing analyst decisions for seismic source discrimination with an optimized learning model

Sustainable development in urban areas requires a wide variety of current and theme-based information for efficient management and planning. In addition, researching the spatial distribution of earthquake (EQ) clusters is an important step in reducing seismic risks and EQ losses through better assessment of seismic hazards, therefore it is desirable to acquire an uncontaminated database of seismic activity. Quarry blasts (QBs) conducted over the mapped area have tainted the seismicity inventory in the northwestern region of Egypt, which is the focus of this paper. Separating these QBs from the EQs is hence preferable for accurate seismicity and risk assessments. Consequently, we present a highly effective ML model for cleaning up the seismicity database, allowing for the accurate delineation of EQ clusters using data from a single seismic station, "AYT", which is part of the Egyptian National Seismic Network. The magnitudes <= 3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le 3$$\end{document} that are very uncertain as EQs or QBs and need a significant amount of time to analyze are the primary focus of the model. In order to find the best way to classify EQs and QBs, the method looks at a number of ML models before settling on the best one using eight features. The results show that the suggested method, which uses the quadratic discrimination analysis model for discriminating, successfully separates EQs and QBs with a 99.4% success rate.