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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. [ABSTRACT FROM AUTHOR]

The train platforming problem is one of the most investigated topics in the train operation field. However, a systematic understanding of how train platforming improves the convenience of passenger transfer procedures is still lacking. To face the rapidly growing transfer demands in the high-speed railway station, this paper proposes a transfer-facilitating-oriented train platforming approach based on a multi-agent train operation simulation model. By evaluating the transfer connection quality using an indicator named Transfer Connection Strength(TCS), which considers transfer distance and transfer time, the proposed transfer-facilitating-oriented train platforming strategies can establish more convenient transfer connections and so improve the transfer service. A track occupation horizon based on the modelling of accurate train running processes is utilized to detect route conflicts and guarantee conflict-free train operations in the simulation model, enabling the solutions' feasibility of train platforming to be taken into account. A series of simulation experiments are conducted using real data of Zhengzhou East station in China. The experiment results show that, the proposed transfer-facilitating-oriented train platforming strategies are capable of obtaining a greater number of convenient transfer connections. The feasibility can be guaranteed by permitted train delays and route conflict detection for the succeeding trains in a forward-looking period. Additionally, the platform assignment plans can be generated with a specified priority of transfer distance or transfer time. [ABSTRACT FROM AUTHOR]