InSAR high coherence zoning-based mountain area disaster identification method along oil and gas pipeline

Abstract

The invention discloses an InSAR high coherence zoning-based mountain area disaster identification method along an oil and gas pipeline, and the method comprises the steps: dividing each year into a summer time period, a transition time period and a winter time period according to the change trend of InSAR coherence, and further bringing the transition time period into the summer time period or the winter time period according to the InSAR coherence of the transition time period; stacking-InSAR processing is carried out on InSAR interference pairs in the summer time period and the winter time period respectively, and part of long-time baseline InSAR interference pairs with good interference quality are introduced; and finally, the acquired Stacking-InSAR results in the summer and winter periods are comprehensively utilized to carry out accurate early recognition work of regional geological disasters. According to the method, the blank of InSAR high-coherence partitioning is filled, and various problems that the extraction time sequence deformation precision is relatively poor and the geological disaster hidden danger is mistakenly recognized and missed recognized due to the fluctuation of the coherence of the InSAR in regions with relatively large ground feature scattering characteristic changes, such as dense vegetation coverage, high-altitude ice and snow coverage or grassland wetland, are solved.

Description

Technical Field

[0001] The invention relates to, in particular to, a method for identifying mountain disasters along an oil and gas pipeline based on InSAR high coherence partitioning. Background Art

[0002] At present, the construction of oil and gas pipelines has entered a peak period. There are more and more pipelines passing through special terrain and complex geological conditions, and the impact of geological disasters has become more and more prominent. Therefore, accurate identification and monitoring of geological disasters in oil and gas pipelines is extremely important to ensure the safe operation of pipelines. Traditional pipeline geological disaster identification methods mainly include daily inspection identification and entrusting external professional teams to investigate and identify. Daily inspections can only detect small-scale water damage disasters with significant deformation; professional investigations are generally carried out once every 2 to 3 years, with a long cycle and poor real-time performance, and new disasters cannot be discovered in time, and the cost is high. Both of these identification methods have the disadvantages of narrow investigation scope, incomplete disaster identification, easy omissions, and low timeliness. Traditional pipeline geological disaster monitoring mainly uses total stations, fiber Bragg gratings, three-dimensional laser scanning and other monitoring methods. Although the monitoring accuracy is high, it is impossible to achieve long-distance and large-scale soil displacement monitoring along the pipeline.

[0003] In recent years, the new remote sensing technology synthetic aperture radar interferometry (InSAR) has been developed. It can identify hidden dangers such as landslides along the pipeline at an early stage without affecting the operation of the pipeline. At the same time, it can understand the surrounding geological environment, grasp the activity range and movement characteristics of disasters such as landslides, and preliminarily determine the spatial position relationship between the affected pipeline and the disaster body. Using InSAR technology to identify and monitor pipeline geological disasters can help make up for the shortcomings of traditional technologies, improve the level of pipeline geological disaster risk management, and provide technical support for pipeline geological disaster monitoring and early warning.

[0004] In recent years, the development of multi-phase InSAR technology has enabled the effective extraction of regional surface deformation rate and deformation sequence through spatiotemporal filtering and deformation modeling of long-term InSAR interference phases, breaking through the limitations of traditional InSAR technology. Among them, the Stacking-InSAR method based on phase stacking technology is widely used in the early identification of large-scale geological disasters due to its simple and efficient data processing process and its ability to effectively suppress spatiotemporal errors.

