The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study’s proposed method and its potential in processing complicated microseismic datasets.

The travel time computation of microseismic waves in different directions (particularly, the diagonal direction) in three-dimensional space has been found to be inaccurate, which seriously affects the localization accuracy of three-dimensional microseismic sources. In order to solve this problem, this research study developed a method of calculating the P-wave travel time based on a 3D high-order fast marching method (3D_H_FMM). This study focused on designing a high-order finite-difference operator in order to realize the accurate calculation of the P-wave travel time in three-dimensional space. The method was validated using homogeneous velocity models and inhomogeneous layered media velocity models of different scales. The results showed that the overall mean absolute error (MAE) of the two homogenous models using 3D_H_FMM had been reduced by 88.335%, and 90.593% compared with the traditional 3D_FMM. On that basis, the three-dimensional localization of microseismic sources was carried out using a particle swarm optimization algorithm. The developed 3D_H_FMM was used to calculate the travel time, then to conduct the localization of the microseismic source in inhomogeneous models. The mean error of the localization results of the different positions in the three-dimensional space was determined to be 1.901 m, and the localization accuracy was found to be superior to that of the traditional 3D_FMM method (mean absolute localization error: 3.447 m) with the small-scaled inhomogeneous model.

The microseismic (MS) monitoring in the coal mining provides a powerful method to identify the coal mine geological hazards as well as to prevent against the illegal mining. The challenge of MS monitoring is to extract characteristics of interest from the collected raw MS signals and locate the MS events using effective algorithms. In order to improve the localization accuracy of microseismic sources, a genetic-simplex hybrid localization algorithm has been elaborated, which employs Genetic algorithm to determine the initial values and then utilizes simplex algorithm to search the optimal locations of MS events. Meanwhile, frequency filtering method is employed to extract the specific frequency components from the raw MS signals so as to calculate the time differences. Experiments have been performed on a planar with the dimension of 5m*5m to validate the proposed method. The experimental results indicate that the hybrid algorithm provides higher localization accuracy compared with the traditional simplex algorithm. The maximum radial error of the hybrid localization algorithm is 0.47 m, while it is 0.94m for the traditional simplex algorithm. And the relative position error of the hybrid positioning algorithm is less than 10%, which is around 10% higher than the traditional simplex algorithm.