基于松鼠搜索算法的跨孔电阻率溶洞探测

doi:10.11720/wtyht.2022.1262

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摘要

针对在基建工程前期探测中,传统物探反演方法对溶洞等不良地质条件的探测精度低的问题,提出了一种基于松鼠搜索算法的跨孔电阻率溶洞探测反演方法,用于改善传统的基于Tikhonov正则化的灵敏度迭代法对初值、噪声敏感、容易陷入局部最优等缺陷。采用小型、大型和串珠状溶洞3种数值算例,对不同智能搜索算法和灵敏度迭代法的探测结果进行了对比分析,并开展了室内模型试验和现场实验验证。研究结果表明:基于松鼠搜索算法的反演方法收敛速度快、精确度高,可显著提高跨孔电阻率溶洞的探测精度。

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	梁森
	陈建华
	李宏涛
	罗威力
	罗盈洲
	艾姣姣
	廖伟

关键词 ：跨孔电阻率法, 松鼠搜索算法, 溶洞, 探测精度

Abstract：

Aiming at the low-detection precision of traditional geophysical prospecting inversion methods for unfavorable geological conditions such as karst caves in the early detection stage of infrastructure projects, this study proposed a cross-hole resistivity detection and inversion method of karst caves based on the squirrel search algorithm to improve the performance of the traditional Tikhonov regularization-based sensitivity iteration method, which is sensitive to initial values and noise and easy to fall into local optimization. The detection results obtained using different intelligent search algorithms and sensitivity iteration methods were compared and analyzed using three numerical examples of small, large, and beaded karst caves. Moreover, an indoor physical model was also built to validate the proposed method. The results show that the inversion method based on the squirrel search algorithm has a high convergence speed and precision and can significantly improve the detection precision of karst caves using the cross-hole resistivity method.

Key words： cross-hole resistivity method squirrel search algorithm karst cave detection precision

收稿日期: 2021-05-12 修回日期: 2021-12-28 出版日期: 2022-10-20

ZTFLH:

P631

基金资助:国家自然科学基金项目(51808147)

通讯作者: 罗威力

作者简介: 梁森(1982-),男,高级工程师,2018年毕业于重庆大学,主要从事土木工程研究工作。Email:6976993@qq.com

引用本文:

梁森, 陈建华, 李宏涛, 罗威力, 罗盈洲, 艾姣姣, 廖伟. 基于松鼠搜索算法的跨孔电阻率溶洞探测[J]. 物探与化探, 2022, 46(5): 1296-1305.
LIANG Sen, CHEN Jian-Hua, LI Hong-Tao, LUO Wei-Li, LUO Ying-Zhou, Ai Jiao-Jiao, LIAO Wei. Detection of karst caves using the cross-hole resistivity method based on the squirrel search algorithm. Geophysical and Geochemical Exploration, 2022, 46(5): 1296-1305.

链接本文:

https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2022.1262 或 https://www.wutanyuhuatan.com/CN/Y2022/V46/I5/1296

Fig.1 观测模式示意

Fig.2 松鼠搜索算法反演流程

Fig.3 不同智能优化算法反演结果

Fig.4 不同智能优化算法的反演迭代结果

Fig.5 灵敏度迭代法反演结果

Fig.6 松鼠搜索算法反演结果

Fig.7 松鼠搜索算法迭代曲线

Table 1 反演结果对比

Fig.8 高密度电法探测仪

Fig.9 溶洞模型

Table 2 各几何因素原型尺寸与模型尺寸对照

Fig.10 充水溶洞模型反演结果(a)与迭代曲线(b)

Fig.11 充气溶洞模型反演结果(a)与迭代曲线(b)

Table 3 测区视电阻率分布

Table 4 钻孔柱状图部分结果

Fig.12 现场实验反演结果

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