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Natural resources research
Kluwer Academic Publishers
Natural resources research

Kluwer Academic Publishers

1520-7439

Natural resources research/Journal Natural resources researchSCI
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    Crushed Volcanic Rock as Soil Remineralizer: A Strategy to Overcome the Global Fertilizer Crisis

    Mosquera Burbano, Diego FelipeTheodoro, Suzi HuffXavier de Carvalho, Andre MundstockRamos, Claudete Gindri...
    14页
    查看更多>>摘要：Transitioning the productive base to a more sustainable agriculture is one of the great challenges of our time. The current conflicts in Eastern Europe have had a major repercussion on the agricultural commodity market with restricted access and a massive cost increase for some fertilizers used in agriculture. This scenario has led to international concern about food shortages, whereby countries that depend on fertilizer imports need to find mechanisms and new technological paths to reduce their dependence on the international market. The use of crushed rock (soil remineralizers) associated with microorganisms is an important alternative in terms of cost reduction, lower impact on the environment and reduction of external dependence on agricultural inputs. The objective of this work was to evaluate the results of different types of inputs for soil fertilization (crushed rock - remineralizer, organic material and conventional - NPK), the production parameters of quinoa culture (Chenopodium quinoa) and this nutritional content of the crop. The experiment was carried out in a greenhouse and the data were subjected to analysis of variance, the Dunnett's test, complex contrasts, and multivariate analyses. The results showed significant increases in grain filling and quinoa yields, in soil fertility, and in the nutrient content of the aerial parts of plants treated with remineralizers. The treatments containing a mixture of remineralizers and organic compost were superior to those without these inputs, suggesting positive interaction among these sources. This approach may help toward adopting new technologies, especially with the current undersupply of soluble fertilizers. The use of local geological sources (crushed rock) has the capacity to reduce the dependence on imported fertilizers, thus helping to increase agri-food sovereignty in countries and adhering to the principles of agroecology at the local and global levels.
      原文链接:
    • NSTL
    • Springer Nature

    Visual Interpretable Deep Learning Algorithm for Geochemical Anomaly Recognition

    Luo, ZijingZuo, RenguangXiong, Yihui
    13页
    查看更多>>摘要：Deep learning algorithms (DLAs) have achieved better results than traditional methods in the field of multivariate geochemical anomaly recognition because of their strong ability to extract feature from nonlinear data. However, most of DLAs are black-box approaches because of the high nonlinearity characteristics of the hidden layer. In addition, the integration of domain knowledge into the DLAs to ensure physical consistency is a challenge for DLAs in geoscience. In this study, we adopted the adversarial autoencoder (AAE) algorithm for geochemical anomaly detection. The interpretability of the model is improved by visualizing features and integrating geological domain knowledge into the loss function of the AAE. The feature visualization method was used to display the changes of information in the model calculation process to further understand the inherent operation law and principle of the neural network. The penalty term was added to the optimized loss function, and the spatiotemporal and genetic relationships between felsic intrusions and mineralization were integrated into the AAE with the aim of improving the geological interpretability of the network. The added penalty item can guide the changes in the stage of data reconstruction and improve the understandability of the results of geologically constrained AAE. In addition, the effectiveness of injecting the concept of physical constraints into the AAE can be verified via feature visualization. A case study in the southern Jiangxi Province and its surrounding areas was performed to identify multivariate geochemical anomalies. The results obtained by the geologically constrained AAE demonstrated a strong spatial correlation with the outcrop of intrusions in the study area, and most of the known mineral deposits are located in or near the highly anomalous areas.
      原文链接:
    • NSTL
    • Springer Nature

    Recognizing Multivariate Geochemical Anomalies Related to Mineralization by Using Deep Unsupervised Graph Learning

