Methods, devices, electronic equipment, and storage media for mapping fault shape variations.

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for mapping fault shape deformation zones, relating to the field of surveying and mapping technology. The method includes: acquiring basic deformation data of a target area; wherein the basic deformation data includes coseismic deformation data and / or DEM data; generating profile lines along the fault vector from the basic deformation data to obtain profile line data; and generating the target fault shape deformation zone of the target area based on the abrupt change points of each profile line in the profile line data. Thus, based on the coseismic deformation data and / or DEM data of the target area, and through the abrupt change points on the profile lines generated along the fault vector, high-precision automated mapping of fault shape deformation zones during coseismic or interseismic stages can be achieved.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping technology, and in particular to a method, apparatus, electronic device, and storage medium for mapping fault shape variation zones. Background Technology

[0002] The sudden movement of active faults causes earthquakes. The direct damage and secondary geological hazards caused by earthquakes are typically distributed linearly along the fault, meaning the closer to the fault where the earthquake originated, the more severe the damage. For example, the density of landslides within the off-fault deformation zone is significantly higher than in areas farther from the fault. Off-fault deformation (OFD) refers to the area of ​​the Earth's surface that deforms during an earthquake. This deformation includes not only ground displacement but may also involve crack formation and surface subsidence, directly affecting the extent and severity of earthquake damage. Accurately identifying and analyzing the damage zones on both sides of the fault is crucial for mitigating the impact of earthquake disasters, assessing geological hazard risks, and developing effective emergency response plans.

[0003] Currently, fault shape deformation zone identification mainly relies on coseismic deformation monitoring technologies, primarily InSAR (Interferometric Synthetic Aperture Radar) deformation analysis. While InSAR offers high monitoring accuracy, the area surrounding the fault is prone to decoherence, making it difficult to accurately determine the deformation region. Furthermore, for historical earthquake events for which pre-earthquake images are unavailable, fault shape deformation zone identification is not possible, requiring manual mapping of the fault shape deformation zones. However, manually extracting and analyzing these fault shape deformation zones is time-consuming and complex, hindering its rapid application in earthquake hazard assessment and emergency response. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, electronic device, and storage medium for drawing fault shape variation zones, so as to achieve high-precision automated drawing of fault shape variation zones.

[0005] In a first aspect, embodiments of the present invention provide a method for drawing fault shape variation zones, including:

[0006] Acquire basic deformation data for the target area; wherein, the basic deformation data includes coseismic deformation data and / or DEM data;

[0007] Profile lines are generated along the fault vector from the deformation baseline data to obtain profile line data;

[0008] Based on the abrupt change points of each profile line in the profile data, the target fault shape change zone of the target area is generated.

[0009] Furthermore, obtain the basic deformation data of the target area, including:

[0010] Acquire pre-earthquake and post-earthquake optical image data covering the target area;

[0011] Preprocessing of pre-earthquake optical image data and post-earthquake optical image data yields first image data and second image data.

[0012] Based on the first and second image data, the coseismic deformation data are determined.

[0013] Furthermore, based on the first image data and the second image data, coseismic deformation data are determined, including:

[0014] The displacement field is obtained by performing cross-correlation calculations on the first and second image data.

[0015] Displacement is estimated based on the displacement field to obtain displacement data;

[0016] Based on the preset fault direction, the displacement data is converted into coseismic deformation data.

[0017] Furthermore, profile lines are generated along the fault vector from the deformation baseline data to obtain profile line data, including:

[0018] Determine the fault vector of the deformation baseline data;

[0019] Multiple profile lines are generated in the deformation baseline data along the fault vector;

[0020] Data is extracted from multiple locations for each profile line to obtain the profile line data.

[0021] Further, the fault vectors of the deformation baseline data are determined, including:

[0022] The first gradient data is obtained by calculating the first derivative of the deformation basis data.

