A geological disaster risk area identification method based on a local search strategy

Abstract

The application discloses a geological disaster risk area identification method based on a local search strategy and belongs to the technical field of geological disaster monitoring and early warning. The identification method comprises the following steps: positioning a seed point in a target area; finding out a similar development condition grid unit r similar to the development condition of the seed point s in a local search window range Omega of the seed point s, forming a similar development condition area, then taking the grid of the area where the seed point s is located as an initial area, iteratively performing morphological dilation, and each time the iteration is performed, one pixel of the current area is dilated, an intersection is obtained by intersecting the dilated current area with the similar development condition area, and the current area is updated by using the intersected area, and after the iteration is terminated, a similar neighborhood of the seed point is obtained; the grid of the area of the similar neighborhood of the seed point is converted into a vector polygon, the boundary is smoothed and optimized, and the identification range of the risk area is obtained. The application focuses on core area calculation, avoids the problem of low identification efficiency of global analysis, and is suitable for rapid screening and dynamic updating of a large range of risk areas.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and early warning technology, and in particular to a method for identifying geological disaster risk zones based on a local search strategy. Background Technology

[0002] With the intensification of climate change and the increase in engineering activities, geological disasters are characterized by "high randomness, strong concealment, and increased destructiveness," and many disasters often occur in areas that are not well understood, posing extremely high unknown risks. How to quickly and accurately identify locations of risk in large-scale, complex mountainous areas has become the primary technical challenge for geological disaster prevention and control.

[0003] Traditional methods for calculating geological hazard risk zones, regardless of whether they use raster or slope units as the basic analysis unit, follow a global calculation strategy, meaning they perform feature calculations across the entire study area. However, geological hazards exhibit typical spatial sparsity—the area of ​​a hazard body usually accounts for only one-thousandth or even less of the total study area, with effective information concentrated only in a very small local region. Global calculation not only consumes computational resources and reduces the efficiency of identifying effective information, but it also easily introduces interference information from irrelevant areas, reducing the reliability of the identification results. Summary of the Invention

[0004] The purpose of this invention is to provide a geological hazard risk zone identification method based on a local search strategy, which focuses on core area calculations and avoids the problem of low efficiency in full-domain analysis and identification. It is suitable for rapid screening and dynamic updating of large-scale risk zones.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for identifying geological hazard risk zones based on a local search strategy includes the following steps: S1. Locate the seed point within the target area. Let the target area be a grid space. ; Obtain each grid cell within the target area Deformation value Determine each grid cell Deformation value Is there any abnormality, and identify areas of abnormal deformation? ; Each connected deformation anomaly region Define the raster cell with the largest deformation value as the seed point s; S2, Seed point neighborhood similarity region search Let the local search window range of seed point s be . ; Within the local search window range of seed point s Within the region, identify the developmentally similar raster cells r that have similar developmental conditions. The region formed by these developmentally similar raster cells r is the developmentally similar region. ; Within the local search window range of seed point s Within, the area of ​​the seed point s is represented by a raster. Initial region Iterative morphological dilation is performed, and during each iteration, the current region is... expansion Each pixel, and regions with similar developmental conditions. Find the intersection and update the current region with the intersection region. When the region no longer expands or reaches the preset maximum number of iterations, the iteration terminates and the similar neighborhood of the seed point is obtained. S3. Convert the similar neighborhood region raster of the seed point into a vector polygon, perform boundary smoothing optimization, and obtain the final risk area identification range.

[0006] Furthermore, the local search window range of seed point s is In, arbitrary grid cell The set of feature vectors is: Where d is the number of feature indicators, and T is the transpose; based on Calculate and find the developmentally similar raster cell r with similar developmental conditions. For grid units with similar developmental conditions The similarity measurement function between the index features of the seed point s and the seed point s is calculated using the following formula: in, The preset weight for the i-th feature indicator; For feature indicators that do not have cyclic characteristics The calculation formula is as follows: For characteristic indicators with cyclic properties The calculation formula is as follows: .

[0007] Furthermore, deformation anomaly regions The recognition function is as follows: in, This is the preset deformation threshold.

