WO2020248817A1 - 基于探地雷达三维图像属性分析的供水管道漏损检测方法 - Google Patents

基于探地雷达三维图像属性分析的供水管道漏损检测方法 Download PDF

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WO2020248817A1
WO2020248817A1 PCT/CN2020/092532 CN2020092532W WO2020248817A1 WO 2020248817 A1 WO2020248817 A1 WO 2020248817A1 CN 2020092532 W CN2020092532 W CN 2020092532W WO 2020248817 A1 WO2020248817 A1 WO 2020248817A1
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radar
attribute
analysis
dimensional
ground penetrating
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PCT/CN2020/092532
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French (fr)
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郑飞飞
申永刚
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浙江大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
    • F17D—PIPE-LINE SYSTEMS; PIPE-LINES
    • F17D5/00—Protection or supervision of installations
    • F17D5/02—Preventing, monitoring, or locating loss
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/12—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G06T2207/10044—Radar image

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  • the invention belongs to the field of nondestructive detection and positioning of urban water supply pipeline leakage in municipal engineering, and in particular relates to a water supply pipeline leakage detection method based on the analysis of the three-dimensional image attribute of ground penetrating radar.
  • the most commonly used non-destructive testing method is the audio-visual method, but this method also has certain shortcomings: one is that it requires a high level of experience for testing engineers, which generally requires more than ten years of experience accumulation; the other is that it is harsh on the environment and misses. There should be no noise interference in the process, and generally only work at night; third, the efficiency of leak detection is low, and it usually takes several days to complete a pipeline of more than one kilometer. These deficiencies of the audio-visual method can no longer meet the rapidly developing urban water supply network construction. As a non-destructive testing method, ground penetrating radar has begun to attract attention due to its high efficiency, rapidity, and environmental tolerance.
  • Ground-penetrating radar is based on the theory of electromagnetic wave propagation, based on the difference in dielectric properties (conductivity, permittivity), and uses the reflection of high-frequency pulsed electromagnetic waves to detect target objects. Since the dielectric constant of soil is 6-10, and the dielectric constant of water is 81, there is a huge difference between the two. Theoretically, the leakage area will form an obvious diffraction wave on the radar image, which can be explained by analyzing the radar image. Diffraction wave on the upper surface to find the leakage point of the pipe network.
  • the imaging effect of the image will always be affected to varying degrees, and problems such as weak or interfered signals of the target object will appear. Leakage information on radar images is often difficult to identify. The main problems of existing radar leak detection technology are:
  • the information contained in the image is single, that is, the images are all waveforms;
  • the image analysis process mainly depends on the experience of the engineering personnel, and the accuracy rate is unstable.
  • the present invention provides a water supply pipeline leakage detection method based on the analysis of the three-dimensional image attributes of the ground penetrating radar.
  • the leakage detection method of the present invention is applied to a water supply pipeline, and can obtain three-dimensional spatial information of pipeline leakage, locate the location of the leakage, and determine the scale of the leakage.
  • the invention changes the traditional two-dimensional image analysis method of the ground penetrating radar in the pipeline leakage detection.
  • the radar is used to scan and remove the DC, zero-time correction, amplitude enhancement, bandpass filtering and other image processing along the pipeline axis to fit around the pipeline.
  • the three-dimensional data volume is used to scan and remove the DC, zero-time correction, amplitude enhancement, bandpass filtering and other image processing along the pipeline axis to fit around the pipeline.
  • the coherent attributes extracted from the constructed three-dimensional image can quantify the similarity of the radar waveform in the axial and vertical directions, so as to obtain the three-dimensional spatial information of pipeline leakage; the extracted instantaneous attributes can highlight the slight changes in horizontal continuity, thereby Accurately locate the location of the leakage; the extracted frequency attributes can get a spectrum containing rich information to help further verify the location of the leakage; the extracted amplitude attributes can perform high-resolution imaging of the leakage characteristics to determine the scale of the pipeline leakage.
  • the present invention is realized in this way, a water supply pipeline leakage detection method based on the three-dimensional image attribute analysis of the ground penetrating radar.
  • the leakage detection method of the water supply pipeline based on the three-dimensional image attribute analysis of the ground penetrating radar includes:
  • Step 1 Obtain the original ground penetrating radar image data of the longitudinal scanning of the water supply pipeline
  • Step 2 Perform noise reduction and filtering on the acquired original image data
  • Step 3 Fit the processed image data into a three-dimensional data volume through interpolation, extract a variety of planar or three-dimensional image attributes, and display them in longitudinal section, transverse section, horizontal section, irregular section and isosurface;
  • Step four Use multi-attribute comprehensive analysis to accurately identify the location and scale of pipeline leakage.