    Guan, QingfengRen, ShuliangChen, LirongYao, Yao...
    21页
    查看更多>>摘要：The spatial structure of geochemical patterns is influenced by various geological processes, one of which may be mineralization. Thus, analysis of spatial geochemical patterns facilitates understanding of regional metallogenic mechanisms and recognition of geochemical anomalies related to mineralization. Convolutional neural networks (CNNs) used in previous studies to extract spatial features require regular data (e.g., raster maps) as input. Due to the complex and diverse geological environment, geochemical samples are inevitably irregularly distributed and even partially missing in many spaces, leading to the inapplicability of CNN-based methods for geochemical anomaly identification. Also, interpolation from samples to regular grids often introduces uncertainties. To address these problems, this study innovatively transformed geochemical sampled point data into graphs and introduced graph learning to extract the geochemical patterns. Correspondingly, a novel framework of geochemical identification named GAUGE (recognition of Geochemical Anomalies Using Graph lEarning) is proposed. To assess the performance of the proposed method, this study recognized anomalies related to Au deposits in the Longyan area, the Wuyishan polymetallic metallogenic belt, China. For a set of regularly distributed samples, GAUGE achieved an accuracy similar to that of a traditional convolution autoencoder. More importantly, GAUGE achieved an area under the curve of 0.833, outperforming one-class support vector machine, isolation forest, autoencoder, and deep autoencoder network for a set of irregularly distributed samples by 10.6, 5.2, 4.8, and 2.5%, respectively. By introducing graph learning into geochemical anomaly recognition, this study provides a new perspective of extracting both spatial structure and compositional relationships of multivariate geochemical patterns, which can be applied directly to irregularly distributed samples in irregularly shaped regions without the need for interpolation. Such an improvement greatly enhances the applicability of machine learning methods in geochemical anomaly recognition, providing support for mineral resources evaluation and exploration.
      原文链接:
    • NSTL
    • Springer Nature

    Unlabeled Sample Selection for Mineral Prospectivity Mapping by Semi-supervised Support Vector Machine

    Chen, LiZhang, HaoChi, YujinTao, Jintao...
    23页
    查看更多>>摘要：Semi-supervised learning (SSL) algorithms can use unlabeled data to improve the performance of supervised learning algorithms for mineral prospectivity mapping with few known mineral deposits or mineralized blocks. However, SSL algorithms are sensitive to unlabeled samples and, in some cases, perform worse than supervised algorithms. In this study, a quasi-Newton method for semi-supervised support vector machine (QN-S3VM) was used in the 3D mineral prospectivity mapping of the Honghai volcanogenic massive sulfide Cu-Zn deposit in eastern Tianshan, northwestern China. Three Euclidean distance-based similarity measures of unlabeled samples to known mineral deposits or mineralized blocks were proposed to select unlabeled samples. The influence of the similarity and number of unlabeled samples on the performance of the QN-S3VM was investigated. The results showed that lower similarity in unlabeled samples yielded enhanced QN-S3VM performance. The performance of the QN-S3VM was affected by the number of unlabeled samples used. However, there was no consistent pattern among them. Compared with random selection, the QN-S3VM trained with unlabeled samples selected by the similarity measure had higher generalization and stability. Among the maximum, minimum, and average similarities, the minimum similarity had the best generalization while the average similarity had the best stability. Therefore, similarity to known mineral deposits or mineralized blocks is a good tool for unlabeled sample selection. This can effectively guarantee the performance of SSL for mineral prospectivity mapping.
      原文链接:
    • NSTL
    • Springer Nature

    Quantifying Uncertainties Linked to the Diversity of Mathematical Frameworks in Knowledge-Driven Mineral Prospectivity Mapping

    Daviran, MehrdadParsa, MohammadMaghsoudi, AbbasGhezelbash, Reza...
    17页
    查看更多>>摘要：Knowledge-driven mineral prospectivity mapping (MPM) has been practiced with diverse algorithms for combining GIS-based predictor layers (i.e., exploration targeting criteria) into predictive models in greenfield areas. Due to the diversity of these algorithms, there is a lack of consensus on the output of methods derived; that is, different algorithms employed generate distinct outputs, rendering the MPM uncertain. This specific type of uncertainty poses certain challenges to selecting reliable exploration targets. Measuring this type of uncertainty and linking it to the decision-making process are, therefore, two issues that merit serious consideration. This study adopted a four-part framework for tackling the above problem, namely (i) generating several predictive models using different algorithms, (ii) measuring uncertainties linked to discrepancies in predictive values derived from different algorithms, (iii) integrating the output of different algorithms into a combined predictive model, and (iv) selecting low-risk target zones based on low values of uncertainty and high combined predictive values. Three algorithms, namely AHP, TOPSIS, and fuzzy logic, were applied to generate three interim predictive models, followed by developing a combined predictive model using the self-organizing map (SOM) technique. Plots of combined predictive values vs. measured uncertainty values were used to select low-risk target zones. These zones cover similar to 5% of the area investigated in this study, predicting a considerable number of its mineral occurrences. This methodology appears to be an appropriate option for enhancing the reliability of predictive knowledge-driven models of mineral prospectivity.
      原文链接:
    • NSTL
    • Springer Nature

    Data Mining of a Geoscience Database Containing Key Features of Gold Deposits and Occurrences in Southwestern Uganda: A Pilot Study

