Microseismic location systems tend to be high-speed and precise. However, the requirement of high precision tends to slow down the calculation speed. Fortunately, metaheuristics are able to alleviate this problem. In ...
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Microseismic location systems tend to be high-speed and precise. However, the requirement of high precision tends to slow down the calculation speed. Fortunately, metaheuristics are able to alleviate this problem. In this research, metaheuristic algorithms are used to improve the performance of cross-correlation stacking (CCS). CCS has able to provide excellent location accuracy as it uses more information in the entire waveform for location. However, this method often requires more calculation time due to its complex mathematical modeling. To overcome this problem, various metaheuristic algorithms (i.e. moth flame optimization (MFO), ant lion optimization (ALO) and grey wolf optimization (GWO)) have been used to improve CCS. It has been found that appropriate control parameters can improve the metaheuristic algorithm performance manyfold. So, these control parameters have been adjusted based on three different perspectives, i.e. success rate (SR), computational efficiency and convergence performance. The results show that these models are able to provide better location efficiency compared to the full grid search (FGS) and particle swarm optimization (PSO) based on ensuring good location accuracy. It is also found that MFO is significantly better than the other metaheuristic algorithms. In addition, the superiority of CCS over traditional location methods is verified through comprehensive tests, and the influence of the speed model and the number of sensors on the location performance of CCS was tested.
As the depth of coal mining increases, the risk of rockbursts escalates, necessitating advancements in microseismic monitoring technologies. This study aims to develop a localization method for microseismic events, pa...
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As the depth of coal mining increases, the risk of rockbursts escalates, necessitating advancements in microseismic monitoring technologies. This study aims to develop a localization method for microseismic events, particularly in low signal-to-noise ratio (SNR) environments prevalent in coal mining operations. The method leverages the Enhanced Dynamic Disturbance Index (EDDI) to improve noise suppression and location accuracy without the necessity of onset time picking. The research introduces a cross-correlation stacking method incorporating EDDI, which enhances the signal's dynamic disturbance attributes for better identification and localization of seismic events. This method contrasts traditional Kirchhoff migration and semblance stacking techniques, which often suffer from noise issues and velocity model inaccuracies. Numerical simulations in a homogenous velocity model were conducted to evaluate the method's performance under varying noise levels and velocity perturbations. The EDDI-based cross-correlation stacking method demonstrated superior resistance to noise and velocity model errors compared to conventional methods. It consistently achieved high localization accuracy, particularly in scenarios with up to 70% noise level, significantly outperforming other tested methods in terms of focusing energy and minimizing localization errors across multiple seismic stations. The EDDI-enhanced cross-correlation stacking method offers a promising solution for high-precision microseismic event localization, especially suitable for low-SNR conditions. While it requires more computational resources, its high accuracy and robustness against noise and velocity errors make it an excellent choice for complex underground environments.
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