Spinal Vertebral Fracture Detection and Fracture Level Assessment Based on Deep Learning

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.

MPFracNet:A Deep Learning Algorithm for Metacarpophalangeal Fracture Detection with Varied Difficulties

Due to small size and high occult,metacarpophalangeal fracturediagnosis displays a low accuracy in terms of fracture detection and locationin X-ray images.To efficiently detect metacarpophalangeal fractures on Xrayimages as the second opinion for radiologists,we proposed a novel onestageneural network namedMPFracNet based onRetinaNet.InMPFracNet,a deformable bottleneck block(DBB)was integrated into the bottleneckto better adapt to the geometric variation of the fractures.Furthermore,an integrated feature fusion module(IFFM)was employed to obtain morein-depth semantic and shallow detail features.Specifically,Focal Loss andBalanced L1 Loss were introduced to respectively attenuate the imbalancebetween positive and negative classes and the imbalance between detectionand location tasks.We assessed the proposed model on the test set andachieved an AP of 80.4%for the metacarpophalangeal fracture detection.To estimate the detection performance for fractures with different difficulties,the proposed model was tested on the subsets of metacarpal,phalangeal andtiny fracture test sets and achieved APs of 82.7%,78.5%and 74.9%,respectively.Our proposed framework has state-of-the-art performance for detectingmetacarpophalangeal fractures,which has a strong potential application valuein practical clinical environments.

Signal-to-noise ratio application to seismic marker analysis and fracture detection

Seismic data with high signal-to-noise ratios （SNRs） are useful in reservoirexploration. To obtain high SNR seismic data, significant effort is required to achieve noiseattenuation in seismic data processing, which is costly in materials, and human and financialresources. We introduce a method for improving the SNR of seismic data. The SNR iscalculated by using the frequency domain method. Furthermore, we optimize and discussthe critical parameters and calculation procedure. We applied the proposed method on realdata and found that the SNR is high in the seismic marker and low in the fracture zone.Consequently, this can be used to extract detailed information about fracture zones that areinferred bv structural analysis but not observed in conventional seismic data.

An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

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).

Height Detection and Analysis of Water Flowing Fractured Zone of Coal Face

Taking 91105 working face as the research object, the observation method of water flowing fractured zone and the layout of mining holes were determined by analyzing the field geological structure. It was shown that the fractured zone height and the ratio given by the measured method were 52.33 and 12.46, respectively. By the numerical simulation method with the software of UDEC, the fractured zone height and the ratio were 42.5 and 10.12. By comparison of measured height data and UDEC numerical simulation, there were some differences between the measured height and the calculated results of UDEC numerical simulation method. The method of simulation can be used as the technical basis for the design of waterproof coal pillar in the future.

P-wave fracture prediction algorithm using pre-stack data with limited azimuth distribution:A case study in the TZ45 area,Tarim Basin,China

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.

Dynamic evolution characteristics of mining-induced fractures in overlying strata detected by radon

<正>For environment protection in mining areas in northwest China,we developed a CTSRM(comprehensive test system by radon measurement) to measure radon radioactivity and detect dynamic evolution characteristics of mining-induced fractures in overlying strata.It was used to simulate the relationship between the dynamic evolution characteristics and radon concentrations of 33201~# coalface at Bulianta coal mine in Inner Mongolia,and feasibility of the method was validate.

Design of comprehensive test system for detecting overlying strata mining-induced fractures on surface with radon gas

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.

Fracture detecting based on Ant Colony Algorithm

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.

Seismic prediction method of multiscale fractured reservoir

普通 prestack 破裂预言方法不能清楚地区分 multiplescale 破裂。在这研究，我们为宏建议一个预言方法 -- 并且 mesoscale 在水库基于破裂密度分发断裂。首先，我们检测 macroscale 破裂(比 1/4 波长大) 用基于 curvelet 变换和 mesoscale 破裂(1/4-1/100 波长) 的 multidirectional 连贯技术使用地震方位角的 anisotropy 技术和 prestack 变细归因，例如，频率变细坡度。然后，我们把获得的破裂密度分布合为一张地图并且评估易变地放大的破裂。到一座折断的水库的一个地震物理模型的方法的申请证明方法克服 prestack 产生的不连续的破裂密度分发的问题地震方位角的 anisotropy 方法，区分破裂精确地放大，并且识别折断的地区。