    Woldai, TsehaieFabbri, Andrea G.
    31页
    查看更多>>摘要：Data mining is a promising new tool in mineral exploration. Here, we combined data-mining procedures with spatial prediction modeling for gold exploration targeting in the Buhweju area in southwestern Uganda. It was employed in a data-rich context of unavoidably partly redundant and correlated information that offered challenges in extracting significant relationships. Our study utilized a database of co-registered digital maps related to gold mineralization. It comprised Landsat TM, Shuttle Radar Topographic Mission (SRTM), and geophysical (radiometric and magnetic) datasets for geological and structural mapping. The locations of 15 orogenic gold deposits and 87 gold occurrences were obtained from the Geological Survey of Uganda database. These were considered direct evidence of the presence of gold mineralization. The geological and geophysical settings at the gold deposit/occurrences locations were based on geological units as host rocks, contacts, and structural elements, together with continuous field values of geophysics, radiometry, and other remotely sensed imagery. A gold exploration targeting proposition (T-p) was defined as: "That a point p within the study area contains a gold deposit given the presence of spatial evidence." All outstanding combinations of spatial evidence were obtained using empirical likelihood ratios. With a data-mining strategy, the ratios were filtered and modeled to identify stronger spatial associations, to rank the study area according to the likelihood of future discoveries, to represent ranking quality, to estimate associated uncertainty, and to select prospective target areas. The empirical likelihood ratios facilitated a transparent strategy for generating prediction patterns and extracting small prospective target areas with higher likelihood of discovery and lower-ranking uncertainty. Conclusions are provided on the knowledge extraction for prospectivity with further data and the challenges of reducing the arbitrariness of decisional steps.
      原文链接:
    • NSTL
    • Springer Nature

    3D Constrained Gravity Inversion and TEM, Seismic Reflection and Drill-Hole Analysis for New Target Generation in the Neves-Corvo VMS Mine Region, Iberian Pyrite Belt

    Marques, FabioDias, PedroCarvalho, JoaoRepresas, Patricia...
    26页
    查看更多>>摘要：Located in the Iberian pyrite belt, the Neves-Corvo mine is a world-class massive sulfide deposit and the largest operating mine in Portugal with underground mining down to 1000 m depth focused on massive and stockwork Cu, Zn, Pb rich ores. Gravimetric data have had a leading role in the discovery of the seven known deposits, together with time-domain electromagnetic (TEM) ground data. In this work, we present the results of a 3D constrained gravity inversion carried out with legacy ground gravity data. The 3D gravity inversions were carried out using an updated density database containing approximately 142,000 measurements. A recently constructed 3D geological model based on reprocessed 2D seismic reflection, 3D seismic, TEM and updated geology from detailed surface mapping and drill-hole data, was used to constrain the inversions. The results show multiple high-density anomalies that may indicate the presence of mineralization at depth. These anomalies were therefore cross-checked with holes previously drilled. Approximately 97% of more than 1000 available surface drill-holes located on or at a distance of less than 200 m from the high-density anomalies intersected mineralization. However, gravity anomalies have been drilled in the past and particularly dense black shales or rhyolitic/gabbroic rocks have been intersected. To increase the success of future drilling, gravimetric anomalies have been correlated spatially with high-conductivity TEM zones and strong-amplitude seismic reflections, because igneous rocks usually present weak-to-moderate conductivity and a massive column of black shales presents a seismic signature quite different from that of mineralization. We concluded that some of these locations represent high-quality targets to consider following up with drilling and further exploration.
      原文链接:
    • NSTL
    • Springer Nature

    Multiple-Point Geostatistics-Based Three-Dimensional Automatic Geological Modeling and Uncertainty Analysis for Borehole Data

    Wang, ZhaoxueLi, ChaolingLi, FengdanJessell, Mark Walter...
    21页
    查看更多>>摘要：The three-dimensional characterization of geological structures is important for determining the distribution of subsurface mineral resources. However, geological structures and geological phenomena have great stochasticity and uncertainty at the microscopic level. Traditional multiple-point geostatistics use mostly three-dimensional conceptual models or two-dimensional sections as training images, while simulations using direct low-dimensional borehole data are lacking. In this paper, we propose a new multiple-point geostatistical method to automatically reconstruct three-dimensional geological models directly from borehole data, which can significantly reduce the complexity of intermediate manual operations. First, the geological structure characteristics in the borehole data are extracted, and then an initial prior model is constructed based on geological constraints. Next, for the non-stationary problem, the mobile local scan approach is proposed to make the simulation nodes scan in a certain range of scaled area to simultaneously achieve the zonal simulation effect and eliminate the discontinuity problem between zonal boundaries. Based on this solution, the whole modeling workflow is designed. Finally, the algorithm is validated using actual plains area geological survey data, compared to other modeling methods, and evaluated for model uncertainty. The results show that the proposed 3D geological modeling method can effectively expose the stratigraphic structural morphology, stratigraphic attributes and overburden relationships. It will provide decision support for resource exploration and reduce exploration costs.
      原文链接:
    • NSTL
    • Springer Nature

