A methane fine concentration field inversion method, system and device based on a laser radar, and a storage medium

Abstract

The application provides a laser radar-based methane fine concentration field inversion method and system, device and storage medium, and belongs to the technical field of atmospheric remote sensing monitoring, and comprises the following steps: acquiring data of the interference degree of a gas in an atmospheric environment on different wave number lasers and data of the sensitivity of different methane detection wavelengths to the atmospheric environment; determining a target methane detection wavelength based on the acquired data of the interference degree of the gas in the atmospheric environment on different wave number lasers and the data of the sensitivity of different methane detection wavelengths to the atmospheric environment; performing differential absorption laser radar observation according to the determined target methane detection wavelength; and performing methane concentration inversion on an observation signal by using a Chebyshev fitting inversion algorithm to obtain an inversion result. The laser radar-based methane fine concentration field inversion method and system can improve the precision of laser radar inversion of methane concentration and provide effective support for methane emission reduction.

Description

Technical Field

[0001] This invention relates to the field of atmospheric remote sensing monitoring technology, and in particular to a method, system, device, and storage medium for methane fine concentration field inversion based on lidar. Background Technology

[0002] Methane (CH4) is a major greenhouse gas. Since the Industrial Revolution, global atmospheric CH4 concentrations have been continuously increasing, making it the second largest global warming factor after carbon dioxide (CO2). Furthermore, methane's global warming potential is 27 to 30 times higher than CO2, making the control of methane emissions crucial for mitigating global temperature rise. Human activities such as agricultural production, fossil fuel production, and waste disposal account for the vast majority of global methane emissions. In August 2021, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report emphasized the importance and necessity of methane emission reduction. Monitoring atmospheric methane concentrations allows for the assessment of methane emissions in different regions and industries, helping governments and businesses develop targeted emission reduction plans, track reduction effectiveness, and improve emission reduction strategies in real time. Moreover, as methane is a global greenhouse gas, accurate methane concentration monitoring contributes to enhancing global climate governance capabilities. Therefore, high-precision methane concentration detection is essential.

[0003] Differential absorption light detection and ranging (DIAL) is internationally recognized as the most powerful tool for observing atmospheric CH4 concentration. Compared with other detection methods, DIAL has advantages such as high detection accuracy, less susceptibility to environmental influences, and high temporal and spatial resolution, thus showing great potential in atmospheric CH4 detection. A DIAL system typically uses two laser beams with similar wavelengths for detection. One laser beam is located at the absorption peak of the gas and is strongly absorbed, while the other laser beam is selected near the absorption trough, resulting in weak or almost no absorption. If the difference between these two wavelengths is minimal, atmospheric backscattering, aerosol and atmospheric molecule extinction effects can be ignored. By analyzing the echo signals of these two laser beams, the gas concentration at different distances can be determined.

[0004] However, the atmosphere contains a wide variety of molecules with overlapping absorption wavelengths, and other gases in the atmosphere can significantly interfere with the measured gas. Furthermore, the degree of interference varies with changes in the detection environment. Therefore, the selection of the lidar detection wavenumber (wavelength) is crucial for the accuracy of methane concentration retrieval. In addition, traditional lidar methane concentration retrieval algorithms are highly susceptible to noise, exhibiting large signal fluctuations and low retrieval accuracy. Summary of the Invention

[0005] To address the above problems, this invention provides a method, system, device, and storage medium for fine methane concentration field inversion based on lidar, optimizing the selection of detection wavelength and inversion algorithm to improve the accuracy of lidar inversion of methane concentration.

[0006] This invention provides a method for retrieving fine methane concentration fields based on lidar, comprising: To obtain data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and the sensitivity of different methane detection wavelengths to the atmospheric environment; The target methane detection wavelength is determined based on the obtained data on the interference of gases in the atmospheric environment on lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment. Differential absorption lidar observations are performed based on the determined target methane detection wavelength. The methane concentration is then inverted using a Chebyshev fitting inversion algorithm based on the observed signal to obtain the inversion results.

[0007] As a further improvement of the present invention, the acquisition of data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment includes acquiring data on the degree of interference of CO2 and H2O in different atmospheric environments to lasers of different wavenumbers.

