Authors:
Jian Wang
1
;
2
;
Yongfeng Wang
1
;
2
;
Guotao Pang
2
;
1
;
Yinji Ba
1
;
2
and
Guiheng Wang
1
;
2
Affiliations:
1
Ministry of Natural Resources Observation and Research Station of Land-Sea, Interaction Field in the Yellow River Estuary, Yantai Shandong 264000, China
;
2
Yantai Center of Coastal Zone Geological Survey, China Geological Survey, Yantai Shandong 264000, China
Keyword(s):
Deep Learning, Identification of Marine Geological Anomalies, Prospecting Prediction, Method.
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.
(More)