Natural gas pipeline geological deformation monitoring method and system based on InSAR technology

Abstract

The invention discloses a natural gas pipeline geological deformation monitoring method and system based on an InSAR technology. The method comprises the following steps: data preprocessing: preprocessing natural gas pipeline SAR image data; differential interference processing: based on the preprocessed data, obtaining a plurality of interference pairs satisfying conditions through a space-time baseline threshold, and improving the signal-to-noise ratio of the image through the differential interference processing; distributed scatterer preprocessing: based on the data after the differential interference processing, improving the coherence of the monitoring area through the distributed scatterer preprocessing; time sequence permanent scatterer processing: based on the data preprocessed by the distributed scatterer, obtaining deformation point cloud of the monitoring area through time sequence permanent scatterer processing; and judging geological deformation of the natural gas pipeline: performing optical remote sensing analysis and geological interpretation analysis in combination with optical remote sensing data and three-dimensional remote sensing data over the years, and determining a hidden danger area influencing gas transmission pipeline facilities in the monitoring area in combination with geomorphic rock stratum analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster deformation monitoring, and particularly to a method and system for monitoring geological deformation of natural gas pipelines based on InSAR technology. Background Art

[0002] The Xiangguosi Gas Storage is located at the junction of Beibei District and Yubei District in Chongqing. It was completed and put into operation in 2013. It is the first underground gas storage in the southwestern region and an important gas source to ensure the winter natural gas supply in the Sichuan-Chongqing region and the Beijing-Tianjin-Hebei region. In September 2021, affected by heavy rainfall, a large number of tensile cracks appeared around the northern section of the injection-production main pipeline within 500 meters in the direction from the No. 5 injection-production station to the gathering and injection station of the gas storage management office. The severely damaged part is about 150 meters away from the production site. The area around the No. 5 injection-production station and the northern section of the injection-production main pipeline belongs to the landslide influence area. After evaluation by local professional institutions, the landslide body is still developing. Due to the continuous changes in the earth's crust and the impact of rainwater, geological disaster hazards will be caused, such as landslides, collapses, debris flows, and unstable slopes. Geological landslides will cause extreme accidents such as the tearing and damage of pipelines and equipment facilities of natural gas pipelines, which will affect the gas supply volume of the gas storage, and may even trigger accidents such as forest fires, with a huge social impact.

[0003] At present, only the monitoring of crack distances is carried out by manually measuring 14 crack distances every three days. Manual measurement has large measurement errors and cannot detect the development of cracks and the deformation of geological bodies in a timely manner. Once extreme accidents such as the tearing and damage of pipelines and equipment facilities are caused by geological landslides, it will affect the gas supply volume of the gas storage by about 14 million cubic meters per day, and may even trigger accidents such as forest fires, with a huge social impact. To prevent problems before they occur, scientific and effective geological disaster monitoring needs to be carried out on the No. 5 injection-production station and surrounding pipelines. Summary of the Invention

[0004] To solve the above problems, the present invention proposes a method and system for monitoring geological deformation of natural gas pipelines based on InSAR technology, which uses advanced technologies such as satellite technology deformation monitoring, Beidou high-precision displacement monitoring, and monitoring and early warning based on intelligent Internet of Things. From the periodic census of the macroscopic geological disaster area to the real-time monitoring of the deformation of the microscopic geological disaster geological body, a complete air-ground integrated monitoring technology application for geological disaster monitoring of oil and gas production facilities is formed, which can achieve early detection and early disposal to ensure the safe and stable operation of the station and pipeline.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for monitoring geological deformation of natural gas pipelines based on InSAR technology, comprising the following steps:

[0007] S1. Data preprocessing: Preprocess the SAR image data of the natural gas pipeline. The preprocessing includes SAR image data format conversion, extraction of the monitoring area range, geocoding, image registration, and image cropping.

[0008] S2. Differential interferometry processing: Based on the preprocessed data, obtain several interferometric pairs that meet the conditions through the spatio-temporal baseline threshold, and improve the signal-to-noise ratio of the image through differential interferometry processing.

[0009] S3. Distributed scatterer preprocessing: Based on the data after differential interferometry processing, improve the coherence of the monitoring area through distributed scatterer preprocessing.

[0010] S4. Temporal permanent scatterer processing: Based on the data after distributed scatterer preprocessing, obtain the deformation point cloud of the monitoring area through temporal permanent scatterer processing.

[0011] S5. Judgment of geological deformation of natural gas pipelines: Based on the data after temporal permanent scatterer processing, conduct optical remote sensing analysis and geological interpretation analysis by combining historical optical remote sensing data and stereo remote sensing data, and determine the potential hazard areas affecting the gas pipeline facilities in the monitoring area by combining geomorphic rock layer analysis.

[0012] Further, in step S1, the SAR image data format conversion includes: decompressing the data of the monitoring area collected by the earth observation satellite and converting it into a single-look complex image.

