Mining area methane emission inversion method based on satellite observation and Gaussian plume model

Abstract

The invention discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

Description

Technical Field

[0001] This invention relates to the field of methane emission model prediction in mining areas, and in particular to a method for inverting methane emissions in mining areas based on satellite observations and a Gaussian plume model. Background Technology

[0002] Methane (CH4) is one of the most significant greenhouse gases; compared to carbon dioxide, methane has a shorter average atmospheric lifetime of approximately 9 years and a higher global warming potential. Therefore, mitigating methane emissions is an effective and rapid intervention to address recent climate change. Since the pre-industrial era, global atmospheric methane concentrations have nearly doubled, making it crucial to improve our understanding and quantification of methane emissions. Coal mining activities contribute approximately 12% of global anthropogenic methane emissions, with 90% of these emissions originating from underground mining operations. Global methane budget studies indicate that coal mining accounts for 32% of total fossil fuel-related methane emissions. Research shows that the widely used Emissions Database for Global Atmospheric Research (EDGAR) v4.2 emissions inventory significantly overestimates methane emissions from coal mining in China. A small number of exceptionally strong point sources (“super-emitters”) account for a large proportion of total emissions and are often easily mitigated. Anomalous process conditions such as equipment failure, design flaws, or operational problems are the main causes of super-emitters. Monitoring emissions from large point sources during coal mining and other processes, and understanding the causes of abnormal emissions, is crucial for advancing and implementing carbon reduction activities.

[0003] Currently, methane emission accounting mainly relies on bottom-up inventory modeling methods based on activity levels and emission factors. However, due to errors in activity level data and lags in emission factor updates that fail to reflect changes in different technological levels, bottom-up inventories suffer from significant uncertainties in regional and industry-specific point source accounting. The development of quantitative remote sensing technology has provided a new method for high spatiotemporal resolution methane emission estimation, enabling the identification of emission information through satellite inversion data. Current research focuses on large regional scales such as global and national levels, primarily employing a combination of satellite observations and atmospheric transport models for inversion. In recent years, studies have been conducted to accurately identify and quantitatively invert emissions from individual oil and gas facilities and coal mines based on sub-kilometer spatial resolution (<1 km²) spaceborne sensor observation data. However, such high-resolution satellite systems are limited by observation strategies, and their spatial coverage is typically confined to pre-defined key monitoring areas.

[0004] In contrast, the Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI-S5P), a global atmospheric pollution monitoring satellite, boasts an imaging swath of 2600 km, providing daily global coverage and demonstrating potential for quantifying methane super-emission sources or regional coal emissions using a top-down approach. Therefore, this invention proposes a remote sensing inversion method for coal mine methane emissions based on TROPOMI-S5P satellite observations, providing an objective estimation method for accurately quantifying methane emissions from point source areas. Summary of the Invention

[0005] The purpose of this invention is to provide a method for inverting methane emissions from mining areas based on satellite observations and a Gaussian plume model. By utilizing atmospheric environment satellite observation data, meteorological data, and methane emission inventory data from coal mining, methane emissions and time-series data of methane emissions are obtained through inversion using a Gaussian plume model. This method solves the problems of existing technologies, such as errors in activity level data and lag in emission factor updates, which fail to reflect changes in different technological levels. It also addresses the significant uncertainties in existing methane emission inventories for regional and industry point source accounting, and their inability to reflect the true emission situation.

