A method for extracting seismic anomalies from multi-source satellite methane column concentration products

Abstract

The application discloses a kind of multi-source satellite methane column concentration product seismic anomaly extraction methods, which comprises the following steps: S1, selecting research area and determining the applicability of methane seismic anomaly information extraction method;S2, prepare multi-source satellite data methane gas product;S3, produce the methane column concentration product of multi-source satellite data fusion;S4, based on the methane column concentration product after fusion, calculate the methane column concentration anomaly index of each pixel in research area;S5, based on anomaly index analysis abnormal high value and predict dangerous area.The application mainly focuses on the channel and orbit advantage of multi-source satellite data, proposes a new methane column concentration seismic anomaly extraction method based on multi-source satellite, solves the problem of different channel satellite methane column concentration data product precision and low single satellite coverage, can provide support for remote sensing gas geochemical earthquake service application extraction, and helps to promote domestic satellite product into earthquake monitoring service.

Description

Technical Field

[0001] This invention belongs to the field of earthquake monitoring technology, specifically relating to a method for extracting earthquake anomalies from multi-source satellite methane column concentration products. Background Technology

[0002] Domestic and international studies have shown that major earthquakes cause changes in the composition of trace gases in the atmosphere, such as CH4 and CO2. These changes can indicate the intensity of tectonic activity and are correlated with the earthquake pre-seismic cycle. Currently, significant progress has been made in remote sensing monitoring of earthquake gases both domestically and internationally, but applications are mainly based on foreign data (AIRS, GOSAT, TOMS, MOPIT, etc.), with limited application of domestic satellite data. Furthermore, most applications focus on satellite methane column concentration data products from single data sources, but these all have some limitations. For example, the shortwave infrared channel is highly sensitive to near-surface atmospheric CH4 but cannot retrieve profiles. The thermal infrared channel is highly sensitive to the middle and upper atmosphere but insensitive to the lower atmosphere. This results in unsatisfactory accuracy of single-channel satellite methane column concentration products.

[0003] At 2:28 AM on May 9, 2018, the GF-5(01) satellite was successfully launched from the Taiyuan Satellite Launch Center. Its primary greenhouse gas monitor (GMI) uses the 1.65μm near-infrared band to observe and retrieve the vertical column concentration of CH4. On-orbit testing results show that the acquired Level 1 spectral data is of high quality, comparable to similar international satellites. The short-wave infrared channel corresponding to this sensor is highly sensitive to CH4 in the lower atmosphere, which can compensate for the shortcomings of some foreign thermal infrared satellites, making the methane gas product data more comprehensive. However, its data acquisition method results in low coverage. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides a method for seismic anomaly extraction from multi-source satellite methane column concentration products, which solves the problems of low accuracy of methane column concentration data products from different channels and low coverage of a single satellite.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0006] A method for extracting seismic anomalies from multi-source satellite methane column concentration products is provided, the method comprising the following steps:

[0007] S1. Select the study area and determine the applicability of the methane seismic anomaly information extraction method;

[0008] S2. Prepare methane gas products from multi-source satellite data;

[0009] S3. Producing methane column concentration products from multi-source satellite data fusion;

[0010] S4. Calculate the methane column concentration anomaly index of each pixel in the study area based on the fused methane column concentration product.

[0011] S5. Analyze abnormally high values ​​and predict dangerous areas based on abnormal index analysis.

[0012] Furthermore, the specific method of step S1 includes the following sub-steps:

[0013] S1-1. Select the study area;

[0014] S1-2. Brief analysis of the tectonic landforms of the study area;

[0015] S1-3. Preliminary determination of the regional applicability of the anomaly extraction method from a regional geological perspective.

[0016] Furthermore, the specific method of step S2 includes the following sub-steps:

[0017] S2-1. Prepare near-surface methane concentration products obtained by shortwave infrared channel inversion;

[0018] S2-2, Prepare methane profile products from thermal infrared channel inversion.

[0019] Furthermore, the specific method of step S3 includes the following sub-steps:

[0020] S3-1, Time-space disk matching of multi-dimensional data;

[0021] S3-2. Convolutional fusion of methane products based on load parameters.