[0008] Furthermore, step S1 also includes the following steps: Based on developmental condition discriminant function Determine whether each seed point s meets the developmental conditions, and retain seed points that meet the preset conditions. The developmental condition discrimination function... The calculation formula is as follows: in, For the j-th developmental condition indicator, is the preset threshold for the j-th developmental condition indicator.

[0009] Furthermore, the development condition indicators include slope, stratigraphic lithology, and slope structure.

[0010] Furthermore, step S2 also includes the following steps: In areas with similar developmental conditions Subsequently, a morphological contraction was performed on the region to reshape areas with similar developmental conditions. shrink Each pixel is used to break up the false connected regions formed by narrow connections.

[0011] Furthermore, step S2 also includes the following steps: After obtaining the similar neighborhood of the seed point s, a morphological dilation is performed on this region to expand the similar neighborhood of the seed point. Each pixel compensates for regions with similar developmental conditions. Boundary loss caused by morphological shrinkage.

[0012] Furthermore, the feature indicators include topographic indicators, geological indicators, and remote sensing indicators; the topographic indicators include slope, aspect, curvature, and topographic relief; the geological indicators include stratigraphic lithology and stratigraphic dip angle; and the remote sensing indicators include spectral reflectance and texture features.

[0013] A geological hazard risk zone identification device based on a local search strategy includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the geological hazard risk zone identification method based on a local search strategy described above.

[0014] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for identifying geological hazard risk zones based on a local search strategy.

[0015] The beneficial effects of this invention are as follows: This invention proposes a geological hazard risk zone identification method based on a local search strategy. This method combines deformation-driven and geographical environment similarity, uses the deformation location as a seed point to search for similar local neighborhood areas, and directly outputs complete unit bodies that meet the geological hazard development conditions. It focuses on core area calculation, avoiding the problem of low efficiency in global analysis and identification, and is suitable for rapid screening and dynamic updating of large-scale geological hazard risk zones. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for identifying geological hazard risk zones based on a local search strategy according to the present invention. Figure 2 This is a schematic diagram of the preliminary process for searching the neighboring similar regions of a seed point in a geological hazard risk zone identification method based on a local search strategy according to the present invention. Figure 3 In this invention, a method for identifying geological hazard risk zones based on a local search strategy is used to identify areas with similar developmental conditions. A diagram illustrating the shrinkage of one pixel; Figure 4 This is a schematic diagram illustrating the similar neighborhood of a seed point obtained in a geological hazard risk zone identification method based on a local search strategy according to the present invention. Figure 5 This is a schematic diagram illustrating the expansion of the similar neighborhood of a seed point by one pixel in a geological hazard risk zone identification method based on a local search strategy according to the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific examples described herein are merely illustrative and not intended to limit the scope of the invention.

[0018] Example 1:

[0019] Please see Figures 1-5 As shown, a method for identifying geological hazard risk zones based on a local search strategy includes the following steps: S1. Locate the seed point within the target area; S2, Seed point neighborhood similarity region search; S3. Convert the similar neighborhood region raster of the seed point into a vector polygon, perform boundary smoothing optimization, and obtain the final risk area identification range.

[0020] To locate the seed point within the target area, step S1 includes the following steps: Let the target area be a grid space. ; Obtain each grid cell within the target area Deformation value Determine each grid cell Deformation value Is there any abnormality, and identify areas of abnormal deformation? ; Each connected deformation anomaly region The raster cell with the largest deformation value is defined as the seed point s. It should be noted that for each connected deformation anomaly region... When there are multiple raster cells with the largest deformation value, one of them is selected as the seed point s.

[0021] Specifically, areas of abnormal deformation The recognition function is as follows: in, This is the preset deformation threshold.

[0022] More specifically, the deformation value v of each grid cell p within the target area is obtained using remote sensing technologies such as InSAR (Inductive Aperture Radar Interferometry), optical remote sensing image change detection, or airborne LiDAR (Lidar) differential DEM (Digital Elevation Model).

