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为提高粒子群算法(Particle Swarm Optimization, PSO)在电磁法数据反演中的寻优能力，结合基因遗传使粒子变异特点形成遗传粒子群算法(Genetic Algorithm Particle Swarm Optimization, GAPSO),将其用于等值反磁通瞬变电磁法(Opposing Coils Transient Electromagnetic Method, OCTEM)探测采空区的数据处理中。对比分析两层、三层和四层地质模型的合成数据反演结果，认为GAPSO相对常规PSO具有更优的搜索效果。结合等值反磁通瞬变电磁法探测采空区实例，联合烟圈反演、软件处理结果和GAPSO反演结果进行对比分析，对5-2煤层附近的综合解释地质异常位置进行钻孔验证。钻孔揭露5-2煤层附近掉钻约1.5 m,为不含水采空区，这与GAPSO反演结果最吻合。研究表明，遗传粒子群算法处理OCTEM数据的结果是可靠的，并且该方法对初始模型依赖低，适用于煤矿采空区的等值反磁通瞬变电磁法勘探。
Abstract：In order to improve the optimization ability of particle swarm optimization(PSO) in electromagnetic data invesion, the genetic particle swarm optimization(GAPSO) is developed by incorporating the genetic variation characteristics of particles, which is used in the data processing of opposing coils transient electromagnetic method(OCTEM) to detect goaf. By analyzing the inversion results of synthetic data of two-layer, three-layer and four-layer geological models, it is considered that GAPSO has better search performance than conventional PSO. Combined with the example of detecting goaf by OCTEM, the results of smoke ring inversion, software processing and GAPSO inversion are compared and analyzed, and the comprehensive interpretation of geological anomaly location near 5-2 coal seam is verified by drilling. It reveals that the drilling hole drops about 1.5 m near the 5-2 coal seam, which is a non-water-bearing goaf, and that matches the GAPSO inversion results. The research demonstrates that the results of OCTEM data processing by GAPSO are reliable, and this method is less dependent on the initial model, which is suitable for opposing coils transient electromagnetic in coal mine goaf detection.
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基本信息:
DOI：
中图分类号:P631.325
引用信息:
[1]董毅,仝景阳,吴佩凝等.薄煤层采空区等值反磁通瞬变电磁GAPSO反演研究[J].工程地球物理学报,2025,22(01):50-55.
基金信息:
陕西省自然科学基金资助项目(编号：2024JC-YBQN-0317); 秦创原引用高层次创新创业人才资助项目(编号：QCYRCXM-2022-285)