[0022] Further, the specific method of step S3-2 is as follows: Based on the spatiotemporal matching data, the methane profile product is processed by convolution of the average kernel function of the short-wave infrared channel load. After convolution processing, according to the formula:

[0023]

[0024] Obtain the vertical dry air methane mixing ratio concentration Xch4 after fusion; where P0 represents the surface pressure, P T It represents the pressure at the top of the atmosphere.

[0025] Furthermore, in step S4, according to the formula:

[0026]

[0027]

[0028] ALICE(x,y,t)=[G(x,y,t)-G ref [(x,y,t)] / σ(x,y,t)

[0029] Anomaly indices, ALICE, were obtained by selecting a certain period before and after the earthquake; among them, G... i (x,y,t) represents the observation time t, corresponding to the CH4 content value at longitude x and latitude y; G ref (x,y,t) represents the background value of CH4 at the observation time t, corresponding to longitude x and latitude y; σ(x,y,t) is the standard deviation at the same location (longitude x, latitude y) and the same time (t); where i is the year and N = number of years.

[0030] Furthermore, the specific method of step S5 includes the following sub-steps:

[0031] S5-1. Analyze the spatiotemporal evolution characteristics of methane column concentration changes in the study area;

[0032] S5-2, Screening for structurally relevant high methane column concentration centers;

[0033] S5-3. Based on the above steps, high-value areas should be given special attention as danger zones.

[0034] The beneficial effects of this invention are as follows:

[0035] This invention focuses on the advantages of multi-source satellite data channels and orbits, and proposes a new method for extracting seismic anomalies based on multi-source satellites. It solves the problems of low accuracy of methane column concentration data products from different channels and low coverage of a single satellite, and can provide support for the extraction of seismic anomalies in remote sensing gas geochemical applications, and help promote the entry of domestic satellite products into earthquake monitoring services. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention;

[0037] Figure 2 This is a geological and geomorphological map of the study area in an embodiment of the present invention; wherein F1: Xianshuihe Fault; F2: Huayingshan Fault; F3: Yingxiu-Beichuan Fault; F4: Matoushan Fault; F5: Chengdu-Deyang Fault; F6: Ninghui Fault; F7: Longquanshan West Edge Fault; F8: Guanxian-Anxian Fault;

[0038] Figure 3 This is a monthly spatiotemporal distribution map of the methane anomaly index in the study area of ​​this invention embodiment;

[0039] Figure 4 This is a spatiotemporal distribution map of the methane anomaly index on an 8-day scale in the study area of ​​this invention embodiment;

[0040] Figure 5 This is a time series plot of the mean methane anomaly index on an 8-day scale in the study area of ​​this invention embodiment;

[0041] Figure 6 This is a time-series distribution of the average methane column concentration over five years on an 8-day scale in the study area of ​​this invention. Detailed Implementation

[0042] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0043] Example

[0044] Reference Figure 1 A method for seismic anomaly extraction from multi-source satellite methane column concentration products is provided, the method comprising the following steps:

[0045] (I) Selecting the study area and determining the applicability of the methane seismic anomaly information extraction method

