Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data

Wang, Ziye; Yang, Fanfan

doi:10.1007/s11004-022-10023-z

Cited by 14 publications

(3 citation statements)

References 72 publications

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“…Many works done on mineral exploration in the mining industry using ML techniques based on geological data. Where geochemical survey data and deep learning techniques are used 36 . This article 37 presents a study comparing the effectiveness of different machine learning techniques for perceiving geochemical irregularities allied to mineralization.…”

Section: Supervised Learningmentioning

confidence: 99%

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Shetty,

Shabari Shedthi,

Shashishekar

2023

jmmf

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The ultimate objective of modern engineering applications in mining and material science is to develop good quality novel materials with desirable qualities. Machine Learning (ML) is used in the mining industry to provide solutions to complex problems of the mining industry and improve the efficiency of the overall system. ML methods are increasingly being used by materials scientists to uncover hidden trends in data and generate predictions. Furthermore, data centric techniques can provide useful insights into the basic processes that influence material behaviour while simultaneously reducing human labour in large data processing. The ability of persons to find new materials and infer complex relationships is important for the development of new materials. Large amounts of machine-readable data must be available to use statistical methodologies to speed materials research. In mining engineering, ML can be used for analyzing geographical data, assessing the risk of rock fall, predicting equipment failures and impact of mining activities on the environment etc. Material science data may be used in a variety of ways, including property prediction, the search for new materials and discovering synthesis methods. Selecting proper machine learning techniques to provide solutions is very important and that is discussed here. The purposes of this paper are to provide a comprehensive list of different ML techniques which are applied for the mining and material science domain.

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Section: Supervised Learningmentioning

confidence: 99%

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Shetty,

Shabari Shedthi,

Shashishekar

2023

jmmf

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“…T HE interpretation of geological elements related to rock, soil, and water is a vital aspect of remote sensing in the geological environment. It plays a significant role in diverse domains such as geological mapping [1], [2], evaluation of off-road trafficability [3], selection of outdoor construction sites [4], [5], and early warning systems for natural disasters [6]. Currently, the interpretation of geological elements such as rocks, soil, and water still relies primarily on traditional methods such as visual interpretation by experts and field investigations [7].…”

mentioning

confidence: 99%

Improving Geological Remote Sensing Interpretation Via a Contextually Enhanced Multiscale Feature Fusion Network

He,

Zhang,

Dong

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Geological remote sensing interpretation plays a pivotal role in the field of regional geological mapping, encompassing the analysis of rock, soil, and water features. However, these geological elements can be obscured by the surrounding geographical environment and can undergo modifications caused by geological activities. The former hinders the effectiveness of satellite remote sensing data, resulting in the invisibility of element features, while the latter leads to the complex distribution of element features and significant spatial variations of geological elements. Consequently, existing deep learning-based models for interpreting geological elements often exhibit limited accuracy. To address these issues, this study proposes the Contextually Enhanced Multiscale Feature Fusion Network (CEMFFNet) for the efficient interpretation of geological elements. First, the context enhancement module is employed to extract abundant feature information and reinforce contextual features, aiming to capture essential features and strengthen their interconnections. Second, the multiscale feature fusion module incorporates the SimAM attention mechanism to adaptively learn features from different channels, emphasizing the feature information that contributes to interpretation results and maximizing the comprehensive and crucial feature information for each element. Extensive experiments demonstrate the superior performance of both the context enhancement module and the multiscale feature fusion module compared to several representative deep learning networks in terms of overall interpretation accuracy on two datasets. The model demonstrated improvements in oPA and mIoU of 2.4% and 2.8%, respectively, on the Landsat 8 dataset, and 3.5% and 3.2%, respectively, on the Sentinel-2 dataset.

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“…In the sixth paper, Wang et al (2022) propose a novel framework for geological mapping based on geochemical survey data. This framework uses a direct sampling multiple-point statistics technique to produce spatially continuous and adequate samples by reconstructing geochemical values at unsampled locations based on sparse geochemical survey data.…”

mentioning

confidence: 99%

Machine Learning-Based Mapping for Mineral Exploration

Zuo,

Carranza

2023

Math Geosci

Self Cite

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We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph convolutional network (GCN). In recent years, RF, a representative shallow machine learning algorithm, and CNN, a representative deep learning approach, have been proved to be powerful tools for ML-based mapping for mineral exploration. In the future, GCN deserves more attention for ML-based mapping for mineral exploration because of its ability to capture the spatial anisotropy of mineralization and its applicability within irregular study areas. Finally, we summarize the original contributions of the six papers comprising this special issue.

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Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data

Cited by 14 publications

References 72 publications

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Improving Geological Remote Sensing Interpretation Via a Contextually Enhanced Multiscale Feature Fusion Network

Machine Learning-Based Mapping for Mineral Exploration

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