Carbon sequestration leakage stress index construction and monitoring method based on air-ground integration

Abstract

This invention relates to the field of geological environment monitoring technology and provides a method for constructing and monitoring carbon sequestration leakage stress indices based on integrated air-ground technology. The method includes the following steps: acquiring SAR and optical time-series remote sensing data; calculating the surface deformation index and vegetation stress index respectively; constructing a comprehensive index through a collaborative enhancement multiplicative model; and identifying leakage risk areas based on index anomalies. This invention effectively suppresses false positives caused by single-factor interference through spatial coupling of physical and ecological signals, significantly improving the accuracy and reliability of monitoring CO2 geological sequestration leaks using remote sensing technology. It provides a core technical solution for achieving large-scale, automated, and low-cost sequestration safety monitoring.

Description

Technical Field

[0001] This invention belongs to the field of geological environment monitoring technology, and in particular relates to a method for constructing and monitoring carbon sequestration leakage stress index based on air-ground integration. Background Technology

[0002] Carbon dioxide capture and storage (CCS) technology is a system of technologies that captures carbon dioxide emitted from industry for resource utilization or geological storage. However, CO2 stored underground carries the potential risk of leaking to the surface through caprock fissures, abandoned wells, and other pathways. Such leaks not only weaken the storage effect but may also threaten surface ecosystems and public safety. Therefore, developing efficient, large-scale, and low-cost CO2 leak monitoring technologies is crucial.

[0003] Currently, monitoring of carbon sequestration areas mainly relies on sparsely deployed ground sensors (such as flux towers and soil gas probes) and periodic manual inspections. However, these methods have limited monitoring range and high costs, making it difficult to achieve real-time, continuous coverage of the entire sequestration area. Remote sensing technology, especially satellite remote sensing, has advantages such as wide coverage, short revisit cycles, and low data costs, providing a new approach for regional-scale leakage monitoring. Existing studies mostly use single remote sensing data sources, such as using optical images to monitor vegetation stress or using synthetic aperture radar (SAR) to monitor surface deformation. However, single-indicator monitoring has significant drawbacks: vegetation stress may be caused by multiple factors such as drought and pests (false positives), while minor surface deformation may also originate from groundwater changes or geological tectonic activity (false positives). How to effectively integrate multi-source remote sensing information to construct a comprehensive monitoring index that can simultaneously respond to physical deformation and ecological vegetation signals and significantly suppress the influence of single interfering factors has become a key technical challenge for improving the reliability of remote sensing monitoring of carbon sequestration leakage and achieving early warning. Summary of the Invention

[0004] The purpose of this invention is to provide a method for constructing and monitoring a carbon sequestration leakage stress index based on air-ground integration, in order to solve the problems mentioned in the background art.

[0005] The present invention is implemented as follows: a method for constructing and monitoring a carbon sequestration leakage stress index based on an integrated air-ground system, comprising the following steps:

[0006] Acquire multi-source remote sensing data of the target carbon sequestration area during the monitoring period, wherein the multi-source remote sensing data includes at least synthetic aperture radar time-series image data and multispectral optical time-series image data;

[0007] Based on synthetic aperture radar time-series image data, a surface deformation rate field is generated by using time-series interferometry technology, and the surface deformation index (DSI) of each pixel is calculated accordingly.

[0008] Based on multispectral optical time-series image data, spectral bands sensitive to vegetation moisture and photosynthetic efficiency are extracted, and the vegetation stress index (VSI) for each pixel is calculated.

[0009] Based on the surface deformation index DSI and the vegetation stress index VSI, a carbon sequestration leakage stress comprehensive index CSL-SSI is constructed through a synergistic enhancement multiplicative model.

[0010] Based on the spatiotemporal distribution and anomaly intensity of the carbon sequestration leakage stress composite index, potential CO2 leakage risk areas are identified and delineated.

[0011] Another objective of this invention is to provide a carbon sequestration leakage stress index construction and monitoring system based on air-ground integration, for implementing the above method, including:

[0012] The data acquisition module is used to acquire multi-source remote sensing data of the target carbon sequestration area during the monitoring period;

[0013] The deformation index calculation module is used to calculate the surface deformation index based on synthetic aperture radar time-series image data;

[0014] The vegetation index calculation module is used to calculate the vegetation stress index based on multispectral optical time-series image data;

[0015] The comprehensive index construction module is used to construct a comprehensive carbon sequestration leakage stress index based on the surface deformation index and the vegetation stress index.

