Lithological classification and chemical component estimation based on the visual features of crushed rock samples

Khorram, Farzaneh; Morshedy, Amin Hossein; Memarian, Hossein; Tokhmechi, Behzad; Zadeh, Hamid Soltanian

doi:10.1007/s12517-017-3116-8

Cited by 13 publications

(2 citation statements)

References 22 publications

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“…Many other works devoted to ore classification problems have been thoroughly reviewed by Patel et al (2017a). For example, Khorram et al (2017) used SVM for classification of three different types of carbonates. The SVM method made the distinction with accuracy up to 89%.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

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The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.

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Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

View full text Add to dashboard Cite

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“…Machine learning technology has been widely applied in lithology classification, with its excellent feature mining and data fitting ability. Examples include extreme learning machines [44,45], logistic regression [46,47], back-propagation neural networks [48,49], support vector machines [50][51][52], and multi-layer perceptrons [53,54]. The commonly used lithologic classification models can be divided into three categories, the space vector type, neural network type, and linear type.…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lin

Jiang

et al. 2023

Remote Sensing

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Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.

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