GPR detection imaging method and device for frozen wall development condition and processing equipment

Abstract

The invention provides a GPR detection imaging method and device for the development condition of a frozen wall, and processing equipment, and is used for additionally considering the correlation between signals on the basis of signals acquired based on GPR detection, and realizing secondary imaging through a cross-correlation back projection algorithm. According to the method, interference caused by complexity conditions of electromagnetic interference detection environments such as shield segments is avoided, so that the resolution ratio and the imaging precision can be effectively improved, the development condition of the frozen wall can be recognized more clearly, and the high-quality data use requirement of rock mass engineering related to AGF is met.

Description

Technical Field

[0001] The present application relates to the field of geology, and in particular to a GPR detection imaging method, device and processing equipment for frozen wall development. Background Art

[0002] Artificial Ground Freezing method (AGF) refers to the use of artificial refrigeration technology to transform natural rock and soil into frozen soil under the action of low temperature to form a frozen wall, so as to temporarily isolate groundwater. It is an important construction method for crossing complex strata. Due to its advantages such as good waterproof effect, strong adaptability and green environmental protection, it has been widely used in different underground projects.

[0003] In actual geotechnical engineering, we often encounter unfavorable geological conditions such as excessive groundwater velocity, water-rich cemented rock layers, gravel layers, local underground cavities, and excessively high soil salt content. Under these conditions, the frozen wall often fails to close on time, the overall strength of the frozen wall does not meet the standard, or there are cavities in the wall, which not only causes significant economic losses, but also greatly threatens construction safety. Therefore, the development of the frozen wall directly determines the success or failure of the freezing project, and early prediction, process detection, and effect evaluation of abnormal frozen wall development are particularly important.

[0004] In traditional schemes, the development of frozen walls is usually judged based on temperature measurement data, and the expansion and thickness of the frozen walls are roughly inferred through experience. However, due to the limited location and number of temperature measurement points, it is impossible to accurately describe the overall development of the frozen area, which can easily lead to misjudgment and omission of some unfrozen areas.

[0005] The principle of geological radar (GPR) is to use the reflection and transmission of ultra-high frequency electromagnetic waves (1MHz~5GHz) to determine the distribution inside the medium. Dielectric constant and conductivity are the two most critical factors affecting the detection effect of GPR. The greater the difference in electromagnetic parameters between the media, the more obvious the reflection and deflection of electromagnetic waves, and the easier it is to detect and identify the two media. GPR plays an increasingly important role in near-surface exploration. The physical and electrical properties of natural frozen soil and unfrozen soil are extremely different. Therefore, as long as GPR detection is carried out on frozen rock and soil at a reasonable location and the data is analyzed and compared, the distribution area of ​​frozen wall anomalies and the development of frozen walls can be found.

[0006] However, the inventors of the present application have discovered that there are still problems in the existing scheme of identifying the development of frozen walls through GPR detection. Specifically, due to the complexity of the detection environment, such as the electromagnetic interference of shield segments, the frozen wall is usually adjacent to the segment area, and the electromagnetic waves need to pass through the segment layer to reach the target soil layer, which will affect the accurate judgment of the detection image results, resulting in limitations in the recognition accuracy and efficiency of the development of the frozen wall. Summary of the invention

[0007] The present application provides a GPR detection and imaging method, device and processing equipment for the development of frozen walls. The method is used to additionally consider the correlation between each signal based on the signals collected by GPR detection, and realize secondary imaging through a cross-correlation back-projection algorithm to avoid interference caused by complex conditions of electromagnetic interference detection environment such as shield segments. This can effectively improve the resolution and imaging accuracy, help to more clearly complete the identification of the development of frozen walls, and meet the high-quality data usage needs of rock engineering involving AGF.

[0008] In a first aspect, the present application provides a GPR detection imaging method for frozen wall development, the method comprising:

[0009] Acquire a first signal obtained by GPR detection processing of the frozen rock and soil body to be detected, wherein the frozen rock and soil body to be detected forms a corresponding frozen wall through AGF processing;

[0010] Performing data preprocessing on the first signal to obtain a second signal to preliminarily enhance signal quality;

[0011] On the basis of the second signal, in the process of imaging processing by using the back projection algorithm, the correlation between the signals of each channel is considered, and the secondary imaging is realized by the cross-correlation back projection algorithm to obtain the reconstructed image;

[0012] The reconstructed image is subjected to secondary signal processing to obtain a third signal to further enhance the signal quality.