[0016] The risk identification module is used to identify potential CO2 leakage risk areas based on the carbon sequestration leakage stress composite index.

[0017] Another objective of this invention is to provide an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the above-described method.

[0018] Another objective of this invention is to provide a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-described method.

[0019] This invention proposes a Carbon Sequestration Leakage Stress Composite Index (CSL-SSI), which for the first time deeply integrates surface deformation (physical signal) and vegetation physiological stress (ecological signal) through a multiplicative model. This model requires that the two signals coexist spatially to generate a high index value, thereby effectively eliminating interference from single factors such as vegetation stress caused solely by drought, pests and diseases, or deformation caused solely by geological activity, significantly reducing the false positive rate of monitoring results. This invention also utilizes free Sentinel series satellite data to achieve large-scale, low-cost, near real-time automated monitoring, providing an efficient and reliable technical tool for the long-term safe operation and risk management of carbon sequestration projects. Attached Figure Description

[0020] Figure 1 A flowchart illustrating the method for constructing and monitoring a carbon sequestration leakage stress index based on air-ground integration, as provided in this embodiment of the invention.

[0021] Figure 2 This is a schematic diagram of the CSL-SSI index synergistic enhancement principle provided in an embodiment of the present invention. A is the vegetation stress index, B is the surface deformation index, C is the spatial overlap analysis, and D is the CSL-SSI comprehensive index.

[0022] Figure 3 This is a schematic diagram of the CSL-SSI index under different risk scenarios provided in the embodiments of the present invention. A1 represents drought stress, A2 represents no deformation, A3 represents moderate CSL-SSI, B1 represents healthy vegetation, B2 represents geological activity, B3 represents low CSL-SSI, C1 represents exposed vegetation stress, C2 represents exposed surface deformation, and C3 represents high CSL-SSI. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0025] like Figure 1 The flowchart shown is a method for constructing and monitoring a carbon sequestration leakage stress index based on air-ground integration, according to an embodiment of the present invention, including the following steps:

[0026] Step S1: Obtain multi-source remote sensing data of the target carbon sequestration area during the monitoring period. The multi-source remote sensing data includes at least synthetic aperture radar (SAR) time-series image data and multispectral optical time-series image data.

[0027] Synthetic aperture radar time-series image data can be obtained from Sentinel-1 satellite, etc., and multispectral optical time-series image data can be obtained from Sentinel-2, Landsat-8 / 9 satellite, etc. It is necessary to obtain historical data for a sufficient period of time (such as 3-5 years) before the implementation of the project as the baseline data, as well as monitoring data after the implementation of the project.

[0028] Step S2: Based on synthetic aperture radar time-series image data, use time-series interferometry techniques (such as PS-InSAR or SBAS-InSAR) to process the data, generate a surface deformation rate field, and calculate the surface deformation index (DSI) for each pixel accordingly.

[0029] The formula for calculating the surface deformation index (DSI) is as follows:

[0030] ;

[0031] in, For pixel position, For the monitoring period, This represents the average deformation rate of the pixel during the monitoring period. This represents the background deformation rate of the pixel during the reference period. The spatial standard deviation of the background deformation rate is given; the absolute value of the DSI characterizes the significance of the deformation anomaly.

[0032] Step S3: Based on multispectral optical time-series image data, extract spectral bands that are sensitive to vegetation moisture and photosynthetic efficiency, and calculate the vegetation stress index (VSI) for each pixel.

[0033] Among them, the Vegetation Stress Index (VSI) is a composite index obtained by standardizing and weighting multiple stress-sensitive spectral indices.

[0034] A preferred embodiment is as follows:

[0035] ;

[0036] in, , , These are the standardized deviations of the current photochemical vegetation index (PRI), water stress index (MSI), and normalized difference vegetation index (NDVI) from their respective historical baseline values ​​for the same period. , , These are the weighting coefficients, and ;

[0037] A positive VSI value indicates the presence of vegetation stress; the higher the value, the stronger the stress.

[0038] Step S4: Based on the surface deformation index (DSI) and vegetation stress index (VSI), construct the carbon sequestration leakage stress composite index (CSL-SSI) through a synergistic enhancement multiplicative model.