[0023] The first gradient data is edge detected using a preset edge detection algorithm to obtain the fault vector.

[0024] Furthermore, based on the abrupt change points of each profile line in the profile data, the target fault shape deformation zone of the target area is generated, including:

[0025] Abrupt change points are identified for each profile line in the profile data to obtain abrupt change point data;

[0026] The steep change point data is connected and closed regions are generated to obtain the initial fault shape deformation zone.

[0027] The initial fault shape variation zone is optimized to obtain the target fault shape variation zone; the optimization process includes smoothing and / or correction.

[0028] Furthermore, abrupt change points are identified for each profile line in the profile data to obtain abrupt change point data, including:

[0029] Gradient calculation is performed on the sub-data corresponding to each profile line in the profile line data to obtain the second gradient data;

[0030] The second gradient data are fitted using a pre-defined robust regression algorithm to obtain the fitted data.

[0031] Based on the preset steep transition point requirements, steep transition points are identified in the fitted data to obtain steep transition point data; among them, the steep transition point requirements include that the two steep transition points on each profile line are located on both sides of the fault and have the maximum gradient value.

[0032] Secondly, embodiments of the present invention also provide a fault shape variation zone mapping device, comprising:

[0033] The data acquisition module is used to acquire the basic deformation data of the target area; wherein, the basic deformation data includes coseismic deformation data and / or DEM data;

[0034] The profile line generation module is used to generate profile lines along the fault vector from the deformation base data to obtain profile line data.

[0035] The deformation zone generation module is used to generate the target fault deformation zone of the target area based on the steep change points of each profile line in the profile line data.

[0036] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the fault shape variation zone drawing method of the first aspect.

[0037] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the fault outline variation zone drawing method of the first aspect.

[0038] The fault shape deformation zone drawing method, apparatus, electronic device, and storage medium provided in this invention can acquire basic deformation data of a target area. This basic deformation data includes coseismic deformation data and / or DEM data. Profile lines are generated along the fault vector from the basic deformation data to obtain profile line data. Based on the abrupt change points of each profile line in the profile line data, the target fault shape deformation zone of the target area is generated. Thus, based on the coseismic deformation data and / or DEM data of the target area, and through the abrupt change points on the profile lines generated along the fault vector, high-precision automated drawing of the fault shape deformation zone during coseismic or interseismic stages can be achieved. Attached Figure Description

[0039] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0040] Figure 1 A flowchart illustrating a method for drawing fault shape variation zones according to an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram illustrating the data source and processing of deformation-based data provided in an embodiment of the present invention.

[0042] Figure 3 A schematic diagram of a profile line generation and numerical extraction process provided in an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of a process for identifying abrupt change points and generating deformation zones, provided by an embodiment of the present invention.

[0044] Figure 5 This is a schematic diagram of a fault shape variation zone drawing device provided in an embodiment of the present invention;

[0045] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0046] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Coseismicity refers to the instantaneous changes that occur in the Earth's crust during fault slippage at the time of an earthquake. Inter-seismicity refers to the time interval between two major earthquakes, during which stress gradually accumulates in the Earth's crust. This invention provides a method, apparatus, electronic device, and storage medium for mapping fault deformation zones. It proposes an efficient automatic extraction technology for coseismic / inter-seismic deformation zones, which can more effectively analyze large amounts of data, identify potential disaster areas, help scientists gain a deeper understanding of the mechanisms of fault activity, assess secondary disasters caused by earthquakes, and provide data support for rapid post-earthquake response and long-term disaster prevention, thereby enabling more scientific geological disaster prevention and emergency decision-making.

[0048] To facilitate understanding of this embodiment, a method for drawing fault shape variation zones disclosed in this embodiment of the invention will first be described in detail.

[0049] This invention provides a method for mapping fault contour variations, which can be executed by an electronic device with data processing capabilities. See also... Figure 1 The diagram shows a method for drawing fault shape variation zones, which mainly includes the following steps S110 to S130:

[0050] Step S110: Obtain basic deformation data of the target area; wherein, the basic deformation data includes coseismic deformation data and / or DEM data.