[0046] First, the study area (the Longmen Nanshan fault zone in the Sichuan-Yunnan region) was selected. Taking the Luxian Mw6.0 earthquake of September 16, 2019 as an example, the area covering the epicenter (29.20°N, 105.34°) was selected (25-35°N, 95-115°E), and the results were obtained. Figure 2The map shown depicts the geological structure of the study area. The terrain is characterized by a combination of plateaus and basins, with the famous Qinghai-Tibet Plateau in the west and the Sichuan Basin in the central and eastern parts. Multiple active tectonic structures are distributed within the region, providing convenient channels for gas spillover. Furthermore, the region is tectonically active and experiences frequent earthquakes. Since 2008, there have been 21 earthquakes of magnitude 6.0 or greater in the region, including the notable 2008 Wenchuan earthquake (magnitude 8.0), the 2010 Yushu earthquake (magnitude 7.1), the 2013 Lushan earthquake (magnitude 7.0), the 2017 Jiuzhaigou earthquake (magnitude 7.0), and the 2021 Qinghai Madoi earthquake (magnitude 7.4). The seismogenic structure of this earthquake is closer to the Huayingshan Fault Zone on the southeastern side of the Sichuan Basin. The Huayingshan Fault Zone, bounded by Hechuan and Linshui, is the boundary fault between two third-order tectonic units: the central Sichuan platform arch and the eastern Sichuan fold belt, and is an important fault zone in eastern Sichuan. The fault strikes 45° northeast, with an overall southeast dip of 30°-70°, exhibiting both right-lateral strike-slip and thrust characteristics. The southwestern segment of this fault zone is more active than the northeastern segment; the epicenter of the Luxian earthquake was located in the southern segment. The Sichuan Basin is one of my country's major natural gas producing areas, with existing conventional gas reservoirs primarily consisting of Sinian, Cambrian, Carboniferous, and Permian-Triassic carbonate rocks, whose natural gas is mainly composed of methane. Earthquakes cause the release of large amounts of natural gas from underground traps into the atmosphere. As earthquakes occur and tectonic stress gradually intensifies, the pressure of internal fluids increases, causing rocks near the fault zone to fracture and increasing fissures. This leads to the rapid migration and diffusion of natural gas from underground traps in the study area along rock fissures, fault zones, unconformities, and other weak areas into the atmosphere. This results in enhanced micro-leakage of natural gas in and around the epicenter, causing a gradual increase in atmospheric methane concentration in the study area, forming an earthquake-induced methane anomaly. Therefore, this region can be selected for earthquake-induced methane anomaly monitoring.

[0047] (II) Preparation of Methane Gas Products from Multi-Source Satellite Data

[0048] The selected satellite data covers the same areas as the satellites passing over the Earth, including a) near-surface methane concentration products retrieved via shortwave infrared channel inversion (e.g., GMI / GF-5 and TANSO-FTS / GOSAT); and b) methane profile products retrieved via thermal infrared channel inversion (e.g., AIRS LEVEL2 atmospheric standard methane column concentration profile products). The data timeframe is at least three months prior to the earthquake.

[0049] (III) Production of methane column concentration products from multi-source satellite data fusion

[0050] First, spatial disk matching of multivariate data was conducted. At the geographic scale, due to the different spatial imaging methods and single-pixel spatial resolutions of the three payloads, especially the GMI / GF-5 and TANSO-FTS / GOSAT operating in the shortwave infrared channel, there is a significant difference compared to the AIRS data in the thermal infrared channel. AIRS imaging observations determine its significant advantage in spatial coverage. Using the latitude and longitude grid data of AIRS pixels as a benchmark, spatial registration was performed between GMI / GF-5 and TANSO-FTS / GOSAT observation pixels and AIRS data according to the spatial nearest neighbor method. Regarding spatial resolution, for the spatially registered pixel pairs, remote sensing data from GMI / GF-5 or TANSO-FTS / GOSAT were fused into the AIRS product and output.

[0051] Methane product convolution fusion based on load parameters. Considering differences in load parameters, based on the above spatiotemporal matching data, the average kernel function (AK) of the short-wave infrared channel load is used to convolve the methane profile products of AIRS pixels. After convolution processing, the result is calculated according to the formula:

[0052]

[0053] Obtain the vertical dry air methane mixture concentration (Xch4) after fusion; where P0 represents the surface pressure, P... T This represents the pressure at the top of the atmosphere. If the spatially registered AIRS pixel has no methane product value, then the methane product (Xch4) from GMI or GOSAT is assigned to the merged product for that spatial location; for other AIRS pixels paired without GMI or GOSAT products, the vertical dry air methane mixing ratio concentration (Xch4) is calculated using the above formula.

[0054] (iv) Calculate the methane column concentration anomaly index for each pixel in the study area

[0055] Based on the fused methane column concentration product and anomaly index algorithm, the anomaly index of each pixel in the study area is calculated according to the following formula.