[0013] Further, in step S1, the image registration includes: selecting the main image, calculating the lookup table of the MLI mosaic data of the main image and the MLI mosaic data of the slave image, calculating the offset between the resampled slave image and the main image, calculating the residual offset between the main and slave images according to the cross-correlation method, adding the generated offset correction file, and resampling the slave image again. After multiple iterative processes, the registration accuracy for interferometry processing is achieved.

[0014] Further, step S2 also includes filtering and phase unwrapping. When performing phase unwrapping, weight distribution needs to be carried out using the coherence obtained after filtering; when performing phase unwrapping for each pixel, a reference point needs to be selected. This reference point must be in a non-deformed or relatively stable position, and the reference point position should be as far away from the deformation area as possible while being located in the center of the image; the deformation amounts calculated through phase unwrapping can be both positive and negative, indicating whether the deformation direction of the target is towards or away from the radar line of sight, and its physical meaning is uplift and subsidence.

[0015] Further, in step S4, the temporal permanent scatterer processing includes: image multi-looking, geocoding, selection of temporal permanent scatterer points, differential interferometry processing of temporal permanent scatterer points, deformation calculation based on temporal permanent scatterer points, phase unwrapping, and spatio-temporal filtering.

[0016] A geological deformation monitoring system for natural gas pipelines based on InSAR technology, comprising:

[0017] A data preprocessing module, configured to preprocess the SAR image data of the natural gas pipeline. The preprocessing includes SAR image data format conversion, monitoring area range extraction, geocoding, image registration, and image cropping;

[0018] A differential interferometry processing module, configured to obtain a number of qualified interferometric pairs based on the preprocessed data through a spatio-temporal baseline threshold, and improve the signal-to-noise ratio of the image through differential interferometry processing;

[0019] A distributed scatterer preprocessing module, configured to improve the coherence of the monitoring area based on the data after differential interferometry processing through distributed scatterer preprocessing;

[0020] A time series permanent scatterer processing module, configured to obtain the deformation point cloud of the monitoring area through time series permanent scatterer processing based on the data after distributed scatterer preprocessing;

[0021] A natural gas pipeline geological deformation judgment module, configured to perform optical remote sensing analysis and geological interpretation analysis based on the data after time series permanent scatterer processing, combined with historical optical remote sensing data and stereo remote sensing data, and determine the hidden danger areas affecting the gas pipeline facilities in the monitoring area by combining geomorphic rock layer analysis.

[0022] Further, in the data preprocessing module, the SAR image data format conversion includes: decompressing the monitoring area data collected by the Earth observation satellite and converting it into a single-look complex image.

[0023] Further, in the data preprocessing module, the image registration includes: selecting a master image, calculating the lookup table of the MLI mosaic data of the master image and the MLI mosaic data of the slave image, resampling the slave image and then calculating the offset between the slave image and the master image, calculating the residual offset between the master and slave images according to the cross-correlation method, adding the generated offset correction file, and resampling the slave image again. After multiple iterative processes, the registration accuracy for interferometry processing is achieved.

[0024] Further, the differential interferometry processing module further includes filtering and phase unwrapping. When performing phase unwrapping, weight allocation needs to be performed using the coherence obtained after filtering; when performing phase unwrapping for each pixel, a reference point needs to be selected, and this reference point must be in a non-deformed or relatively stable position, and the reference point position should be as far away from the deformation area as possible while being located in the center of the image; the deformation amounts calculated through phase unwrapping have both positive and negative values, indicating whether the deformation direction of the target is towards or away from the radar line of sight, and its physical meaning is uplift and subsidence.

[0025] Furthermore, in the temporal permanent scatterer processing module, the temporal permanent scatterer processing includes: image multi-looking, geocoding, temporal permanent scatterer point selection, temporal permanent scatterer point differential interferometry processing, deformation solution based on temporal permanent scatterer points, phase unwrapping, and spatio-temporal filtering.

[0026] The beneficial effects of the present invention are as follows:

[0027] 1. The present invention utilizes advanced technologies such as satellite technology deformation monitoring, Beidou high-precision displacement monitoring, and monitoring and early warning based on intelligent Internet of Things. From the periodic census of macroscopic geological disaster areas to the real-time monitoring of the deformation of microscopic geological disaster geological bodies, a complete air-ground integrated monitoring technology application for geological disaster monitoring of oil and gas production facilities is formed.

[0028] 2. According to the deformation information obtained by the present invention, the deformation development of the monitored area can be known in a timely manner, and there is no need for personnel to monitor the deformation area in real time on site, ensuring the safety of the staff and reducing the workload of the staff.

[0029] 3. The present invention combines the DEM data and real-time image data within the first preset range of the monitored area to calculate the DEM data formed by the image acquisition of the monitored area, and verifies this data with the directly obtained DEM data of the monitored area, reducing the error of the DEM data, thereby improving the accuracy of the deformation monitoring of this solution.

[0030] 4. The present invention obtains the SAR image data and DEM data of the area that needs to be monitored for the natural gas pipeline, generates a settlement deformation map based on the SAR image data and DEM data, obtains the deformation information of the monitored area according to the settlement deformation map, and through this monitoring and management, the deformation situation of the monitored area can be obtained in a timely manner, and relevant measures can be taken in a timely manner to ensure the normal operation of the natural gas pipeline equipment. Brief Description of the Drawings

[0031] Figure 1 Flowchart of the geological deformation monitoring method for the natural gas pipeline in the embodiment of the present invention.