  • the acquisition of the original image data of the ground penetrating radar includes selecting a radar antenna with an appropriate center frequency and a reasonably arranged pipeline axial line survey. Different pipeline buried depths and pipe diameters correspond to different radar antenna frequencies. According to the pipeline buried depth and pipe diameter, a suitable radar antenna model is calculated in advance. The relationship between radar frequency and horizontal resolution and vertical resolution is shown in the following formula:
  • a radar antenna with a center frequency of 800MHz can be used for pipelines with a buried depth of less than 1 meter and a pipe diameter of 60mm or more.
  • the width of the image detected at the buried depth of each track will not be less than half of the depth, so the parallel interval between two adjacent running tracks is about half of the buried depth.
  • the basic processing of the original radar image data in the second step includes DC removal, zero-time correction, amplitude enhancement and band-pass filtering.
  • the Energy decay module in the reflexw gain is used to amplify the amplitude of the deep weak signal, and then the bandpassbutterworth module in the reflexw one-dimensional filter is used to select the frequency signal in a specific range, so as to allow the high frequency signal to pass through to the maximum and the low frequency The signal is attenuated and suppressed.
  • the step 3 fitting a three-dimensional data volume includes firstly establishing a three-dimensional coordinate system of the radar image according to the number of radar image data to be imported, the total number of radar data channels and the total time length using OpendTect software; secondly, the step two is processed Import the radar image data into the software; then confirm the layout spacing of the survey line and the radar data track spacing during radar acquisition, and enter the spacing-related parameters in the Manipulate module of the OpendTect software to determine the corresponding calculation function. Finally, the interpolation of the radar image data is completed through calculation commands, thereby constructing a three-dimensional spatial information map of the underground pipeline, as shown in Figure 7.
  • the step four multi-attribute analysis includes analysis of coherent attributes, instantaneous attributes, frequency attributes and amplitude attributes.
  • the three-dimensional spatial information map constructed in step 3 determine the profile and attribute analysis type of the attribute analysis, and use the Attribute module in the OpendTect analysis function to select the corresponding attribute type.
  • the coherent attribute can quantify the similarity of the radar waveform in the axial and vertical directions, so as to obtain the three-dimensional spatial information of the pipeline leakage, and obtain the preliminary suspected leakage points, as shown in Figure 8. ;
  • instantaneous attributes are used on the longitudinal profile.
  • the instantaneous attributes can highlight the weak changes in horizontal continuity, so as to accurately locate the leakage location, as shown in Figure 9; then use the frequency attributes on the transverse profile, and the frequency attributes can get a The spectrum with rich information helps to further verify the leakage location, as shown in Figure 10. Finally, the amplitude attribute is used on the horizontal section view.
  • the amplitude attribute can perform high-resolution imaging of the leakage characteristics to determine the pipeline leakage scale, such as Shown in Figure 11.
  • Another object of the present invention is to provide a ground penetrating radar image processing system based on the ground penetrating radar three-dimensional image attribute analysis method for water supply pipeline leakage detection.
  • the advantages and positive effects of the present invention are: the leakage detection method of the present invention is applied to pipelines, and three-dimensional spatial information of pipeline leakage can be obtained, and then the pipeline leakage location and scale of leakage can be located.
  • the present invention changes the traditional ground penetrating radar image analysis method in the pipeline leakage detection.
  • the coherent attributes extracted from the constructed three-dimensional image can quantify the similarity of the radar waveform in the axial and vertical directions, thereby obtaining the pipeline leakage
  • the extracted instantaneous attributes can highlight the weak changes in horizontal continuity, so as to accurately locate the leakage location;
  • the extracted frequency attributes can obtain a spectrum with rich information to help further verify the leakage location;
  • the extracted amplitude Property, high-resolution imaging of leakage characteristics can be performed to determine the scale of pipeline leakage.
  • the pipeline leakage detection method can dig out the complex and reliable information of the ground penetrating radar image, improve the imaging effect, and thereby judge the location and scale of the pipeline leakage more accurately and efficiently.
  • the pipeline leakage detection method of the present invention has a good effect on the acquisition of three-dimensional space information of pipeline leakage, the positioning of the pipeline leakage position, and the determination of the leakage scale.
