Traditional geological mapping methods, which rely on field observations and rock sample analysis, are ine fficient for continuous spatial mapping of geological features such as alteration zones. Deep learning models such as convolutional neural networks (CNNs) have ushered in a transformative era in remote sensing data analysis. CNNs excel in automatically extracting features from image data for classification and regression problems. CNNs have the ability to pinpoint specific mineralogical changes attributed to mineralisation processes by discerning subtle features within remote sensing data. Our methodology involves model training using two distinct sets of training samples generated through ground truth data and a fully automated approach through selective principal component analysis (PCA). We also compare CNNs with conventional machine learning models, including k-nearest neighbours, support vector machines, and multilayer perceptron. Our findings indicate that training with a ground truth-based dataset produces more reliable alteration maps. Additionally, we find that CNNs perform slightly better when compared to conventional machine learning models, which further demonstrates the ability of CNNs to capture spatial patterns in remote sensing data e ffectively. We find that Landsat 9 surpasses Landsat 8 in mapping iron oxide areas when employing the CNNs model trained with ground truth data obtained by field surveys. We also observe that using ASTER data with the CNNs model trained on the ground truth-based dataset produces the most accurate maps for two other important types of alteration zones, argillic and propylitic. This underscores the utility of CNNs in enhancing the e fficiency and precision of geological mapping, particularly in discerning subtle alterations indicative of mineralisation processes, especially those associated with critical metal resources. Introduction Geological maps are traditionally crafted through ground surveys and founded on field observations. They frequently incur inevitable errors due to the lack of spatial continuity of the field observations, thus yielding inaccurate representations (Campbell et al., 2005). Recognising these limitations, geologists have been prompted to seek innovative approaches and e fficient methodologies to accurately map geological features, particularly alteration zones (Kesler, 2007; McCuaig et al., 2010). The utilisation of remote sensing data for alteration mapping emerges as a pivotal technique in regional mineral exploration, enabling the precise spatial identification of alteration zones associated with mineralisation processes (Mohamed et al., 2021).

Remote-sensing mineral exploration is critical for identifying economically viable mineral deposits, yet it poses significant challenges for multimodal large language models (MLLMs). These include limitations in domain-specific geological knowledge and difficulties in reasoning across multiple remote-sensing images, further exacerbating long-context issues. To address these, we present MineAgent, a modular framework leveraging hierarchical judging and decision-making modules to improve multi-image reasoning and spatial-spectral integration. Complementing this, we propose MineBench, a benchmark specific for evaluating MLLMs in domain-specific mineral exploration tasks using geological and hyperspectral data. Extensive experiments demonstrate the effectiveness of MineAgent, highlighting its potential to advance MLLMs in remote-sensing mineral exploration.

Ore deposits are generally formed by hydrothermal activity, which also forms various types of alteration zones in the vicinity of such deposits. Identifying alteration zones can clarify the mineralization mechanism and provides information indispensable for mineral resource exploration. The application of an alteration halo accompanied by the alteration of host rocks to the exploration of the Kuroko ore deposits produced many results in Japan (see, e.g., [1]). It is important to identify the alteration zone in the exploration of porphyry copper deposits. More than 50% of global copper production is from porphyry copper deposits, making them the most important copper resource.