HIGH-PRECISION SEISMIC SOURCE LOCALIZATION ALGORITHMS BASED ON NONLINEAR OPTIMIZATION AND 3D DEPTH LAYOUT

Abstract

Addressing the core challenge in seismic monitoring—where the accuracy of seismic source localization is affected by geological medium heterogeneity and geometric constraints of the monitoring network—this study constructs a progressive modeling framework that integrates geometric analysis, nonlinear optimization, and statistical sampling. The study first establishes an algebraic analytical model based on the principle of spherical intersection and, through iterative least-squares methods, reduces the sum of squared positioning residuals in an ideal scenario to 0.02 units. To address the ±0.2% ranging errors encountered in practical engineering applications, the Levenberg–Marquardt algorithm and Monte Carlo sampling are introduced to quantify the anisotropy of the error distribution and reveal the sensitivity of vertical positioning accuracy to station geometric constraints. To further enhance system robustness, the study utilized the Shapiro-Wilk test to remove outliers and employed multi-source data fusion via high-density station network redundancy constraints, significantly reducing vertical positioning errors. Finally, numerical simulations validated the scientific validity of the three-dimensional depth layout of monitoring stations, demonstrating that multi-depth configurations can reduce the average positioning error by 42.84%. The “theoretical modeling—error analysis—engineering optimization” technical chain established in this study provides critical technical support for the development of earthquake early warning systems and three-dimensional monitoring networks.