[0032] Figure 2 Basic principle diagram of InSAR.

[0033] Figure 3 Flowchart of DS-InSAR data processing.

[0034] Figure 4 Flowchart of PS-InSAR data processing.

[0035] Figure 5 Combined diagram of interference baselines.

[0036] Figure 6 Temporal trend diagram of point T1.

[0037] Figure 7 Scatter plot of T2 settlement time series. Specific implementation manners

[0038] In order to have a clearer understanding of the technical features, objectives, and effects of the present invention, the specific implementation manners of the present invention will now be described. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0039] Embodiment 1

[0040] As Figure 1 shown, this embodiment provides a method for monitoring geological deformation of natural gas pipelines based on InSAR technology, including the following steps:

[0041] S1. Data preprocessing: Preprocess the SAR image data of the natural gas pipeline. The preprocessing includes SAR image data format conversion, monitoring area range extraction, geocoding, image registration, and image cropping;

[0042] S2. Differential interferometry processing: Based on the preprocessed data, obtain several interference pairs that meet the conditions through the spatio-temporal baseline threshold, and improve the signal-to-noise ratio of the image through differential interferometry processing;

[0043] S3. Distributed scatterer preprocessing: Based on the data after differential interferometry processing, improve the coherence of the monitoring area through distributed scatterer preprocessing;

[0044] S4. Temporal permanent scatterer processing: Based on the data after distributed scatterer preprocessing, obtain the deformation point cloud of the monitoring area through temporal permanent scatterer processing;

[0045] S5. Judgment of geological deformation of natural gas pipelines: Based on the data after temporal permanent scatterer processing, combine the optical remote sensing data and stereo remote sensing data of previous years for optical remote sensing analysis and geological interpretation analysis, and combine the geomorphic rock layer analysis to determine the hidden danger areas affecting the gas pipeline facilities in the monitoring area.

[0046] Specifically, the method for monitoring geological deformation of natural gas pipelines in this embodiment is described in detail as follows.

[0047] 1. Basic principle of InSAR

[0048] Interferometric Synthetic Aperture Radar (InSAR) is an active microwave remote sensing system, which makes the formed SAR images have structural characteristics and electromagnetic wave scattering characteristics. To facilitate subsequent interferometric calculation and processing of SAR images, each pixel on the SAR image is represented by a complex number:

[0049]

[0050] u is the echo signal, |u| represents the scattering intensity value of the ground resolution cell, that is, the amplitude of the echo signal. ρ is the phase value of the ground resolution cell, corresponding to the non-integer part of the total number of waves of the radar along the slant range propagation direction. and correspond to the real part and the imaginary part of the complex signal respectively. Thus, for the echo signals u1 and u2 received from two different imaging of the same target object, there are the following expressions:

[0051]

[0052]

[0053] R1 and R2 are the distances between the radar and the same target point during the two imaging processes respectively.

[0054] The interferometric fringe image is obtained by performing complex conjugate multiplication between pixels of the registered images. Its mathematical expression is as follows:

[0055]

[0056] The phase in the signal received by the radar consists of two parts: the phase part of the slant range R and the random phase part caused by different scatterers within the resolution cell, that is:

[0057]

[0058]

[0059] In the above formula, λ is the wavelength of the radar system, and represent the random phases of different scatterers within the ground resolution cell at different times. Assuming that the random phases at different times are the same, the interferometric phase difference obtained from the two images can be expressed as (Equation 1):

[0060]

[0061] It can be seen from the above formula that the interferometric phase is a function of the difference in the slant ranges from the radar antenna to the target point during the two imaging processes.

[0062] The spaceborne SAR data is sourced from the sensors of a spaceborne SAR in a repeat orbit. Taking this as an example, the obtained geometric relationships are as Figure 2 shown. A1 and A2 are respectively the positions of the satellite sensor when the satellite passes over the ground point P twice in succession (which can be obtained based on the satellite orbit parameters); H is the height of the satellite from the actual ground; R1 and R2 are respectively the distances from the positions of the two satellite sensors to the ground point P; B is the distance between the two satellite sensors, known as the spatial baseline; B ⊥ is the vertical component of the baseline B along the radar line of sight A1P, known as the vertical baseline; B || is the horizontal component of the baseline B along the radar line of sight A1P, known as the parallel baseline; α is the angle between the baseline B and the horizontal plane; θ is the viewing angle of the radar observing the target P during the satellite's first imaging; h is the elevation of point P from the reference ellipsoid.

