Applying machine learning methods to predict geology using soil sample geochemistry

Lui, Timothy C.C.; Gregory, Daniel D.; Anderson, Marek; Lee, Well-Shen; Cowling, Sharon A.

doi:10.1016/j.acags.2022.100094

Cited by 18 publications

(2 citation statements)

References 17 publications

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“…Results obtained confirm that the SVM, KNN and RF models can handle data asymmetry and produce improved performance in predicting mineral prospectivity in new regions of interest [85][86][87]. To overcome some of the limitations and potentially improve the results, balance techniques, such as the synthetic minority oversampling technique (SMOTE) [88][89][90], could be investigated in forthcoming studies.…”

Section: Discussionmentioning

confidence: 68%

Prospectivity Mapping of Heavy Mineral Ore Deposits Based upon Machine-Learning Algorithms: Columbite-Tantalite Deposits in West- Central Côte d’Ivoire

2022

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This study aimed to model the prospectivity for placer deposits using geomorphic and landscape parameters. Within a geographic information system (GIS), spatial autocorrelation analysis of 3709 geochemical samples was used to identify prospective and non-prospective targets for columbite-tantalite (Nb-Ta) placer deposits of Hana-Lobo (H-L) Geological Complex (West- Central Côte d’Ivoire, West Africa). Based on mineralization system analysis, hydrologic, geomorphologic and landscape parameters were extracted at the locations of the identified targets. Supervised automatic classification approaches were applied, including Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to find a prospectivity model complex enough to capture the nature of the data. Metrics such as cross-validation accuracy (CVA), Receiver Operating Characteristic (ROC) curves, Area Under Curve (AUC) values and F-score values were used to evaluate the performance and robustness of output models. Results of applying machine-learning algorithms demonstrated that predictions provided by the final RF and KNN models were very close (κ = 0.56 and CVA = 0.69; κ = 0.54 and CVA = 0.68, respectively) and those provided by the SVM models were slightly lower with κ = 0.46 and CVA = 0.63. Independent validation results confirmed the slightly higher performance of both KNN and RF prospectivity models, compared to final SVM. Sensitivity analyses of both KNN and RF prospectivity models for medium and high-grade Nb-Ta deposits show a prediction rate of up to 90%.

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Section: Discussionmentioning

confidence: 68%

Prospectivity Mapping of Heavy Mineral Ore Deposits Based upon Machine-Learning Algorithms: Columbite-Tantalite Deposits in West- Central Côte d’Ivoire

2022

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“…Remote sensing methods have been widely used in recent decades, and machine learning (ML) has primarily been used to determine the quantitative relationship between soil salinization and environmental variables while exploring the spatiotemporal change patterns of soil salinization [6][7][8][9]. These methods used include neural networks, regression trees, genetic inheritance, random forest (RF), and random gradient tree enhancement [10][11][12]. These methods have high accuracy in predicting soil salinization in different historical periods and have achieved good monitoring results.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

He,

Ding,

Huang

et al. 2023

Sustainability

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Soil salinization is a serious global issue; by 2050, without intervention, 50% of the cultivated land area will be affected by salinization. Therefore, estimating and predicting future soil salinity is crucial for preventing soil salinization and investigating potential arable land resources. In this study, several machine learning methods (random forest (RF), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost)) were used to estimate the soil salinity in the Werigan–Kuqa River Delta Oasis region of China from 2001 to 2021. The cellular automata (CA)–Markov model was used to predict soil salinity types from 2020 to 2050. The LightGBM method exhibited the highest accuracy, and the overall prediction accuracy of the methods had the following order: LightGBM > RF > GBRT > XGBoost. Moderately saline, severely saline, and saline soils were dominant in the east and south of the research area, while non-saline and mildly saline soils were widely distributed in the inner oasis area. A marked decreasing trend in the soil salt content was observed from 2001 to 2021, with a decreasing rate of 4.28 g/kg·10 a−1. The primary change included the conversion of mildly and severely saline soil types to non-saline soil. The generalized difference vegetation index (51%), Bio (30%), and temperature vegetation drought index (27%) had the greatest influence, followed by variables associated with soil attributes (soil organic carbon and soil organic carbon stock) and terrain (topographic wetness index, slope, aspect, curvature, and topographic relief index). Overall, the CA–Markov simulation resulted exhibited suitable accuracy (kappa = 0.6736). Furthermore, areas with non-saline and mildly saline soils will increase while areas with other salinity levels will continue to decrease from 2020 to 2050. From 2046 to 2050, numerous areas with saline soil will be converted to non-saline soil. These results can provide support for salinization control, agricultural production, and soil investigations in the future. The gradual decline in soil salinization in the research area in the past 20 years may have resulted from large-scale land reclamation, which has turned saline alkali land into arable land and is also related to effective measures taken by the local government to control salinization.

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Mineral Informatics: Origins

Prabhu

Morrison

Hazen

2023

Springer Mineralogy

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Applying machine learning methods to predict geology using soil sample geochemistry

Cited by 18 publications

References 17 publications

Prospectivity Mapping of Heavy Mineral Ore Deposits Based upon Machine-Learning Algorithms: Columbite-Tantalite Deposits in West- Central Côte d’Ivoire

Prospectivity Mapping of Heavy Mineral Ore Deposits Based upon Machine-Learning Algorithms: Columbite-Tantalite Deposits in West- Central Côte d’Ivoire

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

Mineral Informatics: Origins

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