[0008] As a further improvement of the present invention, the interference levels of CO2 and H2O on lasers of different wavenumbers in different atmospheric environments are calculated using the following formula:

[0009] In the formula, Q Interference ， For different atmospheric environments For a given wavenumber laser, the absorption cross-section For different atmospheric environments For a given wavenumber laser, the absorption cross-section For different atmospheric environments For a given wavenumber laser, the interference degree is... Q The higher the value ， This indicates that the less interference CO2 and H2O cause to a laser at a given wavenumber; The sensitivity data of different methane detection wavelengths to the atmospheric environment were calculated using the following formula:

[0010] In the formula, Indicates the first c Atmospheric environment For a given wavenumber laser, the absorption cross-section Indicates all atmospheric conditions The average value, n The atmospheric environment quantity indicates that low sensitivity means that the given detection wavelength remains stable under significant environmental changes.

[0011] As a further improvement of the present invention, determining the target methane detection wavelength based on the obtained data on the interference of gases in the atmospheric environment to lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment includes using a variable step-size genetic algorithm to select the laser radar detection wavenumber to determine the optimal methane detection wavelength.

[0012] As a further improvement of the present invention, the step of using a variable step-size genetic algorithm to select the laser radar detection wavenumber to determine the optimal methane detection wavelength includes: Based on the absorption cross-sectional area of ​​methane to laser under standard conditions and the interference of atmospheric gases to lasers of different wavenumbers, the optimal wavenumber band range was initially determined. Within the defined wavenumber optimization band range, wavenumber intervals and variation ranges and intervals of atmospheric environment including temperature, pressure and sea waves are set. Set the initial band step size, give the solution space range, and use a genetic algorithm to search for a better solution in the solution space. Then, based on the better solution, reduce the solution space range to a preset ratio of the previous solution space range. The step size is gradually shortened, and the genetic algorithm is used to search for a better solution in the solution space. The solution space is then reduced to a preset proportion of the previous solution space based on the better solution, until the step size is the preset final step size. The best solution obtained in the final step is then taken as the optimal methane detection wavelength.

[0013] As a further improvement of the present invention, the step of performing differential absorption lidar observation based on the determined target methane detection wavelength, and then using a Chebyshev fitting inversion algorithm to invert the methane concentration from the observed signal to obtain the inversion result includes: Chebyshev fitting inversion algorithm was used to fit the DAOD of CH4 at different heights to obtain the DAOD value difference of the corresponding layer. By utilizing the high-intensity signals returned from the atmospheric boundary, known conditions are determined, and then conditional adjustment constraints are applied to the DAOD values ​​of each layer.

[0014] As a further improvement of the present invention, the model of the adjustment constraint is as follows:

[0015] In the formula, (i=1,2,3…n, where n represents the layer number) represents the actual optical thickness of the i-th layer. .

[0016] This invention provides a methane fine concentration field inversion system based on lidar, comprising a data acquisition module, a methane detection wavelength selection module, and a methane concentration inversion module, wherein: The data acquisition module is used to acquire data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and data on the sensitivity of different methane detection wavelengths to the atmospheric environment. The methane detection wavelength screening module is used to determine the target methane detection wavelength based on the obtained data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment. The methane concentration inversion module is used to perform differential absorption lidar observation based on the determined target methane detection wavelength, and to perform methane concentration inversion using a Chebyshev fitting inversion algorithm to obtain the inversion result.

[0017] The present invention provides an apparatus comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0018] The present invention provides a computer storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0019] This invention provides a method, system, device, and storage medium for fine methane concentration field inversion based on lidar, achieving high-precision and high-stability methane concentration inversion. 1. An improved wavenumber selection optimization method was adopted to gradually compress the solution space size, which significantly improved the algorithm speed while ensuring the accuracy of wavenumber optimization. The optimal methane detection wavelength with the least interference from other gases and the lowest environmental sensitivity was determined. 2. Chebyshev fitting ensures that the signal curve has the minimum nonlinear error throughout the entire detection range; conditional adjustment constraints further improve the accuracy of the inversion results and solve the problem of large echo signal fluctuation amplitude. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of the method for retrieving fine methane concentration field based on lidar according to an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram illustrating the specific process of the method for retrieving fine methane concentration field based on lidar according to an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the STEP-GA algorithm flow for the methane fine concentration field inversion method based on lidar according to an embodiment of the present invention. Detailed Implementation

[0023] The following describes specific embodiments and appendices. Figure 1-3 The invention is described in detail so that those skilled in the art can more fully understand its purpose, features and effects.