[0039] The formula for calculating the Carbon Sequestration Leakage Stress Composite Index (CSL-SSI) is as follows:

[0040] ;

[0041] in, The vegetation stress index. It is the Earth's surface deformation index. and An adjustable non-negative weighting exponent is used to balance the contributions of the two types of signals, typically... One can be chosen. A value greater than 1 (such as 1.5) can be used to appropriately amplify the weight of the deformation signal; in the formula, " The design ensures that when there is no significant deformation ( The exponential value is dominated by vegetation stress; when deformation signals are present, deformation acts as a multiplier amplifier to amplify the final exponential value, such as... Figure 2 As shown (high-risk scenario: high VSI overlaps with high |DSI|).

[0042] This multiplication model requires both signals to appear together to produce a high value, such as Figure 3 As shown (low-risk scenario: a single signal cannot push up the index);

[0043] Step S5: Based on the spatiotemporal distribution and anomaly intensity of the Carbon Sequestration Leakage Stress Index (CSL-SSI), identify and delineate potential CO2 leakage risk areas:

[0044] The process includes the following:

[0045] Step S51: Based on the baseline data, calculate the historical average CSL-SSI index for the entire region. with standard deviation ;

[0046] Step S52: Set risk warning thresholds, such as... The area was designated as a warning zone. Furthermore, areas within this region where the spatial overlap between high VSI and high |DSI| exceeds a preset ratio (e.g., 70%) are designated as high-risk alarm zones;

[0047] Step S53: Output a spatial distribution map of the risk area and link it with the Geographic Information System (GIS) platform to provide information such as coordinates, area, and risk level to guide the ground verification work;

[0048] Furthermore, embodiments of the present invention also include a result verification and feedback step: using ground-based mobile monitoring equipment or drones to conduct on-site measurements of soil CO2 concentration, plant physiological parameters, etc., in the identified high-risk areas, verifying the monitoring results, and feeding back the verification information to optimize model parameters (such as weights). , , ,index and and warning thresholds).

[0049] Example 2: Taking a region where a carbon dioxide geological storage experiment has been conducted as the research object, the storage area and a surrounding area of ​​approximately 25 km² were selected as the monitoring area. Using publicly available multi-source remote sensing data, the carbon storage leakage stress index construction and monitoring method proposed in this embodiment of the invention was verified and explained.

[0050] Step S1, Data Source and Monitoring Period:

[0051] Synthetic Aperture Radar (SAR) data: 86 scenes of Sentinel-1 satellite ascending and descending SAR images were selected, spanning from January 2019 to December 2022;

[0052] Multispectral optical data: Sentinel-2 satellite imagery, totaling 120 scenes, with a spatial resolution of 10m;

[0053] The period from 2019 to 2020 is used as the baseline period, and the period from 2021 to 2022 is used as the monitoring period.

[0054] Step S2, Calculation results of the Surface Deformation Index (DSI):

[0055] The SBAS-InSAR technique was used to process SAR time-series images to generate the annual average surface deformation rate field of the study area. The results show that:

[0056] The deformation rate in most areas of the study area ranged from -2 mm / a to +2 mm / a; the maximum settlement rate of approximately -7.6 mm / a was detected in a localized area of ​​the sealed area.

[0057] According to the method for calculating the surface deformation index proposed in this embodiment of the invention, the region is identified as a significant deformation anomaly area;

[0058] Step S3, Calculation results of Vegetation Stress Index (VSI):

[0059] Based on Sentinel-2 multispectral imagery, NDVI, PRI, and MSI indices were calculated and compared with historical baseline values ​​for the same period. The results show that:

[0060] In locations spatially adjacent to areas of abnormal deformation, NDVI showed a sustained decrease of 8-15%; PRI and MSI simultaneously exhibited anomalous shift characteristics; based on this, a vegetation stress index (VSI) was constructed, and the VSI in this region was significantly higher than the background value;

[0061] Step S4, Spatial distribution results of CSL-SSI composite index:

[0062] The CSL-SSI index was constructed based on the collaborative enhancement multiplication model, and the results show that:

[0063] In areas where only vegetation abnormalities exist but no obvious deformation, the CSL-SSI value remains at a low level;

[0064] In areas where only deformation anomalies exist but no vegetation response, CSL-SSI did not show a significant increase;

[0065] In areas where deformation anomalies and vegetation stress spatially overlap, CSL-SSI values ​​increase significantly, forming a continuous high-value zone.