[0051] The aforementioned target area is the required study area. If pre- and post-earthquake optical imagery data is available, coseismic deformation data can be prioritized for deformation zone mapping. This involves obtaining coseismic deformation data based on pre- and post-earthquake optical imagery data to map the coseismic fault deformation zone of the target area. If pre-earthquake optical imagery data is unavailable, DEM (Digital Elevation Model) data of the target area can be acquired to map the inter-seismic fault deformation zone. Coseismic fault deformation zone mapping refers to a single earthquake, while inter-seismic fault deformation zone mapping refers to cumulative earthquakes.

[0052] In some possible embodiments, coseismic deformation data can be obtained through the following steps:

[0053] Step S111: Obtain pre-earthquake optical image data and post-earthquake optical image data covering the target area.

[0054] Based on the time of the earthquake, pre-earthquake and post-earthquake optical imagery data can be selected. Optionally, Sentinel-2 satellite imagery before and after the earthquake can be acquired using Google Earth Engine (GEE). In practice, time range, spatial range, and cloud cover control should be set on the GEE platform, prioritizing imagery with low cloud cover and short time intervals between the earthquake and its occurrence. If suitable imagery is unavailable, efforts should be made to ensure that the pre-earthquake and post-earthquake optical imagery data were acquired during the same season to minimize the impact of seasonal variations.

[0055] Step S112: Preprocess the pre-earthquake optical image data and the post-earthquake optical image data to obtain the first image data and the second image data.

[0056] Preprocessing operations such as radiometric calibration, geometric correction, and cropping can be performed on selected images to ensure the consistency and accuracy of image data. The processed first and second image data will be used for coseismic deformation calculation.

[0057] Step S113: Determine the coseismic deformation data based on the first image data and the second image data.

[0058] Coseismic deformation data can be determined based on the differences between the first and second image data. Optionally, cross-correlation can be performed on the first and second image data to obtain the displacement field; then, displacement estimation can be performed based on the displacement field to obtain displacement data; and finally, the displacement data can be converted into coseismic deformation data according to the preset fault direction.

[0059] For cross-correlation calculations, it is possible to perform them using the first image data. I 1( x,y ) and second image data I 2( x,y To calculate the displacement field, cross-correlation is performed between the two data points. The formula for cross-correlation is as follows:

[0060] ;

[0061] in, R ( u,v ) is the cross-correlation function, ( u,v ) is the displacement. I 1( x,y ) is in the first image data ( x,y The reflectance value at () I 2( x+u,y+v ) is in the second image data ( x+u,y+v The reflectance value at ().

[0062] For displacement estimation, a sub-pixel matching algorithm can be used to calculate the coseismic deformation in the east-west and north-south directions: by finding the cross-correlation function. R ( u,v The peak position of the cross-correlation peak is initially estimated at the integer pixel level. To achieve sub-pixel accuracy, interpolation can be performed on the cross-correlation peak, for example, by fitting a quadratic curve or a Gaussian function. The formula for fitting a quadratic curve is as follows:

[0063]

[0064]

[0065] in,( δu,δv ) is the subpixel level corrected displacement, ( u peak ,v peak ) is the cross-correlation function R ( u,v The peak position of ).

[0066] For deformation transformation, the deformation along the fault direction can be calculated as follows: Based on the fault strike angle θ, the deformation in the east-west (E) and north-south (N) directions is converted into deformation along the fault direction (S) and perpendicular to the fault direction (D):

[0067]

[0068] in, d E , d N These are the deformations in the east-west and north-south directions, respectively. d S It is the deformation along the fault direction. d D It is the deformation along the direction perpendicular to the fault.