[0023] For the above technical solution, step S1 also includes the following steps: Based on developmental condition discriminant function Determine whether each seed point s meets the developmental conditions, and retain seed points that meet the preset conditions. The developmental condition discrimination function... The calculation formula is as follows: in, For the j-th developmental condition indicator, is the preset threshold for the j-th developmental condition indicator.

[0024] Specifically, development condition indicators include slope, stratigraphic lithology, and slope structure.

[0025] To perform a search for similar regions in the neighborhood of the seed point, step S2 includes the following steps: Let the local search window range of seed point s be . , where arbitrary grid cell The set of feature vectors is: Where d is the number of feature indicators, and T is the transpose (transpose of the matrix). Within the local search window range of seed point s Internally, based on Calculate and identify developmentally similar raster cells r, and the region formed by these developmentally similar raster cells r is the developmentally similar region. ,in: For grid units with similar developmental conditions The similarity measurement function between the index features of the seed point s and the seed point s is calculated using the following formula: in, The preset weights (weights) for the i-th feature index The local adaptive method is used to determine the search area, i.e., within the local search window. Within the seed point s, the weights are dynamically adjusted based on the characteristic variance of the neighborhood of the seed point s; indicators with large variance are assigned lower weights, and vice versa. For characteristic indicators that do not have cyclical characteristics (i.e., continuous indicators, which need to be normalized due to the different dimensions of each indicator, such as slope), The calculation formula is as follows: For characteristic indicators with cyclic properties (such as slope aspect). The calculation formula is as follows: Within the local search window range of seed point s Within, the area of ​​the seed point s is represented by a raster. Initial region Iterative morphological dilation is performed, and during each iteration, the current region is... expansion Each pixel, and regions with similar developmental conditions. Find the intersection and update the current region with the intersecting region. The iteration terminates when the region stops expanding or reaches the preset maximum number of iterations, thus obtaining the similar neighborhood of the seed point, i.e.: in, For morphological dilation operators.

[0026] like Figure 4 In this embodiment, for example, the maximum number of iterations is preset to 9, and the results of the iterations are R1, R2, R3...R9 in sequence; The value is 1, meaning that during each iteration of expansion, the current region is... Expand by 1 pixel.

[0027] Specifically, the characteristic indicators include topographic indicators, geological indicators, and remote sensing indicators.

[0028] More specifically, topographic indicators include slope, aspect, curvature, and topographic relief; geological indicators include stratigraphic lithology and stratigraphic dip; and remote sensing indicators include spectral reflectance and texture features.

[0029] For the above technical solution, step S2 also includes the following steps: In areas with similar developmental conditions Subsequently, a morphological contraction was performed on the region to reshape areas with similar developmental conditions. shrink Each pixel is used to break up the false connected regions formed by narrow connections; After obtaining the similar neighborhood of the seed point s, a morphological dilation is performed on this region to expand the similar neighborhood of the seed point. Each pixel compensates for regions with similar developmental conditions. Boundary loss caused by morphological shrinkage.

[0030] In this embodiment, by way of example, The value is 1, meaning that in regions with similar developmental conditions, Then, regions with similar developmental conditions will be... Shrink by 1 pixel, such as Figure 3 After obtaining the similar neighborhood of the seed point s, expand the similar neighborhood of the seed point by 1 pixel, such as... Figure 5 .

[0031] In summary, this invention combines deformation-driven computation with geographical similarity, using deformation location as seed point to search for similar local neighborhoods, and directly outputs complete unit bodies that meet the conditions for geological disaster development. By focusing on core area calculations, it avoids the problem of low computational efficiency in full-domain analysis, making it suitable for rapid screening and dynamic updating of large-scale geological disaster risk areas.

[0032] Example 2:

[0033] As an application, a geological hazard risk zone identification device based on a local search strategy includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the geological hazard risk zone identification method based on a local search strategy described in Embodiment 1.

[0034] As an application, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the geological hazard risk zone identification method based on a local search strategy described in Embodiment 1.

[0035] This invention is not limited to the specific embodiments described above. Those skilled in the art can implement this invention using various other specific embodiments based on the content disclosed herein. Therefore, any design that adopts the design structure and concept of this invention and makes some simple changes or modifications falls within the protection scope of this invention.