[0005] However, it should also be pointed out that the control of coherence quality is still a typical problem faced by InSAR data processing. When the similarity of the ground radar echo signals received by the satellite during two passes is low, it often leads to poor InSAR coherence or even complete interference decorrelation, which has an unignorable negative impact on the identification of geological disasters based on InSAR deformation. The low coherence phenomenon of InSAR mainly exists in areas where the scattering characteristics of ground objects change greatly over time. Under natural landform conditions, such areas are often densely covered with vegetation, covered with ice and snow at high altitudes, or grassland wetlands. The fluctuation of InSAR coherence in the above-mentioned areas is prone to poor accuracy in extracting time series deformations and causing misidentification and missed identification of geological disaster hazards. Therefore, how to solve the significant seasonal InSAR decorrelation in high vegetation areas is an unavoidable problem in the early identification of geological disasters in the above-mentioned areas using InSAR technology. At the same time, there have been studies on seasonal geological disaster monitoring based on InSAR technology, which mostly focus on monitoring the periodic frost heave and ablation in frozen soil areas using time series InSAR technology. Few studies have systematically analyzed the impact of seasonal changes in InSAR high coherence partitions on the early identification of geological disasters. Early identification of geological hazards based on seasonal changes in InSAR high-coherence zoning in the oil and gas pipeline field is even rarer. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for identifying mountain disasters along oil and gas pipelines based on InSAR high coherence partitioning.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0008] A method for identifying mountainous area disasters of oil and gas pipelines based on InSAR high coherence partitioning includes the following steps:

[0009] S1. Obtain InSAR time series interference diagram and coherence coefficient diagram of the study area;

[0010] S2. According to the change trend of InSAR coherence, each year is divided into summer period, transition period and winter period, and the transition period is further included into summer period or winter period according to the InSAR coherence;

[0011] S3, phase-weighted stacking deformation extraction using InSAR interferometry in summer and winter periods respectively;

[0012] S4. Use the phase-weighted superposition InSAR deformation results obtained in summer and winter to perform phase-weighted superposition calculations under seasonal coherence differences to accurately identify geological disasters in the study area at an early stage.

[0013] Furthermore, the S1 specifically includes the following steps:

[0014] S11, select the winter and spring SAR images as the common main image, and the other SAR images are registered based on the common main image;

[0015] S12, setting the spatial baseline threshold and the temporal differential interferometry time baseline threshold, using external DEM data and GACOS atmospheric data to remove the terrain phase and correct the atmospheric error, and obtain the differential interferometry result;

[0016] S13, performing adaptive filtering on the differential interference result, and using the minimum cost flow method to untangle the obtained interference result to obtain a differential interference pair;

[0017] S14. According to the coherence change of the interference result, the time series InSAR data of the study area is processed to obtain the time series interference map.

[0018] Furthermore, the S2 specifically includes the following steps:

[0019] S21, using an image supervised classification tool to identify high altitude areas and low altitude areas, and using the image supervised classification tool to distinguish high coherence and low coherence in the coherence coefficient map of each interference pair;

[0020] S22, superimposing the altitude zone division result and the coherence division result, and calculating the coverage area ratio of the high coherence zone in the high altitude zone and the low altitude zone;

[0021] S23. Determine the calculated coverage area ratio. If the high-altitude area has a higher proportion of high coherence than the low-altitude area, it is classified as a summer interference pair according to the InSAR coherence characteristics of the oil and gas pipeline in the mountainous area. If the low-altitude area has a higher proportion of high coherence, it is classified as a winter interference pair.

[0022] Furthermore, the specific method of calculating the coverage area ratio of the high coherence area in the high altitude area and the low altitude area in S22 is:

[0023] P1=S3 / S1

[0024] P2=S4 / S2

[0025] In the formula, S1 is the area of ​​the high altitude zone, S2 is the area of ​​the low altitude zone, S3 is the area covered by the high coherence zone in the high altitude zone, S4 is the area covered by the high coherence zone in the low altitude zone, P1 is the proportion of the high coherence zone in the high altitude zone, and P2 is the proportion of the high coherence zone in the low altitude zone.

[0026] Furthermore, the S3 specifically includes the following steps:

[0027] S31, using multiple SAR images in the study area to perform differential interferometry processing on each of them within a set time baseline to obtain an interferometry pair;

[0028] S32, the unwrapped phase of the linear superposition interference pair, and the weighted average according to the time span are used to obtain the average annual deformation in the study area.

[0029] Furthermore, the calculation method of the average annual deformation in the study area in S32 is:

[0030]

[0031] Where: P rate represents the annual average rate; represents the unwrapped phase value of a single interferometer pair; Δt represents the time baseline of the interferometer pair; N represents the number of interferometer pairs added to the solution.

