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\"When itinerant cave diver James Tighe receives an invitation to billionaire Nathan Joyce's private island, he thinks it must be a mistake. But Tighe's unique skill set makes him a prime candidate for Joyce's high-risk venture to mine a near-earth asteroid--with the goal of kick-starting an entire off-world economy. The potential rewards and personal risks are staggering, but the competition is fierce and the stakes couldn't be higher. Isolated and pushed beyond their breaking points, Tighe and his fellow twenty-first century adventurers--ex-soldiers, former astronauts, BASE jumpers, and mountain climbers--must rely on each other to survive not only the dangers of a multi-year expedition but the harsh realities of business in space. They're determined to transform humanity from an Earth-bound species to a space-faring one--or die trying\"-- Provided by publisher.

What are the main findings? This study constructs a dedicated semantic segmentation dataset of pegmatite dikes for the Yilanlike area by using 0.66 m high-resolution RGB imagery for visual delineation and ZY1F hyperspectral data for spectral constraint and label refinement. The proposed GAD-UNet++ is designed as a pegmatite-dike-oriented semantic segmentation framework rather than a simple combination of existing modules. By integrating GhostNetV2, DFC, Coordinate Attention, and deep supervision within the UNet++ architecture, the model is adapted to address the slender morphology, discontinuous exposure, weak boundaries, background confusion, and class imbalance of pegmatite dikes in high-resolution remote sensing imagery. It achieved an mIoU of 93.11% and an F1-score of 94.95% on the test set, while reducing the number of parameters to 13.20 M and the inference time to 7.36 ms. Based on the segmentation results, 18 potential pegmatite dike enrichment zones were delineated in the study area. What are the implications of the main findings? The results indicate that combining lightweight feature extraction, long-range dependency modeling, and spatial attention enhancement is an effective strategy for segmenting slender geological targets with blurred boundaries and complex backgrounds, such as pegmatite dikes. The proposed dataset construction strategy and segmentation framework provide a practical technical pathway for pegmatite dike identification from high-resolution remote sensing imagery and support remote sensing-based rare-metal prospecting and regional geological interpretation. Pegmatite dikes are important prospecting indicators for rare-metal deposits, whereas traditional methods for pegmatite dike identification are constrained by the limited capability of human visual interpretation to capture information from remote sensing imagery, resulting in low identification accuracy and efficiency. In recent years, global research on semantic segmentation of different surface features and remote sensing-based mineral exploration using deep learning methods and high-resolution remote sensing imagery has made significant progress; however, studies on surface-exposed geological bodies such as pegmatite dikes remain highly insufficient. To address the key problem of efficiently identifying pegmatite dikes in remote sensing imagery, this study proposes an improved model based on UNet++, termed GAD-UNet++. In the field of remote sensing geology, this study constructed a pegmatite dike semantic segmentation dataset based on high-resolution RGB imagery by using 0.66 m RGB imagery for visual delineation and ZY1F hyperspectral data for spectral constraint and label refinement; on this basis, semantic segmentation of surface pegmatite dikes in the Yilanlike area of the South Tianshan Mountains, Xinjiang, was conducted using RGB remote sensing image patches as model input. Specifically, because pegmatite dikes are small targets characterized by slender structures, indistinct boundaries, and sparse regional distribution, this study introduced a lightweight feature extraction structure (GhostNetV2) and a long-range dependency attention module (DFC) at the encoder stage, and further incorporated the Coordinate Attention module (CA) to enhance spatial localization and boundary representation of the targets. Finally, focal cross-entropy loss and a deep supervision strategy were adopted to improve the accuracy of semantic information extraction for pegmatite dikes, as well as the training stability and segmentation accuracy under class-imbalance conditions. The results show that the proposed model achieved an mIoU of 93.11% and an F1-score of 94.95% on the test set. Compared with existing semantic segmentation models, the proposed model achieved superior performance in both identification accuracy and computational efficiency for pegmatite dikes. In addition, this study delineated 18 potential pegmatite dike enrichment zones in the Yilanlike area, providing technical support for remote sensing-based rare-metal prospecting and geological interpretation in the study area.

Meeting future material needs requires expanding metal supply while reducing environmental footprints and improving the efficiency of exploration and resource assessment. The Wadi El-Muweih area, located north of the Aswan region in Egypt’s Eastern Desert, hosts significant iron ore potential within the Late Cretaceous Nubia sandstone (Taref Formation). This study provides a systematic, integrated approach for delineating ironstone extensions to support more targeted field campaigns and responsible development pathways. Remote sensing enabled the rapid screening and mapping of iron-bearing zones, subsequently validated through field observations and mineralogical and geochemical analyses. The iron-bearing middle member of the Taref Formation consists of glauconitic/chamositic and ferruginous sandstones, with ironstone bands occurring in three fining-upward cycles. The mineralogical results indicate chamosite, hematite, and goethite as primary constituents, with detrital quartz and apatite in varying proportions. The geochemical data show high Fe2O3 (avg. 68.83%) and SiO2 (avg. 13.13%), with elevated Al2O3, CaO, and P2O5 compared to Aswan’s oolitic ironstones. The study confirms that massive, oolitic, and conglomeratic ironstone facies formed in a near-shore environment during transgressive–regressive cycles. By combining remote sensing with ground-based validation, the workflow supports sustainable metal technologies by improving discovery efficiency, reducing unnecessary disturbance, and strengthening the geoscientific basis for future iron resource planning.

