Microseismic Source Localization Method Based on Neural Network Algorithm and Dynamic Reduction of Solution Interval

The accuracy of microseismic source localization depends largely on the quality of the velocity model. Due to the anisotropy of the rock mass, the current uniform velocity model is no longer sufficient for high-precision localization. Additionally, the time-varying property of the velocity model will influence the accuracy of the estimated source location. Focusing on these challenges, we propose an iterative source location estimation and simplified anisotropic velocity inversion method based on the neural network algorithm (NNA) and dynamic reduction of solution interval. We first introduce a simplified anisotropic velocity model and establish an objective function for source localization. The t-distribution is embedded in the NNA to increase the probability of jumping out of the local optimum. In each iteration, the solution interval is narrowed down, and then, the source location is estimated by the NNA. The initial solution interval is determined from the inversion results of the uniform velocity model. The performance of the proposed method is evaluated by the numerical and blasting experiments. The location accuracy of the proposed method is at least 40% higher than that of the conventional method. Test results indicate that our method is effective to locate the sources in the areas with heterogeneous and complex media.