Deep convolutions for in-depth automated rock typing

Baraboshkin, E.Y.; Ismailova, Leyla; Orlov, Denis; Zhukovskaya, E. A.; Калмыков, Г. А.; Khotylev, O. V.; Koroteev, Dmitry

doi:10.1016/j.cageo.2019.104330

Cited by 89 publications

(45 citation statements)

References 32 publications

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“…As lithofacies with similar grayscale and textural properties are expected to exhibit similar transport properties, porosity and permeability data from core analysis measurements were used to investigate the transport properties of the classified lithofacies. Figure 11 shows the porosity-permeability cross-plot for core plug samples from the same core data as in our CT images, where different colors correspond to the different lithofacies that have (6) f1-score = 2 × precision × recall precision + recall . been derived from the manual core description.…”

Section: Lithofacies Predictionmentioning

confidence: 99%

Lithology classification of whole core CT scans using convolutional neural networks

Chawshin

Berg

Varagnolo³

et al. 2021

SN Appl. Sci.

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X-ray computerized tomography (CT) images as digital representations of whole cores can provide valuable information on the composition and internal structure of cores extracted from wells. Incorporation of millimeter-scale core CT data into lithology classification workflows can result in high-resolution lithology description. In this study, we use 2D core CT scan image slices to train a convolutional neural network (CNN) whose purpose is to automatically predict the lithology of a well on the Norwegian continental shelf. The images are preprocessed prior to training, i.e., undesired artefacts are automatically flagged and removed from further analysis. The training data include expert-derived lithofacies classes obtained by manual core description. The trained classifier is used to predict lithofacies on a set of test images that are unseen by the classifier. The prediction results reveal that distinct classes are predicted with high recall (up to 92%). However, there are misclassification rates associated with similarities in gray-scale values and transport properties. To postprocess the acquired results, we identified and merged similar lithofacies classes through ad hoc analysis considering the degree of confusion from the prediction confusion matrix and aided by porosity–permeability cross-plot relationships. Based on this analysis, the lithofacies classes are merged into four rock classes. Another CNN classifier trained on the resulting rock classes generalize well, with higher pixel-wise precision when detecting thin layers and bed boundaries compared to the manual core description. Thus, the classifier provides additional and complementing information to the already existing rock type description. Article Highlights A workflow for automatic lithofacies classification using whole core 2D image slices and CNN is introduced. The proposed classifier shows lithology-dependent accuracies. The prediction confusion matrix is exploited as a tool to identify lithofacies classes with similar transport properties and to automatically generate lithofacies hierarchies.

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Section: Lithofacies Predictionmentioning

confidence: 99%

Lithology classification of whole core CT scans using convolutional neural networks

Chawshin

Berg

Varagnolo³

et al. 2021

SN Appl. Sci.

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“…Although the study provided a plan to identify rocks in the geological survey field, the results of this study lacked comparisons with other CNNs model identification results. Baraboshkin compared and tested four heavy-duty CNNs [18]. The accuracy of the algorithm was as high as 95% on the validation set of the Googlenet architecture, but the model requires more resources in the calculation, and is not the best solution for the geological survey field.…”

Section: Realized Thementioning

confidence: 99%

“…This kind of method solves the problem of strong subjectivity and high judgment cost. However, it is still necessary to create rock samples and to obtain the images of rock thin sections under a microscope [18], [21]. Moreover, the identification efficiency needs to improve, and the identification time should be reduced.…”

Section: Realized Thementioning

confidence: 99%

Recognizing Multiple Types of Rocks Quickly and Accurately Based on Lightweight CNNs Model

Fan

Chen

et al. 2020

IEEE Access

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The recognition and classification of rock lithology is an extremely important task of geological surveys. This paper proposes a new method for quickly identifying multiple types of rocks suitable for geological survey work field. Based on the two lightweight convolutional neural networks (CNNs), SqueezeNet and MobileNets, and combined with the transfer learning method, a rock lithology recognition model was established. The model was embedded into a smart phone for testing. This method was used to identify and classify the images of 28 kinds of rocks. Through a comprehensive comparison of the two models, the accuracy of SqueezeNet and MobileNets in the test dataset is 94.55% and 93.27%, respectively. Via the two models, the average recognition time of a single rock image is 557 and 836 milliseconds, and rock images with a recognition accuracy of over 96% accounted for 95% and 93% of the entire test dataset. Compared with the classification method based on rock thin section images, this method does not need to make rock thin sections. This paper meets the requirements of workers to quickly and accurately identify rocks in the work field, which improves the work efficiency and limits identification costs. INDEX TERMS Geological survey, lightweight convolutional neural networks, multiple types, rock recognition.

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“…Zhang Y et al efficiently completed the classification task of potassium feldspar, perlite, plagioclase, and quartz images by combining Transfer Learning technology with the InceptionV3 [27]. In the comparison of the different CNNs-based image classification models, Baraboshkin et al used AlexNet, VGGNet, and Inception to classify 20,000 rock images collected from different regions and strata [28]. Additionally, because of the excellent classification performance of CNNs models in mineral image classification tasks, it is also used for coal gangue discharge [29], [30] and iron ore image classification [31].…”

Section: Section 1 Introductionmentioning

confidence: 99%

Deep Learning Based Mineral Image Classification Combined With Visual Attention Mechanism

et al. 2021

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Mineral image classification technology based on machine vision is an efficient system for ore sorting. With the development of artificial intelligence and computer technology, the deep learning-based mineral image classification system is gradually applied to ore sorting. However, there is a bottleneck in improving classification accuracy, and the feature extraction ability of the CNNs model is relatively limited for multi-category mineral image classification tasks. Therefore, four visual attention blocks are designed and embedded in the existing CNNs model, and new mineral image classification models based on the visual attention mechanism and CNNs are proposed. Then, referring to the building strategies of the different depth ResNet, we build various CNNs model embedding with attention blocks for mineral image classification and visualize the models by Grad-CAM to observe the change in classification weight distributions and classification weight values. Finally, by using the confusion matrices, this experiment systematically evaluates the classification performance of the proposed models and analyzes the misjudgment rate.

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Deep convolutions for in-depth automated rock typing

Cited by 89 publications

References 32 publications

Lithology classification of whole core CT scans using convolutional neural networks

Lithology classification of whole core CT scans using convolutional neural networks

Recognizing Multiple Types of Rocks Quickly and Accurately Based on Lightweight CNNs Model

Deep Learning Based Mineral Image Classification Combined With Visual Attention Mechanism

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