Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods rema...Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods remains challenging,as it requires the estimation of more than eight parameters.Assuming the reservoir to be a weakly anisotropic ORT medium with small contrasts in the background elastic parameters,a new azimuthal elastic impedance equation was first derived using parameter combinations and mathematical approximations.This equation exhibited almost the same accuracy as the original equation and contained only six model parameters:the compression modulus,anisotropic shear modulus,anisotropic compression modulus,density,normal fracture weakness,and tangential fracture weakness.Subsequently,a stepwise inversion method using second-order derivatives of the elastic impedance was developed to estimate these parameters.Moreover,the Thomsen anisotropy parameter,epsilon,was estimated from the inversion results using the ratio of the anisotropic compression modulus to the compression modulus.Synthetic examples with moderate noise and field data examples confirm the feasibility and effectiveness of the inversion method.The proposed method exhibited accuracy similar to that of previous inversion strategies and could predict richer vertical fracture information.Ultimately,the method was applied to a three-dimensional work area,and the predictions were consistent with logging and geological a priori information,confirming the effectiveness of this method.Summarily,the proposed stepwise inversion method can alleviate the uncertainty of multi-parameter inversion in ORT medium,thereby improving the reliability of fracture detection.展开更多
Fracture is one of the most common and unexpected traumas.If not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve injury.Using computer vision technology to detec...Fracture is one of the most common and unexpected traumas.If not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve injury.Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed.However,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult fractures.In this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture detection.The authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local variations.The authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art models.The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).展开更多
This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’diagnostic efficiency.Therefore,a deep-learning-based automated diagnostic systemwithmulti-label segmentatio...This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’diagnostic efficiency.Therefore,a deep-learning-based automated diagnostic systemwithmulti-label segmentation is proposed to recognize the condition of vertebral fractures.The whole spine Computed Tomography(CT)image is segmented into the fracture,normal,and background using U-Net,and the fracture degree of each vertebra is evaluated(Genant semi-qualitative evaluation).The main work of this paper includes:First,based on the spatial configuration network(SCN)structure,U-Net is used instead of the SCN feature extraction network.The attention mechanismandthe residual connectionbetweenthe convolutional layers are added in the local network(LN)stage.Multiple filtering is added in the global network(GN)stage,and each layer of the LN decoder feature map is filtered separately using dot product,and the filtered features are re-convolved to obtain the GN output heatmap.Second,a network model with improved SCN(M-SCN)helps automatically localize the center-of-mass position of each vertebra,and the voxels around each localized vertebra were clipped,eliminating a large amount of redundant information(e.g.,background and other interfering vertebrae)and keeping the vertebrae to be segmented in the center of the image.Multilabel segmentation of the clipped portion was subsequently performed using U-Net.This paper uses VerSe’19,VerSe’20(using only data containing vertebral fractures),and private data(provided by Guizhou Orthopedic Hospital)for model training and evaluation.Compared with the original SCN network,the M-SCN reduced the prediction error rate by 1.09%and demonstrated the effectiveness of the improvement in ablation experiments.In the vertebral segmentation experiment,the Dice Similarity Coefficient(DSC)index reached 93.50%and the Maximum Symmetry Surface Distance(MSSD)index was 4.962 mm,with accuracy and recall of 95.82%and 91.73%,respectively.Fractured vertebrae were also marked as red and normal vertebrae were marked as white in the experiment,and the semi-qualitative assessment results of Genant were provided,as well as the results of spinal localization visualization and 3D reconstructed views of the spine to analyze the actual predictive ability of the model.It provides a promising tool for vertebral fracture detection.展开更多