[0013] In a second aspect, the present application provides a GPR detection imaging device for frozen wall development, the device comprising:

[0014] An acquisition unit, used for acquiring a first signal obtained by performing GPR detection processing on a frozen rock and soil body to be detected, wherein the frozen rock and soil body to be detected forms a corresponding frozen wall through AGF processing;

[0015] A preprocessing unit, used for performing data preprocessing on the first signal to obtain a second signal, so as to preliminarily enhance the signal quality;

[0016] An imaging unit is used to consider the correlation between the signals of each channel in the process of imaging processing by using the back projection algorithm based on the second signal, and to realize secondary imaging by using the cross-correlation back projection algorithm to obtain a reconstructed image;

[0017] The secondary processing unit is used to perform secondary signal processing on the reconstructed image to obtain a third signal to further enhance the signal quality.

[0018] In a third aspect, the present application provides a processing device, including a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method provided in the first aspect of the present application or any possible implementation method of the first aspect of the present application is executed.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which stores multiple instructions, and the instructions are suitable for a processor to load to execute the method provided by the first aspect of the present application or any possible implementation of the first aspect of the present application.

[0020] From the above content, it can be concluded that the present application has the following beneficial effects:

[0021] For the GPR detection and imaging target of frozen wall development, this application, based on the signals collected by GPR detection, additionally considers the correlation between each signal, and realizes secondary imaging through the cross-correlation back-projection algorithm to avoid the interference caused by the complex conditions of electromagnetic interference detection environment such as shield segments. This can effectively improve the resolution and imaging accuracy, help to more clearly complete the identification of frozen wall development, and meet the high-quality data usage needs of rock engineering involving AGF. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of a process of the GPR detection imaging method for frozen wall development in this application;

[0024] Figure 2 A schematic diagram of a scenario for moving from a frozen area to an unfrozen area for GPR detection in this application;

[0025] Figure 3 A schematic diagram of the structure of a GPR detection imaging device for frozen wall development in this application;

[0026] Figure 4 A schematic diagram of the structure of the processing equipment of this application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0028] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0029] The division of modules in this application is a logical division. There may be other division methods when it is implemented in actual applications. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. In addition, the modules or submodules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0030] Before introducing the GPR detection imaging method for frozen wall development provided by the present application, the background content involved in the present application is first introduced.

[0031] The GPR detection and imaging method, device and computer-readable storage medium for frozen wall development provided in the present application can be applied to processing equipment to additionally consider the correlation between each signal based on the signal collected by GPR detection, and realize secondary imaging through a cross-correlation back-projection algorithm to avoid interference caused by complex conditions of electromagnetic interference detection environment such as shield segments. This can effectively improve the resolution and imaging accuracy, help to more clearly complete the identification of frozen wall development, and meet the high-quality data usage requirements of rock engineering involving AGF.

[0032] The GPR detection imaging method for frozen wall development mentioned in the present application can be implemented by a GPR detection imaging device for frozen wall development, or a server, physical host or user equipment (UE) and other different types of processing equipment that integrates the GPR detection imaging device for frozen wall development. Among them, the GPR detection imaging device for frozen wall development can be implemented in hardware or software, and the UE can be a terminal device such as a smart phone, tablet computer, laptop computer, desktop computer or personal digital assistant (PDA), and the processing device can be set in the form of a device cluster.

[0033] It is understandable that, in actual situations, considering that the present application is usually data processing based on the existing data obtained by GPR detection and processing, the processing equipment that executes the GPR detection and imaging method for frozen wall development of the present application, or the processing equipment equipped with the corresponding application service of the GPR detection and imaging method for frozen wall development of the present application, usually only needs to meet the required data processing capabilities. If it involves GPR detection data acquisition and processing, or the output / display of image results, it is obvious that the specific equipment structure and equipment deployment form of the processing equipment can be further adaptively adjusted according to the actual situation.

[0034] As an example, the processing equipment of this application under the equipment cluster solution may specifically include GPR equipment responsible for data collection on site, central processing equipment responsible for data processing in the background, and display equipment responsible for image display on the user side, corresponding to the three major aspects that may be involved when applying the solution of this application, thus having a relatively complete solution deployment feature.

[0035] Next, the GPR detection imaging method for frozen wall development provided by the present application is introduced.

[0036] First, see Figure 1 , Figure 1A schematic flow chart of a GPR detection imaging method for frozen wall development in the present application is shown. The GPR detection imaging method for frozen wall development provided in the present application may specifically include the following steps S101 to S104:

[0037] Step S101, obtaining a first signal obtained by performing GPR detection processing on a frozen rock and soil body to be detected, wherein the frozen rock and soil body to be detected is processed by AGF to form a corresponding frozen wall;

[0038] It is understandable that in engineering work, the application of AGF (artificial ground freezing) may be involved, which uses artificial refrigeration technology to transform natural rock and soil into frozen soil under the action of low temperature to form a frozen wall, so as to temporarily isolate groundwater. In this regard, there is a corresponding demand for frozen wall monitoring to ensure that the project can be carried out under the expected conditions.

