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GIS-based mineral prospectivity mapping (MPM) is a computer-aided methodology for delineating and better constraining target areas deemed prospective for mineral deposits of a particular type. The underlying algorithms are well-established and well-understood, but on the whole, MPM that is a multi-faceted and multi-criteria approach, is faced with a high degree of uncertainty. We distinguish three principal types of uncertainties: (1) data-related (e.g., the sometimes erroneous, inadequate, incomplete, unevenly distributed or poorly resolved nature of the input data); (2) model-related (e.g., the diversity and inherent natural variability of mineral deposits, our lack of complete knowledge of the targeted mineral deposit type, and our imperfect ability to interpret geoscience datasets); and (3) judgment-related (e.g., the influence of cognitive heuristics and biases). In this contribution, we review and characterize the key uncertainties listed above and provide possible solutions as to how they may be recognized and mitigated in the context of MPM. This review also clearly illustrates the need for future studies designed to carefully monitor each step of the MPM process and aims at reducing uncertainty by, for example, (1) using carefully vetted, high-quality input data, (2) developing targeting models based on the best possible understanding of the underlying mineral deposit models and backed by machine learning-based simulations of likely ore-forming processes, and (3) adopting advanced methods such as deep learning algorithms for effective integrating of predictor maps.

There has been in recent years a trend towards adopting deep neural networks for addressing earth science problems. Of the various deep neural networks applied to different problems in earth sciences, this study aimed to demonstrate how to apply the group method of data handling (GMDH) neural networks for mineral prospectivity mapping (MPM). GMDH neural networks are sophisticated, multilayered, robust tools for addressing complex regression problems. However, labeled data for MPM, which constitute multivariate attributes of known deposit and non-deposit sites, are often (if not always) insufficient to train neural networks adequately. Such issue triggers networks' poor generalization; that is, the networks developed fit the labeled data perfectly (i.e., low bias) but cannot predict unseen data accurately (i.e., high variance). Given this, GMDH neural networks were, in this study, coupled with a window-based data augmentation technique, an approach to generate additional geologically constrained labeled samples for MPM, and applied to a district hosting few but giant porphyry copper deposits. It was recognized that coupling the data augmentation technique with GMDH neural networks yielded robust predictive models that can handle the bias–variance tradeoff, making this combined methodology a viable option for MPM in terrains that host few but large mineral deposits.

Unraveling the evolution of Mississippi Valley-type (MVT) hydrothermal system is crucial for understanding ore genesis and exploration. In this paper, we take the Wusihe Pb-Zn deposit in the western Yangtze Block (South China) as a case study, using detailed ore deposit geology, quartz in situ trace elements, and sulfides in situ S-Pb isotopes, to propose a new integrated model for the evolution of MVT hydrothermal system. Four hydrothermal stages were identified in the Wusihe ore district: (I) lamellar pyrite-sphalerite; (II) disseminated, stock-work, and brecciated sphalerite-galena; (III) massive galena, and (IV) veined calcite-bitumen. Within the most representative stage (stage II), Al concentrations in quartz (Q) increase from 8.46-354 ppm (mean 134 ppm) of Q1 to 171-3049 ppm (mean 1062 ppm) of Q2, and then decrease to 3.18-149 ppm (mean 25.4 ppm) of Q3. This trend indicates the role of acid-producing processes that resulted from sulfide precipitation and acid consumption by carbonate buffering. The occurrence of authigenic non-altered K-feldspar provides further evidence that the ore-forming fluids were weakly acidic with pH values of >∼5.5. Moreover, new bulk δ34S values of sulfides (+1.8 to +14.3 ppm) are overall lower than those previously reported (+7.1 to +20.9 ppm), implying that in addition to thermochemical sulfate reduction (TSR), bacterial sulfate reduction (BSR) may play an important role in the formation of S2-. In situ δ34S values show a larger range (-4.3 to +26.6 ppm), and significantly, varies within single grains (up to +12.3 ppm), suggesting mixing of two isotopically distinct S2- end-members produced by TSR and BSR. The diagenetic and hydrothermal early phase (stage I) sulfides were formed within a nearly closed system of BSR, whereas the formation of late phase (stage II and stage III) sulfides was caused by the input of hydrothermal fluids that promoted TSR. New galena in situ Pb isotopic ratios (206Pb/204Pb=18.02-18.19, 207Pb/204Pb = 15.66-15.69, and 208Pb/204Pb=38.14-38.39) suggest that the sources of mineralizing metals in the Wusihe deposit are mainly Proterozoic basement rocks. Hence, a multi-process model (i.e., basin-mountain coupling, fluid mixing, local sulfate reduction, in situ acid-producing and involvement of black shales and carbonate sequences) was responsible for the formation of the Wusihe deposit, while S2- was produced by both TSR and BSR, providing new insights into the evolution of MVT hydrothermal system.