  • the left image in FIG. 5 is an axial cross-sectional view of a pipeline obtained by using the prior art
  • the right image is a phase attribute image obtained by applying attribute analysis on the basis of the left image. Comparing the left and right images, it can be clearly seen that in the dotted box at the same position, the lines in the right image have obvious discontinuities, while the left image has no obvious features or abnormalities.
  • FIG. 1 is a flowchart of a method for detecting leakage of a water supply pipeline based on the analysis of the three-dimensional image attributes of a ground penetrating radar according to an embodiment of the present invention.
  • Figure 2 is a schematic diagram of radar axial acquisition provided by an embodiment of the present invention.
  • Fig. 3 is a schematic diagram of radar lateral acquisition provided by an embodiment of the present invention.
  • Fig. 4 is a schematic diagram of seepage simulation results provided by an embodiment of the present invention.
  • Fig. 5 is a schematic diagram of an experimental platform provided by an embodiment of the present invention.
  • Fig. 6 is a schematic diagram of radar acquisition provided by an embodiment of the present invention.
  • Fig. 7 is a cross-sectional view of a three-dimensional data volume provided by an embodiment of the present invention.
  • Figure 8 is a coherent attribute slice diagram provided by an embodiment of the present invention.
  • Fig. 9 is a cross-sectional view of the instantaneous attributes before and after comparison provided by an embodiment of the present invention.
  • FIG. 10 is a cross-sectional view of frequency attributes provided by an embodiment of the present invention.
  • Fig. 11 is an amplitude attribute slice diagram provided by an embodiment of the present invention.
  • the present invention enhances the recognition of the location and scale of the leakage and improves the imaging effect. It can effectively mine complex and reliable information in the data, and enhance the recognition and imaging effects of leakage.
  • the method for detecting leakage of water supply pipelines based on the analysis of the three-dimensional image attributes of the ground penetrating radar includes the following steps:
  • S101 Obtain the original image data of ground penetrating radar, including selecting a radar antenna with appropriate frequency and arranging survey lines with reasonable spacing;
  • S102 Perform basic processing on the obtained original image data, including DC removal, zero-time correction, amplitude enhancement and band-pass filtering, etc., to ensure that the data has a better signal-to-noise ratio and resolution at the target depth;
  • S103 Fit the processed image data into a three-dimensional data volume through interpolation to realize a variety of planar or three-dimensional attributes, and display it in axial section, vertical section, horizontal section, irregular section and isosurface, etc. the way;
  • S104 Using multi-attribute comprehensive analysis, including attribute information such as coherent attributes, instantaneous attributes, frequency attributes, and amplitude attributes, to accurately detect and identify pipeline leakage.
  • attribute information such as coherent attributes, instantaneous attributes, frequency attributes, and amplitude attributes
  • the acquisition of the original image data of the ground penetrating radar includes selecting a radar antenna with a proper center frequency and a reasonably arranged pipeline axial line survey. Different pipeline buried depths and pipe diameters correspond to different radar antenna frequencies. According to the pipeline buried depth and pipe diameter, a suitable radar antenna model is calculated in advance. The relationship between radar frequency and horizontal resolution and vertical resolution is shown in the following formula:
  • a radar antenna with a center frequency of 800MHz can be used for pipelines with a buried depth of less than 1 meter and a pipe diameter of 60mm or more.
  • the width of the image detected at the buried depth of each track will not be less than half of the depth, so the parallel interval between two adjacent running tracks is about half of the buried depth.
  • the basic processing of original image data includes DC removal, zero-time correction, amplitude enhancement, and band-pass filtering.
  • the Energy decay module in the reflexw gain is used to amplify the amplitude of the deep weak signal, and then the bandpassbutterworth module in the reflexw one-dimensional filter is used to select the frequency signal in a specific range, so as to allow the high frequency signal to pass through to the maximum and the low frequency The signal is attenuated and suppressed.
  • fitting a three-dimensional data volume includes firstly establishing a three-dimensional coordinate system of a radar image using OpendTect software according to the number of radar image data to be imported, the total number of radar data channels, and the total duration; 2. Import the processed radar image data into the software; then confirm the layout spacing of the survey line and the radar data track spacing during radar acquisition, and enter the spacing-related parameters in the Manipulate module of the OpendTect software to determine the corresponding calculation function. Finally, the interpolation of the radar image data is completed through calculation commands, thereby constructing a three-dimensional spatial information map of the underground pipeline, as shown in Figure 7.