[0032] The present invention has the following beneficial effects: BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of a method for identifying mountain disasters along oil and gas pipelines based on InSAR high coherence partitioning according to the present invention.

[0034] Figure 2 The figure is a schematic diagram of the D-InSAR differential interferometry processing flow of the SAR image according to an embodiment of the present invention.

[0035] Figure 3 Schematic diagram of the time series change of coherence in the study area according to an embodiment of the present invention.

[0036] Figure 4 This is a schematic diagram of seasonal fluctuations of interference signals in the study area of ​​an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0038] A method for identifying mountain disasters along oil and gas pipelines based on InSAR high coherence partitioning. Figure 1 As shown, the following steps are included:

[0039] S1. Obtain InSAR time series interference diagram and coherence coefficient diagram of the study area;

[0040] In this embodiment, the SAR images observed by the European Space Agency's Sentinel-1 satellite are used as the data source, and the monitoring time range should be selected as a long-term complete time period spanning the four seasons of spring, summer, autumn and winter, such as January 2017 to December 2020. In addition, considering the problem of satellite orbit errors, the study uses the POD precise orbit data published by the European Space Agency to refine and correct the Sentinel-1 satellite orbit data. When extracting surface deformation, SRTM-DEM data is used to simulate and remove terrain phases. Finally, considering the complex climatic conditions in the study area and the unstable atmospheric factors that are prone to deformation solution errors, the tropospheric delay (ZTD) data with a spatial resolution of GACOS-90m can be introduced to carry out atmospheric correction of InSAR interferograms, weaken the atmospheric delay error, and improve the accuracy of InSAR deformation monitoring.

[0041] Before carrying out time-series InSAR data processing, the collected SAR images need to be processed by D-InSAR differential interferometry, such as Figure 2 As shown, including:

[0042] S11, select the winter and spring SAR images as the common main image, and the other SAR images are registered based on the common main image;

[0043] S12, setting the spatial baseline threshold and the temporal differential interferometry time baseline threshold, using external DEM data and GACOS atmospheric data to remove the terrain phase and correct the atmospheric error, and obtain the differential interferometry result;

[0044] S13, performing adaptive filtering on the differential interference result, and using the minimum cost flow method to untangle the obtained interference result to obtain a differential interference pair;

[0045] S14. According to the coherence change of the interference result, the time series InSAR data of the study area is processed to obtain the time series interference map.

[0046] According to the coherence changes of the interference pattern, the InSAR interference pair is divided into winter and summer seasons, providing a data basis for time series InSAR processing.

[0047] S2. According to the change trend of InSAR coherence, each year is divided into summer period, transition period and winter period, and the transition period is further included into summer period or winter period according to the InSAR coherence;

[0048] InSAR coherence directly affects the extraction of temporal deformation and the identification of disaster hazards. First, based on the filtered InSAR coherence coefficient map with a partial time baseline of 120 days, the temporal InSAR coherence coefficient was analyzed, and the regional InSAR coherence comparison analysis and seasonal division were carried out. According to the coherence of the study area, the coherence area was divided (coherence>0.75 is a high coherence area, and coherence<0.35 is a low coherence area).

[0049] For example, a certain area is only affected by ice and snow cover from February to March each year, and the coherence of some interference pairs is poor, but overall more than 90% of the interference pairs can maintain good coherence throughout the year, so it is defined as a year-round high coherence area. With the changes in topography and the influence of time, InSAR coherence begins to fluctuate seasonally. From January to April each year, low-altitude areas can maintain good coherence, while high-altitude areas have poor coherence. Starting around May each year, the ice and snow in high-altitude areas gradually melt, and the coherence is significantly improved; while the vegetation on both sides of the low-altitude river valley is lush, and the coherence is significantly reduced; that is, the area with good coherence begins to migrate from low altitude to high altitude.

[0050] According to the above analysis, combined with the regional topography, the region can be divided into high coherence zones. The year-round high coherence zone is the area with relatively good InSAR coherence all year round. The InSAR high coherence zone in winter mostly corresponds to the low-altitude area along the river valley; while the InSAR high coherence zone in summer corresponds to the high-altitude area, such as Figure 3 shown.