基于充电导体模型的水力压裂电磁响应特征分析

随着中国非常规、超低渗油气资源的大力开发,压裂监测技术迎来重大发展机遇。电磁法在地球物理探测方面具有成本低、时效性强、探测深度大等优点,但面对大深度压裂环境,电磁压裂监测技术的可行性亟须论证。基于充电导体和电偶极源扩散电磁场理论,首先构建了不同深度和不同长度下的裂缝压裂模型,分析了各模型的地面总场及异常场的电磁响应特征。分析结果表明:利用井地电磁法进行压裂监测时,观测总场对裂缝长度增加引起的电磁响应不敏感,异常场存在低值带;深度为4000 m时,电磁异常小于1%,深度越大异常场衰减越严重,且相邻测点间异常场的绝对值与深度呈负相关,电磁法压裂监测技术难以兼顾探测精度与探测深度。然后,通过建立压裂监测物理模型,分析实测数据电磁异常曲线特征,验证了理论计算的正确性。最后,结合前人理论成果和实践经验,提出了一种井中激发—地面接收的测网式电磁法压裂监测观测系统,对测区范围与测点间距进行合理约束,系统具有野外施工简捷、监测深度大等优势,为电磁法压裂监测提供了新的发展方向。

光纤传感技术在油气田开发中的应用

分布式光纤监测技术在油气田开发中具有无可比拟的优势。从分布式光纤传感技术基本原理和井中布设方式出发,介绍了光纤传感技术在水力压裂、连续油管产液剖面测试、邻井压裂监测、油气井注采监测、储气库安全监测和套损监测等多个石油工程领域应用情况。整体表明光纤监测是一种经济高效的监测技术,具备实时监控油气井全生命周期的潜力,是具有科学性、时代特性和安全高效的生产测井技术。同时指出分布式光纤传感技术在油田应用的未来发展应聚焦于提升传感器性能、丰富光纤监测场景应用、提高资料处理解释水平能力,降低应用成本,进一步发挥光纤在油气田开发中的作用。

用于碳酸盐岩储层裂缝检测的GWO-CS-BP算法及应用研究

碳酸盐岩储层中的裂隙是油气的运移通道和储集空间,对于油气勘探、开发和评价都具有重要的指导意义。针对研究区碳酸盐岩储层裂缝检测的难题,提出灰狼布谷鸟优化BP算法(GWO-CS-BP),该算法是将GWO-CS(grey wolf-cuckoo search algorithm)与BP(back propagation)相结合形成的裂隙检测方法。将含裂缝信息的相干、曲率、倾角、方位角和构型张量等属性作为GWO-CS-BP神经网络的输入数据,在工区地质资料约束下根据测井数据获得裂缝发育水平评价指标,进而对研究区裂缝发育水平进行评价并划分等级。研究区碳酸盐岩储层裂缝发育水平检测结果表明,GWO-CS-BP算法能够综合各属性特点对研究区的裂缝发育水平特征进行二次误差控制,获得裂缝发育水平评价指标f s并将研究区裂缝发育水平划分为3个等级及4个裂缝存在区域。其中,当研究区裂缝发育水平参数的值适中时,即f s的值大于4.0且小于5.8时,C区域最有利于油气的聚集,高产井的分布数量较多。利用GWO-CS-BP算法对研究区的裂缝发育水平进行了精细评价,并得出裂隙发育水平参数f s,实现了GWO-CS算法改进的BP神经网络在裂缝检测中的有效应用。

一种具有压力检测功能的智能医疗夹板设计

传统的医用夹板被用于固定桡骨远端骨折,它只能依靠医生的经验来控制压力,这将直接影响骨折的康复过程。文中设计并制造了一种具有压力和温度检测功能的智能夹板。首先,对用于桡骨骨折固定的传统四种夹板之一的宽夹板进行了重新设计,并通过三维(3 dimension,3D)打印技术进行打印。在人体工程学的基础上,夹板增加了高密度的通气孔、扎线槽、传感器接触点、电路槽和电池。其次,对压力传感器进行选型并设计控制电路,该电路可同时检测多个通道的压力值及当前温度值,并在本地的有机发光二极管(organic light-emitting diode,OLED)屏幕上实时显示。最后,通过蓝牙将采集数据上传到手机,利用大数据分析病人的康复数据,从而得到最优治疗方案。

顺北油田分支断裂及断控储层的识别与描述研究

顺北油气田是断裂控制的断控油气田,其中不少断层发育有分支断裂。分支断裂在常规地震资料上难以发现,属于断距小或者说是比较隐蔽的断裂。因这些断裂带形态与分布不清,断控储集体特征也不清楚,从而使得储层预测与目标优选困难。如何对这类断裂开展识别和描述,进而开展储层预测和目标评价,本次研究尝试从多种方法的组合使用入手,通过反复试验发现,把时频谱蓝化提高地震分辨率处理与方向性边界保持断层增强处理组合使用,能有效提高地震资料分支断裂的识别能力;把梯度结构张量断裂检测处理与最大似然属性断裂检测处理组合使用能很好地对分支断裂进行描述与刻画;把结构张量属性分析与缝洞增强处理组合使用能很好地对洞穴型储层进行有效刻画;GST(Gradient Structural Tensor)相干属性与不连续性属性联合使用实现了对孔洞型储层的预测;体曲率+最大似然属性组合使用实现了对裂缝型储层预测与描述;在不同类型储层预测基础上,通过不同类型储层的叠合得到的总储层预测。