  • the attributes include coherence attributes, instantaneous attributes, frequency attributes, and amplitude attributes.
  • the attributes include coherence attributes, instantaneous attributes, frequency attributes, and amplitude attributes.
  • determine the profile and attribute analysis type of the attribute analysis and use the Attribute module in the OpendTect analysis function to select the corresponding attribute type.
  • the coherent attribute can quantify the similarity of the radar waveform in the axial and vertical directions, so as to obtain the three-dimensional spatial information of the pipeline leakage, and obtain the preliminary suspected leakage points, as shown in Figure 8. ;
  • instantaneous attributes are used on the longitudinal profile.
  • the instantaneous attributes can highlight the weak changes in horizontal continuity, so as to accurately locate the leakage location, as shown in Figure 9; then use the frequency attributes on the transverse profile, and the frequency attributes can get a The spectrum with rich information helps to further verify the leakage location, as shown in Figure 10. Finally, the amplitude attribute is used on the horizontal section view.
  • the amplitude attribute can perform high-resolution imaging of the leakage characteristics to determine the pipeline leakage scale, such as Shown in Figure 11.
  • the method for detecting leakage of water supply pipelines based on the analysis of the three-dimensional image attributes of the ground penetrating radar includes the following steps:
  • Figure 5 is based on the seepage simulation. Two experimental platforms with the size of 1:1 with the seepage model have been established. The area of each platform is 3m ⁇ 3m. The center of the spherical leakage area is 11cm from the bottom of the pipe. The radius is 15cm.
  • Figure 6 shows the use of ground penetrating radar to complete data collection.
  • Fig. 7 shows the processed axial section through interpolation to fit a three-dimensional data volume with high density in space.