[0051] Further, by analyzing regional meteorological information, the average temperature along the oil and gas pipelines in mountainous areas is generally less than 5°C from January to April each year, while the average temperature in high-altitude areas (above 4500m) is as low as -4°C or even below, and regional precipitation is mainly in the form of ice and snow. At the same time, combined with historical optical remote sensing images, it is found that high-altitude areas are mostly covered with ice and snow from January to April each year, and their radar scattering characteristics are unstable, making it difficult to maintain InSAR coherence. At this time, the low-altitude river valley area maintains better coherence due to relatively sparse vegetation in winter, which is more conducive to the identification of coastal landslide and collapse disasters. Starting from May every year, the regional temperature rises above 10℃. After the melting of ice and snow in high-altitude areas, most of them are bare rocks and bare land. At this time, InSAR coherence can be effectively maintained, which is more conducive to the identification of high-level hidden disasters. However, due to the replenishment of ice and snow melt water and relatively abundant precipitation in the low-altitude areas near the river valley, coupled with the rising temperature, the vegetation density on both sides of the river valley increases rapidly, causing the regional InSAR coherence to decrease significantly. This phenomenon will continue until around October every year and enter the next coherence exchange stage. It is worth mentioning that in the period of alternation between winter and summer, there will be a transition period of coherence fluctuation. During this period, due to the rapid changes and poor regularity of the ground objects, the overall coherence will be higher or lower. For interference pairs with too low coherence, they need to be removed to avoid introducing errors, and for interference pairs with better coherence, they can be selectively added to the winter or summer interference group for deformation solution according to the maintenance of their coherence.

[0052] According to the above analysis, the InSAR coherence in high-altitude areas along the oil and gas pipelines in mountainous areas is better in summer, which is conducive to the identification of high-altitude hidden disasters, while the InSAR coherence in river valley areas is better in winter, which is more conducive to the accurate interpretation of disasters such as coastal landslides and collapses.

[0053] Based on the optical remote sensing images of the study area in the current season combined with the image supervision classification tool, block extraction is performed to divide the winter interference pairs and summer interference pairs, such as Figure 4 shown.

[0054] First, the image supervised classification tool is used to identify high-altitude areas and low-altitude areas. Then, the image supervised classification tool is used to distinguish the high coherence and low coherence of the coherence coefficient map of each interference pair (coherence>0.75 is a high coherence area, and coherence<0.35 is a low coherence area).

[0055] Secondly, the division results of the altitude areas are superimposed and analyzed with the division results of coherence, and the proportion of the coverage area of the high coherence areas in the high altitude areas and the low altitude areas is calculated. If the proportion of high coherence in the high altitude areas is greater than that in the low altitude areas, i.e., P1 > P2, it indicates that the coherence in the high altitude areas can be better maintained, and it is classified as a summer interference pair according to the InSAR coherence characteristics of mountain oil and gas pipelines; then if the proportion of high coherence in the low altitude areas is larger, i.e., P1 < P2, it indicates that the coherence in the low altitude areas is better, and it is classified as a winter interference pair.

[0056] P1 = S3 / S1 (Equation 1)

[0057] P2 = S4 / S2 (Equation 2)

[0058] Where: S1 is the area of the high altitude area, S2 is the area of the low altitude area, S3 is the area covered by the high coherence area in the high altitude area, S4 is the area covered by the high coherence area in the low altitude area, P1 is the proportion of the coverage area of the high coherence area in the high altitude area, and P2 is the proportion of the coverage area of the high coherence area in the low altitude area.

[0059] S3. Respectively use the InSAR interference pairs in the summer and winter periods for phase-weighted stacking deformation extraction;

[0060] Stacking-InSAR (phase-weighted stacking) is a time-series InSAR processing technology based on the stacking of interference phases; first, pairwise InSAR differential interferometry is performed on multiple SAR images in the region within the set time baseline, and then the unwrapped phases obtained from the D-InSAR processing are linearly stacked and weighted averaged according to the time span to finally obtain the annual average deformation information in the radar line-of-sight (LOS) direction of the study area (Equation (1)).

