Graph Deep Learning Model for Mapping Mineral Prospectivity

Xu, Ying

doi:10.1007/s11004-022-10015-z

Cited by 57 publications

(15 citation statements)

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“…Thus, the classification algorithm allowed us to predict four tiles and six SBUs with average adjusted (normalized) balanced accuracies of 0.72 and 0.82, respectively. Further development of the classification can be done via Delaunay tessellation 19 or graph theory, 43 which help to represent the pores of the zeolite frameworks, which naturally have a significant weight in the structure of the zeolite and are responsible for its main properties.…”

Section: Resultsmentioning

confidence: 99%

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra

et al. 2023

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Application of machine learning (ML) algorithms to spectroscopic data has a great potential for obtaining hidden correlations between structural information and spectral features. Here, we apply ML algorithms to theoretically simulated infrared (IR) spectra to establish the structure-spectrum correlations in zeolites. Two hundred thirty different types of zeolite frameworks were considered in the study whose theoretical IR spectra were used as the training ML set. A classification problem was solved to predict the presence or absence of possible tilings and secondary building units (SBUs). Several natural tilings and SBUs were also predicted with an accuracy above 89%. The set of continuous descriptors was also suggested, and the regression problem was also solved using the ExtraTrees algorithm. For the latter problem, additional IR spectra were computed for the structures with artificially modified cell parameters, expanding the database to 470 different spectra of zeolites. The resulting prediction quality above or close to 90% was obtained for the average Si−O distances, Si−O− Si angles, and volume of TO 4 tetrahedra. The obtained results provide new possibilities for utilization of infrared spectra as a quantitative tool for characterization of zeolites.

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Section: Resultsmentioning

confidence: 99%

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra

et al. 2023

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“…The range of all possible algorithms is unknowable because there are emerging algorithms and variations of existing ones, either as architectural modifications (e.g., changes in neural network architecture) or as add-ons (e.g., optimization algorithms; Chen et al, 2020;Yin & Li, 2022;Gharehchopogh et al, 2023). An empirical analysis revealed that algorithms used by various authors include (non-exhaustively): Bayes network (Porwal et al, 2006;Yin & Li, 2022); logistic regression (Agterberg & Bonham-Carter, 1999;Carranza & Hale, 2001;Karbalaei Ramezanali et al, 2020;Lin et al, 2020;Zhang et al, 2022c); support vector machines (Zuo & Carranza, 2011;Zhang et al, 2021;Senanayake et al, 2023); tree-based methods, such as random forest, extra trees and XGBoost (Chen & Wu, 2019;Sun et al, 2019;Zhang et al, 2022a); artificial neural networks, such as extreme learning machines (Chen & Wu, 2017); deep learning methods (Xiong et al, 2018;Wang et al, 2020;Yang et al, 2022;Zuo et al, 2022Li et al, 2023;Yin et al, 2023;Zuo & Xu, 2023); and reinforcement learning (Shi et al, 2023). There are also applicative MPM studies that employed ensemble learning, which is an approach to improve outcome reliability by integrating the output of multiple independent models (e.g., Senanayake et al, 2023;Shetty et al, 2023).…”

Section: Review Of Data-driven Mpm Workflowsmentioning

confidence: 99%

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

Zhang,

Lawley,

Bourdeau

et al. 2024

Nat Resour Res

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The primary goal of mineral prospectivity mapping (MPM) is to narrow the search for mineral resources by producing spatially selective maps. However, in the data-driven domain, MPM products vary depending on the workflow implemented. Although the data science framework is popular to guide the implementation of data-driven MPM tasks, and is intended to create objective and replicable workflows, this does not necessarily mean that maps derived from data science workflows are optimal in a spatial sense. In this study, we explore interactions between key components of a geodata science-based MPM workflow on the geospatial outcome, within the modeling stage by modulating: (1) feature space dimensionality, (2) the choice of machine learning algorithms, and (3) performance metrics that guide hyperparameter tuning. We specifically relate these variations in the data science workflow to the spatial selectivity of resulting maps using uncertainty propagation. Results demonstrate that typical geodata science-based MPM workflows contain substantial local minima, as it is highly probable for an arbitrary combination of workflow choices to produce highly discriminating models. In addition, variable domain metrics, which are key to guide the iterative implementation of the data science framework, exhibit inconsistent relationships with spatial selectivity. We refer to this class of uncertainty as workflow-induced uncertainty. Consequently, we propose that the canonical concept of scientific consensus from the greater experimental science framework should be adhered to, in order to quantify and mitigate against workflow-induced uncertainty as part of data-driven experimentation. Scientific consensus stipulates that the degree of consensus of experimental outcomes is the determinant in the reliability of findings. Indeed, we demonstrate that consensus through purposeful modulations of components of a data-driven MPM workflow is an effective method to understand and quantify workflow-induced uncertainty on MPM products. In other words, enlarging the search space for workflow design and experimenting with workflow components can result in more meaningful reductions in the physical search space for mineral resources.