  • Figure 8 is the coherence attribute analysis
  • Figure 9 is the instantaneous attribute analysis
  • Figure 10 is the frequency attribute analysis
  • Figure 11 is the amplitude attribute analysis.

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Abstract

一种基于探地雷达三维图像属性分析的供水管道漏损检测方法，获取供水管道纵向扫描的探地雷达原始图像数据；对获取的原始图像数据进行降噪滤波处理；将处理后的图像数据通过插值拟合成一个三维数据体，提取多种平面或立体的图像属性，并以纵向剖面、横向剖面、水平切面、不规则切面以及等值面显示；利用多属性综合分析，对管道漏损位置、规模进行精确识别，通过多属性综合分析对管道漏损位置和规模进行精确定位。提取频率属性，得到一个包含丰富信息的频谱，进一步验证漏损位置；提取振幅属性，对漏损特征进行高分辨率成像，确定管道漏损规模。

Description

基于探地雷达三维图像属性分析的供水管道漏损检测方法 技术领域
本发明属于市政工程城市供水管道漏损的无损检测定位领域，尤其涉及一种基于探地雷达三维图像属性分析的供水管道漏损检测方法。
背景技术
目前，业内常用的现有技术是这样的：城市供水管道的管理已经成为社会备受关注的热点问题。由于我国的供水管道存在施工、管材和使用时间等方面的问题，常会发生因管道破损而漏水的问题。这一问题不仅导致了水资源的浪费，还带来了一定的安全隐患。为了减轻供水管道漏损产生的影响，需要利用管道漏损检测方法，确定漏损位置。分布式光纤法、相关仪法、示踪气体法、智能球法等需要道路开挖的有损检测方法需要封闭交通，工程量大、成本高，很难在城市里推广，实际应用中更需要采用无损检测方法。目前最常用的无损检测方法是音听法，但该方法也存在一定缺陷：一是对检测工程师听漏的经验要求高，一般需要十年以上的经验积累；二是对环境要求苛刻，听漏过程中不能有噪声干扰，一般只能晚上作业；三是探漏效率低，一般一条一公里以上的管线需要数天才能完成。音听法这些缺陷已经无法满足日益快速发展的城市供水管网建设，探地雷达作为一种无损检测方法因其高效、快速、环境耐受性等优势已开始受到关注。
探地雷达是以电磁波传播理论为基础，以介质电性(电导率、介电常数)差异为前提，利用高频脉冲电磁波的反射探测目标物体。因土体的介电常数为6～10，而水的介电常数为81，两者相差巨大，理论上漏损区域会在雷达图像上形成一个明显的绕射波，可以通过分析解释雷达图像上的绕射波来寻找管网漏损点。但是实际工程中因为地下介质的多样性和复杂性，图像的成像效果总是会受到不同程度的影响，出现目标物体信号微弱或被干扰等问题。雷达图像上 表示漏损的信息常常难以识别。现有雷达探漏技术存在的主要问题是：
(1)图像包含的信息单一，即图像均为波形图；
(2)图像显示的方式单一，即图像均为剖面图；
(3)图像的分析过程主要依赖于工程人员的经验，准确率不稳定。
因此，如何从探地雷达图像数据中获取漏损信息和优化探地雷达图像数据的成像效果，已成为探地雷达检测管道漏损的关键问题。然而，现有技术下图像包含信息和显示方式单一的问题，极大地阻碍了图像成像效果的改善，也因此导致了图像的分析过程往往依赖于个人的经验判断。解决以上问题，不仅能更加高效、准确地检测出管道是否发生漏损及漏损情况，更有利于探地雷达在管道漏损检测方面的推广。
发明内容
针对现有技术存在的问题，本发明提供了一种基于探地雷达三维图像属性分析的供水管道漏损检测方法。通过多属性综合分析对管道漏损位置和规模进行精确定位的方法。本发明的漏损检测方法应用于供水管道上，可以获取管道漏损的三维空间信息、定位漏损位置、确定漏损规模。本发明在管道漏损检测方面改变了传统的探地雷达二维图像分析方式，通过雷达沿管道轴向扫描和去直流、零时校正、振幅增强、带通滤波等图像处理拟合成管道周围的三维数据体。从构建的三维图像中提取的相干属性，可以量化雷达波形在轴向和垂向的相似性，从而获取管道漏损的三维空间信息；提取的瞬时属性，可以突出水平连续性的微弱变化，从而精确定位漏损位置；提取的频率属性，可以得到一个包含丰富信息的频谱，帮助进一步验证漏损位置；提取的振幅属性，可以对漏损特征进行高分辨率成像，从而确定管道漏损规模。
本发明是这样实现的，一种基于探地雷达三维图像属性分析的供水管道漏损检测方法，所述基于探地雷达三维图像属性分析的供水管道漏损检测方法包括：
步骤一，获取供水管道纵向扫描的探地雷达原始图像数据；
步骤二，对获取的原始图像数据进行降噪滤波处理；