It is important to understand the development of joints and fractures in rock masses to ensure drilling stability and blasting effectiveness.Traditional manual observation techniques for identifying and extracting fra...It is important to understand the development of joints and fractures in rock masses to ensure drilling stability and blasting effectiveness.Traditional manual observation techniques for identifying and extracting fracture characteristics have been proven to be inefficient and prone to subjective interpretation.Moreover,conventional image processing algorithms and classical deep learning models often encounter difficulties in accurately identifying fracture areas,resulting in unclear contours.This study proposes an intelligent method for detecting internal fractures in mine rock masses to address these challenges.The proposed approach captures a nodal fracture map within the targeted blast area and integrates channel and spatial attention mechanisms into the ResUnet(RU)model.The channel attention mechanism dynamically recalibrates the importance of each feature channel,and the spatial attention mechanism enhances feature representation in key areas while minimizing background noise,thus improving segmentation accuracy.A dynamic serpentine convolution module is also introduced that adaptively adjusts the shape and orientation of the convolution kernel based on the local structure of the input feature map.Furthermore,this method enables the automatic extraction and quantification of borehole nodal fracture information by fitting sinusoidal curves to the boundaries of the fracture contours using the least squares method.In comparison to other advanced deep learning models,our enhanced RU demonstrates superior performance across evaluation metrics,including accuracy,pixel accuracy(PA),and intersection over union(IoU).Unlike traditional manual extraction methods,our intelligent detection approach provides considerable time and cost savings,with an average error rate of approximately 4%.This approach has the potential to greatly improve the efficiency of geological surveys of borehole fractures.展开更多
Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images,this paper integrates the YOLOv8 model with the Convolution Block Attention Module(CBAM).It proposes ...Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images,this paper integrates the YOLOv8 model with the Convolution Block Attention Module(CBAM).It proposes an intelligent method for detecting fractures and holes,as well as segmenting whole-wellbore images.Firstly,we develop a dataset sample of effective reservoir sections by integrating logging data and conducting data augmentation on fracture and hole samples in ultrasonic logging images.A standardized process procedure for the generation of new samples and model training has been proposed effectively.Subsequently,the improved YOLOv8 model undergoes a process of training and validation.The results indicate that the model achieves average accuracies of 0.910 and 0.884 in target detection and image segmentation tasks,respectively.These findings demonstrate a notable performance improvement compared to the original model.Furthermore,a sliding window strategy is proposed to tackle the challenges of high computational demands and insufficient accuracy in the intelligent processing of full-well ultrasonic images.To manage overlapping regions within the sliding window,we employ the Non-Maximum Suppression(NMS)principle for effective processing.Finally,the model has been tested on actual logging images and demonstrates an enhanced capability to identify irregular fractures and holes,which significantly improves the efficiency of geological feature recognition in the wholewell section ultrasonic logging images.展开更多
Common prestack fracture prediction methods cannot clearly distinguish multiplescale fractures. In this study, we propose a prediction method for macro- and mesoscale fractures based on fracture density distribution i...Common prestack fracture prediction methods cannot clearly distinguish multiplescale fractures. In this study, we propose a prediction method for macro- and mesoscale fractures based on fracture density distribution in reservoirs. First, we detect the macroscale fractures （larger than 1/4 wavelength） using the multidirectional coherence technique that is based on the curvelet transform and the mesoscale fractures （1/4-1/100 wavelength） using the seismic azimuthal anisotropy technique and prestack attenuation attributes, e.g., frequency attenuation gradient. Then, we combine the obtained fracture density distributions into a map and evaluate the variably scaled fractures. Application of the method to a seismic physical model of a fractured reservoir shows that the method overcomes the problem of discontinuous fracture density distribution generated by the prestack seismic azimuthal anisotropy method, distinguishes the fracture scales, and identifies the fractured zones accurately.展开更多
Fractured reservoirs always show anisotropic amplitude features,i.e.the reflection amplitude of seismic waves varies with offset and azimuth （AVOZ）.A noise attenuation fracture inversion algorithm is presented for f...Fractured reservoirs always show anisotropic amplitude features,i.e.the reflection amplitude of seismic waves varies with offset and azimuth （AVOZ）.A noise attenuation fracture inversion algorithm is presented for fracture detection based on P-wave AVOZ.The conventional inversion method always fails when applied to limited azimuth data because of the existence of noise.In our inversion algorithm,special attention is paid to suppressing the noise during inversion,to overcome the limitation of the conventional inversion method on limited azimuth data.Numerical models are employed to illustrate the effectiveness of the method.The inversion algorithm is then applied to Tazhong 45 area field data which is acquired under limited azimuth distribution.Compared with cores and fullbore formation microimage （FMI）,the inverted results （fracture density and orientation） are reasonable,suggesting that the inversion algorithm is feasible for fracture prediction in the Tarim Basin.展开更多