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“…Exploration geochemistry is one of the leading methods in mineral exploration and quantitative prediction of mineral resources, widely used for seeking various types of mineral deposits and conducting mineral evaluations. − Since the 1970s, geologists have gathered extensive high-quality, multiscale, multielement geochemical data by applying exploration geochemistry techniques. Consequently, how to effectively mine the ore-forming information hidden in geochemical exploration data at different scales and identify such information has become the frontier and a hot topic of study. − The related analysis methods mainly include principal component analysis, factor analysis, cluster analysis, , local enrichment index method, spatially weighted principal component analysis, and fractal and multifractal methods. − Among them, fractal and multifractal theory’s anomaly identification and extraction method is one of the most straightforward, most effective, fastest, and most reliable methods.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, how to effectively mine the ore-forming information hidden in geochemical exploration data at different scales and identify such information has become the frontier and a hot topic of study. 5 − 8 The related analysis methods mainly include principal component analysis, 9 factor analysis, 10 cluster analysis, 11 , 12 local enrichment index method, 13 spatially weighted principal component analysis, 14 and fractal and multifractal methods. 15 − 18 Among them, fractal and multifractal theory’s anomaly identification and extraction method is one of the most straightforward, most effective, fastest, and most reliable methods.…”

Section: Introductionmentioning

confidence: 99%

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia

Zhao,

Yin

et al. 2024

ACS Omega

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The focus of exploration geochemistry is an accurate interpretation of geochemical data and the precise extraction of anomaly information related to mineralization from complex geological information. However, geochemical data are component data and exhibit a closure effect. Thus, traditional statistical methods cannot adequately reveal and identify the distribution of deep-seated anomaly information. This paper focuses on the Sidaowanzi area in Inner Mongolia and uses multivariate component data analysis methods to process 1:50 000 soil geochemical data. Using the Exploratory Data Analysis (EDA) method, the spatial distribution and internal structure characteristics of raw, logarithmic, and isometric logarithmic ratio (ILR) transformed data were compared and, coupled with robust principal component analysis (RPCA) and elemental component biplots, the association between element combinations and mineralization indicated by these three types of data was revealed. The S-A method was used to decompose composite anomalies of the ILR transformed RPCA score data to extract the characteristics of elemental combination anomalies and background distribution, and the Fry analysis method was utilized to analyze the dominant mineralization direction within the area. The results show that (1) data transformed using the ILR eliminated the influence of the closure effect, making the data more uniform on a spatial scale and exhibiting characteristics of an approximately normal distribution. (2) The S-A method was further used to decompose the composite anomaly of the PC1 and PC2 principal component combinations. The screened-out anomaly and background fields can essentially reflect the ore-causing anomalies dominated by Au and Cu−Mo mineralization. Moreover, the extracted anomalies and background information closely align with known mineral deposits (prospects) and can effectively identify weakly retarded geochemical anomaly information. (3) Fry analysis based on geochemical anomalies indicates that the dominant mineralization directions, by an assemblage dominated by Au and Cu−Mo, predominantly occur in the NE, NW, and proximate EW orientations. The combined application of the aforementioned three methods for the quantitative analysis of geochemical data aims to explore a transferable methodological system, providing new insights and approaches for further prediction of mineralization potential.

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Graph Deep Learning Model for Mapping Mineral Prospectivity

Cited by 57 publications

References 50 publications

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra

Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia

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