[0039] In this regard, as in the prior art, after AGF processing, GPR (geological radar) detection processing can be carried out on the frozen rock and soil to be detected on site to obtain the original signal for subsequent data processing. For the convenience of explanation, the original signal obtained here is recorded as the first signal, and subsequent signals will also be distinguished by the second signal and the third signal.

[0040] It should be noted that the acquisition processing of the first signal here can be either real-time signal acquisition processing, that is, involving real-time GPR detection processing, or it can be extraction processing of ready-made signals, that is, directly extracting the detection results of the completed GPR detection processing, which can be adjusted according to actual needs.

[0041] As an exemplary embodiment here, the GPR detection processing involved in this application may specifically include the following processing contents:

[0042] After radar selection, survey line layout and detection environment optimization, the specific detection work begins. The GPR host remains close to the surface of the segment and moves forward in a straight line at a uniform speed along the survey line. The speed is no higher than 5cm / s and remains consistent. The distance moved along the survey line is consistent each time and the error does not exceed 5cm. The detection time node starts before freezing and is guaranteed to be detected once every 6 hours during the active freezing period, and each survey line is detected 3 times each time.

[0043] Among them, there are:

[0044] (1) For radar selection, the target detection depth can be determined based on the thickness and strength of the frozen wall design in the freezing project, combined with the design size of the pipe segment, so as to determine the center frequency of the GPR equipment (that is, geological radar). Usually, 400 to 1200 MHz can be selected to take into account both the target depth and resolution of detection.

[0045] (2) For the survey line layout, the distribution and length of the survey lines can be reasonably planned and arranged in combination with the location and estimated thickness of the frozen wall to ensure that the subsequent detection data can fully reflect the development of the frozen area.

[0046] (3) Regarding the optimization of the detection environment, specifically, in order to improve the imaging quality and prevent misjudgment or missed detection results, on the one hand, due to the shielding property of metal conductors on electromagnetic waves, it is necessary to reduce their electromagnetic interference as much as possible. The construction environment of the freezing site can be cleaned up, such as temporarily removing unnecessary scaffolding and steel pipes near the survey line to ensure that there are no large loading equipment around when the detection work is carried out. On the other hand, in order to make the detection results as concise and clear as possible and avoid the appearance of complex multi-layer media, when conducting detection work, the pipe segment insulation layer can be temporarily removed while ensuring that the overall freezing effect is not affected, and then rearranged after the detection work is completed.

[0047] (4) For specific detection work, when performing detection work, the GPR host (transmitting / receiving antenna) can be kept close to the surface of the pipe segment and move forward in a straight line at a uniform speed along the direction of the survey line. The speed shall not exceed 5 cm / s and shall always remain consistent. The distance moved along the survey line shall be consistent each time and the error shall not exceed 5 cm. The detection time node starts before freezing, and during the active freezing period, detection shall be guaranteed once every 6 hours, and each survey line shall be detected 3 times each time to ensure the reliability and stability of the data.

[0048] Regarding the specific detection scheme here, it is worth noting that in conventional GPR detection processing, when detecting the interface between different media, the interface between adjacent media is usually parallel to the survey line layout direction. In the freezing project, the target medium changes along the detection direction, which has an adverse effect on the resolution and accuracy of the detection. In contrast, the specific detection scheme given in this application can effectively avoid this problem and ensure the subsequent high level of imaging accuracy and resolution, so it has better practical value.

[0049] Step S102, performing data preprocessing on the first signal to obtain a second signal to preliminarily enhance signal quality;

[0050] After acquiring the first signal, the present application does not directly carry out the corresponding imaging processing, but also involves the operation of data preprocessing to enhance the signal quality. This setting helps to improve the processing efficiency and processing accuracy of subsequent imaging processing.

[0051] It is understandable that data preprocessing is a common part of data analysis work. The specific preprocessing operations involved can usually adopt existing solutions, or further optimize and improve the existing solutions, or adopt self-developed novel solutions. These are all allowed in actual situations.

[0052] In this way, after completing the data preprocessing and obtaining the second signal, specific imaging processing can be carried out.

[0053] As an exemplary embodiment here, the data preprocessing involved in the present application can specifically include the removal of data points with abnormal noise, the removal of data points with lost content, time zero point correction, background noise removal and gain processing.

[0054] Specifically, there are:

[0055] (1) For the removal of data points with abnormal noise, obviously, its purpose is to remove / filter out data points with abnormal noise, i.e., abnormal noise points, which is one of the more traditional data preprocessing operations;

[0056] (2) For the removal of data points with content loss, obviously, its purpose is to remove / filter out data points with content loss, i.e., content loss points, which is one of the more traditional data preprocessing operations;

[0057] (3) Time zero correction processing, which takes into account that the echo signal may not be on the correct time axis due to the relative position of the transmitting and receiving antennas, so the time zero point / time axis of the data can be corrected;

[0058] (4) Background noise removal, which takes into account the noise of the antenna system itself that may exist in the data, so the background noise can be eliminated by background averaging or filtering;

[0059] (5) Gain processing. Considering that the signal will attenuate when penetrating the medium, increasing the gain can improve the visibility of the later reflected signal. Specifically, time-varying gain or exponential gain can be used to enhance the signal.