[0006] The objective of this invention is achieved through the following technical solution: A method for inverting methane emissions from mining areas based on satellite observations and a Gaussian plume model, the method comprising: S1. Construct a gridded geographic map and acquire satellite-inverted XCH4 dataset, meteorological data, emissions inventory data, and global energy monitoring data; S2. Use emission inventory data and global energy monitoring data to identify methane emission sources on a geographic map, and expand the spatial range centered on the methane emission sources as the study area. S3. Extract the satellite-inverted XCH4 data of the study area from the satellite-inverted XCH4 dataset, and use the atmospheric background concentration calculation method to obtain the methane background concentration and XCH4 enhancement in the geographic map. S4. Identify methane-enhanced plumes from methane emission sources in the study area by combining the spatial distribution of XCH4 enhancement with meteorological data on geographic maps; S5. Construct a Gaussian plume model including a coordinate system. Use the Gaussian plume model to simulate and invert the Gaussian plume wind field of the methane emission source. The X-axis of the coordinate system represents the wind direction, and the Y-axis represents the vertical wind direction. The inversion expression is as follows: ,in Enhance XCH4 at coordinates (x,y). The methane emission rate of the methane emission source. The standard deviation of the wind perpendicular to the y-axis. To increase the wind speed at the centerline of the methane plume; The Gaussian plume model inverts and outputs the methane emission rate of the methane emission source at the current time, and the time series data of methane emission from the methane emission source are obtained sequentially according to the time series.

[0007] To better implement this invention, in method S5, the standard deviation of the wind perpendicular to the y-axis direction is... The expression is as follows: ,in The atmospheric stability coefficient, These are the model parameters.

[0008] Preferably, the Gaussian plume model uses the XCH4 enhanced observations transformed by the input method S3 and the XCH4 enhanced predicted values ​​of the Gaussian plume model to perform constrained inversion using the least squares method. The XCH4 enhanced observations adopt the following transformation formula: , ,in Let be the molar mass constant of air. Let be the molar molecular mass constant of methane. The acceleration due to gravity of methane. The surface pressure of the air column. This refers to the water vapor content.

[0009] Preferably, in method S3, the atmospheric background concentration calculation method is a local adaptive background value calculation method. The method for obtaining the methane background concentration using the local adaptive background value calculation method is as follows: Select a pixel from the XCH4 data retrieved from the satellite inversion of the study area and select the region centered on the pixel. The expression for the methane background concentration BC of that pixel is as follows: ,in The median of the XCH4 data within the region. This represents the average value of XCH4 data within the region. The standard deviation of XCH4 data within the region. This is a preset constant.

[0010] Preferably, in method S3, the background methane concentration at location i is used in the geographic map. The XCH4 enhancement at position i is calculated as follows: ,in XCH4 enhancement at position i, For the XCH4 data at position i in the satellite inversion XCH4 data of the study area, Let be the methane background concentration at position i.

[0011] Preferably, in method S2, a 2.1°×2.1° spatial range centered on the methane emission source is used as the extended spatial range, and the extended spatial range is used as the study area. Satellite inversion XCH4 data for the study area is cropped from the satellite inversion XCH4 dataset, and satellite inversion XCH4 time series data for the study area is obtained sequentially according to the time series.

[0012] Preferably, in method S4, the methane-enhanced plume is an XCH4-enhanced cluster region containing methane emission sources within the study area of ​​the geographic map; an XCH4-enhanced cluster identification model is constructed in the geographic map, and the XCH4-enhanced cluster identification model performs geographic location clustering processing on the XCH4 enhancements within the study area and selects the XCH4-enhanced cluster region containing methane emission sources as the methane-enhanced plume.

[0013] Preferably, in method S5, the Gaussian plume model uses wind field data from meteorological data to construct a coordinate system with the X-axis representing wind direction and the Y-axis representing the vertical wind direction. The wind field data includes wind direction and wind speed.

[0014] Preferably, in method S5, the methane emissions at each time scale are calculated cumulatively according to the time scale of hour, day, week, month or year.

[0015] Preferably, in method S3, the atmospheric background concentration is calculated using the Gaussian curve fitting method, the expression of which is as follows: ,in The XCH4 data in the satellite-retrieved XCH4 data for the study area is denoted as , and the orbital distance corresponding to the XCH4 data in the satellite-retrieved XCH4 data for the study area is denoted as . m, b, A, μ, and σ are the shape parameters estimated by nonlinear least-squares fitting, respectively. The methane background concentration BC is obtained according to the following formula. .