Based on radon gas properties and its existing projects applications, we firstly attempted to apply geo- physical and chemical properties of radon gas in the field of mining engineering, and imported radioac- tive mea...Based on radon gas properties and its existing projects applications, we firstly attempted to apply geo- physical and chemical properties of radon gas in the field of mining engineering, and imported radioac- tive measurement method to detect the development process of the overlying strata mining-induced fractures and their contained water quality in underground coal mining, which not only innovates a more simple-fast-reliable detection method, but also further expands the applications of radon gas detection technology in mining field. A 3D simulation design of comprehensive testing system for detecting strata mining-induced fractures on surface with radon gas (CTSR) was carried out by using a large-scale 3D solid model design software Pro/Engineer (Pro/E), which overcame three main disadvantages of ''static design thought, 2D planar design and heavy workload for remodification design'' on exiting design for mining engineering test systems. Meanwhile, based on the simulation design results of Pro/E software, the sta- bility of the jack-screw pressure bar for the key component in CTSR was checked with a material mechan- ics theory, which provided a reliable basis for materials selection during the latter machining process.展开更多
Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological str...Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological structure-fracture on the borehole image was identified, and quantitative parameters were obtained by HOUGH transform. Several case studies show that the method is feasible.展开更多
The authors studied the potential field boundary identification of the new technology in order to find out the possible fractures or contact zones using the following methods such as tilt derivative,horizontal derivat...The authors studied the potential field boundary identification of the new technology in order to find out the possible fractures or contact zones using the following methods such as tilt derivative,horizontal derivative of tilt derivative,normalized standard deviation and normalized differential method. Combined with Euler deconvolution and small subdomain filtering,the actual data processing results show that these methods are all able to identify wider range extending fractures and obtain abundant geological information. The horizontal derivative of tilt derivative and normalized differential method have a better resolution for the small cutting fractures and lacunae in the studied area. They provide a reliable basis for study of the cutting relationship between fractures.展开更多
基金sponsorship of the National Natural Science Foundation of China(42430809,42274157,42030103,42404132)the Fund of State Key Laboratory of Deep Oil and Gas,China University of Petroleum(East China)(SKLDOG2024-ZYTS-02)+5 种基金the Postdoctoral Fellowship Program of CPSF(GZB20240850)the Postdoctoral Project of Qingdao(QDBSH20240102082)the Fundamental Research Funds for the Central Universities(24CX07004A,24CX06036A)the CNPC Innovation Fund(2024DQ02-0505,2024DQ02-0136)the Innovation fund project for graduate student of China University of Petroleum(East China)the Fundamental Research Funds for the Central Universities(24CX04002A).
文摘Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods remains challenging,as it requires the estimation of more than eight parameters.Assuming the reservoir to be a weakly anisotropic ORT medium with small contrasts in the background elastic parameters,a new azimuthal elastic impedance equation was first derived using parameter combinations and mathematical approximations.This equation exhibited almost the same accuracy as the original equation and contained only six model parameters:the compression modulus,anisotropic shear modulus,anisotropic compression modulus,density,normal fracture weakness,and tangential fracture weakness.Subsequently,a stepwise inversion method using second-order derivatives of the elastic impedance was developed to estimate these parameters.Moreover,the Thomsen anisotropy parameter,epsilon,was estimated from the inversion results using the ratio of the anisotropic compression modulus to the compression modulus.Synthetic examples with moderate noise and field data examples confirm the feasibility and effectiveness of the inversion method.The proposed method exhibited accuracy similar to that of previous inversion strategies and could predict richer vertical fracture information.Ultimately,the method was applied to a three-dimensional work area,and the predictions were consistent with logging and geological a priori information,confirming the effectiveness of this method.Summarily,the proposed stepwise inversion method can alleviate the uncertainty of multi-parameter inversion in ORT medium,thereby improving the reliability of fracture detection.
基金Science and technology plan project of Xi'an,Grant/Award Number:GXYD17.12Open Fund of Shaanxi Key Laboratory of Network Data Intelligent Processing,Grant/Award Number:XUPT-KLND(201802,201803)Key Research and Development Program of Shaanxi,Grant/Award Number:2019GY-021。
文摘Fracture is one of the most common and unexpected traumas.If not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve injury.Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed.However,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult fractures.In this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture detection.The authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local variations.The authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art models.The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).
