Multi-source satellite remote sensing data fusion method for oilfield methane emission feature monitoring

Abstract

The application discloses a multi-source satellite remote sensing data fusion method for oilfield methane emission feature monitoring, belongs to the application of remote sensing technology in the field of environmental monitoring and energy production, and has the technical scheme that comprises the following steps: obtaining high-resolution data of GOSAT and TROPOMI methane column concentration data of an oilfield to be monitored by adopting geographic weighted regression Kriging interpolation; taking the high-resolution GOSAT data as observation values and the mean value of the high-resolution TROPOMI data as a to-be-optimized state quantity, constructing an observation equation, and obtaining a priori Gaussian distribution; calculating the posterior probability distribution of the mean value of the TROPOMI data, obtaining a cost function, deriving the corresponding mean value of the TROPOMI data, using the mean value and the GOSAT data to obtain a bias correction number, finally correcting the bias of each concentration data in the TROPOMI high-resolution data set, and obtaining a high-precision high-resolution methane concentration data set. The application has the beneficial effect that the multi-source satellite remote sensing data fusion method for oilfield methane emission feature monitoring is provided.

Description

Technical Field

[0001] This invention relates to the application of remote sensing technology in environmental monitoring and energy production, and in particular to a method for multi-source satellite remote sensing data fusion for monitoring methane emission characteristics in oil fields. Background Technology

[0002] Methane (CH4) is a radioactive and chemically active gas in the atmosphere. It not only affects the radiation balance of the Earth's climate system but also plays a crucial role in atmospheric and stratospheric chemical processes. It absorbs long-wave radiation from the atmosphere, leading to atmospheric warming and altering the net radiation flux at the top of the atmosphere. Although the concentration of CH4 in the atmosphere is much lower than that of carbon dioxide (CO2), the impact of a unit mass of CH4 on global warming is 28 times that of CO2. With the development of human civilization, the total emissions of CH4 have continued to increase. Before the Industrial Revolution, the concentration of CH4 in the atmosphere was approximately 700 ppbv, while by the 1990s, this figure had risen to 1714 ppbv. A certain amount of methane emission is inevitably accompanied by oil and gas extraction, refining, storage, and transportation. According to statistics, methane emissions from this industry account for more than 30% of total emissions. However, my country has not yet established a methane emission inventory related to oil and gas, and monitoring of methane emissions is severely lacking. Therefore, in the context of "carbon neutrality", obtaining high-quality methane monitoring data in oil fields using existing observation methods has important application prospects.

[0003] Currently, satellite remote sensing is gradually becoming an essential research tool for greenhouse gas monitoring. However, single satellite data has some limitations. For example, Tropomi has high spatial resolution (7 km wide) but relatively large bias (14 ppbv, 0.8%). Conversely, GOSAT data has high accuracy (1.4 ppbv, 0.07%) but lower spatial resolution (2.5° × 2.5°). Using either alone cannot meet the requirements for methane monitoring in oil fields. Therefore, it is essential to combine the advantages of both satellite data to obtain high-precision, high-resolution methane concentration data using data fusion methods. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-source satellite remote sensing data fusion method for monitoring methane emission characteristics in oil fields, which solves the problem that single satellite data cannot simultaneously meet the requirements of high precision and high resolution in oil field methane monitoring scenarios.

[0005] In a first aspect, the present invention provides a multi-source satellite remote sensing data fusion method for monitoring methane emission characteristics in oil fields, characterized in that it includes:

[0006] Acquire methane column concentration data for the monitored area based on GOSAT and TROPOMI;

[0007] Geographically weighted regression kriging interpolation was used to interpolate the gaps in the methane column concentration data, generating high-resolution GOSAT and TROPOMI methane concentration data. Geographically weighted regression kriging interpolation comprehensively considers the relationship between explanatory variables and environmental variables within the region, and assigns different weights to environmental variables at different locations, interpolating the concentration at the location to be predicted, thus obtaining high-resolution methane concentration data.