步骤三，将处理后的图像数据通过插值拟合成一个三维数据体，提取多种平面或立体的图像属性，并以纵向剖面、横向剖面、水平切面、不规则切面以及等值面显示；
步骤四，利用多属性综合分析，对管道漏损位置、规模进行精确识别。
进一步，所述步骤一探地雷达原始图像数据的获取包括选用中心频率合适的雷达天线和布置合理的管道轴向测线。不同的管道埋深和管径对应不同的雷达天线频率，事先根据管道埋深和管径计算出适合的雷达天线型号，雷达频率与水平分辨率、垂直分辨率的关系见下面的公式所示：
Figure PCTCN2020092532-appb-000001
例如埋深1米以内、管径60mm以上的管道可以选用中心频率为800MHz的雷达天线。根据管口的位置确定管道的轴线位置，让天线沿着管道轴线从起点管口一直匀速行驶到终点管口，然后再回到起点管口。理论上，每条轨迹在埋深处探测到的图像宽度不会小于深度的一半，因此两条相邻运行轨迹的平行间隔约为埋深的一半。依此重复多条平行的运行轨迹，其中第一条在管道轴线正上方，然后平行第一条轨迹在左右两侧各等宽度对称运行其他多条轨迹，见图2所示，这种采集方式的效率远远大于传统垂直于管道的横向运行方式，见图3所示。
进一步，所述步骤二原始雷达图像数据的基本处理包括去直流、零时校正、振幅增强和带通滤波。首先对采集到的雷达原始图像信号，利用雷达通用处理 软件reflexw一维滤波中的Subtract-DC-Shift模块将信号的直流分量置零，达到去直流和去零点漂移的目的。而后利用reflexw静校正中的Move start time模块选择直达波的第一负峰值或正峰值作为零时校正点，将该位置的时间设置为0。最后利用reflexw增益中的Energy decay模块将深度的微弱信号进行振幅放大，再通过reflexw一维滤波中的bandpassbutterworth模块对特定范围内的频率信号进行选择，最大限度的让高频信号通过，并对低频信号进行衰减和抑制。
进一步，所述步骤三拟合一个三维数据体包括首先根据准备导入的雷达图像数据的数量、雷达数据总道数和总时长利用OpendTect软件建立一个雷达图像的三维坐标系；其次将步骤二处理过的雷达图像数据导入到该软件；然后确认雷达采集时测线的布置间距和雷达数据的道间距，在OpendTect软件的Manipulate模块中输入与间距相关的参数，从而确定相应的计算函数。最后，通过计算命令完成雷达图像数据的插值，从而构建出一个地下管道三维空间信息图，如图7所示。
进一步，所述步骤四多属性分析包括相干属性、瞬时属性、频率属性和振幅属性的分析。根据步骤三已构建的三维空间信息图，确定属性分析的剖面图和属性分析类型，并利用OpendTect分析功能中的Attribute模块选择相应的属性类型。首先在水平切面图上运用相干属性，相干属性可以量化雷达波形在轴向和垂向的相似性，从而获取管道漏损的三维空间信息，得到初判的疑似漏损点，如图8所示；其次在纵向剖面图上运用瞬时属性，瞬时属性可以突出水平连续性的微弱变化，从而精确定位漏损位置，如图9所示；然后在横向剖面图上运用频率属性，频率属性可以得到一个包含丰富信息的频谱，帮助进一步验证漏损位置，如图10所示；最后在水平切面图上运用振幅属性，振幅属性可以对漏损特征进行高分辨率成像，从而确定管道漏损规模，如图11所示。
本发明的另一目的在于提供一种基于探地雷达三维图像属性分析的供水管道漏损检测方法的探地雷达图像处理系统。
综上所述，本发明的优点及积极效果为：本发明的漏损检测方法应用于管道上，可以获取管道漏损的三维空间信息，进而定位管道漏损位置和漏损规模。 本发明在管道漏损检测方面改变了传统的探地雷达图像的分析方式，从构建的三维图像中提取的相干属性，可以量化雷达波形在轴向和垂向的相似性，从而获取管道漏损的三维空间信息；提取的瞬时属性，可以突出水平连续性的微弱变化，从而精确定位漏损位置；提取的频率属性，可以得到一个包含丰富信息的频谱，帮助进一步验证漏损位置；提取的振幅属性，可以对漏损特征进行高分辨率成像，从而确定管道漏损规模。本管道漏损检测方法能够挖掘探地雷达图像复杂、可靠的信息，改善成像效果，从而对管道漏损的位置和规模等信息的判断更加准确、高效。本发明的管道漏损检测方法对于管道漏损三维空间信息的获取、管道漏损位置的定位、漏损规模的确定具有较好的效果。例如，图5中左图为利用现有技术得到的一张管道轴向剖面图，右图为在左图基础上应用了属性分析得到的一张相位属性图。比较左右两图可以清楚地看出：在相同位置的虚线方框内，右图中的线条存在明显的不连续性，而左图并没有明显的特征或者异常。
附图说明
图1是本发明实施例提供的基于探地雷达三维图像属性分析的供水管道漏损检测方法流程图。
图2是本发明实施例提供的雷达轴向采集示意图。
图3是本发明实施例提供的雷达横向采集示意图。
图4是本发明实施例提供的渗流模拟结果示意图。
图5是本发明实施例提供的实验平台示意图。
图6是本发明实施例提供的雷达采集示意图。