[0056]

[0057]

[0058] ALICE(x,y,t)=[G(x,y,t)-G ref [(x,y,t)] / σ(x,y,t)

[0059] Among them, G i (x,y,t) represents the observation time t, corresponding to the CH4 content value at longitude x and latitude y; G ref(x,y,t) represents the background CH4 value at observation time t, corresponding to longitude x and latitude y; σ(x,y,t) is the standard deviation at the same location (longitude x, latitude y) and the same time (t). Where i is the year, N = number of years, and in this study, N is chosen as 5, meaning the historical 5-year average is used to calculate the background field. G(x,y,t) represents the CH4 content value at observation time t, corresponding to longitude x and latitude y. ALICE is the anomaly index. Taking monthly and 8-day scales as examples, t in the above three formulas corresponds to months and 8 days, respectively. The methane anomaly index before and after the earthquake is calculated using the above formulas. In this case, the monthly scale is calculated by selecting 8 months before the earthquake and the month of the earthquake; the 8-day scale is calculated by selecting approximately 5 months before the earthquake and approximately one week after the earthquake. The spatiotemporal distribution map of the methane anomaly index is obtained, see [link to relevant documentation]. Figure 3 and Figure 4 . Figure 3 For monthly scale results, 2021.01 represents the spatial distribution of the methane anomaly index in the study area in January 2021. Different color levels represent different values; the darker the color, the more obvious the anomaly. 2021.02, 2021.03, ..., 2021.09 are explained above. Figure 4 The results are on an 8-day timescale. March 21, 2021 represents the spatial distribution of methane anomalies in the 8-day data product from March 21 to March 28, 2021. Other dates, such as March 29, 2021, April 6, 2021, and so on, are interpreted similarly. Different color levels represent different values; the darker the color, the more pronounced the anomaly. A detailed analysis of the relationship between the spatial distribution of anomalies and earthquakes will follow in the next step.

[0060] (V) Analysis of Abnormal High Values ​​and Prediction of Hazardous Areas

[0061] The spatial distribution evolution of methane column concentration variations in the active tectonic distribution area of ​​the study region was analyzed, including time-series analysis of anomaly indices and comparative analysis of multi-year historical data. (See attached document.) Figure 3 , 4 5 and 6. A comprehensive analysis was conducted on the anomalous amplitude of the high-value center, its evolution over time, its spatial distribution, and its correlation with faults. The high-value center was then identified as a key area of ​​seismic hazard. Figure 3The results showed that the methane anomaly index was high in the northwestern part of the study area from January to May, then decreased, and then increased again in June. Since July, the high values ​​have tended to be in the central part of the study area, and the high values ​​were mostly near faults. In August, the overall value weakened, but high values ​​were still found in the fault zone and near the epicenter. Comparison with earthquake cases revealed that the anomaly from January to May was likely caused by the May 22, 2021 earthquake in Maduo, Qinghai (epidemcenter: 34.59N, 98.34E) in the northwestern part of the study area, and the subsequent anomaly in July corresponded to the Luxian earthquake on September 16. Further analysis of the 8-day scale results attributed the anomaly before July to the Maduo, Qinghai earthquake. Figure 4 The 8-day results show that on July 11, abnormally high values ​​appeared in the Xianshuihe Fault, Yingxiu-Beichuan Fault area, and surrounding areas in the western and northern parts of the study area. Subsequently, high-value areas in the Luxian epicenter area and near the Huayingshan Fault began to increase rapidly over a large area until July 27. For the following month, high values ​​persisted in the Luxian epicenter area and the Huayingshan Fault. However, within the 8-day period starting August 20, abnormally negative values ​​appeared near the Luxian epicenter and along the fault zone, covering a large area along the fault. This may be due to internal rock fracturing following the release of a large amount of gas, hindering gas emission from the channel, or an interval after gas emission. In subsequent periods, abnormally high values ​​reappeared near the Luxian epicenter. During the Luxian earthquake cycle starting September 13, high values ​​were mainly distributed near the Huayingshan Fault and in the western part of the study area, with a slightly reduced area. In the following cycle, the area of ​​high values ​​decreased rapidly. Figure 5 and Figure 6 The time series plots show the mean methane anomaly index over an 8-day period and the time series plots show the methane column concentration within the region over the same period in the past 5 years, respectively, further validating the analysis. Figure 4 The result. Among them Figure 5 The abnormally high values ​​within the circular region A correspond to the Maduo earthquake in Qinghai. The results in this figure are the same. Figure 4 The circular BCD anomaly can reflect the earthquake preparation process of the Luxian earthquake in Sichuan. Figure 6 The data shows that the methane column concentration in the year of the earthquake (within the dashed box) was significantly higher than the historical five-year levels, further verifying that earthquakes cause an abnormal increase in methane. Analyzing methane changes can reveal the earthquake preparation process. During the earthquake preparation process in Luxian County, the methane anomaly generally exhibited the characteristics of initial enhancement, abnormal strengthening, peak, decay, enhancement, and weakening, consistent with the earthquake preparation process.