[0008] High-resolution GOSAT methane column concentration data were used as observations, and the mean value of high-resolution TROPOMI methane column concentration data was used as the state variable to be optimized to construct the observation equation.

[0009] Based on the observation equation, the prior Gaussian distribution of high-resolution GOSAT and TROPOMI methane column concentration data is obtained. Using Bayes' theorem, the posterior probability distribution of the mean of high-resolution TROPOMI methane column concentration data is calculated, and the cost function is obtained.

[0010] The bias correction for the high-resolution TROPOMI methane column concentration dataset is obtained by inverting the cost function;

[0011] By using the bias correction, the bias of each concentration data in the high-resolution TROPOMI methane column concentration dataset is corrected, resulting in a high-precision, high-resolution methane column concentration dataset.

[0012] Furthermore, high-resolution GOSAT and TROPOMI methane column concentration data were generated, including:

[0013] Select a rectangular area covering the oil field and obtain the methane column concentration data of GOSAT and TROPOMI within this rectangular area;

[0014] High-resolution methane column concentration datasets of 100m×100m were generated by interpolation using the geographic weighted regression kriging method.

[0015] Furthermore, the geographically weighted regression kriging model is shown below:

[0016] Y GWRK (u i ,v i ) = Y GWR (u i ,v i )+ε(u i ,v i )

[0017] Among them, Y GWRK (u i ,v i Y represents the geographically weighted regression Kriging interpolation result.GWR (u i ,v i ) represents the geographically weighted regression fitted value, ε(u) i (v) represents the Kriging interpolation result of the regression residuals; where Y GWR (u i ,v i It is calculated using the following formula:

[0018]

[0019] Among them, (u i ,v i B0(u) represents the coordinates of the i-th point to be interpolated; i ,v i ) represents the intercept; B k (u i ,v i ) is the k-th regression coefficient at the i-th interpolation point; X is x ij In matrix form, Y is the dependent variable vector, and W... i ε is the spatial weight diagonal matrix at point i, representing the spatial weights of the surrounding observations and the point to be interpolated; i It is the regression residual.

[0020] Furthermore, the observation equation is:

[0021]

[0022] Where G represents the high-resolution GOSAT methane column concentration for this oilfield region. Let σ1 be the true value of the mean methane column concentration at high resolution in this region, and σ1 be the random error in the GOSAT observation process. For the true value of the mean methane column concentration at high resolution, the following relationship holds:

[0023] in, σ² is the arithmetic mean of the high-resolution TROPOMI methane column concentration dataset for this oilfield region. Random error.

[0024] Furthermore, the cost function is obtained, which specifically includes:

[0025] Based on the observation equation, the prior probability distribution is derived as follows:

[0026]

[0027] By Bayes' theorem, the posterior probability distribution is obtained:

[0028]

[0029] The cost function is then:

[0030]

[0031] Furthermore, the deviation correction is calculated, specifically including:

[0032] Setting the derivative of the cost function to 0, we get The posterior estimate:

[0033]

[0034] The deviation correction for each high-resolution TROPOMI methane column concentration data is:

[0035]

[0036] Where n is the total number of data points in the high-resolution TROPOMI methane column concentration dataset within the oilfield area.

[0037] Secondly, embodiments of the present invention provide a multi-source satellite remote sensing data fusion device, characterized in that it includes:

[0038] The data acquisition module is used to acquire methane column concentration data for the monitored area based on GOSAT and TROPOMI.

[0039] The data interpolation and regression analysis module is used to interpolate the gaps in methane column concentration data using the geographically weighted regression kriging interpolation method, generating high-resolution GOSAT and TROPOMI methane concentration data.

[0040] A module is built to construct the observation equation by using high-resolution GOSAT methane column concentration data as observations and the mean of high-resolution TROPOMI methane column concentration data as the state variable to be optimized.

[0041] The calculation module is used to obtain the prior Gaussian distribution of high-resolution GOSAT and TROPOMI methane column concentration data based on the observation equation, and to calculate the posterior probability distribution of the mean of high-resolution TROPOMI methane column concentration data using Bayes' theorem, thereby obtaining the cost function.