高压管汇中活动弯管BC直接头失效分析

高压管汇作为压裂设备核心部件之一,比泵头体、泵阀等易损件失效危害更大。为此,以失效活动弯管BC直接头为研究对象,采用硬度检测、金相检验、扫描电镜检测等手段分析了失效直接头材料力学性能和断口形貌,并利用有限元方法计算了直接头轴肩根部含初始缺陷处的应力强度。分析结果表明:BC直接头失效是由应力集中导致的脆性断裂而产生;应力集中、组装配合不当使其受力不均、材料硬度偏高和力学性能不达标,由此造成接头在循环载荷作用下发生脆性断裂;建议严格控制接头原材料复检及产品质量检验,规范产品结构组装标准,定量规范管汇安装标准。所得结论可为压裂高压管汇接头的失效分析提供理论参考。

横波分裂和纵波方位各向异性在油气勘探中的应用

本文首先回顾了横波分裂和纵波方位各向异性应用于油气勘探的发展历程;在此基础上重点讨论了二者在上地壳中的分布情况、相互关系及应用条件.横波分裂是地震波传播经过裂缝时所独有的横波双折射现象,40年的科学研究及生产应用表明这一物理现象在上地壳中广泛存在,并主要存在于近地表1200 m以浅.纵波方位各向异性从浅到深在上地壳中分布差异较大;应用于检测裂缝时仅对含气裂缝发育区敏感,对含其他流体的裂缝区不敏感.针对裂缝发育区的预测,本文利用理论和实验数据分析论证了横波分裂与纵波方位各向异性的关系;阐明在油气勘探中横波分裂分析与纵波方位各向异性预测裂缝储层须首先消除近地表的影响,这在一定程度上限制了二者的应用.同时阐明二者的应用还需要满足近地表相对简单、裂缝储层相对较厚等条件;且需采集非零偏或环形垂直地震剖面(VSP)资料进行标定.即多分量地震数据品质、近地表地震地质条件及储层方位各向异性发育程度是影响横波分裂及纵波方位各向异性应用效果的3个关键因素.

淮南矿区煤层顶板分段压裂水平井抽采技术及效果研究

淮南矿区为典型高瓦斯矿区,煤层碎软、渗透率低、瓦斯含量偏高、抽采难度大,为探讨地面煤层气顶板分段压裂水平井在矿区的技术可行性与瓦斯治理效果,在分析矿区主要煤层13-1煤储层特征基础上,采用应力解除法进行了煤层三向地应力测试,结果显示三向应力场类型主要为σ_(h,max)>σ_(v)>σ_(h,min),具有实施顶板分段压裂水平井技术的充分条件;利用MFrac Suite软件分别模拟了水平段距离煤层1、3、5 m时的压裂缝参数,压裂缝半长最大107.33 m、最小89.47 m,具有理想的压裂效果,说明顶板分段压裂水平井在淮南矿区具有比较好的地质适应性与可行性。以潘一煤矿13-1煤层“L”型顶板分段压裂水平井CBM01井为研究对象,采用井下钻孔检测与数值模拟等手段综合分析了瓦斯治理效果,结果显示CBM01井抽采415 d即显著降低了煤层瓦斯压力与瓦斯含量,距离水平井50、65 m处瓦斯压力由6.4 MPa分别降至2.6、2.7 MPa,降低幅度均超过55%,水平段两侧各15~20 m范围内瓦斯含量由13.5 m^(3)/t降至最大9.11 m^(3)/t、最小6.92 m^(3)/t,平均7.92 m^(3)/t,约10 m范围均降至8 m^(3)/t以下。最后,采用数值模拟方法预测了CBM01井抽采10 a的产气效果及瓦斯治理效果,气井抽采10 a累计产气约272.08×10^(4)m^(3),水平段倾向单侧约150 m范围内气含量均降至8 m^(3)/t以下、压力均降至3 MPa以下。综合研究结果表明,煤层顶板分段压裂水平井技术在淮南矿区瓦斯治理方面均具有较大的优势和应用效果。

强富水区山岭隧道穿越断层破碎带施工技术研究

为降低隧道穿越断层破碎带施工中的涌水突泥风险,该文依托古兜山隧道实体工程,采用“TSP+地质雷达+超前地质钻孔”的地质超前预报技术,探明隧道掌子面前方的地层状况和富水程度,提出富水区隧道穿越断层破碎带施工关键技术。提出隧道洞口明挖法与隧道洞内光面爆破技术及洞内围岩段的双侧壁导坑法、单侧壁导坑法、三台阶七部法的开挖方式;采用S-Ⅴb型隧道衬砌结构型式与全断面帷幕注浆、加强泄水等技术相结合的支护方式,其中的整体承载结构为初期支撑系统和二次模筑衬砌的组合结构。该施工关键技术可有效避免古兜山隧道施工的突水涌泥现象,可指导类似工程的施工。