[0024] Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the event of any discrepancy between the definitions of terms in this invention and their commonly understood meaning by one of ordinary skill in the art to which this invention pertains, the definitions set forth herein shall prevail.

[0025] Traditional lidar inversion algorithms are susceptible to detection noise, with single raw echo signals exhibiting significant fluctuations at different altitudes, which cannot be completely eliminated even after noise reduction. Furthermore, other atmospheric gas components can interfere with lidar methane observations, and complex environments may exacerbate this effect.

[0026] This invention provides a method, system, device, and storage medium for fine methane concentration field inversion based on lidar, optimizes lidar detection wavenumber selection, and improves existing methane concentration inversion algorithms to achieve fine detection of the methane concentration field, thereby serving research on methane emission reduction and global climate change.

[0027] Example 1 As a specific embodiment of the present invention, this embodiment provides a method for retrieving the fine concentration field of methane based on lidar, referring to... Figure 1 and Figure 2 The specific steps are as follows: S100: Acquire data on the interference levels of gases in the atmospheric environment on lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment. Specifically, in this embodiment, data on the interference levels of CO2 and H2O on lasers of different wavenumbers in different atmospheric environments are obtained; the sensitivity data of different methane detection wavelengths to the atmospheric environment includes the sensitivity data to temperature, pressure, and sea level in the atmospheric environment.

[0028] The interference levels of CO2 and H2O on lasers of different wavenumbers in different atmospheric environments are calculated using the following formula:

[0029] In the formula, Q Interference ， For different atmospheric environments Absorption Cross Sections (ACS) for a given wavenumber laser. For different atmospheric environments For a given wavenumber laser, the absorption cross-section For different atmospheric environments The absorption cross-sectional area of ​​a laser at a given wavenumber. The interference levels of CO2 and H2O in different environments are used to determine the impact of these interferences on the laser at a given wavenumber. Q A low value indicates that CO2 and H2O significantly interfere with a laser at a given wavenumber; if the interference level... Q A high value indicates that CO2 and H2O have little interference with a given wavenumber laser.

[0030] The sensitivity of different methane detection wavelengths to atmospheric conditions was calculated using the following formula:

[0031] In the formula, Indicates the first c Atmospheric environment For a given wavenumber laser, the absorption cross-section Indicates all atmospheric conditions The average value, n This refers to atmospheric environmental parameters. Sensitivity is the standard deviation of the methane absorption cross-sectional area fluctuation, primarily used to measure the stability of each detection wavelength under different atmospheric conditions, i.e., the stability of the detection wavelength under significant changes in the atmospheric environment. The unit is generally cm. 2 Low sensitivity indicates that the detection wavelength remains stable under significant environmental changes; the lower the sensitivity, the more stable the detection wavelength. High sensitivity indicates that the detection wavelength cannot remain stable under significant environmental changes; the higher the sensitivity, the more unstable the detection wavelength.

[0032] According to the above scheme, we can obtain data on the interference of the main interfering gases CO2 and H2O in the atmosphere on the detection of methane by lasers of different wavenumbers, and data on the sensitivity of lasers of different wavenumbers to the environment under different atmospheric conditions (at different temperatures, pressures and altitudes). Combining these two atmospheric environmental influences helps to determine the optimal methane detection wavelength.

[0033] For wavenumber optimization, both low sensitivity to the environment and a desired absorption cross-section are required. This meets the inversion requirements. Specifically, the on wavenumber corresponds to... It should be as large as possible, corresponding to the off wavenumber. It should be as small as possible.

[0034] Furthermore, in this embodiment, the defined environmental adaptability R embodies both of the above requirements; a higher environmental adaptability indicates better performance of the selected wavenumber in the environment. R can be calculated using the following formula:

[0035] In the formula, To standardize parameters, , For standard ambient conditions (296 K, 1 atm), gas mixing ratio is based on HITRAN standard air data, 4350.510 cm⁻¹ -1 The sensitivity of the laser to ACS. In other embodiments, the wavenumber of the laser can be chosen arbitrarily to serve a normalizing function, preferably optimizing the wavenumber at the center of the band.

