Pseudo-outcrop Visualization of Borehole Images and Core Scans

Mirkes, Evgeny M.; Gorban, Alexander N.; Levesley, Jeremy; Elkington, Peter; Whetton, James

doi:10.1007/s11004-017-9701-2

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…It proves effective in revealing the morphology of stratigraphic structures, layer properties, and relationships between different geological layers. Mirkes et al [14] demonstrated a pseudo-outcrop visualization method for borehole and full-diameter rock core images, aiming to augment the ubiquitous unwrapped cylinder view and assist non-specialist interpreters. The pseudo-outcrop visualization is equivalent to a nonlinear projection of the image from the borehole to an earth frame of reference, creating a solid volume sliced longitudinally to reveal two or more faces.…”

Section: Related Workmentioning

confidence: 99%

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

Deng,

Han,

Liu

et al. 2024

Electronics

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The use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence of interference noise regions in the images and consequently impacting the classification accuracy. To enhance the analytical efficacy of in-hole images, this paper employs the proposed optimal non-concentric ring segmentation method to establish a new database. This method establishes the transformation function based on the Ansel Adams Zone System and the fluctuation values of the grayscale mean, adjusting the gray-level distribution of images to extract two visual blind spots of different scales. Thus, the inner and outer circles are located with these blind spots to achieve the adaptive acquisition of the optimal ring. Finally, we use the optimal non-concentric ring segmentation method to traverse all original images to obtain the borehole image classification database. To validate the effectiveness of this method, we conduct experiments using various segmentation and classification evaluation metrics. The results show that the Jaccard and Dice of the optimal non-concentric ring segmentation approach are 88.43% and 98.55%, respectively, indicating superior segmentation performance compared to other methods. Furthermore, after employing four commonly used classification models to validate the performance of the new classification database, the results demonstrate a significant improvement in accuracy and macro-average compared to the original database, with the highest increase in accuracy reaching 4.2%. These results fully demonstrate the effectiveness of the proposed optimal non-concentric ring segmentation method.

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Section: Related Workmentioning

confidence: 99%

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

Deng,

Han,

Liu

et al. 2024

Electronics

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“…Javier et al [19] employed drones to create highly accurate and high-resolution 3D models for identifying and interpreting ancient Roman gold mining sites in Northwestern Spain, revealing areas such as excavation sites, canals, reservoirs, and drainage channels. Mirkes et al [20] proposed a semantic segmentation method for rock outcrops that leads to the detection and segmentation of various geometric features, including fractures, faults, and sedimentary layers. Zhang et al [21] state that most existing semantic segmentation methods are based on FCNs, which replace the fully connected layer with fully convolutional layers for pixel-level prediction.…”

Section: Related Studiesmentioning

confidence: 99%

OutcropHyBNet: Hybrid Backbone Networks with Data Augmentation for Accurate Stratum Semantic Segmentation of Monocular Outcrop Images in Carbon Capture and Storage Applications

Madokoro,

Sato,

Nix

et al. 2023

Sensors

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The rapid advancement of climate change and global warming have widespread impacts on society, including ecosystems, water security, food production, health, and infrastructure. To achieve significant global emission reductions, approximately 74% is expected to come from cutting carbon dioxide (CO2) emissions in energy supply and demand. Carbon Capture and Storage (CCS) has attained global recognition as a preeminent approach for the mitigation of atmospheric carbon dioxide levels, primarily by means of capturing and storing CO2 emissions originating from fossil fuel systems. Currently, geological models for storage location determination in CCS rely on limited sampling data from borehole surveys, which poses accuracy challenges. To tackle this challenge, our research project focuses on analyzing exposed rock formations, known as outcrops, with the goal of identifying the most effective backbone networks for classifying various strata types in outcrop images. We leverage deep learning-based outcrop semantic segmentation techniques using hybrid backbone networks, named OutcropHyBNet, to achieve accurate and efficient lithological classification, while considering texture features and without compromising computational efficiency. We conducted accuracy comparisons using publicly available benchmark datasets, as well as an original dataset expanded through random sampling of 13 outcrop images obtained using a stationary camera, installed on the ground. Additionally, we evaluated the efficacy of data augmentation through image synthesis using Only Adversarial Supervision for Semantic Image Synthesis (OASIS). Evaluation experiments on two public benchmark datasets revealed insights into the classification characteristics of different classes. The results demonstrate the superiority of Convolutional Neural Networks (CNNs), specifically DeepLabv3, and Vision Transformers (ViTs), particularly SegFormer, under specific conditions. These findings contribute to advancing accurate lithological classification in geological studies using deep learning methodologies. In the evaluation experiments conducted on ground-level images obtained using a stationary camera and aerial images captured using a drone, we successfully demonstrated the superior performance of SegFormer across all categories.

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“…where ( ) ij f v is the average value on each slice; fij is the value of each point on the slice; mij is the number of points. To judge whether the points in the image are from the same slice, we utilize the method of Mirkes [49]. This method can quickly search for the points from the same slice in a Cartesian coordinate system.…”

Section: Optimal Sinusoid Extractionmentioning

confidence: 99%

An Automatic Method for Core Orientation Based on Planar Geologic Features in Drill-Core Scans and Microresistivity Images

2022

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Drilling cores, the most direct and accurate first-hand data reflecting the subsurface geological information, are of unclear orientation for most boreholes. The image feature comparison method is one of the most widely used methods for core orientation. However, this method is mainly conducted by manual comparison, which is labor-intensive, time-consuming and inaccurate. In this paper, we propose an automatic core orientation method by comparing the planar geologic features (one of the most common features) in drill-core scans and microresistivity images. This method first automatically detects planar geologic features by combing the vertical gradient-based Otsu threshold segmentation, Hough transform and optimal sinusoid extraction and then determines correction difference of the core orientation by comparing the dip azimuths of the optimal sinusoids from the two types of images. This method is successfully applied to actual data. The automatic core orientation results are compared with the manual core orientation results. The results show that this method can eliminate human errors and has higher accuracy, especially for core scan images with non-distinct features. The proposed method has wide application potential in the fields of hydrocarbon exploration and development, scientific drilling research, and basic geological surveys.INDEX TERMS Bedding surfaces, borehole images, core orientation, core scan images, Hough transform.

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Pseudo-outcrop Visualization of Borehole Images and Core Scans

Cited by 4 publications

References 21 publications

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

OutcropHyBNet: Hybrid Backbone Networks with Data Augmentation for Accurate Stratum Semantic Segmentation of Monocular Outcrop Images in Carbon Capture and Storage Applications

An Automatic Method for Core Orientation Based on Planar Geologic Features in Drill-Core Scans and Microresistivity Images

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