Treffer: Homogenized earthquake catalog of Maharashtra state, India.

In this study, empirical relationships are developed to convert body wave magnitudes (M<subscript>b</subscript>), surface wave magnitudes (M<subscript>S</subscript>) and local wave magnitudes (M<subscript>L</subscript>) into moment magnitude (M<subscript>W</subscript>) for Maharashtra state, India, using the general orthogonal regression (GOR) approach. The seismic data analysis of Maharashtra state, spanning from 1967 to 2021, has been meticulously conducted by utilizing Python software. The dataset under scrutiny encompasses a total of 238 seismic events that have been accurately recorded within the geographical confines of Maharashtra. The core of these seismic events lies in their magnitude (M<subscript>W</subscript>) values, an essential metric that unravels the magnitude of energy discharges during seismic occurrences. The basis of this study lies in the adept utilization of diverse seismic data analysis techniques, all orchestrated seamlessly within the Python software environment. Each event includes corresponding M<subscript>b</subscript>–M<subscript>W</subscript>, M<subscript>S</subscript>–M<subscript>W</subscript>, and M<subscript>L</subscript>–M<subscript>W</subscript> magnitude pairs. Established predictive relationships between these observed magnitudes and their corresponding M<subscript>W</subscript> values using linear regression models derived through the GOR method. These models enable the estimation of M<subscript>W</subscript> from available M<subscript>b</subscript>, M<subscript>S</subscript>, or M<subscript>L</subscript> values, thereby allowing magnitude conversion in cases where direct M<subscript>W</subscript> measurements are unavailable. This approach is particularly significant for the homogenization of earthquake catalogs that contain mixed magnitude types and inherent measurement uncertainties. The developed GOR-based conversion relations enable seismic catalogs to be standardized in terms of moment magnitude (M<subscript>W</subscript>), enhancing their utility for seismic hazard assessment and geophysical interpretation. The performance of the proposed equations is validated with an R² value of 0.85, indicating strong reliability and predictive capability of the approach. The methodology presented herein offers a robust framework for regional seismic data analysis and contributes to a deeper understanding of seismicity in the Maharashtra region. [ABSTRACT FROM AUTHOR]

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