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The purpose of mineral prospectivity mapping (MPM) is to discover unknown mineral deposits by means of fusing multisource prospecting information. In recent years, with rapid advancements in artificial intelligence, deep learning algorithms (DLAs) as a groundbreaking technique have exhibited outstanding capabilities in geoscience. However, conventional DLAs for MPM face certain challenges in feature extraction and the fusion of multimodal prospecting data. Moreover, opaque DLAs lead to an insufficient understanding of the predictive results by experts. In this study, a dual-branch convolutional neural network (DBCNN) and its post hoc interpretability were jointly constructed to map gold prospectivity in western Henan Province of China. In particular, channel and spatial attention modules were integrated into two branches to complement the respective advantages of multichannel and high spatial prospecting data for MPM. The Shapley additive explanations (SHAP) framework was then adopted to explain the predictive results by exploring the feature contributions. The comparative experiments illustrated that DBCNN can enhance feature representation and fusion abilities to improve the performance of MPM compared to conventional DLAs. The high-probability areas delineated by the DBCNN model exhibited close spatial relevance with known gold deposits, and the SHAP further confirmed the reliability of the predictive result obtained by the DBCNN model, thereby guiding future gold exploration in this study area.

In recent years, numerous countries have initiated geochemical survey projects, highlighting the importance of identifying geochemical anomalies for the discovery of potential mineral deposits. In addition, anthropogenic activity, missing or inaccurate data, and overburden can lead to local enrichment or deficiency of elements, resulting in false or weak geochemical anomalies. Simultaneously considering spatial and spectrum information in the data can eliminate spectrum differences caused by the data inaccuracy and enhance weak mineralization anomalies. Therefore, introducing spatial-spectrum models is beneficial for leveraging the strengths of both approaches. This study proposes a two-branch fusion network for extracting spatial-spectrum features from a geochemical survey data cube. The spectrum branch consists of a one-dimensional convolutional neural network (1DCNN) that can be utilized to extract geochemical spectrum information within a single pixel, covering major and trace geochemical elements and accounting for both positive and negative geochemical anomalies. The spatial branch is a superpixel graph convolutional network (SGCN), which is composed of internal and external graph convolutions. The SGCN not only can extract spatial relationships between neighboring pixels and even pixels at a long distance, but also takes into account the anisotropy of mineralization. Furthermore, spatial information can smooth out false geochemical anomalies caused by inaccurate or missing data. A case study was conducted to identify mineralization-related geochemical anomalies and validate the proposed hybrid deep learning model in northwestern Hubei Province, China. Experiments have shown that (1) superpixel segmentation is an effective tool for geochemical anomalies identification, (2) the incorporation of spectrum- and spatial-based methods contributes to the model’s ability to discern anomalies and backgrounds within the geochemical data cube, improving its accuracy in anomaly detection, and (3) the identified anomalous areas provide clues for future mineralization searches.

Mineral prospectivity mapping (MPM) is designed to reduce the exploration search space by combining and analyzing geological prospecting big data. Such geological big data are too large and complex for humans to effectively handle and interpret. Artificial intelligence (AI) algorithms, which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration, have demonstrated excellent performance in MPM. However, AI-driven MPM faces several challenges, including difficult interpretability, poor generalizability, and physical inconsistencies. In this study, based on previous studies, we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence (XAI) models for MPM by embedding domain knowledge throughout the AI-driven MPM, from input data to model design and model output. This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process, thereby improving model interpretability and performance. Overall, the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process, presenting a valuable and promising area for future research designed to improve MPM.

Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt (SFMB) (China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potential for mineral exploration because large areas in this belt are covered by forest. A new predictive model for mapping skarn-type Fe deposit prospectivity in this belt was developed and focused on in this study, using five criteria as evidence: (1) the contact zones of Yanshanian granites (GRANITE); (2) the contact zones within the late Paleozoic marine sedimentary rocks and the carbonate formations (FORMATION); (3) the NE-NNE-trending faults (FAULT); (4) the zones of skarn alterations (SKARN); and (5) the aeromagnetic anomaly (AEROMAGNETIC). The fuzzy weights of evidence (FWofE) method, developed from the classical weights of evidence (WofE) and based on fuzzy sets and fuzzy probabilities, could provide smaller variances and more accurate posterior probabilities and could effectively minimize the uncertainty caused by omitted or wrongly assigned data and be more flexible than the WofE. It is an efficient and widely used method for mineral potential mapping. Random forests (RF) is a new and useful method for data-driven predictive mapping of mineral prospectivity method, and needs further scrutiny. Both prospectivity results respectively using the FWofE and RF methods reveal that the prediction model for the skarn-type Fe deposits in the SFMB is successful and efficient. Both methods suggested that the GRANITE and FORMATION are the most valuable evidence maps, followed by SKARN, AEROMAGNETIC, and FAULT. This is coincident with the skarn-type Fe deposit mineral model in the SFMB. The unstable performance experienced when FORMATION was omitted might indicate that the highest uncertainty and risk in follow-up exploration is related to the sequences. In addition, the performance of the RF method for the skarn-type Fe deposits prospectivity in the SFMB is better than the FWofE; therefore, it could be used to guide further exploration of skarn-type Fe prospects in the SFMB.

It is assumed that variable fluid pressure states in space control the spatiotemporal evolution of hydrofracture, and their responses on mineralization. In this study, mineral precipitation caused by rapid fluid pressure reduction is incorporated into a cellular automaton model to explore the effects of various spatially structured fluid pressure rates on the behavior of hydrofracture and mineralization. We explore a range of spatial structures, which vary from essentially random to strong spatial structure. In the model, fluid pressure increase induces local hydrofracture to nearest neighbors, and fluid mass is conserved among the cell affected. As the model evolves, internally connected networks merge and a correlation length is established as the system approaches the failure condition and identifies the percolation threshold. The correlation length of the internally connected network scales with the degree of spatial structure of fluid pressure increase rates, resulting in earlier arrival to the percolation threshold for strong spatial structures. At the percolation threshold, large‐scale fluid pressure reduction induces mineral precipitation and facilitate the formation of the spatially structured geochemical patterns. The degree of spatial structure of geochemical patterns correlates with the spatial structure of fluid pressure increase rates because mineral precipitation from fluid pressure reduction occurs over larger scale. The proposed model offers an intuitive and potentially important tool for understanding the role of spatially structured fluid pressure increase and reduction on hydrothermal mineralization patterns in fault‐related ore systems. Sudden fluid pressure reduction associated with hydrofracture affects metal solubility, triggering transient physical and chemical changes in ore‐forming fluids that can enhance the mineralization process. In this work, we investigate the influence of spatially structured fluid pressure on hydrofracture and mineralization. Using a cellular automaton model, we explore five different spatially structured scenarios in fluid pressure increase rates on the evolved spatial geochemical pattern development and their statistical properties. Simulation results show that the scale of interacting hydrofractures evolves from a large number of small‐scale failures to a small number of large‐scale failures with the enhancement of the spatially structured fluid pressure increase rate. Fluid pressure reduction induced by hydrofracture promote cyclic mineral precipitation and further form spatially structured geochemical patterns. Accordingly, the degree of spatial structure of geochemical patterns become stronger and stronger with the enhancement of the spatially structured fluid pressure increase rate because the occurrence of larger‐scale mineral precipitation resulted from fluid pressure reduction in larger‐scale failure cells. The proposed model provides strong insights into hydrothermal mineralization pattern in many fault‐related ore systems. The simplified fluid flow cellular automaton model can give a near‐perfect expression for hydrothermal mineralization processThe occurrence of percolation threshold is of great significance to the degree and scale of mineralizationThe spatial structure of fluid pressure is critically important for producing highly spatial structured geochemical pattern The simplified fluid flow cellular automaton model can give a near‐perfect expression for hydrothermal mineralization process The occurrence of percolation threshold is of great significance to the degree and scale of mineralization The spatial structure of fluid pressure is critically important for producing highly spatial structured geochemical pattern