[0063] According to Figure 2 , the expressions for the vertical baseline B ⊥ and the parallel baseline B || can be obtained (Equation 1):

[0064] B ⊥ = B cos(θ - α)

[0065] B || = B sin(θ - α)

[0066] In triangle △A1A2P, according to the cosine theorem, the following (Equation 2) can be obtained:

[0067]

[0068] Also, since R is much larger than B, so can be neglected and is approximately equal to 0; from the right triangle in Figure 2 , it can be known that R2 has the following expression:

[0069]

[0070] From the above equation, it can be known that R1 - R2 = ΔR = B || , and R1 is much larger than R1 - R2. Therefore, it can be known that (Equation 2) can be transformed into:

[0071]

[0072] According to the transformation of (Equation 1), it can be obtained:

[0073]

[0074] So, the magnitude of the θ value can be known:

[0075]

[0076] Moreover, the elevation h of ground point P is expressed as follows:

[0077] h = H - R1cosθ

[0078] Therefore, the elevation value of point P can be obtained as above.

[0079] The geometric principle of InSAR is to use the phase information of repeatedly observing the same point and the surface elevation formula in the geometric relationship to obtain the elevation of the ground observation point. That is, the phase of InSAR is a function of distance. The distance from the satellite to the ground can be obtained through the phase information, and the change in the distance leads to the phase shift. Therefore, the change in the distance from the satellite to the ground can be measured by accurately measuring the phase shift.

[0080] 2. Introduction to InSAR Technology

[0081] 2.1 Differential Radar Interferometry Technology D-InSAR

[0082] D-InSAR uses the phase information provided by the synthetic aperture radar complex data as the information source, utilizes the phase difference information of the complex radar image, and according to the InSAR set relationship, through image processing, data processing, and geometric transformation, etc., extracts the minute deformation information of the ground target from one or more interferometric fringe maps containing information such as the terrain and deformation of the target area.

[0083] As Figure 3 shown, the main steps of D-InSAR data processing are as follows:

[0084] (1) Selection of the master image. According to certain rules, select the master image, and the others are used as auxiliary images.

[0085] (2) Registration and cropping

[0086] ① Registration and cropping of the master and slave images. Register the master image with the slave image and crop them into the same area;

[0087] ② DEM data processing. Resample the DEM data to the same sampling interval as the master image, then register it with the master image and crop it into the same area as the master image.

[0088] (3) Calculation of the interferometric phase. Multiply the registered master and auxiliary images conjugately and calculate the interferometric phase for each pixel to obtain the interferometric phase map.

[0089] (4) Generation of the differential interferogram. Use the spatial baseline parameters and the earth ellipsoid parameters to remove the flat-earth phase; use the registered DEM to remove the terrain phase to obtain the differential interferogram.

[0090] (5) Filtering and phase unwrapping. Adaptive filtering is performed on the differential interferogram to remove the noise that interferes with the phase values, obtaining the differential interferogram of the wrapped phase. Phase unwrapping is carried out using methods such as the branch cut method and the least squares method.

[0091] (6) Calculation of line-of-sight deformation. The unwrapped phase, combined with the radar wavelength-related parameters and external data, is converted into the deformation in the radar line-of-sight direction.

[0092] (7) Vertical conversion. Based on the InSAR geometric relationship, the deformation in the line-of-sight direction is converted into the vertical deformation.

[0093] (8) Geocoding and datum correction. Geocoding is performed on the vertical deformation using the registered DEM, converting the monitoring results into the results in the geodetic coordinate system; using high-precision leveling data such as leveling and GPS to correct the geocoded results, thereby obtaining the final surface deformation data.

[0094] 2.2 Preprocessing of Distributed Scatterers JSInSAR

[0095] The preprocessing technology of distributed scatterers (JSInSAR) is a DS target processing method, which mainly consists of two parts: time series InSAR preprocessing and PSI technology. Among them, the time series InSAR preprocessing technology includes three key steps: joint pixel signal modeling, goodness-of-fit test, and spatial adaptive filtering. Its specific implementation details are significantly different from those of SqueeSAR. The processing object of JSInSAR is the joint pixel block, rather than a single pixel like SqueeSAR. In SqueeSAR preprocessing, the maximum likelihood method is used to obtain the optimized interferometric phase, but this method can only process 1D pixels and cannot be applied to the optimization of joint pixel phases.

[0096] JSInSAR adopts the Joint Subspace Projection (JSP) method. Its principle is to estimate the time series phase based on the projection from the joint signal subspace to the joint noise subspace. This method can make good use of the phase information of surrounding points and can well recover the interferometric phase even under large registration errors. Therefore, the JSInSAR technology has low requirements for the registration accuracy.

[0097] The processing steps of the JSInSAR technology include the following four aspects: establishment of the joint pixel signal model, goodness-of-fit test for joint pixels, adaptive filtering for joint pixels, and phase optimization for joint pixels. A signal model is established for the pixel block. The pixel values of an N×N pixel block are rearranged, and then modeled using a formula. Different from the statistical test method for a single pixel, in order to perform a statistical test for joint pixels, the JSInSAR technology uses a method of Time-Series Likelihood Ratios (TSLR) test to perform the goodness-of-fit test for the joint pixel vector, and then estimates the phase covariance matrix. Finally, the joint subspace projection method is used to restore the interferometric phase.