[0069] For situations where pre-earthquake images are unavailable, high-precision DEM data can be obtained using UAV LiDAR, SfM (Structure from Motion) technology, or Gaofen-7 satellite data, covering an area of ​​approximately 2 kilometers on both sides of the fault. UAV LiDAR, also known as airborne LiDAR, is suitable for areas with abundant vegetation cover and offers high accuracy. SfM technology uses UAVs to capture images covering the target area, thereby obtaining DEM data. Gaofen-7 satellite data is inexpensive and has a lower cost. Users can choose one of these methods—UAV LiDAR, SfM technology, or Gaofen-7 satellite data—to obtain DEM data for the target area based on their specific needs.

[0070] In practical implementation, the UAV photos acquired by SFM technology and the forward and backward image data from Gaofen-7 satellite can be processed into DEM data using software such as Agisoft Metashape. This mainly includes photo alignment, dense point cloud generation, DEM generation, and orthophoto generation. For UAV LiDAR, software such as TerraSolid can be used to separate ground points and non-ground feature points from the airborne LiDAR point cloud data. Then, the ground points are used to construct a DEM using irregular triangular meshes or grids. This DEM data can be used to monitor terrain changes around faults and assist in the identification of steep transition points and the mapping of fault deformation zones in subsequent steps.

[0071] Step S120: Generate profile lines along the fault vector from the deformation base data to obtain profile line data.

[0072] When acquiring profile data, if coseismic deformation data exists, the profile data can be acquired based solely on the coseismic deformation data, meaning the DEM data is not processed; if coseismic deformation data does not exist, the profile data is acquired based on the DEM data.

[0073] In some possible embodiments, step S120 above may include the following steps:

[0074] Step S121: Determine the fault vector of the deformation basis data.

[0075] The aforementioned fault vector can be manually drawn or automatically identified. In one possible implementation, the fault vector can be obtained through the following process: calculating the first derivative of the deformation baseline data to obtain the first gradient data; and then performing edge detection on the first gradient data using a preset edge detection algorithm to obtain the fault vector. The edge detection algorithm can be selected based on actual needs and is not limited here.

[0076] Step S122: Generate multiple profile lines in the deformation base data along the fault vector.

[0077] High-density profile lines are generated along the fault vector. The spacing and length of the profile lines can be determined based on the resolution of the deformation / DEM data. Generally, the spacing of the profile lines is set to match the image resolution, such as 1 meter or 0.5 meters, to ensure sufficient spatial resolution. The length of the profile lines is typically set to cover a sufficient area on both sides of the fault, such as 2 kilometers, to capture the complete features of the deformation region. The spacing and length of the profile lines can be adjusted appropriately according to the fault characteristics and research needs.

[0078] Step S123: Extract data from multiple locations for each profile line to obtain profile line data.

[0079] Data extraction can include: extracting the deformation value of each point on each profile line based on coseismic deformation data; or extracting the elevation value of each point on each profile line based on DEM data. Specific implementation methods can be as follows:

[0080] Deformation extraction: Along each profile line, obtain the deformation value of each point on the profile line;

[0081] Elevation value extraction: Using high-precision DEM data, the elevation value of each point on the profile line is obtained;

[0082] Data storage: The location, deformation / elevation values ​​of each profile line are stored as arrays or tables to provide a data foundation for subsequent identification of steep change points.

[0083] Step S130: Generate the target fault shape variation zone of the target area based on the steep change points of each profile line in the profile line data.

[0084] In some possible embodiments, step S130 above may include the following steps:

[0085] Step S131: Identify steep change points for each profile line in the profile line data to obtain steep change point data.

[0086] First, gradient calculation can be performed on the sub-data corresponding to each profile line in the profile data to obtain the second gradient data; then, a preset robust regression algorithm is used to fit the second gradient data to obtain the fitted data; then, according to the preset steep change point requirements, steep change points are identified on the fitted data to obtain steep change point data; wherein, the steep change point requirements include that the two steep change points on each profile line are located on both sides of the fault and the gradient value is the maximum.