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) This invention utilizes atmospheric environment satellite observation data, meteorological data, and coal mine methane emission inventory data to obtain methane emission and methane emission time series data through Gaussian plume model inversion. This solves the problems of existing technologies, such as the inability to reflect changes in different technology levels due to errors in activity level data and the lag in emission factor updates. It also solves the problem that existing methane emission inventories have great uncertainty in regional and industry point source accounting and cannot reflect the true emission situation.

[0017] (2) The present invention can obtain methane emission data by inverting the Gaussian plume model based on real-time acquired satellite XCH4 data and meteorological data, which increases the frequency of obtaining methane emission data and has higher real-time performance. It can provide key technologies and data support for regional and industry methane estimation and emission reduction policy formulation. The method of the present invention is not affected by the inconsistency and accuracy of activity level data. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the methane-enhanced plume in study areas hh and xhs, as exemplified in the embodiments. Figure 3 This is a schematic diagram illustrating the inversion of methane emission rates in a study area using a Gaussian plume model, as exemplified in the embodiments. Figure 4 This is a comparison chart of methane emissions and coal mining volume in study areas hh and xhs, as exemplified in the embodiments. Figure 5 The example illustrates a comparison of methane emissions obtained using the method of the present invention with other methods for study areas hh and xhs. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to embodiments: Example like Figure 1 As shown, a method for inverting methane emissions from mining areas based on satellite observations and a Gaussian plume model is described, the method comprising: S1. Construct a gridded geographic map and acquire the satellite-inverted XCH4 dataset, meteorological data, emissions inventory data, and global energy monitoring data. XCH4 refers to the average dry air mole fraction of methane column in the atmosphere. In this embodiment, the satellite-inverted XCH4 dataset is obtained by inverting Level 2 using the Copernicus Sentinel-5 Precursor Tropospheric Monitoring Instrument (TROPOMI-S5P). The meteorological data includes three meteorological elements: wind speed, wind direction, and surface solar radiation. The meteorological data is obtained from model reanalysis. In this embodiment, the reanalysis meteorological data uses the fifth-generation European Centre for Medium-Range Weather Forecasts Global Climate and Atmosphere Reanalysis dataset (ERA5) and / or the Modern Research and Applications Retrospective Analysis, Version 2 (MERRA-2) meteorological data. The model reanalysis meteorological data is resampled to the same spatial resolution as the satellite-inverted XCH4 dataset using bilinear interpolation. Emissions inventory data can be selected from the EDGAR emissions inventory data, a global greenhouse gas and air pollutant emissions database jointly developed by the Joint Research Centre for Europe (JRC) and the Netherlands Environmental Assessment Agency (PBL). Global energy monitoring data serves as supplementary data to the emissions inventory data. In this embodiment, the GEM global coal mine tracking dataset is used to trace the emissions inventory from the global energy monitoring data and supplement it with the energy emissions inventory. The global energy monitoring data includes coal mining type, latitude and longitude information of the coal mine location, and predicted coal mining depth information.

[0020] In this embodiment, the TROPOMI-S5P Level 2 inverted XCH4 product (https: / / www.earthdata.nasa.gov) for the study area on the study date was collected and organized as the atmospheric environment satellite inverted XCH4 dataset, with a spatial resolution of 7 km × 3.5 km. Only XCH4 measurement data with a quality assessment value (qa_value) greater than 0.5 were selected for subsequent analysis. The ERA5 0.25°×0.25° spatial resolution hourly meteorological reanalysis data product (https: / / cds.climate.copernicus.eu / datasets) and the 0.5°×0.625° spatial resolution MERRA-2 hourly meteorological reanalysis data product (https: / / gmao.gsfc.nasa.gov / reanalysis / MERRA-2 / ) for the study date were also collected and organized, including 10m... Three meteorological elements—U (east-west) and V (north-south) wind vectors, and surface solar radiation—were used as the meteorological dataset for model reanalysis. The dataset was spatially interpolated using bilinear interpolation to unify its spatial resolution to the same level as the XCH4 dataset retrieved from atmospheric environment satellites. Monthly EDGARv8.0 coal mining methane emission inventory data with a spatial resolution of 0.1°×0.1° for the study area during the study period and GFEI fuel extraction inventory data during the study period were collected and compiled as the coal mining methane emission inventory dataset. The GEM global coal mine tracking dataset, including information on coal mine location, type, and mining depth, was also collected and compiled as global energy monitoring data.