The idea of mineral exploration, which is called “exploration philosophy” in the Western countries, is the thoughts, the methodology, technology, goals and organization that guide mineral exploration. The three basic elements of mineral exploration are “what to find”, “where to find” and “how to find”. The concept of mineral exploration is gradually changing with the development of these three elements that provide a powerful driving force to change mineral exploration concepts, methods and technology. Innovation of mineral exploration concepts is the result of continuing exploration and development keeping pace with the times. The combination of “mathematical geology” and “information technology” can be called “digital geology”. Digital geology is the data analysis component of geological science. Geological data science is a science that uses the general methodology of data to study geology based on the characteristics of geological data and the needs of geological field work. Digital mineral exploration is the application of digital geology in mineral exploration to reduce ore-finding uncertainty.

Seafloor massive sulfide (SMS) deposits formed by hydrothermal circulation generate measurable self-potential (SP) anomalies in seawater, providing an effective geophysical indicator of sulfide mineralization. In this study, a remotely operated vehicle (ROV)-borne SP survey was conducted at the Yuhuang hydrothermal field on the Southwest Indian Ridge to investigate the spatial distribution of SMS mineralization. The survey operated at a near-bottom altitude of approximately 10 m, substantially lower than that typically achieved by autonomous underwater vehicles (AUVs) or towed systems, enabling high-resolution data acquisition with improved signal quality. To efficiently discretize complex seafloor topography under irregular data coverage, an adaptive octree mesh was employed, enabling computationally efficient three-dimensional inversion over a large survey area and recovery of the subsurface source current density distribution. The inversion results resolve a main anomaly zone spatially correlated with known SMS mineralization, as well as an additional anomaly zone that was not resolved by previous surveys and suggests potential mineralization. Anomalies associated with known mineralization show good spatial agreement with independent near-bottom observations and drilling results. The results demonstrate that ROV-borne SP surveying combined with adaptive meshing and three-dimensional inversion provides a reliable approach for imaging SMS mineralization in deep-sea environments.

Deriving meaningful mineralization information from raw geospatial datasets is fundamental to the sustainable evaluation and management of mineral resources. As a cornerstone of mineral resource evaluation, identifying geochemical anomalies often faces the significant challenge of sampling bias in practical applications. Strong spatial unevenness often leads to information loss in traditional geostatistical models, where critical anomaly structures may be over-smoothed or obscured. To address this limitation, this study proposes a knowledge-driven adaptive direct sampling (KD-ADS) framework. This approach functions as a geospatial context-aware reconstruction engine. It integrates a multi-factor knowledge-driven weighting system to prioritize regions with high information value and incorporates a dynamic context-aware neighborhood module that adapts to local statistical characteristics. Using 1268 samples from the Jiulian Mountains tungsten metallogenic belt, ablation studies demonstrate the individual contributions of the knowledge-driven weighting and adaptive neighborhood modules to improving reconstruction accuracy and spatial connectivity. Comparative experiments with the traditional direct sampling (DS) algorithm demonstrate that KD-ADS achieves a more accurate reconstruction of geochemical fields and better preserves discrete high-value mineralization anomalies and spatial heterogeneity under sampling-bias conditions. This approach improves the reproducibility of mineralization enrichment patterns and enhances computational efficiency, providing data science-driven support for sustainable mineral exploration and resource allocation.

This paper presents a novel approach for identifying lithium bearing minerals in Qahavand Playa, Iran, using ASTER satellite imagery. The method utilizes reference spectra extracted from Clayton Valley Playas in the United States, a well-known lithium rich area, as input for mineral detection. Image processing was performed using Matched Filtering (MF) method to map potential lithium bearing regions in Qahavand Playa. Results from this remote sensing approach demonstrate strong correlation (R2= 0.94) with field verification and geochemical analysis, confirming the effectiveness of using reference spectra from established lithium deposits for identifying similar mineralogical signatures in unexplored areas. This study highlights the importance of remote sensing techniques in exploration of strategic mineral resources such as lithium, which is essential for electric vehicle batteries and energy storage systems. Overall, the results demonstrate the potential of cross regional spectral transfer as a powerful tool for guiding targeted mineral exploration in unexplored playa environments.

The Baiyun gold deposits in the Qingchengzi ore concentration area have significant deep exploration potential. Transient electromagnetic (TEM) exploration is conducted to identify ore-bearing strata and ore-controlling structures in the mining area’s periphery. Previous tests have revealed that it is challenging to penetrate the conductive layer to obtain deep information using a conventional in-loop TEM method with an induction coil. Field data are acquired using a high-temperature superconductor (HTS) superconductive quantum interference device (SQUID) magnetometer with a fixed-loop configuration. The exploration results indicate that the SQUID TEM system can detect the distribution of ore-bearing strata, and the ore-control structures within deep formations. The paper explores two extension areas in the east and west of the Baiyun thrust nappe structural belt. The inversion results reveal that the Baiyun gold deposit is significantly compressed, with noticeable thrusts and faults. Since the thrust fault zone extends to the south stably, the lower section has a gentle dip angle favorable area for deep prospecting.

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.