[0087] When performing gradient calculations, the first derivative of the deformation / elevation values ​​on the profile line can be calculated to obtain the gradient value:

[0088]

[0089] in, g ( x ) is location x The gradient value at Δ x It is the distance between adjacent points. d ( x ) is location x Deformation / elevation values ​​at the location.

[0090] After obtaining the second gradient data, robust linear regression analysis can be performed, including data fitting, identification of steep transition points, and determination of the unique steep transition point, as follows:

[0091] Data fitting: Robust regression algorithms (such as M-estimation or Theil-Sen estimation) are used to fit the gradient data corresponding to each profile line in the second gradient data to reduce the impact of outliers;

[0092] Alternating point identification: By analyzing the gradient change rate, a threshold is set. T g To determine significant changes in the gradient; when | g ( x )|> T g At that time, the current position is considered to be x There is a point of abrupt change;

[0093] Determining the unique abrupt change point: In order to ensure that there is only one abrupt change point on each side of the fault for each profile line, the point with the largest gradient value can be selected as the abrupt change point; that is, select one point with the largest gradient value on each side of the fault for each profile line as the abrupt change point.

[0094] It should be noted that the above robust regression algorithm and threshold... T g All settings can be customized according to actual needs; no restrictions are imposed here.

[0095] Step S132: Connect the steep change points and generate closed regions from the steep change point data to obtain the initial fault shape change zone.

[0096] For connecting abrupt transition points: all abrupt transition points on the cross-section lines can be connected in spatial order to form the boundary lines of the fault's outward transition zone. Here, spatial order refers to the positional relationship; that is, connecting all abrupt transition points on the left side of the fault into one line, and connecting all abrupt transition points on the right side of the fault into one line, results in two boundary lines.

[0097] For generating closed regions: Topological processing functions of GIS (Geographic Information System) software can be used to close the boundary lines and generate polygonal surfaces. One or more closed regions (i.e., polygonal surfaces) can be generated, and these closed regions are the initial fault deformation zones.

[0098] Step S133: Optimize the initial fault shape variation zone to obtain the target fault shape variation zone; wherein, the optimization process includes smoothing and / or correction processing.

[0099] For smoothing: The boundary lines of the generated initial fault shape deformation zone are smoothed to eliminate unnecessary jaggedness or abrupt changes and achieve boundary optimization.

[0100] For correction processing: The initial fault deformation zone boundary line can be fine-tuned by combining the elevation information in the coseismic deformation data and / or DEM data to ensure that the boundary of the deformation zone accurately reflects the actual surface change characteristics.

[0101] Final output: The optimized target fault shape variation zone can be output in vector format, which can be conveniently used for mapping, analysis and further research.

[0102] Vector graphics are a graphic format that uses mathematical objects such as points, lines, curves, and polygons to represent images. Vector graphics do not become distorted when enlarged or reduced because their shapes are defined based on mathematical formulas.

[0103] Common vector formats include, but are not limited to:

[0104] 1) ESRI Shapefile (.shp): One of the most commonly used GIS vector data formats, supporting geometric types such as points, lines, and polygons.

[0105] 2) GeoJSON: An open standard format based on JSON for encoding various geographic data structures.

[0106] 3) KML / KMZ: The file format used by Google Earth, suitable for sharing geolocation information on the web.

[0107] 4) DXF: AutoCAD's drawing exchange format, widely used in the field of engineering design.

[0108] 5) SVG: An XML-based vector image format suitable for use in web pages and vector graphics editors.

[0109] The fault shape deformation zone drawing method provided in this invention can acquire basic deformation data of a target area. This basic deformation data includes coseismic deformation data and / or DEM data. Profile lines are generated along the fault vector from the basic deformation data to obtain profile line data. Based on the abrupt change points of each profile line in the profile line data, the target fault shape deformation zone of the target area is generated. Thus, based on the coseismic deformation data and / or DEM data of the target area, and through the abrupt change points on the profile lines generated along the fault vector, high-precision automated drawing of the fault shape deformation zone during coseismic or interseismic stages can be achieved.