[0021] S2. Using emission inventory data and global energy monitoring data, determine methane emission sources on a geographic map, and use the methane emission source as the center to expand the spatial range as the study area. In some embodiments, the spatial range of 2.1° × 2.1° latitude and longitude centered on the methane emission source (i.e., the spatial range with a longitude span of 2.1° and a latitude span of 2.1° centered on the methane emission source) is used as the expanded spatial range, and this expanded spatial range is used as the study area. Satellite inversion XCH4 data for the study area is cropped from the satellite inversion XCH4 dataset (this embodiment mainly studies the study area, so satellite inversion XCH4 data for the study area is cropped or extracted). The satellite inversion XCH4 time series data for the study area is obtained sequentially according to the time series (the method of this invention can obtain the methane emission rate at the current moment; in order to obtain the methane emission time series data, the satellite inversion XCH4 time series data for the study area needs to be obtained sequentially according to the time series when acquiring the data).

[0022] In this embodiment, two different geographical locations were selected and methane emission sources were determined according to methods S1 and S2, resulting in two typical mining areas: study area hh (Inner Mongolia ** coal mining area) and study area xhs (Xinjiang ** field mining area). The basic information on methane emission sources in the two study areas (including study area hh and study area xhs) is shown in the table below:

[0023] S3. Extract satellite-retrieved XCH4 data for the study area from the satellite-retrieved XCH4 dataset, and use atmospheric background concentration calculation methods to obtain the methane background concentration and XCH4 enhancement on the geographic map. Atmospheric background concentration calculation methods include the local adaptive background value calculation method, the Gaussian curve fitting method, and the four-region average method.

[0024] In some embodiments, the atmospheric background concentration calculation method employs a local adaptive background value calculation method (considering that methane background information in the actual environment is usually non-homogeneous, the local adaptive background value calculation method is used to determine the background concentration). The method for obtaining the methane background concentration using the local adaptive background value calculation method is as follows: Select a pixel from the XCH4 data retrieved from the satellite inversion of the study area and select a region centered on the pixel (e.g., a 21×21 pixel region centered on the pixel). The expression for the methane background concentration BC of that pixel is as follows: ,in The median of the XCH4 data within the region. This represents the average value of XCH4 data within the region. The standard deviation of XCH4 data within the region. This is a preset constant. If the average value of XCH4 data within the region... Median of XCH4 data Difference divided by standard deviation If the methane background concentration BC of a pixel is greater than 0.3, the median of the XCH4 data is selected. Otherwise, according to The background methane concentration BC is calculated using the formula.

[0025] In some embodiments, the atmospheric background concentration calculation method can also select the Gaussian curve fitting method (which is preferred in cases where the correlation between satellite-observed enhanced plumes and simulated plumes obtained by the local adaptive background value calculation method is relatively poor). The expression for the Gaussian curve fitting method is as follows: ,in The XCH4 data in the satellite-retrieved XCH4 data for the study area is denoted as , and the orbital distance corresponding to the XCH4 data in the satellite-retrieved XCH4 data for the study area is denoted as . m, b, A, μ, and σ are the shape parameters estimated by nonlinear least-squares fitting, respectively. The methane background concentration BC is obtained according to the following formula. Another method for calculating atmospheric background concentration is the four-region average method, which uses the average value of two different regions surrounding the methane emission source as the background value.

