Automated lithology extraction from core photographs

Thomas, A.; Rider, M.; Curtis, A.; MacArthur, Alasdair

doi:10.3997/1365-2397.29.6.51281

Cited by 22 publications

(12 citation statements)

References 4 publications

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“…Secondary physical and chemical analyzes, such as hardness, acid tests and other nondestructive geochemical techniques, can also be performed to confirm the mineralogy of the lithofacies (Croudace and Rothwell, 2015;McPhee et al, 2015;Amao et al, 2016). Although physical analysis cannot be analyzed in machine learning, machine learning is an excellent tool for replicating visual pattern analysis and performing more robust and unbiased classification (Thomas et al, 2011;Baraboshkin et al, 2018;Martin et al, 2021). Visual-based machine learning, specifically deep neural networks, has been widely applied in the past 5 years to perform lithological classification (de Lima et al, 2019;Baraboshkin et al, 2020;Koeshidayatullah et al, 2020).…”

Section: Core-based Lithofacies Analysismentioning

confidence: 99%

“…A set of works that aims to classify rocks by different types of features. A set of works extracted different color distributions and intensity (e.g., color histogram, hue, saturation) from rock samples and used different algorithms based on statistics (Singh et al, 2004;Harinie et al, 2012), SVM (Lobos et al, 2016;Patel et al, 2016;Patel et al, 2017), combination of statistics and machine learning (Prince et al, 2005;Thomas et al, 2011;Seleznev et al, 2020), to perform lithology classification. In addition, previous works applied LeNet (named CIFAR in the publication) and other convolutional neural networks to classify granite tiles (Ferreira and Giraldi, 2017) and rock images (Zhang et al, 2017).…”

Section: Core-based Lithofacies Analysismentioning

confidence: 99%

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FaciesViT: Vision transformer for an improved core lithofacies prediction

et al. 2022

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Lithofacies classification is a fundamental step to perform depositional and reservoir characterizations in the subsurface. However, such a classification is often hindered by limited data availability and biased and time-consuming analysis. Recent work has demonstrated the potential of image-based supervised deep learning analysis, specifically convolutional neural networks (CNN), to optimize lithofacies classification and interpretation using core images. While most works have used transfer learning to overcome limited datasets and simultaneously yield a high-accuracy prediction. This method raises some serious concerns regarding how the CNN model learns and makes a prediction as the model was originally trained with entirely different datasets. Here, we proposed an alternative approach by adopting a vision transformer model, known as FaciesViT, to mitigate this issue and provide improved lithofacies prediction. We also experimented with various CNN architectures as the baseline models and two different datasets to compare and evaluate the performance of our proposed model. The experimental results show that the proposed models significantly outperform the established CNN architecture models for both datasets and in all cases, achieving an f1 score and weighted average in all tested metrics of 95%. For the first time, this study highlights the application of the Vision Transformer model to a geological dataset. Our findings show that the FaciesViT model has several advantages over conventional CNN models, including (i) no hyperparameter fine-tuning and exhaustive data augmentation required to match the accuracy of CNN models; (ii) it can work with limited datasets; and (iii) it can better generalize the classification to a new, unseen dataset. Our study shows that the application of the Vision transformer could further optimize image recognition and classification in the geosciences and mitigate some of the issues related to the generalizability and the explainability of deep learning models. Furthermore, the implementation of our proposed FaciesViT model has been shown to improve the overall performance and reproducibility of image-based core lithofacies classification which is significant for subsurface reservoir characterization in different basins worldwide.

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Section: Core-based Lithofacies Analysismentioning

confidence: 99%

Section: Core-based Lithofacies Analysismentioning

confidence: 99%

FaciesViT: Vision transformer for an improved core lithofacies prediction

et al. 2022

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“…In the field of underground engineering, knowing the geological conditions around the project mainly relies on geological drilling. The extracted core provides the most reliable lithology information [1], and the lithology of the drill core reflects the lithology of the geological structure in the drilling area. The lithological data determines the location of the underground project and the orientation of the axis of the underground structure in the early stage of construction and provides a valuable reference for the treatment of adverse geological conditions during the construction process.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to manual feature extraction, the convolutional kernel of CNN can automatically extract deep-level features [33]. Thomas et al [1] used gray-scale images of drill cores to classify three lithologies, carbonate-cement, shale and sandstone, and obtained a classification accuracy of 94%, which is an early attempt to predict lithology from drill core images. Zhang et al [34] identified three selected lithologies, sandstone, shale, and conglomerate, using convolutional neural networks on a dataset of 1500 on a two-dimensional gray-scale image dataset of 64 pixels in height and width and obtained 95% prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based lithology classification of drill core images

Wang

et al. 2022

PLoS ONE

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Drill core lithology is an important indicator reflecting the geological conditions of the drilling area. Traditional lithology identification usually relies on manual visual inspection, which is time-consuming and professionally demanding. In recent years, the rapid development of convolutional neural networks has provided an innovative way for the automatic prediction of drill core images. In this work, a core dataset containing a total of 10 common lithology categories in underground engineering was constructed. ResNeSt-50 we adopted uses a strategy of combining channel-wise attention and multi-path network to achieve cross-channel feature correlations, which significantly improves the model accuracy without high model complexity. Transfer learning was used to initialize the model parameters, to extract the feature of core images more efficiently. The model achieved superior performance on testing images compared with other discussed CNN models, the average value of its Precision, Recall, F1−score for each category of lithology is 99.62%, 99.62%, and 99.59%, respectively, and the prediction accuracy is 99.60%. The test results show that the proposed method is optimal and effective for automatic lithology classification of borehole cores.

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“…Various approaches have been developed to study full-bore core images by a computer due to rapid quantity and quality data increase. The images may be used for automated rock typing (Baraboshkin et al, 2020;Lepistö, 2005;Patel et al, 2017;Thomas et al, 2011), (Alzubaidi et al, 2021) and different properties distribution analysis (Egorov, 2019;Khasanov et al, 2016;Prince and Chitale, 2008;Wieling, 2013). Most of them are based on an analysis of a separated core column, usually stored in a box.…”

Section: Introductionmentioning

confidence: 99%

Core Box Image Recognition and its Improvement with a New Augmentation Technique

Baraboshkin,

Demidov,

Orlov

et al. 2022

Preprint

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Most methods for automated full-bore rock core image analysis (description, colour, properties distribution, etc.) are based on separate core column analyses. The core is usually imaged in a box because of the significant amount of time taken to get an image for each core column. The work presents an innovative method and algorithm for core columns extraction from core boxes. The conditions for core boxes' imaging may differ tremendously. Such differences are disastrous for machine learning algorithms which need a large dataset describing all possible data variations. Still, such images have some standard features -a box and core. Thus, we can emulate different environments with a unique augmentation described in this work. It is called template-like augmentation (TLA). The method is described and tested on various environments, and results are compared on an algorithm trained on both "traditional" data and a mix of traditional and TLA data. The algorithm trained with TLA data provides better metrics and can detect core on most new images, unlike the algorithm trained on data without TLA. The algorithm for core column extraction implemented in an automated core description system speeds up the core box processing by a factor of 20.

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Automated lithology extraction from core photographs

Cited by 22 publications

References 4 publications

FaciesViT: Vision transformer for an improved core lithofacies prediction

FaciesViT: Vision transformer for an improved core lithofacies prediction

Deep learning based lithology classification of drill core images

Core Box Image Recognition and its Improvement with a New Augmentation Technique

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