A local magnitude scale has been developed for Eastern Dharwar Craton (EDC) and adjoining areas around Southern Peninsular Shield, India, to improve the scale used previously in this region. Measurements, 1333 of peak amplitudes on simulated Wood-Anderson instruments located in Andhra Pradesh and Telangana (APTS) network for 91 earthquakes recorded at 26 seismic stations have been used to determine a new distance correction curve for routine in calculating the local magnitudes. The new M_L scale has been derived using the Grey Wolf Optimization (GWO), a swarm intelligence-based global optimization technique for the first time: \({M}_{\mathrm{L }}=\mathrm{log}A+1.2588\mathrm{log}R +0.0002789R-2.2265\), where A is the amplitude measured in millimeters, and R is the hypocentral distance in kilometers. The new M_L scale derived here is valid up to 400 km. This study has been carried out using both singular value decomposition (SVD) and GWO to obtain scale coefficients and station corrections. The newly derived scale has a drop of 21.37% in the overall standard deviation of all magnitude residuals when station corrections are considered in comparison to the previously used scale. A new M_L-M_W relationship was derived for local magnitudes above 2.5, and the relationship was M_W α 0.9224*M_L. In our study, the results obtained using GWO gave less standard deviation of all magnitude residuals for the Eastern Dharwar Craton, thus proving to be better than the conventional SVD approach.

已为东达尔瓦克拉通 (EDC) 和印度南部半岛地盾附近的邻近地区制定了局部震级标度，以改进先前在该地区使用的震级。位于安得拉邦和特兰加纳 (APTS) 网络的模拟 Wood-Anderson 仪器对 26 个地震台站记录的 91 次地震的峰值振幅的 1333 次测量已被用于确定新的距离校正曲线，用于计算当地震级。新的M _L尺度是使用灰狼优化 (GWO) 推导出来的，这是一种首次基于群体智能的全局优化技术：\({M}_{\mathrm{L }}=\mathrm{log}A +1.2588\mathrm{log}R +0.0002789R-2.2265\)，其中A是以毫米为单位测量的振幅，R是以公里为单位的震源距离。新的中号_大号这里派生规模有效期可达400公里。本研究使用奇异值分解 (SVD) 和 GWO 进行，以获得比例系数和台站改正。与以前使用的比例相比，当考虑站点改正时，新得出的比例在所有震级残差的总体标准偏差上下降了 21.37%。对于高于 2.5 的局部震级，推导出新的M _L - M _W关系，该关系为M _W α 0.9224* M _L. 在我们的研究中，使用 GWO 获得的结果给出了东达尔瓦克拉通所有震级残差的较小标准偏差，因此证明比传统的 SVD 方法更好。