[0110] For ease of understanding, please refer to the following: Figures 2 to 4 The above-mentioned method for drawing the variable zones of fault shapes is described in detail.

[0111] See Figure 2The diagram illustrates the data source and processing flow for deformation baseline data. For coseismic events, Google Earth Engine can be used to acquire pre-earthquake and post-earthquake images (i.e., pre-earthquake optical image data and post-earthquake optical image data). Based on these images, coseismic surface deformation is calculated to obtain east-west and north-south deformation components. Then, through deformation transformation, the fault strike surface deformation component (i.e., coseismic deformation data) is obtained. For inter-earthquake events, DEM data can be acquired using GF7 (Gaofen-7 satellite) and a drone platform. Specifically, the drone platform includes a camera and LiDAR. Drone photos taken using SFM technology and forward and backward view images from GF7 can be used to generate DEM data through image registration, 3D point cloud reconstruction, and other processes. LiDAR point cloud data is generated through point cloud filtering and other processes.

[0112] See Figure 3 The diagram illustrates a process for generating and extracting profile lines. First, the spacing and length of the profile lines are determined. Then, high-density profile lines are generated along the fault vector in the deformation data / DEM data. After that, numerical extraction is performed, namely, the deformation data corresponding to the deformation data and the elevation value corresponding to the DEM data are extracted to obtain the deformation / elevation values ​​at different profile line distances (i.e., different positions). Subsequently, curve fitting and steep change point identification can be performed on the profile line data.

[0113] See Figure 4 The diagram illustrates a process for identifying abrupt change points and generating deformation zones. For abrupt change point identification, gradient detection (i.e., gradient calculation) is performed first, followed by robust fitting regression to obtain abrupt change point data. For deformation zone generation, abrupt change points are first connected to form the boundary line of the fault deformation zone, and then a closed region is generated to obtain the initial fault deformation zone. Finally, the initial fault deformation zone is optimized to obtain the final target fault deformation zone.

[0114] This invention proposes an automatic method for mapping fault deformation zones during coseismic and interseismic phases. This method utilizes multi-source data combined with seismic deformation characteristics to accurately map fault deformation areas, providing data and technical support for crustal deformation research. First, during the coseismic phase, this method uses Google Earth Engine to acquire Sentinel-2 satellite images before and after the earthquake. After geometric correction and band selection, the east-west and north-south components of coseismic deformation are calculated. Then, combined with the fault strike angle, the east-west and north-south deformation values ​​are converted into deformation values ​​along the fault direction. This process fully considers the fault's geometric structure and the displacement characteristics generated by the earthquake. During the interseismic phase, this method uses structured light photogrammetry, airborne LiDAR technology, and Gaofen-7 stereo image pairs to acquire high-precision DEM data covering an area of ​​approximately 2 kilometers on both sides of the fault. The high-resolution DEM captures subtle topographic changes around the fault, providing a data foundation for the precise location of the deformation zone. After acquiring coseismic deformation data and high-precision interseismic DEM data, this method generates high-density profile data along the fault vector. The spacing of the profile lines is consistent with the resolution of the deformation and DEM data, and the profile line length is typically set to about 1-2 kilometers to ensure the accuracy of the profile analysis. Subsequently, by reading the deformation profile values ​​or elevation profile values, gradient detection and robust linear regression are performed on each profile line to obtain the location of the unique abrupt change points on both sides of the fault. Finally, these abrupt change points are connected to form a closed region, thus generating the fault deformation zone.