[0036] S200. Based on the acquired data on the interference of gases in the atmospheric environment with lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment, determine the target methane detection wavelength and set the target methane detection wavelength as the optimal methane detection wavelength. An optimization algorithm is employed to determine the optimal methane detection wavelength, minimizing the adverse effects of atmospheric conditions and interfering gases during detection. In this embodiment, a Variable Step Genetic Algorithm (STEP-GA) is used to select the optimal methane detection wavelength for the lidar. This embodiment utilizes the STEP-GA algorithm to improve search efficiency in the solution space, obtain better solutions, and gradually compress the solution space based on the solution results, achieving rapid search for better solutions and progressively improving wavenumber optimization accuracy. In this embodiment, the STEP-GA algorithm is used to solve for the optimal detection wavelength, refining the wavenumber selection optimization into a single-objective programming problem. Constraints include a signal-to-noise ratio greater than 30 dB and an interference level Q greater than 5000, while maximizing the on / off wavenumber environmental adaptability R is the optimization objective.

[0037] Specifically, in combination Figure 3 ,include: S201. Based on the absorption capacity of CH4 to laser and the anti-interference ability of laser to CO2 and H2O under standard environment (296 K, 1 atm), the optimal wavenumber band range is preliminarily determined. Among them, CH4's ability to absorb laser light is utilized The absorption cross-sectional area (ACS) of a laser at a given wavenumber is used to represent the laser's resistance to interference from CO2 and H2O. Q express.

[0038] if The absorption cross-sectional area (ACS) of a laser at a given wavenumber is greater than 1 × 10⁻⁶. -23 cm 2 And the interference degree Q is greater than 5×10 3 If the band is selected, it is retained as a band for wavenumber optimization; otherwise, the band is excluded.

[0039] 4000~4700 cm -1 The band range is the band range from which the bands to be optimized are selected.

[0040] In an optional embodiment, for further verification, the temperature is measured at 4000~4700 cm. -1 The band settings are as follows: wavenumber interval is 0.1 cm. -1 The temperature range was 260–330 K, with an interval of 1 K; the pressure range was 0.6–1 atm, with an interval of 0.001 atm; and the altitude range was 0–3 km, with an interval of 100 m. The influence of different atmospheric environmental conditions on the methane absorption cross-section of lasers at different wavenumbers was studied using the HITRAN database. The results showed that the atmospheric environment had no significant effect on the methane absorption of laser light. At 4200 cm⁻¹… -1 and 4330 cm -1 The presence of distinct strong absorption bands near the band indicates that this band is suitable for searching for the on wavenumber.

[0041] S202. Set the environmental parameters for wavenumber optimization. Between 4000 and 4700 cm -1 The following environmental settings were configured within the optimized band range: wavenumber interval of 0.1 cm. -1 The temperature range is 260~330 K, with an interval of 1 K; the pressure range is 0.6~1 atm, with an interval of 0.001 atm; the altitude range is set to 0~3 km, with an interval of 100 m.

[0042] S203. Set the initial band step size to 1 cm. -1 Given a solution space, a genetic algorithm is used to search for a better solution within the solution space. The bands containing the top 10% of solutions (the top 10% with the highest environmental adaptability R) are retained. Based on the better solutions, the solution space is narrowed down to 10% of the original range. The better solutions are the bands with better environmental adaptability R.

[0043] The absorption cross-section (ACS) of CH4 for a given wavelength of laser light is closely related to the atmospheric environment (including temperature, pressure, and altitude). Therefore, ensuring the stability of the ACS under changing environmental conditions is an important optimization objective when determining the optimal wavenumber. In S100, the standard deviation of the absorption cross-section fluctuation is defined as sensitivity. A low sensitivity indicates that the ACS can remain stable under significant environmental changes, meaning the given detection wavelength is more stable. For wavenumber optimization, it is required that the methane detection wavelength has low sensitivity to the environment, and that the obtained absorption cross-section meets the inversion requirements; that is, the larger the environmental adaptability R, the better.

[0044] S204, set the step size to 0.1 cm. -1 0.01 cm -1 and 0.001 cm -1 And repeat S203.

[0045] The final optimized result is 4333.703 cm. -1 As the on wave number, 4334.192 cm -1 As the off wavenumber, the absorption capacity of the on and off wavenumbers differs by an average of 41 times under various environments for CH4 detection, which is about 8 times higher than that of the commonly used atmospheric detector DIAL. This means that the detection wavenumber is more sensitive to CH4 concentration and will bring higher signal-to-noise ratio and spatiotemporal resolution.