[0060] In this way, through the above data preprocessing operations, the differences between frozen soil and unfrozen soil can be further highlighted in specific applications, which helps to improve the subsequent imaging quality.

[0061] Step S103, based on the second signal, in the process of imaging processing using the back projection algorithm, the correlation between the signals of each channel is considered, and secondary imaging is achieved by using the cross-correlation back projection algorithm to obtain a reconstructed image;

[0062] In the imaging process, reverse projection processing is involved. It is understandable that reverse projection processing itself is a processing link also involved in the prior art. However, the inventors of the present application found that the reverse projection processing or reverse projection algorithm adopted by the existing scheme only considers the combination (accumulated result) of the response amplitude (vector form) of each channel as the final imaging result, but does not consider the inherent correlation in the vector, so there are limitations in imaging accuracy and resolution.

[0063] In this case, the imaging processing involved in the present application, in the process of performing imaging processing through the reverse projection algorithm, additionally considers / pays attention to the correlation between the signals of each channel, and calculates the combined results of the response amplitudes through a specially designed cross-correlation back-projection algorithm as a new imaging result output to obtain improved imaging accuracy and resolution.

[0064] The output data of the imaging processing here is itself also a signal and can be recorded as a reconstructed image.

[0065] Next, we can combine the GPR detection process to gain a deeper understanding of the imaging process here.

[0066] refer to Figure 2 A schematic diagram of a scenario of moving from a frozen area to an unfrozen area for GPR detection in the present application is shown. During the detection process, the following contents may be involved:

[0067] The designated detection area is divided into m×n pixels, each pixel has a width of dx and a height of dz, thus forming a plane model with the frozen area on the left and the unfrozen area on the right. Starting from x=0, the radar moves forward, continuously emitting and receiving electromagnetic waves.

[0068] Corresponding to the limited working range of the GPR main lobe, the detection scanning process can be divided into four different stages:

[0069] 1) Phase I: Detection of the frozen area below, characterized by a single medium, stable response amplitude (RA), and no deflection;

[0070] 2) Stage II: At the boundary where the main lobe range intersects with the unfrozen area, the response amplitude is stratified, resulting in deflection;

[0071] 3) Phase III: The main lobe signal contains both frozen and unfrozen areas, indicating that the scanning coverage gradually transitions to the unfrozen area;

[0072] 4) Phase IV: Detect the unfrozen area below and complete the comprehensive scanning process.

[0073] GPR has three scanning modes, corresponding to which three types of data can be obtained, namely A-scan data (single-channel waveform data), B-scan data (two-dimensional profile data) and C-scan data (three-dimensional data).

[0074] As an exemplary embodiment, the back projection algorithm involved above may specifically include the following contents:

[0075] Assume that the designated detection area is divided into m×n pixels, and the two-way travel time (TWT) of pixel A in the p-th channel signal of the A-scan data is τ A,P It can be expressed as follows:

[0076]

[0077] Among them, ε1 is the relative dielectric constant of the frozen soil area, dimensionless, dx is the pixel width, dz is the pixel height, c is the propagation speed of light in vacuum, i A is the horizontal coordinate of pixel A, j A is the ordinate of pixel A,

[0078] In this case, the response amplitude RA corresponding to each pixel A,P It can be expressed as follows:

[0079] RA A，p =s p (t=τ A，p ),

[0080] Among them, S p The reflected electromagnetic wave recorded by the GPR moving to the pth channel signal,

[0081] Then, considering that the transmitted radar signal has a main lobe characteristic, if a certain pixel (e.g., pixel B) is outside the main lobe of the A-scan, the value of the response amplitude may appear to be zero. In general, the response amplitude of a certain pixel in phase I can be expressed as follows:

[0082]

[0083] Where θ is the maximum coverage distance of the radar main lobe signal,

[0084] When the GPR enters phases II and III, the signal is refracted due to the change of the medium along the measurement line. For example, for pixel D, the two-way propagation time of pixel D in A-scanα can be expressed as follows:

[0085]

[0086] Where ε2 is the relative dielectric constant of the unfrozen soil area, dimensionless,

[0087] The electromagnetic wave is emitted from the αth synthetic aperture position and passes through the refraction point R(i R , j R ) arrives at point D(i D , j D ), and then reaches the GPR antenna for reception. R , j R ) can be determined by an iterative method. When the electromagnetic wave enters the unfrozen area, the response amplitude of the pixel can be described as follows:

[0088]