[0115] The innovation of this method lies in its use of advanced deformation analysis techniques combined with multi-source data (optical imagery, lidar, DEM, etc.) to automatically map the deformation zones of faults during coseismic and interseismic stages. The results not only possess high accuracy but also provide a scientific basis for subsequent seismic activity analysis and fault research.

[0116] Corresponding to the above-described method for drawing fault shape variations, this embodiment of the invention also provides a device for drawing fault shape variations. See also... Figure 5 The diagram shows a structural schematic of a fault profile variation mapping device, which includes:

[0117] The data acquisition module 501 is used to acquire the basic deformation data of the target area; wherein, the basic deformation data includes coseismic deformation data and / or DEM data;

[0118] The profile line generation module 502 is used to generate profile lines along the fault vector from the deformation basis data to obtain profile line data.

[0119] The deformation zone generation module 503 is used to generate the target fault deformation zone of the target area based on the steep change points of each profile line in the profile line data.

[0120] The fault shape deformation zone drawing device provided in this invention can acquire basic deformation data of a target area; wherein, the basic deformation data includes coseismic deformation data and / or DEM data; profile lines are generated along the fault vector from the basic deformation data to obtain profile line data; and the target fault shape deformation zone of the target area is generated based on the steep transition points of each profile line in the profile line data. In this way, based on the coseismic deformation data and / or DEM data of the target area, and through the steep transition points on the profile lines generated along the fault vector, high-precision automated drawing of the fault shape deformation zone at the coseismic or interseismic stage can be achieved.

[0121] Furthermore, the data acquisition module 501 is specifically used for: acquiring pre-earthquake optical image data and post-earthquake optical image data covering the target area; preprocessing the pre-earthquake optical image data and post-earthquake optical image data to obtain first image data and second image data; and determining coseismic deformation data based on the first image data and second image data.

[0122] Furthermore, the data acquisition module 501 is also used to: perform cross-correlation calculation on the first image data and the second image data to obtain the displacement field; perform displacement estimation based on the displacement field to obtain displacement data; and convert the displacement data into coseismic deformation data according to the preset fault direction.

[0123] Furthermore, the aforementioned profile line generation module 502 is specifically used for: determining the fault vector of the deformation base data; generating multiple profile lines in the deformation base data along the fault vector; and extracting data from multiple locations for each profile line to obtain profile line data.

[0124] Furthermore, the profile line generation module 502 is also used to: calculate the first derivative of the deformation base data to obtain the first gradient data; and perform edge detection on the first gradient data using a preset edge detection algorithm to obtain the fault vector.

[0125] Furthermore, the aforementioned deformation zone generation module 503 is specifically used for: identifying steep change points for each profile line in the profile line data to obtain steep change point data; connecting steep change points and generating closed regions in the steep change point data to obtain the initial fault shape deformation zone; and optimizing the initial fault shape deformation zone to obtain the target fault shape deformation zone; wherein, the optimization process includes smoothing and / or correction processing.

[0126] Furthermore, the aforementioned deformation zone generation module 503 is also used to: perform gradient calculation on the sub-data corresponding to each profile line in the profile line data to obtain second gradient data; fit the second gradient data using a preset robust regression algorithm to obtain fitted data; and identify steep change points on the fitted data according to preset steep change point requirements to obtain steep change point data; wherein, the steep change point requirements include that the two steep change points on each profile line are located on both sides of the fault and have the maximum gradient value.

[0127] The fault shape variable zone drawing device provided in this embodiment has the same implementation principle and technical effect as the aforementioned fault shape variable zone drawing method embodiment. For the sake of brevity, any parts not mentioned in the fault shape variable zone drawing device embodiment can be referred to the corresponding content in the aforementioned fault shape variable zone drawing method embodiment.

[0128] like Figure 6 As shown, an electronic device 600 provided in this embodiment of the invention includes: a processor 601, a memory 602 and a bus. The memory 602 stores a computer program that can run on the processor 601. When the electronic device 600 is running, the processor 601 and the memory 602 communicate through the bus. The processor 601 executes the computer program to realize the above-mentioned fault shape change zone drawing method.