Claims

1. A method for identifying geological hazard risk zones based on a local search strategy, characterized in that: Includes the following steps: S1. Locate the seed point within the target area. Let the target area be a grid space. ; Obtain each grid cell within the target area Deformation value Determine each grid cell Deformation value Is there any abnormality, and identify areas of abnormal deformation? ; Each connected deformation anomaly region Define the raster cell with the largest deformation value as the seed point s; S2, Seed point neighborhood similarity region search Let the local search window range of seed point s be . ; Within the local search window range of seed point s Within the region, based on the similarity of indicator features, similar developmental condition-similar raster units r are identified. The region formed by these similar developmental condition-similar raster units r is the similar developmental condition region. ; Within the local search window range of seed point s Within, the area of ​​the seed point s is represented by a raster. Initial region Iterative morphological dilation is performed, and during each iteration, the current region is... expansion Each pixel, and regions with similar developmental conditions. Find the intersection and use the intersecting region. Update and replace the current region Meanwhile, during the iterative expansion process, when the intersecting regions... When the expansion stops or the preset maximum number of iterations is reached, the iteration terminates, and the similar neighborhood of the seed point is obtained. S3. Convert the similar neighborhood region raster of the seed point into a vector polygon, perform boundary smoothing optimization, and obtain the final risk area identification range.

2. The method for identifying geological hazard risk zones based on a local search strategy according to claim 1, characterized in that: The local search window range of seed point s is In, arbitrary grid cell The set of feature vectors is: Where d is the number of feature indicators, and T is the transpose; based on Calculate and find the developmentally similar raster cell r with similar developmental conditions. For grid units with similar developmental conditions The similarity measurement function between the index features of the seed point s and the seed point s is calculated using the following formula: in, The preset weight for the i-th feature indicator; For feature indicators that do not have cyclic characteristics The calculation formula is as follows: For characteristic indicators with cyclic properties The calculation formula is as follows: 。 3. The method for identifying geological hazard risk zones based on a local search strategy according to claim 1, characterized in that: Deformation anomaly area The recognition function is as follows: in, This is the preset deformation threshold.

4. A method for identifying geological hazard risk zones based on a local search strategy according to any one of claims 1 to 3, characterized in that: Step S1 also includes the following steps: Based on developmental condition discriminant function Determine whether each seed point s meets the developmental conditions, and retain seed points that meet the preset conditions. The developmental condition discrimination function... The calculation formula is as follows: in, For the j-th developmental condition indicator, is the preset threshold for the j-th developmental condition indicator.

5. The method for identifying geological hazard risk zones based on a local search strategy according to claim 4, characterized in that: The developmental condition indicators include slope, stratigraphic lithology, and slope structure.

6. The method for identifying geological hazard risk zones based on a local search strategy according to claim 1, characterized in that: Step S2 also includes the following steps: In areas with similar developmental conditions Subsequently, a morphological contraction was performed on the region to reshape areas with similar developmental conditions. shrink Each pixel is used to break up the false connected regions formed by narrow connections.

7. The method for identifying geological hazard risk zones based on a local search strategy according to claim 6, characterized in that: Step S2 also includes the following steps: After obtaining the similar neighborhood of the seed point s, a morphological dilation is performed on this region to expand the similar neighborhood of the seed point. Each pixel compensates for regions with similar developmental conditions. Boundary loss caused by morphological shrinkage.

8. The method for identifying geological hazard risk zones based on a local search strategy according to claim 2, characterized in that: The feature indicators include topographic indicators, geological indicators, and remote sensing indicators; the topographic indicators include slope, aspect, curvature, and topographic relief; the geological indicators include stratigraphic lithology and stratigraphic dip angle; and the remote sensing indicators include spectral reflectance and texture features.

9. A geological hazard risk zone identification device based on a local search strategy, characterized in that: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for identifying geological hazard risk zones based on a local search strategy as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the geological hazard risk zone identification method based on a local search strategy as described in any one of claims 1 to 8.

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