[0098] Therefore, the JSInSAR technology has a stronger ability to suppress decorrelation, so it can obtain more robust phase estimation results, effectively improve the spatial density of deformation monitoring points in low-coherence areas, improve the quality and signal-to-noise ratio of deformation monitoring points, and at the same time do not affect the acquisition of deformation monitoring points in high-coherence areas.

[0099] 2.3 Persistent Scatterer Interferometry Technology PSInSAR

[0100] The PS-InSAR technology essentially classifies the ground targets corresponding to a set of time-series SAR images in the study area into two categories: PS and non-PS. Those ground targets (PS) that are less affected by decorrelation and atmospheric delay and can still maintain a certain phase stability over a long time are extracted. Using the characteristic that their interferometric correlation is almost unaffected by the temporal baseline and spatial baseline, differential interferometry and modeling are carried out at discrete PS points, the phase components of each signal are separated, and finally the deformation information of each PS is extracted. Then, the deformation situation of the entire study area is obtained by interpolation.

[0101] Such as Figure 4 shown, the main data processing steps of PS-InSAR are as follows:

[0102] (1) Generate differential interferograms

[0103] Among the given multiple SAR images, first select one as the common master image according to certain principles, and the other images as slave images. Then, register the master and slave images, and conjugate multiply the registered two images to obtain the original interferogram. Finally, remove the flat-earth phase and topographic phase in the original interferogram to obtain the time-series differential interferogram of the study area.

[0104] (2) Identification and selection of PS points

[0105] Identifying and selecting PS points is a crucial step in PS-InSAR, which is very important for the subsequent calculation of settlement amounts. On the one hand, as many PS points as possible should be selected; on the other hand, it is also necessary to avoid selecting some unstable points as PS points, which may cause deviations in the results.

[0106] (3) Deformation amount estimation

[0107] After the PS points are selected, the deformation phase can be separated from the phase information of these points, and then the deformation amount in the line-of-sight (LOS) direction can be calculated.

[0108] 3. InSAR data processing flow of this embodiment

[0109] (1) Data preprocessing

[0110] The content includes SAR image data format conversion, monitoring area range extraction, geocoding, and image registration.

[0111] Before data processing, the Sentinel-1A data (observation data of Sentinel-1 satellite) needs to be decompressed. At this time, it cannot be directly processed and needs to be converted into a single-look complex image through processing software. Since Sentinel data is composed of one burst after another, normally a complete Sentinel data scene is 27 bursts. Therefore, for the convenience of registration and other processing, 8 ascending-track bursts and 4 descending-track bursts that are common before and after the offset are extracted from the data using processing software.

[0112] Image registration is the most crucial step before interference processing operations. Its registration accuracy is proportional to the quality of the interferogram. High registration accuracy will reduce the processing difficulty of subsequent steps. The accuracy of ordinary SAR data needs to reach 0.2 pixels to avoid coherence loss of more than 5%. However, since Sentinel-1A data is composed of 3 IWs and 27 bursts, its registration accuracy requirements are higher than those of ordinary SAR data. The registration accuracy in the azimuth direction must reach 1‰, otherwise it will cause interference fringe jump phenomena in the interferogram.

[0113] To ensure meeting the registration accuracy requirements, in this monitoring, the registration lookup table is calculated using terrain information, and then refined according to the intensity matching method. The above method is iteratively processed to improve the accuracy. The specific steps are as follows: select the master image, calculate the lookup table of the MLI mosaic data of the master image and the MLI mosaic data of the slave image, calculate the offset between the slave image and the master image after resampling the slave image, calculate the residual offset between the master and slave images according to the cross-correlation method, add the generated offset correction file, and resample the slave image again. After multiple iterative processes, the registration accuracy for interference processing is achieved, providing good image data for subsequent operations.

[0114] The interferogram without any operation mainly contains five phase components. Among them, the topographic phase can be removed by using high-precision DEM data. The STRM-DEM data is used to achieve the purpose of removing the topographic phase, and operations such as geocoding are carried out.

[0115] The area of a complete Sentinel image is 62500 square kilometers. After cropping, a small image area covering the study area is obtained, which greatly improves the processing efficiency.

[0116] (2) D-InSAR batch interference, batch differencing, batch filtering, and batch unwrapping.

[0117] To obtain a better differential interferogram, appropriate spatio-temporal baseline thresholds need to be set to obtain several interferometric pairs that meet the conditions. Considering that Sentinel data is flown once every 12 days, setting the time baseline threshold to 48 days can ensure a sufficient number of interferometric pairs. The spatial baseline threshold is set to 150. Under the restrictions of the above parameters, a total of 380 interferometric pairs are finally obtained, as Figure 5 shown.

[0118] The interferometric pairs obtained in the previous step are subjected to differential interferometry processing. To reduce the interference of noise on the interferogram, multi-looking is required to improve the signal-to-noise ratio of the image. After setting the azimuth-range ratio to 10:2 for multi-looking processing, the influence of speckle noise can be effectively eliminated, the signal-to-noise ratio is improved, and the ground object targets are displayed more clearly.