[0066] Step S5, Example of numerical calculation of CSL-SSI index:

[0067] To further illustrate the feasibility of the method proposed in this embodiment of the invention, a typical pixel within the aforementioned high-risk area is selected for numerical calculation.

[0068] The average surface deformation rate of this pixel during the monitoring period was -7.6 mm / a, corresponding to a background deformation rate of -1.2 mm / a in the baseline period. The spatial standard deviation of the background deformation rate in the study area was 2.1 mm / a. Therefore:

[0069] ;

[0070] Take its absolute value and use it in subsequent calculations;

[0071] Based on multispectral data, the following was calculated at this pixel:

[0072] ; ; ;

[0073] In weighting coefficients , , Under the following conditions:

[0074] ;

[0075] Take adjustment index ,based on get:

[0076] ;

[0077] Step S6, Risk Assessment Explanation:

[0078] Based on the statistical characteristics of the baseline CSL-SSI, the historical average value of the study area is approximately 0.35, and the standard deviation is approximately 0.28. When the CSL-SSI value exceeds 1.2, it is identified as a high-risk anomaly area.

[0079] Therefore, the CSL-SSI value of this pixel is 1.66, which is significantly higher than the threshold level, and it is identified as a high-risk CO2 leakage stress area.

[0080] Example 3: Comparison of the method proposed in this embodiment with the prior art:

[0081] Using the same monitoring area and the same remote sensing dataset from Example 2, comparative analysis was conducted using the following three methods:

[0082] Method A (Single InSAR): This method identified multiple deformation anomaly areas in the study area, some of which were analyzed to be related to groundwater changes or tectonic activity, but there were many false positive areas.

[0083] Method B (single vegetation index): This method is highly sensitive to drought and seasonal changes. It identified multiple abnormal vegetation patches in the study area, but most of these areas were not accompanied by surface deformation, making it difficult to determine whether they were related to CO2 leakage.

[0084] Method C (Example of the present invention): This method only forms high CSL-SSI values ​​in the spatially overlapping areas of deformation anomalies and vegetation stress, effectively eliminating abnormal areas caused by a single factor, identifying fewer high-risk areas but with stronger spatial continuity, and highly correlated with the spatial location of the sealed area;

[0085] Comparison conclusion:

[0086] The comparative results show that, compared with existing single remote sensing monitoring methods, the method proposed in this embodiment of the invention significantly reduces the false positive identification rate, can more accurately reflect the complex physical-ecological response caused by CO2 leakage, and is more suitable for long-term, automated, and large-scale safety monitoring of carbon sequestration projects.

[0087] Example 4: Based on Example 1, a carbon sequestration leakage stress index construction and monitoring system based on air-ground integration is provided, including:

[0088] The data acquisition module is used to acquire multi-source remote sensing data of the target carbon sequestration area during the monitoring period;

[0089] The deformation index calculation module is used to calculate the surface deformation index based on synthetic aperture radar time-series image data;

[0090] The vegetation index calculation module is used to calculate the vegetation stress index based on multispectral optical time-series image data;

[0091] The comprehensive index construction module is used to construct a comprehensive carbon sequestration leakage stress index based on the surface deformation index and the vegetation stress index.

[0092] The risk identification module is used to identify potential CO2 leakage risk areas based on the carbon sequestration leakage stress composite index.

[0093] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0094] Example 5: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, complete the steps described in Example 1.

[0095] Example 6: A computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps described in Example 1.

[0096] The electronic device proposed in this invention can be a fixed device such as a server or workstation, or a mobile terminal with professional processing software installed. It should be understood that in this invention, the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The memory can include read-only memory and random access memory (RAM), providing instructions and data to the processor; a portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0097] In implementation, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions. The steps of the method disclosed in the embodiments of this invention can be directly manifested as execution by the hardware processor, or by a combination of hardware and software modules in the processor. The software modules can be located in mature storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The storage medium is located in memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, detailed descriptions are not provided here. Those skilled in the art will recognize that the units, i.e., algorithm steps, of the various examples described in the embodiments disclosed in the embodiments of this invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0098] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] In the several embodiments provided in this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between the apparatuses or units may be electrical, mechanical, or other forms.