[0061]

[0062] In the formula: P rate represents the annual average rate; represents the unwrapped phase value of a single interference pair; Δt represents the time baseline of the interference pair; N represents the number of interference pairs added to the solution.

[0063] Since InSAR atmospheric delay noise, orbital errors, etc. often show high-frequency noise in the time dimension and present autocorrelation characteristics in the spatial domain, through the stacking and weighted averaging processing of the unwrapped phases, the above-mentioned noise can be effectively suppressed and the accuracy of the calculated deformation can be improved.

[0064] S4. Use the obtained phase-weighted stacking InSAR deformation results in the summer and winter periods for phase-weighted stacking calculation under seasonal coherence differences to accurately identify geological disasters in the study area at an early stage.

[0065] In order to achieve a more accurate disaster identification rate, if the Stacking-InSAR deformation results of all interference pairs are extracted first, the result is a single annual average deformation rate result, which cannot effectively separate the seasonal deformation information. In view of the problem of missed identification and misidentification of geological disasters in mountainous areas with obvious seasonal changes in landforms, based on the above-mentioned seasonal division rules of interference pairs, the interference pairs are first effectively distinguished by season, and then the interference pairs of different seasons are processed by Stacking-InSAR to obtain the results of deformation in different seasons, effectively separating the seasonal deformation information. Therefore, in order to obtain InSAR interference pairs containing more deformation components, the SAR image data obtained are firstly processed by full interference using the 120d time baseline to obtain a certain number of InSAR interference pairs; secondly, the number of low-coherence and severe atmospheric noise interference pairs is removed; further, the number of winter interference pairs and summer interference pairs is divided according to the quality and coherence of the interference pattern, and the full interference, winter, and summer interference pairs are processed by Stacking-InSAR respectively, and finally a comparative analysis is carried out on the quality and quantity of deformation rate, identification of geological disasters, etc.

[0066] Stacking solution of short baseline interferometer pair:

[0067] According to the InSAR coherence characteristics of the oil and gas pipeline area in the mountainous area, the InSAR interferometric pairs with a 60-day time baseline are first selected, and the InSAR deformation under winter, summer and full interferometric conditions are respectively solved by the Stacking-InSAR method. Finally, the Stacking-InSAR deformation data of winter (Win), summer (Sum) and full interferometric (Total) can be obtained, forming a short-baseline Stacking-InSAR deformation result map, where the color represents the magnitude of the movement, blue represents movement away from the satellite direction, red represents movement close to the satellite direction, and the mask area is the terrain shadow and overlapping area caused by radar side imaging and the InSAR low coherence area (average coherence is less than 0.5). Then, by analyzing the above results, the annual average deformation rate range of short-baseline Win-Stacking, Sum-Stacking and Total-Stacking can be obtained, and all its disaster hazards are circled and classified. The disaster hazard classification includes high-position hidden unstable slopes, coastal landslides, etc.

[0068] Stacking optimization solution of long baseline interferometer:

[0069] Due to the large number of general disasters along the oil and gas pipelines in mountainous areas, some developing slow creep deformation bodies may not have obvious deformation signals in a short time baseline, resulting in the omission of small-scale moving targets by using only short-time baseline combinations. In order to explore the solution to the above problem, a long-term baseline InSAR interferometer with a length of 60 to 120 days is introduced. However, due to the complexity of the topography and landforms along the oil and gas pipelines in mountainous areas, the coherence of the long-term baseline interferometer is difficult to maintain, and it is necessary to selectively add long-term baseline interferometers with better coherence to participate in the deformation solution. Finally, the Stac king-InSAR deformation data of Win, Sum and Total after adding the long baseline are obtained, and the annual average deformation rate range of the long baseline Win-Stacking, Sum-Stacking and Total-Stacking is obtained, and all the disaster hazards are circled and classified. The disaster hazard classification includes high-position hidden unstable slopes, coastal landslides, etc.