[0042] The solution module inverts the cost function to obtain the bias correction for the high-resolution TROPOMI methane column concentration dataset;

[0043] The correction module uses deviation correction numbers to correct the deviation of each concentration data in the high-resolution TROPOMI methane column concentration dataset, resulting in a high-precision, high-resolution methane column concentration dataset.

[0044] Thirdly, embodiments of the present invention provide a computer device, characterized in that it includes: a processor and a memory, wherein the processor is used to execute a program for a multi-source satellite remote sensing data fusion method to implement the multi-source satellite remote sensing data fusion method.

[0045] Fourthly, embodiments of the present invention provide a storage medium, characterized in that the storage medium stores one or more programs, which can be executed by one or more processors to implement the multi-source satellite remote sensing data fusion method.

[0046] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: The present invention successfully absorbs the advantages of both types of satellite data by comprehensively utilizing geographically weighted regression Kriging interpolation, variational inversion based on Bayes' theorem, and GOSAT / TROPOMI methane column concentration data, and produces a high-precision, high-resolution methane concentration dataset for oilfield monitoring areas, providing technical support for methane emission monitoring in the energy industry. Attached Figure Description

[0047] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings listed below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a multi-source satellite remote sensing data fusion method for monitoring methane emission characteristics in oil fields, as described in this embodiment of the invention.

[0049] Figure 2 This is a structural block diagram of a multi-source satellite remote sensing data fusion device according to an embodiment of the present invention;

[0050] Figure 3 This is a graph showing the methane column concentration data after fusion in an embodiment of the present invention;

[0051] Figure 4 Taking the Shengli Oilfield area in Dongying as an example, this invention employs a multi-source satellite remote sensing data fusion method for monitoring methane emission characteristics in oilfields to obtain a high-precision, high-resolution methane concentration monitoring dataset. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. Of course, the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0053] Example 1:

[0054] See Figure 1 This invention provides a multi-source satellite remote sensing data fusion method for monitoring methane emission characteristics in oil fields, characterized by the following specific steps:

[0055] Step S1: Obtain methane column concentration data for the area to be monitored based on GOSAT and TROPOMI;

[0056] Step S2: Use geographic weighted regression kriging interpolation to interpolate the gaps in the methane column concentration data to generate high-resolution GOSAT and TROPOMI methane concentration data. Geographic weighted regression kriging interpolation comprehensively considers the relationship between explanatory variables and environmental variables within the region, and assigns different weights to environmental variables at different locations to interpolate the concentration at the location to be predicted, thereby obtaining high-resolution methane concentration data.

[0057] First, a rectangular area covering the oil field was selected, and methane column concentration data from GOSAT and TROPOMI satellites within this rectangular area were obtained. Then, a high-resolution 100m × 100m dataset was interpolated using geographic weighted regression kriging.

[0058] The geographically weighted regression kriging model is shown below:

[0059] Y GWRK (u i ,v i ) = Y GWR (u i ,v i )+ε(u i ,v i )1

[0060] In Formula 1, Y GWPK (u i ,v i Y represents the geographically weighted regression Kriging interpolation result. GWR (u i ,v i ) represents the geographically weighted regression fitted value, ε(u) i (v) represents the Kriging interpolation result of the regression residuals, where Y GWR (u i ,v i It is calculated using the following formula:

[0061]

[0062] In Equation 2, (u i ,v i B0(u) represents the coordinates of the i-th point to be interpolated; i ,v i ) represents the intercept; B k(u i ,v i ) is the k-th regression coefficient at the i-th interpolation point, estimated using weighted least squares; X is x ij In matrix form, Y is the dependent variable vector, and W... i ε is the spatial weight diagonal matrix at point i, representing the spatial weights of the surrounding observations and the point to be interpolated. The weights can be determined using methods such as Gaussian function or inverse distance weighting; i It is the regression residual.