[0089] GPR measurement generates n sets of A-scan data, such as Figure 2 As shown in Figure 1, the response of each imaging point in the signal is distributed in the n sets of A-scan data generated by this GPR measurement. For example, for a given point A, that is, corresponding to pixel A, at least n two-way propagation times must be calculated. These TWT data are used to index and retrieve the information corresponding to point A in the corresponding A-scan data, thereby generating an n×1 vector representing point A, that is, [RA A,1 ,RA A,2 ,…RA A,n ],

[0090] In this case, the standard back-projection algorithm has n groups of response amplitudes corresponding to pixel A (n here corresponds to n in the previous m×n pixels, that is, the same specific value), and the vectors are combined into an imaging output result, as shown in the following formula:

[0091]

[0092] However, the inventors of the present application have found that the existing back projection method (BP) only combines these n vectors to obtain the final imaging result, but does not consider the correlation between the various A-scan signals, which limits the imaging accuracy and resolution.

[0093] In this regard, in order to emphasize the intrinsic correlation between the acquired vectors, the present application improves the back-projection algorithm and implements it by constructing a correlation matrix V.

[0094] Specifically, as an exemplary embodiment here, the correlation between the signals (A-scan signals) can be specifically represented by a correlation matrix V constructed by the following formula:

[0095]

[0096] In this way, through the cross-correlation operation, the amount of calculation is increased from n to n×(n-1) / 2, and the correlation superposition process can be expressed as follows:

[0097]

[0098] Among them, E A CBP is the sum of the response amplitudes calculated by the cross-correlation back-projection algorithm, i and j are the row and column numbers of the current element in the correlation matrix v, respectively.

[0099] It can be seen that the present application performs cross-correlation back projection based on the designed correlation matrix V, and obtains E considering the correlation between each signal. A CBP For pixel A, a higher-precision response amplitude calculation that is more in line with the deep-level situation is achieved, which can, on the whole, promote a balance between imaging accuracy and resolution.

[0100] Specifically, the imaging processing involved in this application, or the improved back projection algorithm involved, can clearly image, suppress most of the clutter interference, avoid the interference caused by the complex conditions of the electromagnetic interference detection environment such as shield segments, and effectively improve the imaging accuracy and resolution. Secondly, for the unconventional stratified medium model of the vertical freezing front, it can also better image the position of the frozen soil-unfrozen soil boundary and the development morphology of the frozen wall, improve the readability of the detection results, and more intuitively highlight the stratified areas and abnormal areas.

[0101] Step S104, performing secondary signal processing on the reconstructed image to obtain a third signal to further enhance signal quality.

[0102] It can be understood that after completing the imaging processing of the second signal, further data enhancement can be continued on the reconstructed image. It can be understood that the data enhancement processing here is recorded as secondary processing, and the specific strategies / rules involved in the specific data enhancement operation are different from the previous data preprocessing. It is necessary to consider not only the data characteristics of the input data as the reconstructed image obtained after the previous imaging processing, but also the input requirements involved in the subsequent data analysis work, rather than staying at the level of broadly improving data quality as conventionally understood.

[0103] Further, as an exemplary embodiment here, the signal secondary processing involved in the present application may specifically include filtering processing and data enhancement processing;

[0104] Filtering processing includes high-pass filtering, low-pass filtering, band-pass filtering and direct wave removal;

[0105] Data enhancement processing includes frequency domain conversion, elimination of random noise through convolution operations, and comparison of data at different time points.

[0106] In the filtering process, specifically, there are:

[0107] (1) For high-pass filtering and low-pass filtering, the appropriate cutoff frequency is pre-selected according to the characteristics of the data to remove high-frequency noise and low-frequency drift in the data;

[0108] (2) For bandpass filtering, it is to retain the effective signal within a specific frequency band and further reduce the noise;

[0109] (3) As for removing direct waves, it is considered that direct waves may mask important reflected signals, especially in near-surface detection. Therefore, differential filtering and other methods can be used to remove them.

[0110] It can be understood that the above-mentioned filtering processing means is a relatively common filtering method in signal filtering processing, which is integrated with the data enhancement means to achieve the effect of effectively and better realizing the secondary enhancement of the signal.

[0111] In the data enhancement process, specifically, there are:

[0112] (1) Frequency domain conversion is aimed at frequency analysis. Through methods such as Fourier transform, the signal is converted to the frequency domain to enhance the signal characteristics and facilitate signal analysis.

[0113] (2) For eliminating random noise by convolution operation, it is to further eliminate the random noise in the signal by convolution operation;

[0114] (3) For the comparison of data at different time points, it can also be referred to as differential processing. For the evaluation of freezing effect, the time difference method can be used to compare the data at different time points, so as to highlight the changes in the frozen area more conveniently and intuitively.

