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

Liu, Jie-ran; Cui, Yi-an; Xie, Jing; Zhang, Peng-fei; Liu, Jian-xin

doi:10.1007/s11771-023-5432-3

J. Cent. South Univ.

2023

DOI: 10.1007/s11771-023-5432-3

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

Jie-ran Liu,

Yi-an Cui,

Jing Xie

et al.

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2024

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Cited by 3 publications

References 34 publications

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Time-lapse self-potential signals from microbial processes: A laboratory perspective

Guo,

Cui,

Zhang

et al. 2024

Journal of Applied Geophysics

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Time-lapse self-potential signals from microbial processes: A laboratory perspective

Guo,

Cui,

Zhang

et al. 2024

Journal of Applied Geophysics

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Time‐lapse inversion of self‐potential data through particle filtering

Cui,

Peng,

Xie

2024

Geophysical Prospecting

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In environmental sciences, comprehending the movement of subsurface contaminants is crucial for formulating effective remediation measures. The self‐potential (SP) method has become a common tool for delineating landfill contamination plumes. Contaminant diffusion or migration represents dynamic processes, with corresponding SP responses evolving over time. However, conventional SP interpretation approaches have predominantly relied on static single‐frame inversion, overlooking the temporal correlation in time‐series SP data and resulting in cumulative errors. To tackle this challenge, we introduce a novel method for time‐lapse inversion of SP data leveraging particle filtering. This approach recursively refines the priori state model through posteriori observations to achieve precise estimations of dynamic models. Specifically, a spherical polarization model is deployed to establish the state equations of underground contaminant diffusion and transport models, whereas the observation model is derived through forward modeling. The proposed method is validated using two synthetic examples and one lab‐measured dataset. The findings demonstrate the efficacy of the time‐lapse inversion algorithm in precisely estimating dynamic models, outperforming static single‐frame inversion based on the particle swarm optimization algorithm. The posteriori distribution of particles approximates a bell‐shaped distribution, with the true state closely positioned near the peak probability. Therefore, we affirm that conducting time‐lapse inversion of time‐series SP data through particle filtering is an effective and dependable approach for accurately estimating dynamic model states.

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Three-dimensional analytical solution of self-potential from regularly polarized bodies in a layered seafloor model

Zhang,

Cui,

Xie

et al. 2024

Geosci. Model Dev.

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Abstract. The self-potential (SP) method is a sensitive geophysical technique to locate seafloor polymetallic sulfide deposits. Reasonable SP forward modeling can provide a good foundation for the inversion and interpretation of the measured data. Based on the mirror image theory, we proposed a method to derive the three-dimensional analytical solution of the SP generated by regularly polarized bodies in layered media, which is explained in detail within the context of the models. We discussed the analytical solutions for different types of layered models, considering variations in the number of layers and the distribution of sources. A lab-based oxidation–reduction experiment was conducted to record SP data. These data are used to simulate the SP generated by seafloor massive sulfide (SMS) deposits and validate the previous analytical solution. The result shows that the measured SP data match the analytical solution well, demonstrating the correctness of the proposed method and the corresponding analytical solution. This approach is significant for achieving fast and precise forward modeling and inversion in SMS explorations.

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

Cited by 3 publications

References 34 publications

Time-lapse self-potential signals from microbial processes: A laboratory perspective

Time-lapse self-potential signals from microbial processes: A laboratory perspective

Time‐lapse inversion of self‐potential data through particle filtering

Three-dimensional analytical solution of self-potential from regularly polarized bodies in a layered seafloor model

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