[0070] Experimental results analysis

[0071] 1. Seasonal division and analysis of long and short time baseline InSAR results

[0072] First, the large deformation and small deformation disaster results of short baseline Stacking are compared respectively. For example, in the comparison of large deformation disaster results of short baseline Stacking, the local enlarged images of typical disasters such as coastal large-scale deformation landslide groups, coastal single landslides, high-level hidden wedge disasters and high-level unstable sliding bodies of short baseline Win-Stacking, short baseline Sum-Stacking, short baseline Total-Stacking and optical images are compared respectively, and the combined deformation information of each interference pair in the previous article is further integrated to obtain the characteristics of the Stacking-InSAR large deformation and disaster identification results of the 60d short-time baseline interference pair.

[0073] In the comparison of short baseline Stacking small deformation disaster results, the local enlarged images of typical disasters such as coastal landslides (winter identification), coastal landslides (summer identification), high-level hidden disasters (summer identification) and high-level hidden disasters (winter identification) of short baseline Win-Stacking, short baseline Sum-Stacking, short baseline Total-Stacking and optical images are compared, and the combined deformation information of each interference pair mentioned above is further integrated to obtain the characteristics of the Stacking-InSAR small deformation and disaster identification results of the 60d short-time baseline interference pair.

[0074] Then, the Stacking-InSAR deformation and disaster identification results of the 60-120d long-term baseline interferometry with good coherence are added. Specifically, the coastal landslide disaster and high-level hidden disaster group are respectively analyzed and identified by using short baseline Win-Stacking, short baseline Sum-Stacking, long baseline Win-Stacking, and long baseline Sum-Stacking with optical remote sensing images. Thus, the characteristics of the Stacking-InSAR deformation and disaster identification results of the 60-120d long-term baseline interferometry are obtained, and the connection and difference with the characteristics obtained by the 60d short-term baseline interferometry are compared. It is analyzed whether the overall disaster identification law changes after adding the long-term interferometry, whether the deformation signal of the hidden danger with a small deformation rate increases after adding the long-term interferometry, and whether the conventional full-interferometry Stacking-InSAR disaster identification rate is improved.

[0075] Finally, the results and distribution of disaster identification using the seasonal partitioning Stacking-InSAR method are compared with the existing field survey of geological disasters in the area. At the same time, in order to further verify the reliability of identifying geological disaster hazards, it is possible to collect multiple historical optical remote sensing images of the study area and analyze whether there are identifiable slope deformation characteristics or potential movement trends on the time-series optical remote sensing images. For example, if there are phenomena such as the collapse of the front edge of the slope becoming more obvious, and the tensile cracks on the rear edge of the slope developing significantly and gradually expanding, it indicates that the above-mentioned landslide is in a state of continuous movement and is a typical slow-moving landslide disaster. If some of the disaster hazards do not show obvious signs of movement on the time-series optical images, it means that such hazards are usually in the early stage of slow deformation of the slope, and they are mostly potential movement landslides or high-level hidden disasters with small deformation magnitudes monitored by InSAR.

[0076] In order to further quantitatively understand the recognition rate of geological disasters in the experimental area by different interference combination modes, we can quantitatively count the disaster recognition of Stacking-InSAR under short baseline and added long baseline conditions, as well as the proportion of different types of disasters in different interference mode combination modes.

[0077] Analysis 1: In the Stacking disaster identification statistics table, the number of short baseline Stacking and long baseline Stacking in winter identification, summer identification, full interference identification, winter and summer repeated identification and winter and summer total identification are counted respectively.

[0078] Analysis 2: In the statistical table of the proportion of different types of disasters, short baseline Stacking and long baseline Stacking are respectively counted in the "proportion of coastal landslides identified by Stacking in winter" and "proportion of high-level hidden dangers identified by Stacking in summer".

[0079] Analysis 3: In the comparison analysis table of seasonal disaster identification, the repetition rate of winter and summer disasters of short baseline stacking and long baseline stacking are counted and compared with the newly identified disaster rate of full interference. Among them: definition of winter and summer disaster repetition identification rate = number of winter and summer disaster repetition identification / total number of winter and summer identified disasters; new disaster identification rate = (total number of winter and summer identified disasters - number of fully interfered disaster identifications) / total number of winter and summer identified disasters.