Information about the relative importance of each criterion or the weights of criteria can have a significant influence on the ultimate rank of alternatives. Accordingly, assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems. Three methods are commonly used for assessing the weights of criteria: objective, subjective, and integrated methods. In this study, an objective approach is proposed to assess the weights of criteria, called SPC method (Symmetry Point of Criterion). This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence on decision-making. The SPC methodology is systematically presented and supported by detailed calculations related to an artificial example. To validate the developed method, we used our numerical example and calculated the weights of criteria by CRITIC, Entropy, Standard Deviation and MEREC methods. Comparative analysis between these methods and the SPC method reveals that the developed method is a very reliable objective way to determine the weights of criteria. Additionally, in this study, we proposed the application of SPC method to evaluate the efficiency of the multi-criteria partitioning algorithm. The main idea of the evaluation is based on the following fact: the greater the uniformity of the weights of criteria, the higher the efficiency of the partitioning algorithm. The research demonstrates that the SPC method can be applied to solving different multi-criteria problems.

Major, minor, and trace element abundances in titanite crystals from four granitic plutons in southern Yidun arc, SW China, have been determined using electron microprobe and laser ablation-inductively coupled plasma-mass spectrometry. The selected plutons are the Cretaceous Xiuwacu (CXWC) pluton, with quartz vein-type Mo mineralization (economic-Mo), the Tongchanggou (TCG) pluton, with porphyry-type Mo mineralization (economic-Mo), the Triassic Pulang (PL) pluton, with porphyry-type Cu mineralization (subeconomic-Mo), and the Triassic Xiuwacu (TXWC) pluton, without any Mo mineralization (Mo-barren). Our study reveals that the chemical compositions of titanite crystals from these plutons such as REE, Sr, Ga, δEu, δCe, Fe2O3/Al2O3, halogens, and Mo can be used to track magma compositions, oxidation states, metal fertility, and crystallization history. The data from this study also show that titanite crystals from these plutons with different potential of Mo mineralization have similar Mo contents and exhibit an irregular variation between Mo and Sr abundances (indicating non-Mo enrichment in the residual melt during the progressive crystallization) for some Mo-mineralized plutons. Our new observations support the recent hypothesis that high initial Mo contents in magma and the enrichment of Mo in residual melts formed by fractional crystallization are not the only requirements to form a granite-related Mo ore deposit. Efficient extraction of the residual melts, possibly facilitated by high concentrations of magmatic F is also critical to the ore formation. Evidence for high-F concentration in felsic magma, which facilitates melt and fluid separation and economic Mo mineralization during magma evolution, may be traced by the presence of F-rich titanite crystals in the two Mo-mineralized granite plutons (CXWC and TCG). These new findings from this study confirm that titanite is indeed a good petrogenetic and metallogenic indicator. However, in light of the limited contribution of metal fertility to Mo mineralization, we suggest that titanite Mo concentrations should be used along with other crucial proxies, such as titanite F contents and Fe2O3/Al2O3 ratios to better evaluate the Mo-mineralized potential of granites.

Background Africa is blessed with vast arable land and enriched with valuable natural resources encompassing both renewable (like water, forests, and fisheries) and non-renewable (such as minerals, coal, gas, and oil). Under the right conditions, a natural resource boom should serve as an important driver for growth, development, and the transition from cottage industry to factory output. However, despite its wealth, Africa is often associated with the notion of a resource curse. Negative outcomes are often linked with mineral wealth. This paper investigates the causes of adverse health outcomes in resource-rich regions. The study provides empirical support for the natural resource curse with particular emphasis on the environmental health risks in Africa. We explore the multifaceted connections among mineral deposits, environmental risks, conflict events and population dynamics, shedding light on the complexities of resource-rich areas. Results We amalgamate georeferenced data pertaining to 22 specific mineral deposits with information on the prevalence of reliance on compromised infrastructures at a spatial resolution of 0.5 ∘ × 0 . 5 ∘ for all of Africa between 2000 and 2017. Through comprehensive econometric analysis of environmental health risk factors, including reliance on contaminated water sources, open defecation, unimproved sanitation, particulate matter concentration, and carbon concentration, we uncover the intricate pathways through which mineral deposits impact public health. Our findings revealed the significant role of in-migration in mediating environmental health risks. Moreover, we found that the activities of extractive companies amplify certain environmental risks including reliance on unimproved sanitation and practices and particulate matter concentration. Conflict events emerge as a key mediator across all environmental health risks, underlining the far-reaching consequences of instability and violence on both local communities and the environment. Conclusion The study contributes to the discourse on sustainable development by unraveling the nuanced associations between mineral wealth and health challenges. By drawing attention to the intricate web of factors at play, we provide a foundation for targeted interventions that address the unique environmental and health challenges faced by mineral-rich communities.