[0129] Specifically, the memory 602 and processor 601 mentioned above can be general-purpose memory and processor, without any specific limitations here.

[0130] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program performs the fault contour mapping method described in the preceding method embodiments. The computer-readable storage medium includes various media capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), RAM, magnetic disk, or optical disk.

[0131] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0132] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0135] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0136] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for drawing fault shape variation zones, characterized in that, include: Acquire basic deformation data for the target area; wherein, the basic deformation data includes coseismic deformation data and / or DEM data; The deformation baseline data is used to generate profile lines along the fault vector to obtain profile line data; Based on the abrupt change points of each profile line in the profile line data, the target fault shape deformation zone of the target area is generated; The step of generating the target fault deformation zone of the target region based on the abrupt change points of each profile line in the profile line data includes: Abrupt change points are identified for each profile line in the profile line data to obtain abrupt change point data; The abrupt change point data is connected and closed regions are generated to obtain the initial fault shape deformation zone; The initial fault shape variation zone is optimized to obtain the target fault shape variation zone; wherein, the optimization process includes smoothing and / or correction processing; The deformation baseline data is used to generate profile lines along the fault vector to obtain profile line data, including: Determine the fault vector of the deformation baseline data; Multiple profile lines are generated in the deformation baseline data along the fault vector; Data is extracted from multiple locations for each profile line to obtain the profile line data; Determining the fault vector of the deformation baseline data includes: The first gradient data is obtained by calculating the first derivative of the deformation basis data; The first gradient data is subjected to edge detection using a preset edge detection algorithm to obtain the fault vector; For each profile line in the profile line data, abrupt change points are identified to obtain abrupt change point data, including: Gradient calculation is performed on the sub-data corresponding to each profile line in the profile line data to obtain the second gradient data; The second gradient data is fitted using a preset robust regression algorithm to obtain the fitted data; According to the preset steep transition point requirements, the fitted data is subjected to steep transition point identification to obtain the steep transition point data; wherein, the steep transition point requirements include that the two steep transition points on each of the profile lines are located on both sides of the fault and the gradient value is the maximum.

2. The method for drawing fault shape variation zones according to claim 1, characterized in that, The acquisition of the basic deformation data of the target region includes: Acquire pre-earthquake optical image data and post-earthquake optical image data covering the target area; The pre-earthquake optical image data and the post-earthquake optical image data are preprocessed to obtain first image data and second image data. Based on the first image data and the second image data, determine the coseismic deformation data.

3. The method for drawing fault shape variation zones according to claim 2, characterized in that, The step of determining coseismic deformation data based on the first image data and the second image data includes: The displacement field is obtained by performing cross-correlation calculations on the first image data and the second image data. Displacement is estimated based on the displacement field to obtain displacement data. Based on the preset fault direction, the displacement data is converted into coseismic deformation data.

4. A fault shape variable zone drawing device, using the fault shape variable zone drawing method according to any one of claims 1-3, characterized in that, include: The data acquisition module is used to acquire basic deformation data of the target area; wherein, the basic deformation data includes coseismic deformation data and / or DEM data; The profile line generation module is used to generate profile lines along the fault vector from the deformation basis data to obtain profile line data. The deformation zone generation module is used to generate the target fault deformation zone of the target area based on the steep change points of each profile line in the profile line data. The deformation zone generation module is specifically used for: identifying steep change points for each profile line in the profile line data to obtain steep change point data; connecting steep change points and generating closed regions in the steep change point data to obtain an initial fault shape deformation zone; and optimizing the initial fault shape deformation zone to obtain the target fault shape deformation zone; wherein the optimization process includes smoothing and / or correction processing.

5. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the fault shape variation zone drawing method according to any one of claims 1-3.

6. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the fault shape variation zone drawing method according to any one of claims 1-3.

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