文摘This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’diagnostic efficiency.Therefore,a deep-learning-based automated diagnostic systemwithmulti-label segmentation is proposed to recognize the condition of vertebral fractures.The whole spine Computed Tomography(CT)image is segmented into the fracture,normal,and background using U-Net,and the fracture degree of each vertebra is evaluated(Genant semi-qualitative evaluation).The main work of this paper includes:First,based on the spatial configuration network(SCN)structure,U-Net is used instead of the SCN feature extraction network.The attention mechanismandthe residual connectionbetweenthe convolutional layers are added in the local network(LN)stage.Multiple filtering is added in the global network(GN)stage,and each layer of the LN decoder feature map is filtered separately using dot product,and the filtered features are re-convolved to obtain the GN output heatmap.Second,a network model with improved SCN(M-SCN)helps automatically localize the center-of-mass position of each vertebra,and the voxels around each localized vertebra were clipped,eliminating a large amount of redundant information(e.g.,background and other interfering vertebrae)and keeping the vertebrae to be segmented in the center of the image.Multilabel segmentation of the clipped portion was subsequently performed using U-Net.This paper uses VerSe’19,VerSe’20(using only data containing vertebral fractures),and private data(provided by Guizhou Orthopedic Hospital)for model training and evaluation.Compared with the original SCN network,the M-SCN reduced the prediction error rate by 1.09%and demonstrated the effectiveness of the improvement in ablation experiments.In the vertebral segmentation experiment,the Dice Similarity Coefficient(DSC)index reached 93.50%and the Maximum Symmetry Surface Distance(MSSD)index was 4.962 mm,with accuracy and recall of 95.82%and 91.73%,respectively.Fractured vertebrae were also marked as red and normal vertebrae were marked as white in the experiment,and the semi-qualitative assessment results of Genant were provided,as well as the results of spinal localization visualization and 3D reconstructed views of the spine to analyze the actual predictive ability of the model.It provides a promising tool for vertebral fracture detection.
基金supported by the National Natural Science Foundation of China(No.52474172).
文摘It is important to understand the development of joints and fractures in rock masses to ensure drilling stability and blasting effectiveness.Traditional manual observation techniques for identifying and extracting fracture characteristics have been proven to be inefficient and prone to subjective interpretation.Moreover,conventional image processing algorithms and classical deep learning models often encounter difficulties in accurately identifying fracture areas,resulting in unclear contours.This study proposes an intelligent method for detecting internal fractures in mine rock masses to address these challenges.The proposed approach captures a nodal fracture map within the targeted blast area and integrates channel and spatial attention mechanisms into the ResUnet(RU)model.The channel attention mechanism dynamically recalibrates the importance of each feature channel,and the spatial attention mechanism enhances feature representation in key areas while minimizing background noise,thus improving segmentation accuracy.A dynamic serpentine convolution module is also introduced that adaptively adjusts the shape and orientation of the convolution kernel based on the local structure of the input feature map.Furthermore,this method enables the automatic extraction and quantification of borehole nodal fracture information by fitting sinusoidal curves to the boundaries of the fracture contours using the least squares method.In comparison to other advanced deep learning models,our enhanced RU demonstrates superior performance across evaluation metrics,including accuracy,pixel accuracy(PA),and intersection over union(IoU).Unlike traditional manual extraction methods,our intelligent detection approach provides considerable time and cost savings,with an average error rate of approximately 4%.This approach has the potential to greatly improve the efficiency of geological surveys of borehole fractures.
基金supported by the National Natural Science Foundation of China(Grant Nos.12334019,12304496).