[0026] In method S3, the present invention utilizes the methane background concentration at location i in a geographic map. The calculated XCH4 enhancement at position i (XCH4 enhancement refers to an increase in methane concentration compared to the methane background concentration; XCH4 enhancement is a positive value) is expressed as follows: ,in XCH4 enhancement at position i, . For the XCH4 data at position i in the satellite inversion XCH4 data of the study area, Let be the methane background concentration at position i. In this embodiment, the unit of XCH4 enhancement is generally ppb, which is different from the unit of XCH4 enhancement in the Gaussian plume model of this embodiment. Therefore, it is necessary to convert the XCH4 enhancement in method S3. The XCH4 enhancement observation value (the XCH4 enhancement observation value is the XCH4 enhancement input to the Gaussian plume model, which is converted from the XCH4 enhancement by the conversion formula) adopts the following conversion formula: , ,in Let be the molar mass constant of air. Let be the molar molecular mass constant of methane. The acceleration due to gravity of methane. The surface pressure of the air column. This refers to the water vapor content.

[0027] S4. Identify methane-enhanced plumes from methane emission sources in the study area by combining the spatial distribution of XCH4 enhancements with meteorological data on the geographic map. Preferably, the methane-enhanced plume is an XCH4 enhancement cluster region containing methane emission sources within the study area on the geographic map; an XCH4 enhancement cluster identification model is constructed on the geographic map, which performs geographic clustering processing on the XCH4 enhancements within the study area and selects the XCH4 enhancement cluster region containing methane emission sources as the methane-enhanced plume. In the two study mining areas, hh and xhs, such as... Figure 2As shown, examples of methane-enhanced plumes identified in two research mining areas at different times are illustrated. The arrows in the figure indicate wind direction. Overall, satellite observations can capture information on point source emission plumes from coal mines at certain times. It can be seen that the distribution of methane-enhanced plumes from different emission points varies at different times due to factors such as emission intensity, wind field, and surrounding environment. It is important to note that the highest captured plume enhancement values ​​are not always located at the grid point where the coal mine is located or at the location closest to the coal mine. This may be related to wind speed and direction, as well as diffusion during the transport process. It may also be due to a certain offset between the actual ventilation opening and the coal mine location recorded in the GEM, causing a deviation between the high concentration points detected by the satellite and the location of a single emission source.

[0028] S5. Construct a Gaussian plume model with a coordinate system. The Gaussian plume model uses wind field data from meteorological data to construct a coordinate system with the X-axis representing wind direction and the Y-axis representing the vertical wind direction. The wind field data includes wind direction and wind speed. The Gaussian plume model performs Gaussian plume wind field simulation and inversion for methane emission sources. The X-axis of the coordinate system represents wind direction, and the Y-axis represents the vertical wind direction. The Gaussian plume model uses the XCH4 enhancement from input method S3 to convert to XCH4 enhanced observations. The XCH4 enhanced observations and the XCH4 enhanced predictions from the Gaussian plume model are then used for constrained inversion using the least squares method. The XCH4 enhanced observations are converted using the following formula: , ,in Let be the molar mass constant of air. Let be the molar molecular mass constant of methane. The acceleration due to gravity of methane. The surface pressure of the air column. This refers to the water vapor content.

[0029] The inversion expression for the Gaussian plume model is as follows: ,in XCH4 enhancement at coordinates (x,y) (in this embodiment, the unit is g / m²). The methane emission rate of the methane emission source (in this example, the unit is g / s). The standard deviation of the wind perpendicular to the y-axis. Wind speed at the centerline of the methane-enhanced plume (unit: m / s in this embodiment). Standard deviation of the wind perpendicular to the y-axis. The expression is as follows: ,in The atmospheric stability coefficient (based on the Pasquill–Gifford stability classification method to classify and determine the source environment). These are the model parameters. Figure 3 This displays the observed and simulated plume conditions (hh) in the study area on a certain date. Figure 3 In the figures, (a) the original XCH4 plume simulated by the Gaussian plume model; (b) the simulated plume corresponding to the observation spatial resolution; and (c) XCH4 observed by the TROPOMI-S5P. According to the survey, the study area hh adopts a shift-based continuous mining mode, and methane emissions during coal mining have a significant continuous characteristic, spanning the entire mining cycle. Therefore, the instantaneous emissions captured by satellite observations can be considered as the sum of emissions from multiple adjacent coal mines simultaneously, and the mixed plume effect of multi-source emissions is considered in the Gaussian plume simulation. On a certain date at 13:30 local time, the area was affected by southwest winds, and the concentration peak regions of the simulated plume and the actual observed plume matched well (R=0.73). The methane emission rate of hh in the study area was obtained as 518.44±81.28 t / d.