[0115] In general, it can be seen from the above scheme that for the GPR detection and imaging target of frozen wall development, this application, based on the signals collected by GPR detection, additionally considers the correlation between each signal, and realizes secondary imaging through the cross-correlation back-projection algorithm to avoid the interference caused by the complex conditions of electromagnetic interference detection environment such as shield segments. This can effectively improve the resolution and imaging accuracy, help to more clearly complete the identification of frozen wall development, and meet the high-quality data usage needs of rock engineering involving AGF.

[0116] After completing the imaging processing and secondary signal processing, it is understandable that we can start with the specific data application link, the most important of which is obviously the identification, analysis and processing of the development of the frozen wall.

[0117] In this regard, as an exemplary embodiment, after performing secondary signal processing on the reconstructed image in step S104 to obtain a third signal to further enhance the signal quality, the method of the present application may further include:

[0118] Based on the third signal, further data analysis and processing are carried out;

[0119] The data analysis and processing includes reflection signal analysis, frozen wall development identification and differential imaging, specifically:

[0120] (1) Reflection signal analysis: Based on the signal characteristics including intensity and phase changes, the changes in the medium before and after freezing are analyzed. Strong reflection areas correspond to frozen areas or abnormal structures.

[0121] (2) The identification of frozen wall development is based on the relationship between reflection time and velocity, and the frozen wall thickness and abnormal frozen wall development areas are estimated. The detection imaging results are verified by combining the temperature measurement and temperature field inversion method.

[0122] (3) Differential imaging specifically performs background subtraction on the radar data before and after freezing to eliminate the unchanged background signal.

[0123] It can be understood that in the embodiment here, in addition to the identification of the development of the frozen wall based on the high-precision third signal obtained previously, it may also involve the identification of frozen areas / abnormal structures, and further background removal based on the frozen areas. In this way, in terms of details, it provides specific practical solutions from three aspects for the data application that may be involved in this application. It can provide high-quality data support for different types of underground engineering projects such as mines, deep mountains, emergency rescue or municipal subways, which will help to advance underground engineering projects more safely, stably and efficiently, and therefore has better practical value.

[0124] Among them, it is necessary to understand that for the three aspects of data analysis operations here, in actual applications, you can use the existing solutions, or make further optimizations and improvements on the basis of the existing solutions, or you can use novel self-developed solutions. These are all possible and can be configured according to the actual user needs.

[0125] The above is an introduction to the GPR detection and imaging method for frozen wall development provided by the present application. In order to better implement the GPR detection and imaging method for frozen wall development provided by the present application, the present application also provides a GPR detection and imaging device for frozen wall development from the perspective of functional modules.

[0126] See also Figure 3 , Figure 3 This is a structural schematic diagram of a GPR detection imaging device for frozen wall development in this application. In this application, the GPR detection imaging device 300 for frozen wall development may specifically include the following structure:

[0127] An acquisition unit 301 is used to acquire a first signal obtained by performing GPR detection processing on a frozen rock and soil body to be detected, wherein the frozen rock and soil body to be detected forms a corresponding frozen wall through AGF processing;

[0128] The preprocessing unit 302 is used to perform data preprocessing on the first signal to obtain a second signal to preliminarily enhance the signal quality;

[0129] An imaging unit 303 is used to perform imaging processing using a back projection algorithm based on the second signal, taking into account the correlation between the signals, and to achieve secondary imaging using a cross-correlation back projection algorithm to obtain a reconstructed image;

[0130] The secondary processing unit 304 is used to perform secondary signal processing on the reconstructed image to obtain a third signal to further enhance signal quality.

[0131] As an exemplary embodiment, the back-projection algorithm includes the following:

[0132] Assume that the designated detection area is divided into m×n pixels, and the round-trip propagation time τ of pixel A in the p-th channel signal of the A-scan data is A,P It is expressed as follows:

[0133]

[0134] Among them, ε1 is the relative dielectric constant of the frozen soil area, dx is the pixel width, dz is the pixel height, c is the propagation speed of light in vacuum, i A is the horizontal coordinate of pixel A, j A is the ordinate of pixel A,

[0135] The response amplitude RA corresponding to each pixel A,P It is expressed as follows:

[0136] RA A，p =s p (t=τ A，p ),

[0137] Among them, S p The reflected electromagnetic wave recorded by the GPR moving to the pth channel signal,

[0138] The response of each imaging point in the signal is distributed in n groups of A-scan data generated by GPR measurement. Corresponding to pixel A, there are n groups of response amplitudes, and the vector combination is an imaging output result, which is expressed as follows:

[0139]

[0140] As another exemplary embodiment, the correlation between the signals of each channel is represented by a correlation matrix V constructed by the following formula:

[0141]

[0142] The relevant superposition process is expressed as follows:

[0143]

[0144] Among them, E A CBP is the sum of the response amplitudes calculated by the cross-correlation back-projection algorithm, i and j are the row and column numbers of the current element in the correlation matrix v, respectively.