[0046] Ignoring time complexity, in order to optimize the 4000~4700 cm band -1 The data within the specified range requires the examination of at least 700,000 wavelengths. For each wavelength, the spectrum needs to be simulated under 28,000 different environments. Preliminary estimates suggest this will take several years. The STEP-GA algorithm used in this embodiment significantly reduces computational time complexity and can reliably obtain optimal solutions. It exhibits good anti-interference capabilities against CO2 and H2O, with their average impact on detection accuracy not exceeding 4.6‰ and 5.26‰, respectively. It demonstrates good environmental adaptability; a +1 K temperature error results in a maximum CH4 volume concentration error of 2.94 ppb, and a +0.001 atm pressure error results in a maximum CH4 volume concentration error of 2.73 ppb.

[0047] An improved wavenumber optimization algorithm was adopted to gradually compress the solution space size, which significantly improved the algorithm speed while ensuring the accuracy of wavenumber optimization. The optimal methane detection wavelength was determined to be less affected by CO2 and H2O interference, less sensitive to the environment, and most adaptable to the environment.

[0048] S300. Based on the optimal methane detection wavelength determined in S200, differential absorption lidar observation is performed. The observed signal is then used to invert the methane concentration using a Chebyshev fitting inversion algorithm to obtain the inversion result. In target methane monitoring areas, such as oilfields, high-precision methane concentration inversion is achieved through long-term observation using a differential absorption lidar system. The lidar observation signals, along with real-time temperature and pressure data, are then input into a Chebyshev inversion algorithm (CFIA) to obtain a high-precision, fine-grained methane concentration field. During monitoring, the lidar system had a linewidth of 50 MHz, a pulse energy of 75 mJ, and on and off wavenumbers of 4333.703 cm⁻¹. -1 and 4334.192cm -1 The repetition frequency is 20Hz, and the telescope aperture is 1m. First, the acquired signal is integrated over time to improve the signal-to-noise ratio. Then, the DAOD is fitted to different integration intervals using a Chebyshev kernel.

[0049] Specifically, including: S301. Using the Chebyshev fitting inversion algorithm, Chebyshev fitting is performed on the DAOD (Differential Absorption Optical Depth) of CH4 at different heights to obtain the difference in DAOD values ​​for the corresponding layers. By performing Chebyshev fitting on the DAOD of CH4 at different heights, the accuracy and stability of CH4 profile concentration inversion for each layer can be improved, thereby increasing the precision of CH4 profile concentration inversion.

[0050] Chebyshev uses orthogonal polynomials as the fitting kernel, which can be defined by the contour integral, as follows:

[0051] In the formula, n is a positive integer representing the order of the Chebyshev polynomial, x is the independent variable of the function, i is the imaginary unit, and t is the integration variable.

[0052] The DAOD values ​​in different integration intervals are fitted using a fitting kernel, and the difference between the DAOD values ​​of two adjacent heights is used as the difference in the DAOD values ​​of the corresponding layer.

[0053] For example, if the height resolution of each floor is set to 10 m, then .

[0054] S302. Using the high-intensity signal returned from the atmospheric boundary, determine the known conditions, and then apply conditional adjustment constraints to the DAOD values ​​of each layer. The conditional adjustment constraint model is as follows:

[0055] In the formula, (i=1,2,3…n, where n represents the layer number) represents the actual optical thickness of the i-th layer. .

[0056] The known conditions are the conditions required to establish the conditional adjustment constraint model, including the DAOD and signal-to-noise ratio weights for each layer.

[0057] Represented as:

[0058] In the formula, It is the calculated DAOD value for each layer. v It is the DAOD value of each layer relative to The deviation.

[0059] After adjustment, the DAOD value of each layer is obtained. Then, other inversions within the integration time are calculated through a similar process. Assuming the integration time is m, the average DAOD value of each layer is taken as the DAOD value corresponding to that layer, and the inversion results are obtained.

[0060] Finally, the methane concentration was calculated using the differential absorption optical thickness after adjustment constraints.

[0061] Furthermore, for a single acquired signal, the conditional equation is as follows:

[0062] In the formula, represents the true optical thickness of the i-th layer, n represents the layer number, and b0 is the negative of the DAOD value between 300m and the boundary layer height.

[0063] Set the conditional adjustment parameters:

[0064] In the formula, A is the coefficient matrix, L is the DAOD difference matrix for each layer, A0 is the negative of the DAOD value between 300m and the boundary layer height, W is the closure difference matrix, and V is the deviation matrix of the DAOD difference for each layer.