    Data-Driven Predictive Modeling of Lithofacies and Fe In-Situ Grade in the Assen Fe Ore Deposit of the Transvaal Supergroup (South Africa) and Implications on the Genesis of Banded Iron Formations

    Nwaila, Glen T.Zhang, Steven E.Bourdeau, Julie E.Negwangwatini, Elekanyani...
    27页
    查看更多>>摘要：The Assen Fe ore deposit is a banded iron formation (BIF)-hosted orebody, occurring in the Penge Formation of the Transvaal Supergroup, located 50 km northwest of Pretoria in South Africa. Most BIF-hosted Fe ore deposits have experienced post-depositional alteration including supergene enrichment of Fe and low-grade regional metamorphism. Unlike most of the known BIF-hosted Fe ore deposits, high-grade hematite (> 60% Fe) in the Assen Fe ore deposit is located along the lithological contacts with dolerite intrusions. Due to the variability in alteration levels, identifying the lithologies present within the various parts of the Assen Fe ore deposit, specifically within the weathering zone, is often challenging. To address this challenge, machine learning was applied to enable the automatic classification of rock types identified within the Assen Fe ore mine and to predict the in-situ Fe grade. This classification is based on geochemical analyses, as well as petrography and geological mapping. A total of 21 diamond core drill cores were sampled at 1 m intervals, covering all the lithofacies present at Assen mine. These were analyzed for major elements and oxides by means of X-ray fluorescence spectrometry. Numerous machine learning algorithms were trained, tested and cross-validated for automated lithofacies classification and prediction of in-situ Fe grade, namely (a) k-nearest neighbors, (b) elastic-net, (c) support vector machines (SVMs), (d) adaptive boosting, (e) random forest, (f) logistic regression, (g) Naive Bayes, (h) artificial neural network (ANN) and (i) Gaussian process algorithms. Random forest, SVM and ANN classifiers yield high classification accuracy scores during model training, testing and cross-validation. For in-situ Fe grade prediction, the same algorithms also consistently yielded the best results. The predictability of in-situ Fe grade on a per-lithology basis, combined with the fact that CaO and SiO2 were the strongest predictors of Fe concentration, support the hypothesis that the process that led to Fe enrichment in the Assen Fe ore deposit is dominated by supergene processes. Moreover, we show that predictive modeling can be used to demonstrate that in this case, the main differentiator between the predictability of Fe concentration between different lithofacies lies in the strength of multivariate elemental associations between Fe and other oxides. Localized high-grade Fe ore along with lithological contacts with dolerite intrusion is indicative of intra-basinal fluid circulation from an already Fe-enriched hematite. These findings have a wider implication on lithofacies classification in weathered rocks and mobility of economic valuable elements such as Fe.
      原文链接:
    • NSTL
    • Springer Nature

    Lithological Mapping Using a Convolutional Neural Network based on Stream Sediment Geochemical Survey Data

    Wang, XuepingZuo, RenguangWang, Ziye
    16页
    查看更多>>摘要：Mapping of lithological units is a significant challenge for geological tasks. Stream sediment geochemical survey data contain abundant geological information that can help delineate lithological units. In this study, a convolutional neural network (CNN) was applied to map the lithological units in the Daqiao gold District, West Qinling Orogen, China, based on stream sediment geochemical data, in which each sample includes the concentrations of 15 trace elements (Cu, Pb, Zn, Ag, Mo, Sn, W, Mn, Ba, As, Sb, Bi, Cd, Au, and Hg). The training samples were firstly constructed with a certain window size by randomly selecting locations within each lithological unit. A CNN model was then established based on AlexNet to classify the lithologic categories. The classification map showed that 7 lithological units were correctly distinguished with an overall classification accuracy of 90.0%, suggesting that (1) stream sediment geochemical survey data of only trace element concentrations are useful for lithological mapping, and (2) a CNN can extract effectively geochemical characteristics from geochemical survey data. This study confirms the potential of a CNN as an effective method for geological mapping based on geochemical survey data.
      原文链接:
    • NSTL
    • Springer Nature