[0063] In practice, the geographic weighted regression fitting result is calculated first, then ordinary kriging interpolation is performed on the regression residuals, and finally the two are added together to obtain the geographic weighted regression kriging interpolation result.

[0064] Step S3: Using the high-resolution GOSAT methane column concentration data generated in Step 2 as the observed values ​​and the mean value of the high-resolution TROPOMI methane column concentration data as the state variable to be optimized, construct the observation equation.

[0065] The mean values ​​of the high-resolution TROPOMI methane column concentration data were calculated using variational inversion based on the high-resolution TROPOMI methane column concentration dataset.

[0066] The specific observation equations are as follows:

[0067]

[0068] Where G represents the high-resolution GOSAT methane column concentration for this oilfield region. Let σ1 be the true value of the mean methane column concentration at high resolution in this region, and σ1 be the random error in the GOSAT observation process, estimated by the accuracy of GOSAT products. For the true value of the mean methane column concentration at high resolution in TROPOMI, the following relationship holds:

[0069]

[0070] in, σ² is the arithmetic mean of the high-resolution TROPOMI methane column concentration interpolation dataset for this oilfield region. The random error is estimated by the accuracy of the TROPOMI product.

[0071] Step S4: Based on the observation equation, obtain the prior Gaussian distribution of the high-resolution GOSAT and TROPOMI methane column concentration data. Using Bayes' theorem, calculate the posterior probability distribution of the mean of the high-resolution TROPOMI methane column concentration data, and obtain the cost function, specifically:

[0072] Based on the equation obtained in step S3, the prior probability distribution is derived as follows:

[0073]

[0074] Then, using Bayes' theorem, the posterior probability distribution is obtained:

[0075]

[0076] The cost function is:

[0077]

[0078] Step S5: Obtain the bias correction for the high-resolution TROPOMI methane column concentration dataset by inverting the cost function, specifically as follows:

[0079] Setting the derivative of the cost function to 0, we get The posterior estimate:

[0080]

[0081] The correction for each high-resolution TROPOMI methane column concentration data is:

[0082]

[0083] Where n is the total number of data points in the high-resolution TROPOMI methane column concentration dataset within the oilfield area.

[0084] Step S6: Use the deviation correction number to correct the deviation of each concentration data in the high-resolution TROPOMI methane column concentration dataset to obtain a high-precision, high-resolution methane column concentration dataset.

[0085] Currently, there is no single oilfield methane concentration monitoring dataset that simultaneously meets the requirements of high accuracy and high resolution. TROPOMI offers high spatial resolution (7 km wide), but its bias is relatively large (14 ppbv, 0.8%). Conversely, GOSAT data offers high accuracy (1.4 ppbv, 0.07%), but its spatial resolution is low (2.5° × 2.5°). Neither of these datasets alone can meet the requirements for oilfield methane monitoring. This invention, however, can produce a methane concentration dataset with both high spatial resolution (7 km wide) and high accuracy (1.4 ppbv, 0.07%), such as... Figure 4 As shown.

[0086] Example 2:

[0087] See Figure 2 This invention provides a multi-source satellite remote sensing data fusion device 100, characterized in that it includes:

[0088] Data acquisition module 101 is used to acquire methane column concentration data of the area to be monitored based on GOSAT and TROPOMI.

[0089] The data interpolation and regression analysis module 102 is used to interpolate the gaps in the methane column concentration data using the geographically weighted regression kriging interpolation method to generate high-resolution GOSAT and TROPOMI methane concentration data.

[0090] Module 103 is used to construct observation equations by using high-resolution GOSAT methane column concentration data as observations and the mean of high-resolution TROPOMI methane column concentration data as state variables to be optimized.

[0091] The calculation module 104 is used to obtain the prior Gaussian distribution of high-resolution GOSAT and TROPOMI methane column concentration data based on the observation equation, and to calculate the posterior probability distribution of the mean of high-resolution TROPOMI methane column concentration data using Bayes' theorem, thereby obtaining the cost function.