[0030] This invention utilizes the XCH4 enhancement conversion from input method S3 to XCH4 enhancement observations and the XCH4 enhancement predictions from the Gaussian plume model. A constrained inversion is then performed using the least squares method (preferably linear least squares fitting). The fitted methane plume enhancement data obtained through linear least squares fitting is then used to invert the instantaneous (current) methane emission rate using the inversion expression of the Gaussian plume model, thereby obtaining the inverted methane emission amount from the coal mine at the current moment. The Gaussian plume model inversion outputs the current time methane emission rate from the methane emission source, and the time-series data of methane emission amounts from the methane emission source are obtained sequentially according to the time series. The methane emission time series data of the methane emission source is obtained according to methods S1 to S5. Then, the methane emission amount at each time scale is calculated cumulatively according to the time scale of hour, day, week, month or year. For example, if the methane emission source emits for 8 hours during the day, the methane emission time series data of the methane emission source for the 8 hours of the day can be obtained, and the cumulative methane emission amount for the day can be obtained. Similarly, the cumulative methane emission amount for the week, month and year can also be obtained.

[0031] Figure 4 The comparison between methane emissions from coal mining and coal production in the two study areas obtained from the inversion shows that, overall (black line represents production, red line represents emissions), the methane emission estimates based on the remote sensing inversion method proposed in this invention are consistent with the changes in coal mining production. Figure 5This diagram shows a comparison of the inversion results of the method of this invention in different mining areas with EDGAR, GFEI inventory data, and emissions calculated using different emission factors. The IPCCfactor in the diagram is an estimate based on IPCC 2006 and national greenhouse gas emission factor data. The emissions calculated by Liu et al. (2024) and Ma et al. (2020) are based on the emission factors given in the studies of Liu et al. (2024) and Ma et al. (2020), respectively. The results show that the CH4 emission estimates obtained by the method of this invention are largely consistent with the emission factor calculations provided by Liu et al. in areas containing underground coal mines, and are close to the emission factor calculations provided in the article by Ma et al. (2020), and even closer to the 1.2 m³ / t emission factor used in the study by Ma et al. (2020). In contrast, the estimation results of this study show a larger discrepancy with the emissions obtained from EDGAR, GFEI inventories, and IPCC factors. This may be mainly because the IPCC 2006 emission factor update is relatively outdated and cannot reflect the changes in emission factors brought about by technological changes in recent years. In this study, when calculating emissions based on IPCC factors, open-pit coal mines directly used the 2 m³ / t emission factor from the National Greenhouse Gas Emission Factor Database, while underground coal mines, based on the mining depth predicted by GEM (both greater than 400m), adopted the IPCC-recommended emission factor of 25 m³ / t. This resulted in the IPCC-predicted emissions being significantly higher than the emission inventory results. Previous studies have indicated that the emission factors of underground coal mines in China have been declining, with the average emission factor decreasing from 5.48 kg / t in 2011 to 4.48 kg / t in 2019 (Liu et al., 2024). In recent years, the mining model of "extracting coalbed methane first and then mining coal" has been widely adopted in Shanxi, Inner Mongolia, Shaanxi, and other regions, which can significantly reduce methane emissions during the mining process. Numerous studies have also shown that the EDGAR and GFEI methane emission inventories significantly overestimate coal mining areas in Shanxi, Inner Mongolia, and other regions (Han et al., 2024; Hu et al., 2024; Tu et al., 2024), which is consistent with the results estimated based on the method provided in this invention.