[0119] Coherence reflects the change degree of ground targets imaged at two time points and can be used to judge the quality of the interferogram. In areas with high coherence, the phase difference can accurately reflect the deformation amount of the target during this period. In the processing software, the interferometric coherence can be directly estimated from the normalized interferogram and the co-registered intensity image, and the corresponding coherence map can be generated. The quality of the filtered interferogram is much higher than that before filtering, and the deformation results of which areas are more accurate can be determined according to the coherence map.

[0120] To enhance the quality of the differential interferogram and improve the clarity of the interference fringes, filtering operations are usually performed on the interferogram. Currently, in the field of InSAR, common filtering methods include the Goldstein method, the Adaptive method, and the Boxcar method. Among them, the most advanced method is the Goldstein adaptive filtering, and this algorithm can be used to filter the interferogram in most cases. However, it should be noted that too strong filtering will also introduce errors by eliminating the interference fringes. In areas with rugged terrain, it is usually better to use smaller filtering windows of 16 or 32. In flat terrain areas or differential interferograms where the terrain phase has been removed, it is better to use larger filtering windows such as 64. Since the terrain phase information remaining in the interferogram has been eliminated through the terrain phase simulated by STRM DEM data before the filtering operation, the size of the filtering window adopted is 64×64 pixels. After filtering, the noise is suppressed and the interference phase gradient is greatly improved.

[0121] Phase unwrapping is to obtain the true value of the phase, and its purpose is to obtain reliable deformation information. When performing phase unwrapping for each pixel, a reference point needs to be selected. The reference point must be selected strictly according to the following criteria. First, the reference point must be in an undeformed or relatively stable position. Second, the position of the reference point should be as far away from the deformation area as possible while being located in the center of the image. After phase unwrapping, we can convert it into the result in the LOS upward direction. It should be noted that the obtained deformation quantity is relative to the reference point, which also verifies from the side the reason why the reference point needs to be in the stable area. The calculated deformation quantities can be both positive and negative, indicating the change of the observed target position in space. The positive and negative values of the deformation result indicate whether the deformation direction of the target is towards or away from the radar line of sight, and its physical meaning is uplift and subsidence. In this case, the minimum cost flow unwrapping is adopted. During the unwrapping process, the coherence obtained after filtering needs to be used for weight allocation. Unwrapping errors are likely to occur in areas with low coherence. It can be seen that the quality of the interferogram coherence determines the difficulty of unwrapping. This is also why we must obtain high-quality results from the initial registration operation because errors accumulate continuously, and mistakes in previous operations will cause trouble for subsequent steps. After a series of operations such as filtering, unwrapping, detrending, and removing the atmosphere, the quality of the interferogram changes from the original phase chaos to clear phase.

[0122] (3) Preprocessing of distributed scatterers (JSInSAR).

[0123] Since all traditional multi-temporal InSAR techniques have the same defect, that is, the distribution of high-coherence pixel points in the image is not uniform, showing a high density in urban areas and areas with exposed rocks, while a low density in low-coherence areas such as non-urban areas. Therefore, in order to increase the density of deformation monitoring points in low-coherence areas such as non-urban areas, Ferretti et al. proposed the second-generation multi-temporal InSAR technique (SqueeSAR technique) in 2011, which extracts deformation information from permanent scatterers (PS) and distributed scatterers (DS) through spatial domain adaptive filtering and maximum likelihood methods. However, this technique can only process single pixels and has high requirements for registration accuracy. By using a new second-generation time series InSAR technique, joint scatterer InSAR (JSInSAR) can effectively overcome the above disadvantages. This technique uses the coherence information of adjacent pixels and can still effectively improve the signal-to-noise ratio of distributed scatterers and increase the spatial density of monitoring points in low-coherence areas even under large registration errors, while obtaining deformation information in both high-coherence and low-coherence areas. Compared with the time series point selection results of ordinary PS-InSAR, the time series points of JSInSAR are significantly denser.

[0124] (4) Processing of time series permanent scatterer (PSInSAR) data. Perform PSInSAR processing on the data preprocessed by JSInSAR in the previous step to obtain the deformation point cloud of the monitoring area. The content includes image multi-looking, geocoding, selection of time series permanent scatterer PS points, differential interferometric processing of permanent scatterer PS points, deformation solution based on PS points, phase unwrapping, spatio-temporal filtering, etc.

[0125] 4. Verification example

[0126] Through the previous InSAR analysis, there are 20 areas with certain ground deformations in the section where the pipeline and injection-production station are located. Later, through indoor analysis combined with regional geological data and optical remote sensing, 26 field investigation points with potential hidden dangers were selected. These 26 field points with larger deformations or potential hidden dangers were verified on-site one by one. A total of 18 geological disaster hidden danger points were found. At the same time, the geological disaster hidden dangers of the line were classified into three types and the main potential hazard sources and key monitoring sections were summarized based on the actual situation, and targeted follow-up monitoring and inspection measures were proposed.