This book argues that mining area conflicts of Sub-Saharan African countries, like Sierra Leone, and their impacts on mineral development and policy stem from cultural differences in land governance. Extractive industries operating under state laws, which have roots in colonial policies, clash with customary land governance system through landlord-stranger relations.

Lithium is one of the important strategic energy metals, which is in short supply in China. There are three major types of lithium deposits: brine and salt lake type, highly differentiated granite or pegmatite type, and carbonate-clay type. In recent years, some new types of lithium deposits have also begun to receive great attention and subject recent research. There are many crypto-explosive breccia pipe type deposits in the world, including copper, gold, lead, zinc, tungsten and tin deposits, but little is known about this type of lithium deposit. This paper introduces the latest research results of the Weilasituo Sn−Li−Rb polymetallic deposit in Inner Mongolia (NE China), which occurs in the middle-southern section of the Great Xing’an Range metallogenic belt. A remarkable feature of this deposit is the coexistence of various mineralization types, including granite type Rb and Sn−Zn, hydrothermal crypto-explosive breccia pipe type Li−Rb, quartz vein type Sn−Zn and sulfide vein type Pb−Zn−Ag mineralization. Among them, hydrothermal crypto-explosive breccia pipe type Li-Rb deposit is currently very rare at home and abroad, which is likely a new type of rare metal deposit that worthy of our attention. This paper systematically summarizes the geology, alteration and mineralization, geochemistry, isotopes and geochronology of the Weilasituo deposit, and establishes a new petrogenic and metallogenic model.

Digging Earth: Extractivism and Resistance on Indigenous lands of the Americas is a collection of essays and artists' contributions that documents the practices of extractivism on indigenous lands of the American continent, and the opposition to the politics of land appropriation and exploitation, by indigenous movements, activists and artists. Authors and artists address the extractivism of neo-colonial operations, its impact on local and indigenous communities and their environment, while tracing back its practices to settler colonialism in the Americas, ​and the vision of the natural world as ready to plunder. In addition to the economic impact, some contributions look at extractivism from the point of view of the extraction of cultural knowledge and ontologies. Artists and authors highlight topics of indigenous sovereignty, land rights, environmental justice, the stewardship of the land, and the history of indigenous environmental practices. The diversity of the contributors' backgrounds brings fresh perspectives to the issues surrounding the practices of the extractive industries and the exploitation of indigenous lands and resources. Their reflections and analyses convey the urgency of rethinking our politics towards the earth and its resources, as we are warned of an approaching collective ecocide.

The identification and mapping of geochemical anomaly patterns have emerged as a more precise and efficient approach for mineral exploration, with deep learning algorithms being extensively employed in this realm. However, existing methodologies require further investigation regarding model interpretability and correlation with established mineral control factors. This paper proposes a regional geochemical anomaly identification method based on the memory-augmented autoencoder (MemAE), incorporating geological controlling factors. Firstly, the MemAE model is introduced to address the excessive generalization capability of the traditional autoencoder (AE) model. Secondly, utilizing multifractal singularity theory, a nonlinear functional relationship between faults and mineral deposits is established. This relationship reveals the controlling effect of faults on mineralization and it is incorporated as a constraint term in the MemAE's loss function. Finally, the constructed geochemical anomaly identification model is employed to delineate prospective mineralization areas, with comparative studies conducted on AE, MemAE, and geologically constrained MemAE models. The results demonstrate that the geologically constrained MemAE exhibits superior performance, achieving an AUC of 0.802. The eight delineated mineralization prospective areas show strong concordance with actual distributions. The proposed method, which considers geological controlling factors, effectively enhances model interpretability and demonstrates excellent geochemical anomaly identification capabilities. Consequently, this approach can be considered a viable methodology for mineral exploration.