图7是本发明实施例提供的三维数据体剖面图。
图8是本发明实施例提供的相干属性切片图。
图9是本发明实施例提供的瞬时属性前后对比剖面图。
图10是本发明实施例提供的频率属性剖面图。
图11是本发明实施例提供的振幅属性切片图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
本发明通过提取探地雷达图像的各种属性信息，增强对漏损位置和规模的识别，并改善成像效果。能有效挖掘数据中复杂、可靠的信息，增强对漏损的识别和成像效果。
下面结合附图对本发明的技术方案作详细的描述。
如图1所示，本发明实施例提供的基于探地雷达三维图像属性分析的供水管道漏损检测方法包括以下步骤：
S101：探地雷达原始图像数据的获取，包括选用频率合适的雷达天线，布置间距合理的测线；
S102：将得到的原始图像数据做基本处理，包括去直流、零时校正、振幅增强和带通滤波等，以保证数据在目标深度有着较好的信噪比和分辨率；
S103：将处理后的图像数据通过插值拟合成一个三维数据体，以实现多种平面或立体的属性，并以轴向剖面、垂向剖面、水平切面、不规则切面以及等值面等显示方式；
S104：利用多属性综合分析，包括相干属性、瞬时属性、频率属性和振幅属性等属性信息，以对管道漏损进行精准检测和识别。
在本发明的优选实施例中，探地雷达原始图像数据的获取包括选用中心频率合适的雷达天线和布置合理的管道轴向测线。不同的管道埋深和管径对应不同的雷达天线频率，事先根据管道埋深和管径计算出适合的雷达天线型号，雷达频率与水平分辨率、垂直分辨率的关系见下面的公式所示：
Figure PCTCN2020092532-appb-000002
例如埋深1米以内、管径60mm以上的管道可以选用中心频率为800MHz的雷达天线。根据管口的位置确定管道的轴线位置，让天线沿着管道轴线从起点管口一直匀速行驶到终点管口，然后再回到起点管口。理论上，每条轨迹在埋深处探测到的图像宽度不会小于深度的一半，因此两条相邻运行轨迹的平行间隔约为埋深的一半。依此重复多条平行的运行轨迹，其中第一条在管道轴线正上方，然后平行第一条轨迹在左右两侧各等宽度对称运行其他多条轨迹，见图2所示，这种采集方式的效率远远大于传统垂直于管道的横向运行方式，见图3所示。
在本发明的优选实施例中，原始图像数据的基本处理包括去直流、零时校正、振幅增强和带通滤波。首先对采集到的雷达原始图像信号，利用雷达通用处理软件reflexw一维滤波中的Subtract-DC-Shift模块将信号的直流分量置零，达到去直流和去零点漂移的目的。而后利用reflexw静校正中的Move start time模块选择直达波的第一负峰值或正峰值作为零时校正点，将该位置的时间设置为0。最后利用reflexw增益中的Energy decay模块将深度的微弱信号进行振幅放大，再通过reflexw一维滤波中的bandpassbutterworth模块对特定范围内的频率信号进行选择，最大限度的让高频信号通过，并对低频信号进行衰减和抑制。
在本发明的优选实施例中，拟合一个三维数据体包括首先根据准备导入的雷达图像数据的数量、雷达数据总道数和总时长利用OpendTect软件建立一个雷达图像的三维坐标系；其次将步骤二处理过的雷达图像数据导入到该软件； 然后确认雷达采集时测线的布置间距和雷达数据的道间距，在OpendTect软件的Manipulate模块中输入与间距相关的参数，从而确定相应的计算函数。最后，通过计算命令完成雷达图像数据的插值，从而构建出一个地下管道三维空间信息图，如图7所示。
在本发明的优选实施例中，属性包括相干属性、瞬时属性、频率属性和振幅属性。根据步骤三已构建的三维空间信息图，确定属性分析的剖面图和属性分析类型，并利用OpendTect分析功能中的Attribute模块选择相应的属性类型。首先在水平切面图上运用相干属性，相干属性可以量化雷达波形在轴向和垂向的相似性，从而获取管道漏损的三维空间信息，得到初判的疑似漏损点，如图8所示；其次在纵向剖面图上运用瞬时属性，瞬时属性可以突出水平连续性的微弱变化，从而精确定位漏损位置，如图9所示；然后在横向剖面图上运用频率属性，频率属性可以得到一个包含丰富信息的频谱，帮助进一步验证漏损位置，如图10所示；最后在水平切面图上运用振幅属性，振幅属性可以对漏损特征进行高分辨率成像，从而确定管道漏损规模，如图11所示。
下面结合附图对本发明的技术方案作进一步的描述。
如图4-图11所示，本发明实施例提供的基于探地雷达三维图像属性分析的供水管道漏损检测方法包括以下步骤：
(1)建立一个管道底部漏损的渗漏模型，包括：管道顶部埋深0.5m，管径75mm，管内充满水，周围介质的饱和体积含水量0.35，管道中间位置正下方设置一个漏水小孔，管道内壁设置一个不变的压力水头。图4是10小时的渗流模拟结果，其中直线和原点表示管道，虚线区域为渗流区域。