[0127] In the area 200 meters southwest of the potential pile TX4 of the natural gas pipeline in Area A, the deformation rate range from March 2017 to June 2022 is -26.09 mm / year to 22.92 mm / year. The location of the maximum deformation point is 106.043488°E, 29.896669°N, and the maximum cumulative settlement is -124 mm.

[0128] The longitude and latitude of point T1 are: 106.041931°E, 29.897009°N, located beside the gas transmission pipeline in the monitoring area; as Figure 6 shown, this location shows a continuous settlement trend, with a settlement rate of -13.04 mm / year, an accumulated settlement of -53.72 mm, and a single-month deformation value of +13.1 mm in June 2022.

[0129] The longitude and latitude of point T2 are: 106.043205°E, 29.897009°N, 107 meters away from the gas transmission pipeline in the monitoring area; as Figure 7 shown, this location shows a continuous settlement trend, with a settlement rate of -21.66 mm / year, an accumulated settlement of -116.42 mm, and a single-month deformation value of -1.89 mm in June 2022.

[0130] Embodiment 2

[0131] This embodiment provides a geological deformation monitoring system for natural gas pipelines based on InSAR technology, including:

[0132] A data preprocessing module configured to preprocess the SAR image data of the natural gas pipeline. The preprocessing includes SAR image data format conversion, extraction of the monitoring area range, geocoding, image registration, and image cropping;

[0133] A differential interferometry processing module configured to obtain a number of interferometric pairs that meet the conditions based on the preprocessed data, and improve the signal-to-noise ratio of the image through differential interferometry processing;

[0134] A distributed scatterer preprocessing module configured to improve the coherence of the monitoring area based on the data after differential interferometry processing through distributed scatterer preprocessing;

[0135] A time series permanent scatterer processing module configured to obtain the deformation point cloud of the monitoring area through time series permanent scatterer processing based on the data after distributed scatterer preprocessing;

[0136] A natural gas pipeline geological deformation judgment module configured to perform optical remote sensing analysis and geological interpretation analysis based on the data after time series permanent scatterer processing, combined with historical optical remote sensing data and stereo remote sensing data, and determine the hidden danger areas that affect the gas transmission pipeline facilities in the monitoring area by combining geomorphic rock layer analysis.

[0137] Preferably, in the data preprocessing module, the SAR image data format conversion includes: decompressing the data of the monitoring area collected by the earth observation satellite and converting it into a single-look complex image.

[0138] Preferably, in the data preprocessing module, image registration includes: selecting a master image, calculating the lookup tables of the MLI stitching data of the master image and the MLI stitching data of the slave image, resampling the slave image and then calculating the offset between the slave image and the master image, calculating the residual offset between the master and slave images according to the cross-correlation method, adding the generated offset correction file, resampling the slave image again, and achieving the registration accuracy for interferometric processing after multiple iterative processes.

[0139] Preferably, the differential interferometric processing module further includes filtering and phase unwrapping. When performing phase unwrapping, the coherence obtained after filtering is used for weight distribution; when performing phase unwrapping for each pixel, a reference point needs to be selected, and this reference point must be in a non-deformed or relatively stable position, and the position of the reference point should be as far away from the deformation area as possible while being located in the center of the image; the deformation amounts calculated by phase unwrapping have both positive and negative values, indicating whether the deformation direction of the target is towards or away from the radar line of sight, and its physical meaning is uplift and subsidence.

[0140] Preferably, in the time series persistent scatterer processing module, time series persistent scatterer processing includes: image multi-looking, geocoding, time series persistent scatterer point selection, time series persistent scatterer point differential interferometric processing, deformation calculation based on time series persistent scatterer points, phase unwrapping, and spatio-temporal filtering.

[0141] Embodiment 3

[0142] This embodiment is based on Embodiment 1:

[0143] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the natural gas pipeline geological deformation monitoring method based on InSAR technology in Embodiment 1. Among them, the computer program can be in the form of source code, object code, executable file, or some intermediate form, etc.

[0144] Embodiment 4

[0145] This embodiment is based on Embodiment 1:

[0146] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for monitoring geological deformation of natural gas pipelines based on the InSAR technology in Embodiment 1. Among them, the computer program can be in the form of source code, object code, executable file or some intermediate form, etc. The storage medium includes: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the storage medium does not include electrical carrier signals and telecommunication signals.

[0147] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. A method for monitoring geological deformation of natural gas pipelines based on InSAR technology, characterized in that It includes the following steps: S1. Data preprocessing: Preprocess the SAR image data of the natural gas pipeline. The preprocessing includes SAR image data format conversion, monitoring area range extraction, geocoding, image registration, and image cropping; S2. Differential interferometry processing: Based on the preprocessed data, obtain several interferometric pairs that meet the conditions through the spatio-temporal baseline threshold, and improve the signal-to-noise ratio of the image through differential interferometry processing; S3. Distributed scatterer preprocessing: Based on the data after differential interferometry processing, improve the coherence of the monitoring area through distributed scatterer preprocessing; S4. Temporal permanent scatterer processing: Based on the data after distributed scatterer preprocessing, obtain the deformation point cloud of the monitoring area through temporal permanent scatterer processing; S5. Judgment of geological deformation of natural gas pipeline: Based on the data after temporal permanent scatterer processing, combine the optical remote sensing data and stereo remote sensing data of previous years to conduct optical remote sensing analysis and geological interpretation analysis, and combine the geomorphic rock layer analysis to determine the hidden danger areas affecting the gas pipeline facilities in the monitoring area.