[0145] As another exemplary embodiment, the GPR detection process includes the following processing contents:

[0146] After radar selection, survey line layout and detection environment optimization, the specific detection work begins. The GPR host remains close to the surface of the segment and moves forward in a straight line at a uniform speed along the survey line. The speed is no higher than 5cm / s and remains consistent. The distance moved along the survey line is consistent each time and the error does not exceed 5cm. The detection time node starts before freezing and is guaranteed to be detected once every 6 hours during the active freezing period, and each survey line is detected 3 times each time.

[0147] As another exemplary embodiment, data preprocessing includes removal processing of data points with abnormal noise, removal processing of data points with content loss, time zero point correction processing, background noise removal processing and gain processing.

[0148] As another exemplary embodiment, the signal secondary processing includes filtering processing and data enhancement processing;

[0149] Filtering processing includes high-pass filtering, low-pass filtering, band-pass filtering and direct wave removal;

[0150] Data enhancement processing includes frequency domain conversion, elimination of random noise through convolution operations, and comparison of data at different time points.

[0151] As another exemplary embodiment, the apparatus further includes an application unit 305, configured to:

[0152] Based on the third signal, further data analysis and processing are carried out;

[0153] Among them, data analysis and processing include reflection signal analysis, frozen wall development identification and differential imaging;

[0154] Reflection signal analysis specifically analyzes changes in the medium before and after freezing based on signal characteristics including intensity and phase changes. Strong reflection areas correspond to frozen areas or abnormal structures.

[0155] The identification of frozen wall development is based on the relationship between reflection time and velocity, and the frozen wall thickness and abnormal frozen wall development areas are estimated. The detection imaging results are verified by combining the temperature measurement and inversion temperature field method.

[0156] Differential imaging specifically performs background subtraction on the radar data before and after freezing to eliminate the unchanged background signal.

[0157] This application also provides a processing device from the perspective of hardware structure, see Figure 4 , Figure 4 401, a computer program stored in the memory 402, and an input / output device 403. The processor 401 is used to execute the computer program stored in the memory 402. Figure 1The steps of the GPR detection imaging method for frozen wall development in the corresponding embodiment; or, the processor 401 is used to execute the computer program stored in the memory 402 to implement the following Figure 3 Corresponding to the functions of each unit in the embodiment, the memory 402 is used to store the processor 401 executing the above Figure 1 The computer program required for the GPR detection imaging method for frozen wall development in the corresponding embodiment.

[0158] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 402, and executed by the processor 401 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0159] The processing device may include, but is not limited to, a processor 401, a memory 402, and an input / output device 403. Those skilled in the art will appreciate that the illustration is merely an example of a processing device and does not constitute a limitation on the processing device, and may include more or fewer components than shown in the illustration, or a combination of certain components, or different components. For example, the processing device may also include a network access device, a bus, etc., and the processor 401, the memory 402, the input / output device 403, etc. are connected via a bus.

[0160] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the processing device, and uses various interfaces and lines to connect various parts of the entire device.

[0161] The memory 402 can be used to store computer programs and / or modules. The processor 401 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 402 and calling the data stored in the memory 402. The memory 402 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the processing device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0162] When the processor 401 is used to execute the computer program stored in the memory 402, the following functions can be implemented:

[0163] Acquire a first signal obtained by GPR detection processing of the frozen rock and soil body to be detected, wherein the frozen rock and soil body to be detected forms a corresponding frozen wall through AGF processing;

[0164] Performing data preprocessing on the first signal to obtain a second signal to preliminarily enhance signal quality;

[0165] On the basis of the second signal, in the process of imaging processing by using the back projection algorithm, the correlation between the signals of each channel is considered, and the secondary imaging is realized by the cross-correlation back projection algorithm to obtain the reconstructed image;

[0166] The reconstructed image is subjected to secondary signal processing to obtain a third signal to further enhance the signal quality.

[0167] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the GPR detection imaging device, processing equipment and corresponding units described above for the frozen wall development situation can refer to the following Figure 1 The description of the GPR detection imaging method for the frozen wall development in the corresponding embodiment will not be repeated here in detail.

[0168] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0169] To this end, the present application provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the present application as follows: Figure 1 The steps of the GPR detection imaging method for frozen wall development in the corresponding embodiment, the specific operation can be referred to as follows Figure 1 The description of the GPR detection imaging method for the frozen wall development in the corresponding embodiment will not be repeated here.

[0170] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0171] Due to the instructions stored in the computer-readable storage medium, the present application can be executed. Figure 1 The steps of the GPR detection imaging method for frozen wall development in the corresponding embodiment, therefore, the present application can be implemented as follows Figure 1 The beneficial effects that can be achieved by the GPR detection imaging method for frozen wall development in the corresponding embodiment are detailed in the previous description and will not be repeated here.