文摘Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images,this paper integrates the YOLOv8 model with the Convolution Block Attention Module(CBAM).It proposes an intelligent method for detecting fractures and holes,as well as segmenting whole-wellbore images.Firstly,we develop a dataset sample of effective reservoir sections by integrating logging data and conducting data augmentation on fracture and hole samples in ultrasonic logging images.A standardized process procedure for the generation of new samples and model training has been proposed effectively.Subsequently,the improved YOLOv8 model undergoes a process of training and validation.The results indicate that the model achieves average accuracies of 0.910 and 0.884 in target detection and image segmentation tasks,respectively.These findings demonstrate a notable performance improvement compared to the original model.Furthermore,a sliding window strategy is proposed to tackle the challenges of high computational demands and insufficient accuracy in the intelligent processing of full-well ultrasonic images.To manage overlapping regions within the sliding window,we employ the Non-Maximum Suppression(NMS)principle for effective processing.Finally,the model has been tested on actual logging images and demonstrates an enhanced capability to identify irregular fractures and holes,which significantly improves the efficiency of geological feature recognition in the wholewell section ultrasonic logging images.
基金This research was financially supported by the National Natural Science Foundation of China （No. 41474112） and the National Science and Technology Major Project （No. 2017ZX05005-004）.
文摘Common prestack fracture prediction methods cannot clearly distinguish multiplescale fractures. In this study, we propose a prediction method for macro- and mesoscale fractures based on fracture density distribution in reservoirs. First, we detect the macroscale fractures （larger than 1/4 wavelength） using the multidirectional coherence technique that is based on the curvelet transform and the mesoscale fractures （1/4-1/100 wavelength） using the seismic azimuthal anisotropy technique and prestack attenuation attributes, e.g., frequency attenuation gradient. Then, we combine the obtained fracture density distributions into a map and evaluate the variably scaled fractures. Application of the method to a seismic physical model of a fractured reservoir shows that the method overcomes the problem of discontinuous fracture density distribution generated by the prestack seismic azimuthal anisotropy method, distinguishes the fracture scales, and identifies the fractured zones accurately.
文摘Fractured reservoirs always show anisotropic amplitude features,i.e.the reflection amplitude of seismic waves varies with offset and azimuth （AVOZ）.A noise attenuation fracture inversion algorithm is presented for fracture detection based on P-wave AVOZ.The conventional inversion method always fails when applied to limited azimuth data because of the existence of noise.In our inversion algorithm,special attention is paid to suppressing the noise during inversion,to overcome the limitation of the conventional inversion method on limited azimuth data.Numerical models are employed to illustrate the effectiveness of the method.The inversion algorithm is then applied to Tazhong 45 area field data which is acquired under limited azimuth distribution.Compared with cores and fullbore formation microimage （FMI）,the inverted results （fracture density and orientation） are reasonable,suggesting that the inversion algorithm is feasible for fracture prediction in the Tarim Basin.
基金support for this work provided by the Fundamental Research Funds for the Central Universities(China University of Mining & Technology) (No. 2010ZDP02B02)the State Key Laboratory of Coal Resources and Safe Mining(No. SKLCRSM08X02)
文摘Based on radon gas properties and its existing projects applications, we firstly attempted to apply geo- physical and chemical properties of radon gas in the field of mining engineering, and imported radioac- tive measurement method to detect the development process of the overlying strata mining-induced fractures and their contained water quality in underground coal mining, which not only innovates a more simple-fast-reliable detection method, but also further expands the applications of radon gas detection technology in mining field. A 3D simulation design of comprehensive testing system for detecting strata mining-induced fractures on surface with radon gas (CTSR) was carried out by using a large-scale 3D solid model design software Pro/Engineer (Pro/E), which overcame three main disadvantages of ''static design thought, 2D planar design and heavy workload for remodification design'' on exiting design for mining engineering test systems. Meanwhile, based on the simulation design results of Pro/E software, the sta- bility of the jack-screw pressure bar for the key component in CTSR was checked with a material mechan- ics theory, which provided a reliable basis for materials selection during the latter machining process.
文摘Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological structure-fracture on the borehole image was identified, and quantitative parameters were obtained by HOUGH transform. Several case studies show that the method is feasible.
文摘The authors studied the potential field boundary identification of the new technology in order to find out the possible fractures or contact zones using the following methods such as tilt derivative,horizontal derivative of tilt derivative,normalized standard deviation and normalized differential method. Combined with Euler deconvolution and small subdomain filtering,the actual data processing results show that these methods are all able to identify wider range extending fractures and obtain abundant geological information. The horizontal derivative of tilt derivative and normalized differential method have a better resolution for the small cutting fractures and lacunae in the studied area. They provide a reliable basis for study of the cutting relationship between fractures.
