The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
In order to accurately identify the rock, it is necessary to study the identification method of the rock. The rock identification method, the thin slice microscopic image technique, the electron probe analysis method ...In order to accurately identify the rock, it is necessary to study the identification method of the rock. The rock identification method, the thin slice microscopic image technique, the electron probe analysis method or the X-ray powder crystal diffraction method cannot accurately determine the rock. An X-ray powder diffraction method combined with thin-film microscopic image technique and rock identification method was proposed. The X-ray powder diffraction method was combined with the thin-film microscopic image technique to identify the rock, and the microscopic image technique was used to determine the rock. The particle size, structure, shape, mineral color and structure, determine the type of rock, and then determine the mineral and mineral content of the rock by X-ray powder diffraction method, name the rock, and complete the identification of the rock. The experimental results show that the X-ray powder diffraction method or the thin-film microscopic image technique can not accurately determine the rock and combine the X-ray powder diffraction method with the thin-film microscopic image technology to identify the rock. Improve the accuracy of rock identification results.展开更多
Fractures are critical to subsurface activities such as oil and gas extraction,geothermal energy production,and carbon storage.Hydraulic fracturing,a technique that enhances fluid production,creates complex fracture n...Fractures are critical to subsurface activities such as oil and gas extraction,geothermal energy production,and carbon storage.Hydraulic fracturing,a technique that enhances fluid production,creates complex fracture networks within rock formations containing natural discontinuities.Accurately distinguishing between hydraulically induced fractures and pre-existing discontinuities is essential for understanding hydraulic fracture mechanisms.However,this remains challenging due to the interconnected nature of fractures in three-dimensional(3D)space.Manual segmentation,while adaptive,is both labor-intensive and subjective,making it impractical for large-scale 3D datasets.This study introduces a deep learning-based progressive cross-sectional segmentation method to automate the classification of 3D fracture volumes.The proposed method was applied to a 3D hydraulic fracture network in a Montney cube sample,successfully segmenting natural fractures,parted bedding planes,and hydraulic fractures with minimal user intervention.The automated approach achieves a 99.6%reduction in manual image processing workload while maintaining high segmentation accuracy,with test accuracy exceeding 98%and F1-score over 84%.This approach generalizes well to Brazilian disc samples with different fracture patterns,achieving consistently high accuracy in distinguishing between bedding and non-bedding fractures.This automated fracture segmentation method offers an effective tool for enhanced quantitative characterization of fracture networks,which would contribute to a deeper understanding of hydraulic fracturing processes.展开更多
The diagnosis and effective management of inflammatory bowel disease(IBD) requires a combination clinical, endoscopic, histological, biological, and imaging data. While endoscopy and biopsy remains the gold standard f...The diagnosis and effective management of inflammatory bowel disease(IBD) requires a combination clinical, endoscopic, histological, biological, and imaging data. While endoscopy and biopsy remains the gold standard for diagnosis of IBD, imaging plays a central role in the assessment of extra mural disease, in disease surveillance and in the assessment of response to medical treatments, which are often expensive. Imaging is also vital in the detection and diagnosis of disease related complications, both acute and chronic. In this review, we will describe, with illustrative images, the imaging features of IBD in adults, with emphasis on upto-date imaging techniques focusing predominantly on cross sectional imaging and new magnetic resonance imaging techniques.展开更多
Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy fro...Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-展开更多
We present a new optical microscope in which the light transmitted by a sample-scanned transmission confocal microscope is frequency-tripled by SiOx nanocrystallites in lieu of being transmitted by a confocal pinhole....We present a new optical microscope in which the light transmitted by a sample-scanned transmission confocal microscope is frequency-tripled by SiOx nanocrystallites in lieu of being transmitted by a confocal pinhole. This imaging technique offers an increased contrast and a high scattered light rejection. It is demonstrated that the contrast close to the Sparrow resolution limit is enhanced and the sectioning power are increased with respect to the linear confocal detection mode. An experimental implementation is presented and compared with the conventional linear confocal mode.展开更多
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
文摘In order to accurately identify the rock, it is necessary to study the identification method of the rock. The rock identification method, the thin slice microscopic image technique, the electron probe analysis method or the X-ray powder crystal diffraction method cannot accurately determine the rock. An X-ray powder diffraction method combined with thin-film microscopic image technique and rock identification method was proposed. The X-ray powder diffraction method was combined with the thin-film microscopic image technique to identify the rock, and the microscopic image technique was used to determine the rock. The particle size, structure, shape, mineral color and structure, determine the type of rock, and then determine the mineral and mineral content of the rock by X-ray powder diffraction method, name the rock, and complete the identification of the rock. The experimental results show that the X-ray powder diffraction method or the thin-film microscopic image technique can not accurately determine the rock and combine the X-ray powder diffraction method with the thin-film microscopic image technology to identify the rock. Improve the accuracy of rock identification results.
基金supported through the Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grants 341275,CRDPJ 543894-19NSERC/Energi Simulation Industrial Research Chair program.
文摘Fractures are critical to subsurface activities such as oil and gas extraction,geothermal energy production,and carbon storage.Hydraulic fracturing,a technique that enhances fluid production,creates complex fracture networks within rock formations containing natural discontinuities.Accurately distinguishing between hydraulically induced fractures and pre-existing discontinuities is essential for understanding hydraulic fracture mechanisms.However,this remains challenging due to the interconnected nature of fractures in three-dimensional(3D)space.Manual segmentation,while adaptive,is both labor-intensive and subjective,making it impractical for large-scale 3D datasets.This study introduces a deep learning-based progressive cross-sectional segmentation method to automate the classification of 3D fracture volumes.The proposed method was applied to a 3D hydraulic fracture network in a Montney cube sample,successfully segmenting natural fractures,parted bedding planes,and hydraulic fractures with minimal user intervention.The automated approach achieves a 99.6%reduction in manual image processing workload while maintaining high segmentation accuracy,with test accuracy exceeding 98%and F1-score over 84%.This approach generalizes well to Brazilian disc samples with different fracture patterns,achieving consistently high accuracy in distinguishing between bedding and non-bedding fractures.This automated fracture segmentation method offers an effective tool for enhanced quantitative characterization of fracture networks,which would contribute to a deeper understanding of hydraulic fracturing processes.
文摘The diagnosis and effective management of inflammatory bowel disease(IBD) requires a combination clinical, endoscopic, histological, biological, and imaging data. While endoscopy and biopsy remains the gold standard for diagnosis of IBD, imaging plays a central role in the assessment of extra mural disease, in disease surveillance and in the assessment of response to medical treatments, which are often expensive. Imaging is also vital in the detection and diagnosis of disease related complications, both acute and chronic. In this review, we will describe, with illustrative images, the imaging features of IBD in adults, with emphasis on upto-date imaging techniques focusing predominantly on cross sectional imaging and new magnetic resonance imaging techniques.
文摘Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-
基金The Si0x nanocrystals and clusters were deposited by D. Scuderi, 0. Albert, A. Dos Santos and J. Etchepare at the L0A. We thank Bertrand Reynier, Unité de Mécanique, ENSTA, France, for sample characterization by electron microscopy.
文摘We present a new optical microscope in which the light transmitted by a sample-scanned transmission confocal microscope is frequency-tripled by SiOx nanocrystallites in lieu of being transmitted by a confocal pinhole. This imaging technique offers an increased contrast and a high scattered light rejection. It is demonstrated that the contrast close to the Sparrow resolution limit is enhanced and the sectioning power are increased with respect to the linear confocal detection mode. An experimental implementation is presented and compared with the conventional linear confocal mode.