[0172] The above is a detailed introduction to the GPR detection imaging method, device, processing equipment and computer-readable storage medium for frozen wall development provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for technicians in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A GPR detection imaging method for frozen wall development, characterized in that: The method comprises: Acquire a first signal obtained by GPR detection processing of a frozen rock and soil body to be detected, wherein the frozen rock and soil body to be detected forms a corresponding frozen wall through AGF processing; Performing data preprocessing on the first signal to obtain a second signal to preliminarily enhance signal quality; Based on the second signal, in the process of imaging processing by using the back projection algorithm, the correlation between the signals of each channel is considered, and secondary imaging is achieved by using the cross-correlation back projection algorithm to obtain a reconstructed image; The reconstructed image is subjected to secondary signal processing to obtain a third signal, so as to further enhance the signal quality.

2. The method according to claim 1, characterized in that The back-projection algorithm includes the following contents: Assume that the designated detection area is divided into m×n pixels, and the round-trip propagation time τ of pixel A in the p-th channel signal of the A-scan data is A,P It is expressed as follows: Among them, ε1 is the relative dielectric constant of the frozen soil area, dx is the pixel width, dz is the pixel height, c is the propagation speed of light in vacuum, i A is the horizontal coordinate of the pixel A, j A is the ordinate of the pixel A, The response amplitude RA corresponding to each pixel A,P It is expressed as follows: RA A,p =s p (t=τ A，p ), Among them, S p The reflected electromagnetic wave recorded by the GPR moving to the p-th channel signal, The response of each imaging point in the signal is distributed in n groups of A-scan data generated by GPR measurement. Corresponding to the pixel A, there are n groups of response amplitudes, and the vector combination is an imaging output result, which is expressed as follows:

3. The method according to claim 2, characterized in that The correlation between the signals is expressed by the correlation matrix V constructed by the following formula: The relevant superposition process is expressed as follows: Among them, E A CBP is the sum of the response amplitudes calculated by the cross-correlation back-projection algorithm, and i and j are the row number and column number of the current element in the correlation matrix v, respectively.

4. The method according to claim 1, characterized in that: The GPR detection process includes the following processing contents: After radar selection, survey line layout and detection environment optimization, the specific detection work begins. The GPR host remains close to the surface of the segment and moves forward in a straight line at a uniform speed along the survey line. The speed is no higher than 5cm / s and remains consistent. The distance moved along the survey line is consistent each time and the error does not exceed 5cm. The detection time node starts before freezing and is guaranteed to be detected once every 6 hours during the active freezing period, and each survey line is detected 3 times each time.

5. The method according to claim 1, characterized in that The data preprocessing includes the removal of data points with abnormal noise, the removal of data points with lost content, time zero point correction, background noise removal and gain processing.

6. The method according to claim 1, characterized in that The signal secondary processing includes filtering processing and data enhancement processing; The filtering process includes high-pass filtering, low-pass filtering, band-pass filtering and direct wave removal; The data enhancement process includes frequency domain conversion, elimination of random noise through convolution operation and comparison of data at different time points.

7. The method according to claim 1, characterized in that After performing secondary signal processing on the reconstructed image to obtain a third signal to further enhance signal quality, the method further includes: Based on the third signal, further data analysis and processing are carried out; Wherein, the data analysis and processing includes reflection signal analysis, frozen wall development identification and differential imaging; The reflection signal analysis specifically analyzes the changes in the medium before and after freezing based on signal characteristics including intensity and phase changes, and the strong reflection area corresponds to the frozen area or abnormal structure; The identification of frozen wall development is specifically based on the relationship between reflection time and velocity, and the frozen wall thickness and the abnormal frozen wall development area are estimated, and the detection imaging results are verified by combining the temperature measurement inversion temperature field method; The differential imaging specifically performs background subtraction processing on the radar data before and after freezing to eliminate the unchanged background signal.

8. A GPR detection imaging device for frozen wall development, characterized in that: The device comprises: An acquisition unit, used for acquiring a first signal obtained by performing GPR detection processing on a frozen rock and soil body to be detected, wherein the frozen rock and soil body to be detected forms a corresponding frozen wall through AGF processing; A preprocessing unit, configured to perform data preprocessing on the first signal to obtain a second signal, so as to preliminarily enhance signal quality; An imaging unit, configured to, based on the second signal, perform imaging processing by using a back projection algorithm, taking into account the correlation between the signals of each channel, and to achieve secondary imaging by using a cross-correlation back projection algorithm to obtain a reconstructed image; The secondary processing unit is used to perform secondary signal processing on the reconstructed image to obtain a third signal to further enhance signal quality.

9. A processing device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the method according to any one of claims 1 to 7 when calling the computer program in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to any one of claims 1 to 7.

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