[0092] Solver Module 105 inverts the cost function to obtain the bias correction for the high-resolution TROPOMI methane column concentration dataset;

[0093] The correction module 106 uses the deviation correction number to correct the deviation of each concentration data in the high-resolution TROPOMI methane column concentration dataset, thus obtaining a high-precision, high-resolution methane column concentration dataset.

[0094] For the specific functions of each module, please refer to the relevant descriptions in the above method embodiments, which will not be repeated here.

[0095] The modules in the multi-source satellite remote sensing data fusion device 1 in this application embodiment can be individually or entirely merged into one or more modules, or some of the modules can be further divided into at least two functionally smaller units to achieve the same operation without affecting the technical effect of the embodiments of this application. The above modules are based on logical function division. In practical applications, the function of one module can also be implemented by at least two units, or the function of at least two modules can be implemented by one module. In other embodiments of this application, the multi-source satellite remote sensing data fusion device 1 may also include other modules or units. In practical applications, these functions can also be implemented with the assistance of other modules, and can be implemented collaboratively by at least two modules.

[0096] Example 3:

[0097] Figure 3This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. The computer device includes a processor and a memory. The processor is used to execute a program for a multi-source satellite remote sensing data fusion method to implement the multi-source satellite remote sensing data fusion method.

[0098] Figure 3 The computer device 200 shown includes at least one processor 201, a memory 202, at least one network interface 204, and other user interfaces 203. The various components in the computer device 200 are coupled together via a bus system 205. It is understood that the bus system 205 is used to implement communication between these components. In addition to a data bus, the bus system 205 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 3 The general labeled all buses as Bus System 205.

[0099] The user interface 203 may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0100] It is understood that the memory 202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 202 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0101] In some implementations, memory 202 stores elements, executable units or data structures, or subsets thereof, or extended sets thereof: operating system 2021 and application program 2022.

[0102] Operating System 2021 includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. Application Program 2022 includes various applications, such as media players and browsers, used to implement various application functions. Programs implementing the methods of this invention can be included in Application Program 2022.

[0103] In this embodiment of the invention, by calling the program or instructions stored in the memory 202, specifically the program or instructions stored in the application program 2022, the processor 201 is used to execute the multi-source satellite remote sensing data fusion method provided in each method embodiment.

[0104] The methods disclosed in the above embodiments can be applied to or implemented by processor 601. Processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the hardware or by instructions in software form within processor 601. Processor 601 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or by a combination of hardware and software units in the decoding processor. The software units can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 602. Processor 601 reads the information in memory 602 and, in conjunction with its hardware, completes the steps of the above method.

[0105] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0106] Example 4:

[0107] This invention provides a storage medium characterized in that the storage medium stores one or more programs, which can be executed by one or more processors to implement the multi-source satellite remote sensing data fusion method. The storage medium may include volatile memory, such as random access memory; it may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; or it may include a combination of the above types of memory.

[0108] When one or more programs in the storage medium can be executed by one or more processors to implement the multi-source satellite remote sensing data fusion method mentioned in the above method embodiments.

[0109] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented 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 implementations should not be considered beyond the scope of the invention.

[0110] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0111] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-source satellite remote sensing data fusion method for monitoring methane emission characteristics in oil fields, characterized in that, include: Acquire methane column concentration data for the monitored area based on GOSAT and TROPOMI; High-resolution GOSAT and TROPOMI methane concentration data were generated by interpolating the gaps in the methane column concentration data using the geographically weighted regression kriging interpolation method. High-resolution GOSAT methane column concentration data were used as observations, and the mean value of high-resolution TROPOMI methane column concentration data was used as the state variable to be optimized to construct the observation equation. Based on the observation equation, the prior Gaussian distribution of high-resolution GOSAT and TROPOMI methane column concentration data is obtained. Using Bayes' theorem, the posterior probability distribution of the mean of high-resolution TROPOMI methane column concentration data is calculated, and the cost function is obtained. The bias correction for the high-resolution TROPOMI methane column concentration dataset is obtained by inverting the cost function; By using the bias correction, the bias of each concentration data in the high-resolution TROPOMI methane column concentration dataset is corrected, resulting in a high-precision, high-resolution methane column concentration dataset.