(2)图5是在渗流模拟的基础上，建立了2个与渗流模型1：1大小的实验平台，每个平台的面积为3m×3m，球形漏损区域的球心距管底11cm，半径为15cm。
(6)图6为利用探地雷达完成数据采集。
(7)在得到原始数据后，将所有剖面编辑为相同时窗和距离，并做基本处理，包括去直流，零时校正，振幅增强，带通滤波。
(8)图7是处理后的轴向剖面通过插值拟合成一个空间上具有高密度的三维数据体。
(9)利用多属性分析，完成对三维数据体的解释工作。图8是相干属性分析，图9是瞬时属性分析，图10是频率属性分析，图11是振幅属性分析。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (5)

  1. 一种基于探地雷达三维图像属性分析的供水管道漏损检测方法，其特征在于，
    所述基于探地雷达三维图像属性分析的供水管道漏损检测方法包括：
    步骤一，获取供水管道纵向扫描的探地雷达原始图像数据；
    步骤二，对获取的原始图像数据进行降噪滤波处理；
    步骤三，将处理后的图像数据通过插值拟合成一个三维数据体，提取多种平面或立体的图像属性，并以纵向剖面、横向剖面、水平切面、不规则切面以及等值面显示；
    步骤四，利用多属性综合分析，对管道漏损位置、规模进行精确识别；
    所述步骤一探地雷达原始图像数据的获取包括根据埋深和管径选用中心频率合适的雷达天线、布置合理的管道轴向测线；
    所述探地雷达原始图像数据的获取包括选用中心频率合适的雷达天线和布置合理的管道轴向测线；不同的管道埋深和管径对应不同的雷达天线频率，根据管道埋深和管径计算出适合的雷达天线型号，雷达频率与水平分辨率、垂直分辨率的关系见下面的公式所示：
    反射系数
    Figure PCTCN2020092532-appb-100001
    水平分辨率
    Figure PCTCN2020092532-appb-100002
    垂直分辨率
    Figure PCTCN2020092532-appb-100003
    波速
    Figure PCTCN2020092532-appb-100004
    时间
    Figure PCTCN2020092532-appb-100005
    根据管口的位置确定管道的轴线位置，让天线沿着管道轴线从起点管口一直匀速行驶到终点管口，然后再回到起点管口。
  2. 如权利要求1所述的基于探地雷达三维图像属性分析的供水管道漏损检测方法，其特征在于，所述步骤二原始图像数据的降噪滤波处理包括去直流、 零时校正、振幅增强和带通滤波；
    首先对采集到的雷达原始图像信号，利用雷达通用处理软件reflexw一维滤波中的Subtract-DC-Shift模块将信号的直流分量置零，达到去直流和去零点漂移的目的；利用reflexw静校正中的Move start time模块选择直达波的第一负峰值或正峰值作为零时校正点，将该位置的时间设置为0；最后利用reflexw增益中的Energy decay模块将深度的微弱信号进行振幅放大，再通过reflexw一维滤波中的bandpassbutterworth模块对特定范围内的频率信号进行选择，最大限度的让高频信号通过，并对低频信号进行衰减和抑制。
  3. 如权利要求1所述的基于探地雷达三维图像属性分析的供水管道漏损检测方法，其特征在于，所述步骤三拟合一个三维数据体包括首先根据准备导入的雷达图像数据的数量、雷达数据总道数和总时长利用OpendTect软件建立一个雷达图像的三维坐标系；其次将步骤二处理过的雷达图像数据导入到该软件；然后确认雷达采集时测线的布置间距和雷达数据的道间距，在OpendTect软件的Manipulate模块中输入与间距相关的参数，确定相应的计算函数；最后，通过计算命令完成雷达图像数据的插值，构建出一个地下管道三维空间信息图。
  4. 如权利要求1所述的基于探地雷达三维图像属性分析的供水管道漏损检测方法，其特征在于，所述步骤四多属性分析包括相干属性、瞬时属性、频率属性和振幅属性的分析；
    根据步骤三已构建的三维空间信息图，确定属性分析的剖面图和属性分析类型，并利用OpendTect分析功能中的Attribute模块选择相应的属性类型；首先在水平切面图上运用相干属性，相干属性量化雷达波形在轴向和垂向的相似性，获取管道漏损的三维空间信息，得到初判的疑似漏损点；其次在纵向剖面图上运用瞬时属性，瞬时属性突出水平连续性的微弱变化，然后在横向剖面图上运用频率属性，频率属性得到一个包含丰富信息的频谱。
  5. 一种应用权利要求1所述基于探地雷达三维图像属性分析的供水管道漏损检测方法的探地雷达图像处理系统。
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