Identification and Prospecting Prediction of Marine Geological Anomalies Based on Deep Learning

Jian Wang, Jian Wang, Yongfeng Wang, Yongfeng Wang, Guotao Pang, Guotao Pang, Yinji Ba, Yinji Ba, Guiheng Wang, Guiheng Wang

2025

Abstract

The need for deep-sea mineral exploration has become more urgent as marine resources become increasingly scarce. In order to effectively identify marine geological anomalies and improve the accuracy of prospecting prediction, this study proposes a multi-modal data fusion method based on deep learning to achieve anomaly identification and prospecting prediction. Based on the fusion of multi-source data such as ocean seismic waves, magnetism, gravity, etc., the method adopts adaptive feature extraction technology, and uses a double-branch prediction network to perform anomaly identification and mineral enrichment prediction. Finally, the results of this paper show that the system performs well in multi-regional seabed geological data, among which the enrichment of copper ore is 3.5%, and the enrichment of nickel ore is 1.2%. The comprehensive analysis shows that the model and its integrated platform have strong robustness in the complex marine environment, which can effectively improve the efficiency of mineral exploration.

Paper Citation

in Harvard Style

Wang J., Wang Y., Pang G., Ba Y. and Wang G. (2025). Identification and Prospecting Prediction of Marine Geological Anomalies Based on Deep Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 51-58. DOI: 10.5220/0013535200004664

in Bibtex Style

@conference{incoft25,
author={Jian Wang and Yongfeng Wang and Guotao Pang and Yinji Ba and Guiheng Wang},
title={Identification and Prospecting Prediction of Marine Geological Anomalies Based on Deep Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={51-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013535200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Identification and Prospecting Prediction of Marine Geological Anomalies Based on Deep Learning
SN - 978-989-758-763-4
AU - Wang J.
AU - Wang Y.
AU - Pang G.
AU - Ba Y.
AU - Wang G.
PY - 2025
SP - 51
EP - 58
DO - 10.5220/0013535200004664
PB - SciTePress