2. The multi-source satellite remote sensing data fusion method according to claim 1, characterized in that, Generate high-resolution GOSAT and TROPOMI methane column concentration data, including: Select a rectangular area covering the oil field and obtain the methane column concentration data of GOSAT and TROPOMI within this rectangular area; High-resolution methane column concentration datasets of 100m×100m were generated by interpolation using the geographic weighted regression kriging method.

3. The multi-source satellite remote sensing data fusion method according to claim 2, characterized in that, The geographically weighted regression kriging model is shown below: Y GWRK (u i ,v i )=Y GWR (u i ,v i )+ε(u i ,v i ) Among them, Y GWRK (u i ,v i Y represents the geographically weighted regression Kriging interpolation result. GWR (u i ,v i ) represents the geographically weighted regression fitted value, ε(u) i (v) represents the Kriging interpolation result of the regression residuals; where Y GWR (u i ,v i It is calculated using the following formula: Among them, (u i ,v i B0(u) represents the coordinates of the i-th point to be interpolated; i ,v i ) is the intercept; Bk(u i ,v i ) is the k-th regression coefficient at the i-th interpolation point; X is x ij In matrix form, Y is the dependent variable vector, and W... i ε is the spatial weight diagonal matrix at point i, representing the spatial weights of the surrounding observations and the point to be interpolated; i It is the regression residual.

4. The multi-source satellite remote sensing data fusion method according to claim 3, characterized in that, The observation equation is: Wherein, G represents the GOSAT methane concentration in this oilfield region. Let σ1 be the true value of the mean methane column concentration in the TROPOMI region, and σ1 be the random error in the GOSAT observation process. For the true value of the mean methane column concentration in the TROPOMI region, the following relationship holds: in, σ² is the arithmetic mean of the high-resolution TROPOMI methane column concentration dataset for this oilfield region. Random error.

5. The multi-source satellite remote sensing data fusion method according to claim 3, characterized in that, The cost function is obtained, specifically including: Based on the observation equation, the prior probability distribution is derived as follows: By Bayes' theorem, the posterior probability distribution is obtained: The cost function is then:

6. The multi-source satellite remote sensing data fusion method according to claim 5, characterized in that, The deviation correction is calculated, specifically including: Setting the derivative of the cost function to 0, we get The posterior estimate: The deviation correction for each high-resolution TROPOMI methane column concentration data is: Where n is the total number of data points in the high-resolution TROPOMI methane column concentration dataset within the oilfield area.

7. A multi-source satellite remote sensing data fusion device, characterized in that, include: The data acquisition module is used to acquire methane column concentration data for the monitored area based on GOSAT and TROPOMI. The data interpolation and regression analysis module is used to interpolate the gaps in methane column concentration data using the geographically weighted regression kriging interpolation method, generating high-resolution GOSAT and TROPOMI methane concentration data. A module is built to construct the observation equation by using high-resolution GOSAT methane column concentration data as observations and the mean of high-resolution TROPOMI methane column concentration data as the state variable to be optimized. The calculation module is used to obtain the prior Gaussian distribution of high-resolution GOSAT and TROPOMI methane column concentration data based on the observation equation, and to calculate the posterior probability distribution of the mean of high-resolution TROPOMI methane column concentration data using Bayes' theorem, thereby obtaining the cost function. The solution module inverts the cost function to obtain the bias correction for the high-resolution TROPOMI methane column concentration dataset; The correction module uses deviation correction numbers to correct the deviation of each concentration data in the high-resolution TROPOMI methane column concentration dataset, resulting in a high-precision, high-resolution methane column concentration dataset.

8. A computer device, characterized in that, include: A processor and a memory, the processor being configured to execute a program for a multi-source satellite remote sensing data fusion method to implement the multi-source satellite remote sensing data fusion method according to any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the multi-source satellite remote sensing data fusion method according to any one of claims 1 to 7.

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