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Outline
To address the issues of a complex solution space, strong nonlinearity, and easy entrapment in local optima in microseismic localization, the application of swarm intelligence optimization algorithms in microseismic localization was systematically investigated. By comparing the performance of the sparrow search algorithm (SSA), differential evolution (DE) algorithm, and particle swarm optimization (PSO) algorithm in single P-wave localization, it was found that PSO algorithm exhibited the best performance in terms of solution stability. To further increase the localization accuracy and enhance algorithm robustness, S-waves were introduced to construct a dual-wave joint localization model, reducing the inversion space from four dimensions to three dimensions. The experimental results indicate that the dual-wave joint localization significantly outperformed the single P-wave localization, where PSO algorithm achieves the lowest average localization error in the dual-wave joint localization and demonstrated good adaptability. To overcome the shortcomings of the PSO algorithm, such as premature convergence and sensitivity to initial values, an adaptive improved particle swarm optimization (APSO) algorithm integrating opposition-based learning and chaotic mapping was proposed. Opposition-based learning was utilized to generate a high-quality initial population, while chaotic mapping achieved the dynamic adjustment of the inertia weight to balance the global exploration and local exploitation capabilities. The numerical experimental results indicate that the average localization error of APSO algorithm at multiple typical source points is lower than that of the standard PSO algorithm, with the average localization error reduced by approximately 62.4%, and it exhibits stronger stability and convergence efficiency. This method provides an effective tool for high-precision microseismic localization in deep rock mass engineering and has promising prospects for engineering application.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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