2. The natural gas pipeline geological deformation monitoring method based on the InSAR technology according to claim 1, wherein In step S1, the SAR image data format conversion includes: decompressing the monitoring area data collected by the Earth observation satellite and converting it into a single-look complex image.

3. The geological deformation monitoring method for natural gas pipelines based on InSAR technology according to claim 1, wherein, In step S1, the image registration includes: selecting the main image, calculating the lookup table of the MLI mosaic data of the main image and the MLI mosaic data of the slave image, calculating the offset between the resampled slave image and the main image, calculating the residual offset between the main and slave images according to the cross-correlation method, adding the generated offset correction file, and resampling the slave image again. After multiple iterative processes, the registration accuracy of the interferometry processing is achieved.

4. A method for monitoring geological deformation of natural gas pipelines based on InSAR technology according to claim 1, characterized in that, Step S2 also includes filtering and phase unwrapping. When performing phase unwrapping, the coherence obtained after filtering is used for weight distribution; when performing phase unwrapping for each pixel, a reference point needs to be selected. This reference point must be in a non-deformed or relatively stable position, and the reference point position should be as far away from the deformation area as possible while being located in the center of the image; the deformation amounts calculated by phase unwrapping can be both positive and negative, indicating whether the deformation direction of the target is towards or away from the radar line of sight, and its physical meaning is uplift and subsidence.

5. A method for monitoring geological deformation of natural gas pipelines based on InSAR technology according to claim 1, characterized in that, In step S4, the temporal permanent scatterer processing includes: image multi-looking, geocoding, temporal permanent scatterer point selection, temporal permanent scatterer point differential interferometry processing, deformation calculation based on temporal permanent scatterer points, phase unwrapping, and spatio-temporal filtering.

6. A geological deformation monitoring system for natural gas pipelines based on InSAR technology, characterized in that, It includes: A data preprocessing module configured to preprocess the SAR image data of the natural gas pipeline. The preprocessing includes SAR image data format conversion, monitoring area range extraction, geocoding, image registration, and image cropping; A differential interferometry processing module configured to obtain several interferometric pairs that meet the conditions through the spatio-temporal baseline threshold based on the preprocessed data, and improve the signal-to-noise ratio of the image through differential interferometry processing; A distributed scatterer preprocessing module configured to improve the coherence of the monitoring area through distributed scatterer preprocessing based on the data after differential interferometry processing; The temporal permanent scatterer processing module is configured to obtain the deformation point cloud of the monitoring area through temporal permanent scatterer processing based on the data after distributed scatterer preprocessing; The natural gas pipeline geological deformation judgment module is configured to perform optical remote sensing analysis and geological interpretation analysis based on the data after temporal permanent scatterer processing, combined with historical optical remote sensing data and stereo remote sensing data, and determine the potential hazard areas affecting the gas pipeline facilities in the monitoring area by combining geomorphic rock layer analysis.

7. A geological deformation monitoring system for natural gas pipelines based on InSAR technology according to claim 6, characterized in that, In the data preprocessing module, the SAR image data format conversion includes: decompressing the data of the monitoring area collected by the earth observation satellite and converting it into a single-look complex image.

8. The natural gas pipeline geological deformation monitoring system based on InSAR technology according to claim 6, characterized in that In the data preprocessing module, the image registration includes: selecting the master image, calculating the lookup table of the MLI mosaic data of the master image and the MLI mosaic data of the slave image, resampling the slave image and then calculating the offset between the slave image and the master image, calculating the residual offset between the master and slave images according to the cross-correlation method, adding the generated offset correction file, and resampling the slave image again. After multiple iterative processes, the registration accuracy for interferometric processing is achieved.

9. The natural gas pipeline geological deformation monitoring system based on InSAR technology according to claim 6, characterized in that, The differential interferometric processing module further includes filtering and phase unwrapping. When performing phase unwrapping, the coherence obtained after filtering is used for weight allocation; when performing phase unwrapping for each pixel, a reference point needs to be selected. This reference point must be in a non-deforming or relatively stable position, and the position of the reference point should be as far away from the deformation area as possible while being located in the center of the image; the deformation amounts calculated by phase unwrapping can be either positive or negative, indicating whether the deformation direction of the target is towards or away from the radar line of sight, and its physical meaning is uplift and subsidence.

10. The natural gas pipeline geological deformation monitoring system based on InSAR technology according to claim 6, characterized in that, In the temporal permanent scatterer processing module, the temporal permanent scatterer processing includes: image multi-looking, geocoding, temporal permanent scatterer point selection, temporal permanent scatterer point differential interferometric processing, deformation calculation based on temporal permanent scatterer points, phase unwrapping, and spatio-temporal filtering.

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