[0065] w It is the closure difference between the sum of the calculated DAOD values ​​for each layer and the actual sum of the DAOD values ​​for each layer, calculated as follows:

[0066] In the formula, This is the calculated DAOD value for each layer, where n represents the number of layers.

[0067] According to the Lagrange multiplier method and the conditional extremum method, we can obtain:

[0068] In the formula, φ Let XX be the multiplier and K be the multiplier. P These are the weights in the calculation process, where,

[0069]

[0070] In the formula, This represents the SNR (signal-to-noise ratio) of each layer. P can be obtained by comparing the signal strength of each layer with the signal strength at the boundary layer; P is a diagonal matrix.

[0071] V It can be obtained through the following formula:

[0072] The methane concentration inversion algorithm in this embodiment has high accuracy and good robustness. By using Chebyshev fitting and conditional adjustment constraints, the interference of noise is further reduced, and the inversion accuracy is improved. To verify the superiority of the proposed algorithm, real CH4 concentration profile data were collected at the observation site using an UAV-based Aircore system. A high-precision cavity ring-down spectrometer (Picarro G2401) was used to analyze the air samples from the active Aircore. Through spatiotemporal matching, the results of in-situ measurements were quantitatively compared with those of traditional methods and the CFIA algorithm. Compared to the significant fluctuations of traditional methods, the CH4 concentration profile retrieved by the CFIA algorithm showed better stability and good consistency with the trends of the observations collected by Aircore. The correlation coefficient increased from 0.42 to 0.91, significantly reducing the volatility of traditional algorithms. CFIA demonstrated a significant reduction in MAE (mean absolute error), with a maximum reduction of 121.51 ppb compared to traditional algorithms.

[0073] Compared to traditional methods, the data trends show that traditional methods are more significantly affected by noise at higher altitudes, while the trend of CFIA remains relatively stable. Beyond 450 m, CFIA demonstrates a significant reduction in MAE, with a maximum MAE reduction of 121.51 ppb for CH4 profiles at considerable distances. For CH4 detection, the on and off wavenumber absorption capabilities differ by an average of 41 times under various conditions, representing an improvement of approximately 8 times compared to the commonly used atmospheric sounding method DIAL. This indicates greater sensitivity to CH4 concentration, resulting in a higher signal-to-noise ratio and better spatiotemporal resolution.

[0074] Furthermore, after obtaining the inversion results, the results are stored in a historical database for later analysis of methane concentration changes.

[0075] Lidar technology enables precise measurement of methane concentration fields, overcoming the shortcomings of traditional gas concentration measurement methods such as low temporal and spatial resolution and complex operation. This invention presents a lidar-based method for retrieving fine methane concentration fields. Employing a differential absorption lidar system, it optimizes the selection of detection wavenumbers and the inversion algorithm to achieve high-precision methane detection. This method can be applied to oil fields and the chemical industry to monitor methane concentration changes in emission areas, providing data support for analyzing spatiotemporal evolution and methane concentration characteristics, and constructing spatiotemporal distribution maps of methane concentration.

[0076] Example 2 As a specific embodiment of the present invention, this embodiment provides a methane fine concentration field inversion system based on lidar, including a data acquisition module, a methane detection wavelength selection module, and a methane concentration inversion module, wherein: The data acquisition module is used to acquire data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and data on the sensitivity of different methane detection wavelengths to the atmospheric environment. The methane detection wavelength screening module is used to determine the target methane detection wavelength based on the obtained data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment. The methane concentration inversion module is used to perform differential absorption lidar observation based on the determined target methane detection wavelength, and to perform methane concentration inversion using a Chebyshev fitting inversion algorithm to obtain the inversion result.

[0077] The lidar-based methane fine concentration field inversion system of this embodiment can improve the accuracy of lidar inversion of methane concentration and obtain accurate methane concentration inversion results.

[0078] Example 3 As a specific embodiment of the present invention, this embodiment provides an apparatus, including a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it implements the steps of the methane fine concentration field inversion method based on lidar in Embodiment 1.

[0079] Example 4 As a specific embodiment of the present invention, this embodiment provides a computer storage medium. The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the lidar-based methane fine concentration field inversion method in Embodiment 1, thereby accurately inverting the methane concentration.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any modifications or equivalent changes made based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.

