Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network

Zhang, Shuai; Carranza, Emmanuel John M.; Wei, Hudie; Xiao, Keyan; Yang, Fan; Xiang, Jie; Yang, Xu

doi:10.1007/s11053-020-09789-y

Cited by 66 publications

(23 citation statements)

References 58 publications

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“…[23], [24], [25] employed various unsupervised learning methods in machine learning (ML), by reconstructing the geochemical data to extract the anomaly and mining the spatial locations of the anomaly data, to carry out the prediction research. In addition, [26] employed the method combining supervised learning and unsupervised learning to conduct a prediction research on mineral resources.…”

Section: Introductionmentioning

confidence: 99%

“…For example, [28] proposed geological prospecting data augmentation techniques based on replication and noise addition as well as built convolutional neural network (CNN) models for geoscience data mining and integration. [26] aimed at the deep learning problems with few samples. The training data were constructed based on unsupervised learning approach, so that DNN can be effectively applied to mineral prediction.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Class Prediction of Mineral Resources Based on Deep Learning

et al. 2022

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Big data-driven technologies, especially machine learning and deep learning technologies, have been extensively employed in mineral prospectivity prediction. Several approaches have been proposed to learn the deep characteristics of geoscience data, enhance the accuracy of prediction and reduce uncertainty. Nevertheless, the approaches always contain the following two limitations. Firstly, the formation of mineral resources often involves the coupling of multiple factors on a certain spatio-temporal scale, resulting in rare labelled deposits and insufficient number of training samples. Secondly, training Deep Neural Network (DNN) is very challenging. Many approaches are subject to weak interpretability and lack of organic combination with geoscience knowledge. To address these two problems, we propose Geo-Rnet and GCAE (Geological Convolutional Autoencoder). Geo-Rnet is a multi-class mineral prospectivity prediction approach based on improved DNN. GCAE is able to effectively augment multi-disciplinary geoscience data by constructing upon an optimized Convolutional Autoencoder. The experimental results show that most of prospective areas predicted by Geo-Rnet overlap with the labelled mineralization locations, with an average accuracy of 91.1%. In addition, 89.98% of the ore deposits are located in the predicted areas. The results indicate the effectiveness of Geo-Rnet and GCAE for multi-class prediction of mineral resources. Finally, we classify the target area into several mineral prospectiviy areas according to their different mineral types. The research provides an innovative approach for mineral prospectivity prediction in the target area.INDEX TERMS Deep neural network, Geo-Rnet, multi-class, mineral prediction.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Class Prediction of Mineral Resources Based on Deep Learning

et al. 2022

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“…Taking a convolutional neural network (CNN) as an example, it is a type of feedforward neural network that contains convolution computation and a depth structure. CNN is popular in mineral exploration based on its sharedweights architecture and translation invariance, which can extract inner relationships from complex geological features, explore hidden metallogenic information, and describe the patterns that may be ignored by traditional machine learning methods [12,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

Wang

Liu

2021

Remote Sensing

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Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.

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“…Machine learning algorithms (MLA) have emerged as powerful tools to deal with massive datasets and recurring tasks in recent years. Several works have used MLA to solve different geoscienti c problems, such as geological mapping (e.g., Costa et al, 2019;Cracknell et al, 2014;Kuhn et al, 2020Kuhn et al, , 2018Radford et al, 2018), data-driven mineral prospectivity mapping (e.g., Brandmeier et al, 2020;Laborte, 2016, 2015;Prado et al, 2020;Rodriguez-Galiano et al, 2015;Zhang et al, 2021), anomaly detection, among many others (see Dramsch, 2020, and references therein). Speci cally, in mineralogy, MLA have been used for mineral identi cation and classi cation from rock thin sections images (e.g., Borges and Aguiar, 2019;Rubo et al, 2019a) or from drill cores (e.g., Koch et al, 2019), and for the calculation of mineral formulas, e.g., for amphiboles (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Qmin: A machine learning-based application for mineral chemistry data processing and analysis

Silva

Ferreira

Costa

et al. 2021

Preprint

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Mineral chemistry analysis is a valuable tool in several phases of mineralogy and mineral prospecting studies. This type of analysis can point out relevant information, such as concentration of the chemical element of interest in the analyzed phase and, thus, the predisposition of an area for a given commodity. Due to this, considerable amount of data has been generated, especially with the use of electron probe micro-analyzers (EPMA), either in research for academic purposes or in a typical prospecting campaign in the mineral industry. We have identified an efficiency gap when manually processing and analyzing mineral chemistry data, and thus, we envisage this research niche could benefit from the versatility brought by machine learning algorithms. In this paper, we present Qmin, an application that assists in increasing the efficiency of mineral chemistry data processing and analysis stages through automated routines. Our code benefits from a hierarchical structure of classifiers and regressors trained by a Random Forest algorithm developed on a filtered training database extracted from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, maintained by the Max Planck Institute for Chemistry. To test the robustness of our application, we applied a blind test with more than 11,000 mineral chemistry analyses compiled for diamond prospecting within the scope of the Diamante Brasil Project of the Geological Survey of Brazil. The blind test yielded a balanced classifier accuracy of ca. 99% for the minerals known by Qmin. Therefore, we highlight the potential of machine learning techniques in assisting the processing and analysis of mineral chemistry data.

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Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network

Cited by 66 publications

References 58 publications

Multi-Class Prediction of Mineral Resources Based on Deep Learning

Multi-Class Prediction of Mineral Resources Based on Deep Learning

Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

Qmin: A machine learning-based application for mineral chemistry data processing and analysis

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