Deep multimodal fusion for 3D mineral prospectivity modeling: Integration of geological models and simulation data via canonical-correlated joint fusion networks

Data-driven three-dimensional (3D) mineral prospectivity modeling (MPM) employs diverse 3D exploration indicators to express geological architecture and associated characteristics in ore systems. The integration of 3D geological models with 3D computational simulation data enhances the effectiveness...

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Vydáno v:Computers & geosciences Ročník 188; s. 105618
Hlavní autoři: Zheng, Yang, Deng, Hao, Wu, Jingjie, Xie, Shaofeng, Li, Xinyue, Chen, Yudong, Li, Nan, Xiao, Keyan, Pfeifer, Norbert, Mao, Xiancheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.06.2024
Témata:
3D geological models
Canonical correlation analysis
case studies
China
Geodynamic simulation data
gold
Mineral prospectivity modeling
mineralization
Multimodal fusion
prediction
China
Mineral prospectivity modeling
3D geological models
Geodynamic simulation data
Multimodal fusion
Canonical correlation analysis
ISSN:0098-3004
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Shrnutí:Data-driven three-dimensional (3D) mineral prospectivity modeling (MPM) employs diverse 3D exploration indicators to express geological architecture and associated characteristics in ore systems. The integration of 3D geological models with 3D computational simulation data enhances the effectiveness of 3D MPM in representing the geological architecture and its coupled geodynamic processes that govern mineralization. Despite variations in modality (i.e., data source, representation, and information abstraction levels) between geological models and simulation data, the cross-modal gap between these two types of data remains underexplored in 3D MPM. This paper presents a novel 3D MPM approach that robustly fuses multimodal information from geological models and simulation data. Acknowledging the coupled and correlated nature of geological architectures and geodynamic processes, a joint fusion strategy is employed, aligning information from both modalities by enforcing their correlation. A joint fusion neural network is devised to extract maximally correlated features from geological models and simulation data, fusing them in a cross-modality feature space. Specifically, correlation analysis (CCA) regularization is utilized to maximize the correlation between features of the two modalities, guiding the network to learn coordinated and joint fused features associated with mineralization. This results in a more effective 3D mineral prospectivity model that harnesses the strengths from both modalities for mineral exploration targeting. The proposed method is evaluated in a case study of the world-class Jiaojia gold deposit, NE China. Extensive experiments were carried out to compare the proposed method with state-of-the-art methods, methods using unimodal data, and variants without CCA regularization. Results demonstrate the superior performance of the proposed method in terms of prediction accuracy and targeting efficacy, highlighting the importance of CCA regularization in enhancing predictive power in 3D MPM. •Casting 3D mineral prospectivity modeling (MPM) as a deep multimodal fusion problem.•Joint fusion network for integrating features of 3D geological models and simulation data.•Canonical correlation analysis regularization to guide network fusion of the cross-modal gap.•Maximizing the correlation of multimodal information boosts the performance of 3D MPM.
Bibliografie:ObjectType-Article-1
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ISSN:0098-3004
DOI:10.1016/j.cageo.2024.105618