Claims

1. A method for retrieving the fine concentration field of methane based on lidar, characterized in that, The method includes: To obtain data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and the sensitivity of different methane detection wavelengths to the atmospheric environment; The target methane detection wavelength is determined based on the obtained data on the interference of gases in the atmospheric environment on lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment. Differential absorption lidar observations are performed based on the determined target methane detection wavelength. The methane concentration is then inverted using a Chebyshev fitting inversion algorithm based on the observed signal to obtain the inversion results.

2. The method for inverting the fine concentration field of methane based on lidar according to claim 1, characterized in that, The acquisition of data on the interference of gases in the atmospheric environment on lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment includes acquiring data on the interference of CO2 and H2O on lasers of different wavenumbers in different atmospheric environments.

3. The method for inverting the fine concentration field of methane based on lidar according to claim 2, characterized in that, The interference levels of CO2 and H2O on lasers of different wavenumbers in different atmospheric environments were calculated using the following formula: In the formula, Q Interference ， For different atmospheric environments For a given wavenumber laser, the absorption cross-section For different atmospheric environments For a given wavenumber laser, the absorption cross-section For different atmospheric environments For a given wavenumber laser, the interference degree is... Q The higher the value ， This indicates that the less interference CO2 and H2O cause to a laser at a given wavenumber; The sensitivity data of different methane detection wavelengths to the atmospheric environment were calculated using the following formula: In the formula, Indicates the first c Atmospheric environment For a given wavenumber laser, the absorption cross-section Indicates all atmospheric conditions The average value, n The atmospheric environment quantity indicates that low sensitivity means that the given detection wavelength remains stable under significant environmental changes.

4. The method for inverting the fine concentration field of methane based on lidar according to claim 1, characterized in that, The target methane detection wavelength is determined based on the obtained data on the interference of gases in the atmospheric environment with different wavenumber lasers and the sensitivity data of different methane detection wavelengths to the atmospheric environment. This includes using a variable step-size genetic algorithm to select the laser radar detection wavenumber to determine the optimal methane detection wavelength.

5. The method for methane fine concentration field inversion based on lidar according to claim 4, characterized in that, The selection of the optimal methane detection wavelength using a variable step-size genetic algorithm for lidar detection includes: Based on the absorption cross-sectional area of ​​methane to laser under standard conditions and the interference of atmospheric gases to lasers of different wavenumbers, the optimal wavenumber band range was initially determined. Within the defined wavenumber optimization band range, wavenumber intervals and variation ranges and intervals of atmospheric environment including temperature, pressure and sea waves are set. Set the initial band step size, give the solution space range, and use a genetic algorithm to search for a better solution in the solution space. Then, based on the better solution, reduce the solution space range to a preset ratio of the previous solution space range. The step size is gradually shortened, and the genetic algorithm is used to search for a better solution in the solution space. The solution space is then reduced to a preset proportion of the previous solution space based on the better solution, until the step size is the preset final step size. The best solution obtained in the final step is then taken as the optimal methane detection wavelength.

6. The method for inverting the fine concentration field of methane based on lidar according to claim 1, characterized in that, The step involves performing differential absorption lidar observations based on the determined target methane detection wavelength, and then using a Chebyshev fitting inversion algorithm to retrieve the methane concentration from the observed signal. The retrieved results include: Chebyshev fitting inversion algorithm was used to fit the DAOD of CH4 at different heights to obtain the DAOD value difference of the corresponding layer. By utilizing the high-intensity signals returned from the atmospheric boundary, known conditions are determined, and then conditional adjustment constraints are applied to the DAOD values ​​of each layer.

7. The method for inverting the fine concentration field of methane based on lidar according to claim 6, characterized in that, The model for the adjustment constraint is as follows: In the formula, (i=1,2,3…n, where n represents the layer number) represents the actual optical thickness of the i-th layer. .

8. A methane fine concentration field inversion system based on lidar, characterized in that, The system includes a data acquisition module, a methane detection wavelength screening module, and a methane concentration inversion module, wherein: The data acquisition module is used to acquire data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and data on the sensitivity of different methane detection wavelengths to the atmospheric environment. The methane detection wavelength screening module is used to determine the target methane detection wavelength based on the obtained data on the degree of interference of gases in the atmospheric environment to lasers of different wavenumbers and the sensitivity data of different methane detection wavelengths to the atmospheric environment. The methane concentration inversion module is used to perform differential absorption lidar observation based on the determined target methane detection wavelength, and to perform methane concentration inversion using a Chebyshev fitting inversion algorithm to obtain the inversion result.

9. An apparatus comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

10. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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