Improving Marine Mineral Delineation with Planar Self-Potential Data and Bayesian Inversion

Zhang, Lijuan; Feng, Shengfeng; Xu, Shengcai; Huang, Dingyu; Li, Hewang; Su, Ying; Xie, Jing

doi:10.3390/min15121330

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Open AccessArticle

Improving Marine Mineral Delineation with Planar Self-Potential Data and Bayesian Inversion

by

Lijuan Zhang

^1,2,

Shengfeng Feng

^1,2,*,

Shengcai Xu

^1,2,

Dingyu Huang

^1,2,

Hewang Li

^1,2,

Ying Su

^1,2 and

Jing Xie

^3,*

¹

School of Architecture and Electrical Engineering, Hezhou University, Hezhou 542899, China

²

Guangxi University Engineering Research Center for Green and Low-Carbon Urban Regeneration Construction, Hezhou University, Hezhou 542899, China

³

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

^*

Authors to whom correspondence should be addressed.

Minerals 2025, 15(12), 1330; https://doi.org/10.3390/min15121330

Submission received: 18 November 2025 / Revised: 11 December 2025 / Accepted: 17 December 2025 / Published: 18 December 2025

(This article belongs to the Section Mineral Exploration Methods and Applications)

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Abstract

The exploration of marine minerals, essential for sustainable development, requires advanced techniques for accurate resource delineation. The self-potential (SP) method, sensitive to mineral polarization, has been increasingly deployed using autonomous underwater vehicles. This approach enables dense planar SP data acquisition, offering the potential to reduce inversion uncertainties through enhanced data volume. This study investigates the benefits of inverting planar SP datasets for improving the spatial delineation of subsurface deposits. An analytical solution was derived to describe SP responses of spherical polarization models under a planar measurement grid. An adaptive Markov chain Monte Carlo algorithm within the Bayesian framework was employed to quantitatively assess the constraints imposed by the enriched dataset. The proposed methodology was validated through two synthetic cases, along with a laboratory-scale experiment that monitored the redox process of a spherical iron–copper model. The results showed that, compared to single-line data, the planar data reduced the average error in parameter means from 10.9% and 6.4% to 4.1% and 1.7% for synthetic and experimental cases, respectively. In addition, the 95% credible intervals of model parameters narrowed by nearly 50% and 40%, respectively.

Keywords: self-potential; marine minerals; Bayesian inversion; Markov chain Monte Carlo

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MDPI and ACS Style

Zhang, L.; Feng, S.; Xu, S.; Huang, D.; Li, H.; Su, Y.; Xie, J. Improving Marine Mineral Delineation with Planar Self-Potential Data and Bayesian Inversion. Minerals 2025, 15, 1330. https://doi.org/10.3390/min15121330

AMA Style

Zhang L, Feng S, Xu S, Huang D, Li H, Su Y, Xie J. Improving Marine Mineral Delineation with Planar Self-Potential Data and Bayesian Inversion. Minerals. 2025; 15(12):1330. https://doi.org/10.3390/min15121330

Chicago/Turabian Style

Zhang, Lijuan, Shengfeng Feng, Shengcai Xu, Dingyu Huang, Hewang Li, Ying Su, and Jing Xie. 2025. "Improving Marine Mineral Delineation with Planar Self-Potential Data and Bayesian Inversion" Minerals 15, no. 12: 1330. https://doi.org/10.3390/min15121330

APA Style

Zhang, L., Feng, S., Xu, S., Huang, D., Li, H., Su, Y., & Xie, J. (2025). Improving Marine Mineral Delineation with Planar Self-Potential Data and Bayesian Inversion. Minerals, 15(12), 1330. https://doi.org/10.3390/min15121330

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Improving Marine Mineral Delineation with Planar Self-Potential Data and Bayesian Inversion

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