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Inversion of self-potential anomalies from regular geometric objects by using whale optimization algorithm

利用鲸鱼优化算法的规则几何物体自然电位反演

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Abstract

The whale optimization algorithm (WOA) is one of the meta-heuristic algorithms that achieve parameters optimization by simulating the feeding behavior of humpback whales. The WOA can be applied to self-potential (SP) data inversion for regular polarized geometric objects (i.e., sphere, horizontal cylinder, and vertical cylinder), which can assist in exploring subsurface geological objects. The WOA was first applied to perform parameter inversion on three models: the sphere, the vertical cylinder, and the combination of both models. The optimization process of the vertical cylinder model parameters was analyzed, and the convergence behavior of the WOA was discussed. Secondly, laboratory-measured data from three sets of physical models were used for parameters inversion, and a comparison was made with two other optimization algorithms to demonstrate the advantages of the WOA. Finally, the WOA was applied to process a set of field data. The WOA algorithm was employed for the inversion of SP data inversion from numerical experiments, physical experiments, and field examples. The inversion results demonstrate that the proposed WOA inversion has good stability and effectiveness in solving the self-potential inversion problem.

摘要

自然电场法对地下水和地下污染相关的流动电位和氧化还原电位非常敏感, 可以高效经济的实现对地下水和污染物的探测和定位. 鲸鱼优化算法是一种元启发式算法, 通过模拟座头鲸的捕食行为实现参数优化. 将鲸鱼算法应用于规则极化几何体(即球体、 水平圆柱体和垂直圆柱体)的自然电场数据反演, 可以快速实现对地下目标体的精细勘探. 首先, 利用鲸鱼算法对球体、 垂直圆柱体以及组合模型进行了参数反演测试, 并对垂直柱体模型参数的优化过程进行了统计分析, 讨论了鲸鱼算法的收敛性. 然后, 利用 3 组实验室观测的物理模型数据进行进一步的参数反演测试, 与另外 2 种优化算法进行对比分析表明鲸鱼算法具有明显的优势. 最后, 将鲸鱼算法用于某场地实测自然电场数据的处理解释, 实测数据反演结果得到了开挖验证, 说明反演算法的实用性较好. 反演测试还表明, 基于鲸鱼优化算法的自然电场反演具有一定的抗噪声能力, 在噪声条件下还能保持良好的收敛性. 该方法可以实现对地下目标体快速精确反演定位, 具有良好的实用性, 可以广泛应用于地下水和地下污染调查.

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Authors and Affiliations

  1. School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China

    Jie-ran Liu  (刘桔燃), Yi-an Cui  (崔益安), Jing Xie  (谢静), Peng-fei Zhang  (张鹏飞) & Jian-xin Liu  (柳建新)

  2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Detection, Central South University, Changsha, 410083, China

    Jie-ran Liu  (刘桔燃), Yi-an Cui  (崔益安), Jing Xie  (谢静), Peng-fei Zhang  (张鹏飞) & Jian-xin Liu  (柳建新)

  3. Key Laboratory of Metallogenic Prediction of Nonferrous Metals (Ministry of Education), Central South University, Changsha, 410083, China

    Jie-ran Liu  (刘桔燃), Yi-an Cui  (崔益安), Jing Xie  (谢静), Peng-fei Zhang  (张鹏飞) & Jian-xin Liu  (柳建新)

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  1. Jie-ran Liu  (刘桔燃)
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Contributions

LIU Jie-ran conducted the literature review and wrote the first draft of the manuscript. CUI Yi-an provided the concept and edited the draft of the manuscript. XIE Jing provided the measured SP data of lab-based physical experiments. XIE Jing and ZHANG Peng-fei edited the draft of the manuscript. LIU Jian-xin oversaw the orderly conduct of the research.

Corresponding author

Correspondence to Yi-an Cui  (崔益安).

Ethics declarations

LIU Jie-ran, CUI Yi-an, Xie Jing, ZHANG Peng-fei, and LIU Jian-xin declare that they have no conflict of interest.

Additional information

Foundation item: Projects(42174170, 41874145, 42130810) supported by the National Natural Science Foundation of China

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Liu, Jr., Cui, Ya., Xie, J. et al. Inversion of self-potential anomalies from regular geometric objects by using whale optimization algorithm. J. Cent. South Univ. 30, 3069–3082 (2023). https://doi.org/10.1007/s11771-023-5432-3

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  • DOI: https://doi.org/10.1007/s11771-023-5432-3

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