[0080] Analysis 4: In the comparative analysis table of disaster identification in different time baselines, the "disaster repetition rate in different time baselines" and "new disaster rate in long baseline compared with short baseline" of Win-Stacking, Sum-Stacking and Total-Stacking are statistically analyzed.

[0081] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0084] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0085] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for identifying mountainous area disasters of oil and gas pipelines based on InSAR high coherence partitioning, characterized in that: The steps include: S1. Obtain InSAR time series interference diagram and coherence coefficient diagram of the study area; S2. According to the change trend of InSAR coherence, each year is divided into summer period, transition period and winter period, and the transition period is further included into summer period or winter period according to the InSAR coherence; S3, phase-weighted stacking deformation extraction using InSAR interferometry in summer and winter periods respectively; S4. Use the phase-weighted superposition InSAR deformation results obtained in summer and winter to perform phase-weighted superposition calculations under seasonal coherence differences to accurately identify geological disasters in the study area at an early stage.

2. The method for identifying mountainous area disasters of oil and gas pipelines based on InSAR high coherence partitioning according to claim 1 is characterized in that: The S1 specifically includes the following steps: S11, select the winter and spring SAR images as the common main image, and the other SAR images are registered based on the common main image; S12, setting the spatial baseline threshold and the temporal differential interferometry time baseline threshold, using external DEM data and GACOS atmospheric data to remove the terrain phase and correct the atmospheric error, and obtain the differential interferometry result; S13, performing adaptive filtering on the differential interference result, and using the minimum cost flow method to untangle the obtained interference result to obtain a differential interference pair; S14. According to the coherence change of the interference result, the time series InSAR data of the study area is processed to obtain the time series interference map.

3. The method for identifying mountain disasters along oil and gas pipelines based on InSAR high coherence partitioning according to claim 1 is characterized in that: The S2 specifically includes the following steps: S21, using an image supervised classification tool to identify high altitude areas and low altitude areas, and using the image supervised classification tool to distinguish high coherence and low coherence in the coherence coefficient map of each interference pair; S22, superimposing the altitude zone division result and the coherence division result, and calculating the coverage area ratio of the high coherence zone in the high altitude zone and the low altitude zone; S23. Determine the calculated coverage area ratio. If the high-altitude area has a higher proportion of high coherence than the low-altitude area, it is classified as a summer interference pair according to the InSAR coherence characteristics of the oil and gas pipeline in the mountainous area. If the low-altitude area has a higher proportion of high coherence, it is classified as a winter interference pair.

4. The method for identifying mountain disasters along oil and gas pipelines based on InSAR high coherence partitioning according to claim 3 is characterized in that: The specific method of calculating the coverage area ratio of the high coherence area in the high altitude area and the low altitude area in S22 is: P1=S3 / S1 P2=S4 / S2 In the formula, S1 is the area of ​​the high altitude zone, S2 is the area of ​​the low altitude zone, S3 is the area covered by the high coherence zone in the high altitude zone, S4 is the area covered by the high coherence zone in the low altitude zone, P1 is the proportion of the high coherence zone in the high altitude zone, and P2 is the proportion of the high coherence zone in the low altitude zone.

5. The method for identifying mountain disasters along oil and gas pipelines based on InSAR high coherence partitioning according to claim 1 is characterized in that: The S3 specifically includes the following steps: S31, using multiple SAR images in the study area to perform differential interferometry processing on each of them within a set time baseline to obtain an interferometry pair; S32, the unwrapped phase of the linear superposition interference pair, and the weighted average according to the time span are used to obtain the average annual deformation in the study area.

6. The method for identifying mountain disasters along oil and gas pipelines based on InSAR high coherence partitioning according to claim 5 is characterized in that: The calculation method of the annual average deformation in the study area of ​​S32 is: Where: P rate represents the annual average rate; represents the unwrapped phase value of a single interferometer pair; Δt represents the time baseline of the interferometer pair; N represents the number of interferometer pairs added to the solution.

Images