[0062] This invention primarily leverages the advantages of multi-source satellite data channels and orbits, proposing a novel method for extracting seismic anomalies in methane column concentration based on multi-source satellites. A spatially extensive image is selected as the reference image. Spatially, the nearest neighbor method is used to spatially register the observed pixels of the multi-source image with the reference image data. Temporally, corresponding time data is selected. Finally, based on the spatiotemporal matching data, the average kernel function (AK) of the shortwave infrared channel payload is used to convolve the methane profile products of the thermal infrared image pixels. This method solves the problems of low accuracy of methane column concentration data products from different satellite channels and low coverage of a single satellite. It can provide support for the extraction of methane column concentration data in remote sensing gas geochemical seismic operations and helps promote the entry of domestically produced satellite products into earthquake monitoring services.

[0063] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0064] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for extracting seismic anomalies from multi-source satellite methane column concentration products, characterized in that, Includes the following steps: S1. Select the study area and determine the applicability of the methane seismic anomaly information extraction method; S2. Prepare methane gas products from multi-source satellite data; S3. Producing methane column concentration products from multi-source satellite data fusion; S4. Calculate the methane column concentration anomaly index of each pixel in the study area based on the fused methane column concentration product. S5. Analyze abnormally high values ​​and predict dangerous areas based on abnormal index analysis; The specific method of step S3 includes the following sub-steps: S3-1, Time-space disk matching of multi-dimensional data; S3-2. Convolutional fusion of methane products based on load parameters; The specific method for step S3-2 is as follows: Based on spatiotemporal matching data, the methane profile product is processed by convolution of the average kernel function of the short-wave infrared channel load. After convolution processing, according to the formula: ； Obtain the vertical dry air-methane mixing ratio concentration Xch4 after fusion; where, Represents surface pressure, It represents the pressure at the top of the atmosphere.

2. The method for seismic anomaly extraction from multi-source satellite methane column concentration products according to claim 1, characterized in that, The specific method of step S1 includes the following sub-steps: S1-1. Select the study area; S1-2. Brief analysis of the tectonic landforms of the study area; S1-3. Preliminary determination of the regional applicability of the anomaly extraction method from a regional geological perspective.

3. The method for extracting seismic anomalies from multi-source satellite methane column concentration products according to claim 1, characterized in that, The specific method of step S2 includes the following sub-steps: S2-1. Prepare near-surface methane concentration products obtained by shortwave infrared channel inversion; S2-2, Prepare methane profile products from thermal infrared channel inversion.

4. The method for extracting seismic anomalies from multi-source satellite methane column concentration products according to claim 1, characterized in that, In step S4, according to the formula: ； Obtaining the anomaly index ;in, Represents observation time The corresponding longitude is Latitude is CH4 content value; Represents observation time The corresponding longitude is Latitude is Background values ​​at CH4; σ(x, y, t) are for the same location (longitude is...) Latitude is At the same time ( The standard deviation corresponding to ) is: i = year, N = number of years.

5. The method for seismic anomaly extraction from multi-source satellite methane column concentration products according to claim 1, characterized in that, The specific method of step S5 includes the following sub-steps: S5-1. Analyze the spatiotemporal evolution characteristics of methane column concentration changes in the study area; S5-2, Screening for structurally relevant high methane column concentration centers; S5-3. Based on the above steps, high-value areas should be given special attention as danger zones.

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