[0032] 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 inverting methane emissions from mining areas based on satellite observations and a Gaussian plume model, characterized in that: The methods include: S1. Construct a gridded geographic map and acquire satellite-inverted XCH4 dataset, meteorological data, emissions inventory data, and global energy monitoring data; S2. Use emission inventory data and global energy monitoring data to identify methane emission sources on a geographic map, and expand the spatial range centered on the methane emission sources as the study area. S3. Extract the satellite-inverted XCH4 data of the study area from the satellite-inverted XCH4 dataset, and use the atmospheric background concentration calculation method to obtain the methane background concentration and XCH4 enhancement in the geographic map. S4. Identify methane-enhanced plumes from methane emission sources in the study area by combining the spatial distribution of XCH4 enhancement with meteorological data on geographic maps; S5. Construct a Gaussian plume model including a coordinate system. Use the Gaussian plume model to simulate and invert the Gaussian plume wind field of the methane emission source. The X-axis of the coordinate system represents the wind direction, and the Y-axis represents the vertical wind direction. The inversion expression is as follows: ,in Enhance XCH4 at coordinates (x,y). The methane emission rate of the methane emission source. The standard deviation of the wind perpendicular to the y-axis. To increase the wind speed at the centerline of the methane plume; The Gaussian plume model inverts and outputs the methane emission rate of the methane emission source at the current time, and the time series data of methane emission from the methane emission source are obtained sequentially according to the time series.

2. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: In method S5, the standard deviation of the wind perpendicular to the y-axis is... The expression is as follows: ,in The atmospheric stability coefficient, These are the model parameters.

3. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: The Gaussian plume model uses the XCH4 enhanced observations transformed by input method S3 and the XCH4 enhanced predictions of the Gaussian plume model to perform constrained inversion using the least squares method. The XCH4 enhanced observations are transformed using the following formula: , ,in Let be the molar mass constant of air. Let be the molar molecular mass constant of methane. The acceleration due to gravity of methane. The surface pressure of the air column. This refers to the water vapor content.

4. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: In method S3, the atmospheric background concentration is calculated using a local adaptive background value calculation method. The method for obtaining the methane background concentration using this method is as follows: Select a pixel from the XCH4 data retrieved from the satellite inversion database of the study area and select the region centered on that pixel. The expression for the methane background concentration BC of that pixel is as follows: ,in The median of the XCH4 data within the region. This represents the average value of XCH4 data within the region. The standard deviation of XCH4 data within the region. This is a preset constant.

5. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: In method S3, the methane background concentration at location i is used in the geographic map. The XCH4 enhancement at position i is calculated as follows: ,in XCH4 enhancement at position i, For the XCH4 data at position i in the satellite inversion XCH4 data of the study area, Let be the methane background concentration at position i.

6. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: In method S2, a 2.1°×2.1° spatial range centered on the methane emission source is used as the extended spatial range, which is then used as the study area. Satellite-inverted XCH4 data for the study area is cropped from the satellite-inverted XCH4 dataset, and time-series data of satellite-inverted XCH4 for the study area is obtained sequentially according to the time series.

7. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: In method S4, the methane-enhanced plume is the XCH4-enhanced cluster region containing methane emission sources within the study area of ​​the geographic map. An XCH4-enhanced cluster identification model is constructed in the geographic map. The XCH4-enhanced cluster identification model performs geographic clustering processing on the XCH4 enhancements within the study area and selects the XCH4-enhanced cluster region containing methane emission sources as the methane-enhanced plume.

8. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: In method S5, the Gaussian plume model uses wind field data from meteorological data to construct a coordinate system with the X-axis representing wind direction and the Y-axis representing the vertical wind direction. The wind field data includes wind direction and wind speed.

9. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: In method S5, methane emissions are calculated cumulatively over time scales of hours, days, weeks, months, or years.

10. The method for inverting methane emissions from mining areas based on satellite observation and a Gaussian plume model according to claim 1, characterized in that: In method S3, the atmospheric background concentration is calculated using the Gaussian curve fitting method, the expression of which is as follows: ,in The XCH4 data in the satellite-retrieved XCH4 data for the study area is denoted as , and the orbital distance corresponding to the XCH4 data in the satellite-retrieved XCH4 data for the study area is denoted as . m, b, A, μ, and σ are the shape parameters estimated by nonlinear least-squares fitting, respectively. The methane background concentration BC is obtained according to the following formula. .

Images