Microseismic event locations using grid-searching method and Newton-Raphson-based optimizer

Abstract

Microseismic event location plays a pivotal role in industrial applications, such as coal mining and hydraulic fracturing, by revealing subsurface fracture dynamics through the spatiotemporal analysis of seismic events. As a cornerstone of microseismic monitoring, accurate event localization enables critical insights into underground structural integrity. Traditional arrival-time-based methods employ optimization algorithms to minimize residuals between observed and theoretical arrival times. While this classical approach has proven effective, its accuracy is often compromised by two key limitations: suboptimal initial iteration values and inaccuracies in velocity parameter estimation. To address these challenges, we propose an innovative localization method integrating a grid-searching strategy with a Newton-Raphson-based optimizer. Our approach begins by generating initial iterative vectors—comprising event coordinates and velocity parameters—through a systematic grid-searching technique. Subsequently, the Newton-Raphson optimizer refines these estimates within a four-dimensional search space to achieve high-precision inversion results. The efficacy of the proposed method was rigorously evaluated using both synthetic and field datasets, with comparative analyses conducted against four established localization techniques. Experimental results demonstrate that our method significantly enhances localization accuracy and robustness, reliably inverting both event locations and velocity parameters. These findings provide a valuable technical reference for advancing microseismic monitoring systems, offering improved precision in industrial applications.

Keywords

Microseismic event location / Grid-searching method / Newton-Raphson-based optimizer

Funding

This research was supported by the National Natural Science Foundation of China (42474189), the Open Fund Project of State Key Laboratory for Fine Exploration and Intelligent Development of Coal Research (SKLCRSM23KFA04), and the Science and Technology Innovation Team of Shandong Earthquake Agency (TD202404).

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