[0100] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to the prior art or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing and monitoring a carbon sequestration leakage stress index based on an integrated air-ground system, characterized in that, Includes the following steps: Acquire multi-source remote sensing data of the target carbon sequestration area during the monitoring period, wherein the multi-source remote sensing data includes at least synthetic aperture radar time-series image data and multispectral optical time-series image data; Based on synthetic aperture radar time-series image data, a surface deformation rate field is generated by using time-series interferometry technology, and the surface deformation index (DSI) of each pixel is calculated accordingly. Based on multispectral optical time-series image data, spectral bands sensitive to vegetation moisture and photosynthetic efficiency are extracted, and the vegetation stress index (VSI) for each pixel is calculated. Based on the surface deformation index DSI and the vegetation stress index VSI, a carbon sequestration leakage stress comprehensive index CSL-SSI is constructed through a synergistic enhancement multiplicative model. Based on the spatiotemporal distribution and anomaly intensity of the carbon sequestration leakage stress composite index, potential CO2 leakage risk areas are identified and delineated.

2. The method for constructing and monitoring the carbon sequestration leakage stress index based on air-ground integration as described in claim 1, characterized in that, The formula for calculating the surface deformation index (DSI) is as follows: ； in, For pixel position, For the monitoring period, This represents the average deformation rate of the pixel during the monitoring period. This represents the background deformation rate of the pixel during the reference period. The spatial standard deviation of the background deformation rate.

3. The method for constructing and monitoring the carbon sequestration leakage stress index based on air-ground integration as described in claim 1, characterized in that, The Vegetation Stress Index (VSI) is a composite index, obtained by weighted summation of the current time period's photochemical vegetation index deviation, water stress index deviation, and normalized vegetation index negative deviation.

4. The method for constructing and monitoring the carbon sequestration leakage stress index based on air-ground integration as described in claim 1, characterized in that, The Carbon Sequestration Leakage Stress Composite Index (CSL-SSI) is constructed using the following formula: ； in, The vegetation stress index. It is the Earth's surface deformation index. and It is an adjustable non-negative weighting index.

5. The method for constructing and monitoring the carbon sequestration leakage stress index based on air-ground integration as described in claim 4, characterized in that, The non-negative weight index The value of is greater than 1.

6. The method for constructing and monitoring the carbon sequestration leakage stress index based on air-ground integration as described in claim 1, characterized in that, The steps of identifying and delineating potential CO2 leakage risk areas specifically include: Statistical characteristics of the Carbon Sequestration Leakage Stress Composite Index (CSL-SSI) were calculated based on historical benchmark data. Set at least two risk warning thresholds and divide risk zones into different levels based on the situation where the Carbon Sequestration Leakage Stress Index (CSL-SSI) exceeds the threshold. For areas with the highest risk level, the spatial overlap between abnormal vegetation stress and abnormal surface deformation is further assessed, and a high-risk leak alarm zone is only confirmed when the overlap exceeds a preset ratio.

7. The method for constructing and monitoring the carbon sequestration leakage stress index based on air-ground integration as described in claim 6, characterized in that, It also includes the following steps: Use ground-based or drone-based mobile monitoring equipment to conduct on-site verification measurements of the identified risk areas; Based on the verification results, optimize the weights of the Vegetation Stress Index (VSI), the parameters of the synergistic enhancement multiplicative model, or the risk warning threshold.

8. A system for constructing and monitoring a carbon sequestration leakage stress index based on air-ground integration, used to implement the method for constructing and monitoring a carbon sequestration leakage stress index based on air-ground integration as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire multi-source remote sensing data of the target carbon sequestration area during the monitoring period; The deformation index calculation module is used to calculate the surface deformation index based on synthetic aperture radar time-series image data; The vegetation index calculation module is used to calculate the vegetation stress index based on multispectral optical time-series image data; The comprehensive index construction module is used to construct a comprehensive carbon sequestration leakage stress index based on the surface deformation index and the vegetation stress index. The risk identification module is used to identify potential CO2 leakage risk areas based on the carbon sequestration leakage stress composite index.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the steps of the method as described in any one of claims 1-7.

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