Quantitative characterization method for roughness of high-speed long-range landslide movement path

Abstract

The application discloses a kind of high-speed remote landslide movement path roughness degree quantitative characterization method, belong to geological disaster technical field, first obtain the remote sensing image data of target landslide, the DEM digital elevation data of target landslide and the landslide boundary data of target landslide, after processing, the elevation data of target landslide movement path is obtained, and it is de-trended, the elevation data of landslide movement path microtopography is obtained;According to the elevation data of landslide movement path microtopography, based on the fractal dimension calculation method of joint contour line is calculated, the fractal dimension after de-trend of target landslide movement path is obtained, and the path roughness coefficient of target landslide is calculated.The application calculation process is simple, wide application range, can quantify the roughness degree of high-speed remote landslide movement path under different spatial scales, and fully reveal the influence of microtopography in the process of landslide movement, provide more comprehensive roughness characterization.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of geological disasters, and particularly relates to a quantitative characterization method for roughness of a high-speed long-range landslide movement path. BACKGROUND

[0002] High-speed long-range landslide refers to a geological phenomenon that large-scale bedrock mass on high and steep slopes in high mountain and canyon areas moves in a fluid-like state in the form of debris flow at a high or extremely high speed and over a long distance. Such geological disasters have high concealment in the source area, suddenness and great harmfulness in events. Although the occurrence frequency is low, due to the movement characteristics, the high-speed long-range landslide is affected by the terrain and interacts with the surface materials along the way during the high-speed movement, often resulting in transformation between disasters and forming a disaster chain with long-range effects. The formation of the disaster chain significantly enlarges the influence range and complexity of the disaster, and the harm caused by the disaster is difficult to estimate. The quantitative characterization of the roughness of the high-speed long-range landslide movement path can accurately determine kinematic and dynamic parameters such as the maximum movement speed, the farthest movement distance, and the maximum accumulation range, realize the analysis and prediction of the high-speed long-range landslide disaster risk, and determine the disaster risk degree.

[0003] The existing characterization method for the roughness of the landslide movement path is mainly a geometric statistical method. The method is based on high-precision three-dimensional terrain data of the landslide path, converts the geometric characteristics of the path surface into quantifiable roughness indexes, and thus realizes the quantification of the roughness of the movement path. However, the method ignores the local changes of the movement path surface, and it is difficult to accurately capture the micro-topographic changes, especially in complex terrain and micro-topography. On the other hand, the method has limitations at different scales. The quantification result of the roughness is affected by the selected regional scale. At a large scale, local details may be ignored, and at a small scale, the micro-undulations may be overemphasized. Moreover, when processing large-scale and high-resolution three-dimensional terrain data, the calculation complexity is high, and it is often difficult to efficiently and accurately quantify the roughness of the movement path. SUMMARY

[0004] In view of the above problems in the prior art, the quantitative characterization method for the roughness of the high-speed long-range landslide movement path provided by the present application solves the problems of complex calculation, incomplete consideration of terrain scales, and lack of fine characterization of micro-topographic features in the quantitative characterization of the roughness of the high-speed long-range landslide movement path.

[0005] To achieve the above-mentioned application purposes, the technical scheme adopted by the present application is as follows: a quantitative characterization method for the roughness of a high-speed long-range landslide movement path, comprising the following steps:

[0006] S1: select a target landslide and its surrounding area, and obtain remote sensing image data of the target landslide, DEM digital elevation data of the target landslide and landslide boundary data of the target landslide;

[0007] S2: obtain elevation data of the target landslide movement path according to the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide and the landslide boundary data of the target landslide;

[0008] S3: the elevation data of the target landslide movement path is de-trended to obtain the elevation data of the micro-terrain of the landslide movement path;

[0009] S4: according to the elevation data of the micro-terrain of the landslide movement path, based on the calculation method of the joint profile line fractal dimension, the de-trended fractal dimension of the target landslide movement path is obtained;

[0010] S5: according to the de-trended fractal dimension of the target landslide movement path, the path roughness coefficient of the target landslide is calculated, and the quantitative characterization of the roughness of the high-speed long-distance landslide movement path is completed.

[0011] The beneficial effects of the present application are: the present application quantitatively characterizes the roughness of the high-speed long-distance landslide movement path based on the path roughness coefficient, only needs to count the average relief height and average extension length of the micro-terrain on the landslide movement path, and based on the joint fractal model h-L method of the joint profile line roughness characterization method in the field of rock mass mechanics, the de-trended fractal dimension of the high-speed long-distance landslide movement path and the path roughness coefficient of the landslide are calculated, the calculation process is simple, the application range is wide, the roughness of the high-speed long-distance landslide movement path can be revealed under different spatial scales, the influence of the micro-terrain in the landslide movement process is fully revealed, and a more comprehensive roughness characterization is provided, which has strong theoretical significance and practical application value.

[0012] Further: the specific steps of S1 are as follows:

[0013] S101: on the satellite image, select the target landslide and its surrounding area, and obtain the remote sensing image data of the target landslide according to the set spatial pixel resolution, and obtain the DEM digital elevation data of the target landslide according to the set elevation data sampling interval;

[0014] S102: in the satellite image, the landslide boundary of the target landslide is labeled by using the polygon tool to obtain the landslide boundary data of the target landslide.

[0015] The above further scheme has the beneficial effect that: by acquiring remote sensing image data of the target landslide and DEM digital elevation data of the target landslide, more comprehensive geological information can be provided, ensuring sufficient detail and accuracy, and the landslide boundary data of the target landslide can acquire accurate landslide area.

[0016] Further, the specific steps of S2 are as follows:

[0017] S201: superimpose the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide, and keep the coordinate system consistent to obtain a map view of the target landslide;

[0018] S202: according to the map view of the target landslide, extend along the main sliding direction of the landslide from the landslide shear outlet of the target landslide to obtain a longitudinal profile line segment of the target landslide;

[0019] S203: translate the longitudinal profile line segment of the target landslide to the adjacent mountain area of the target landslide to approximately obtain a target landslide movement path line segment;

[0020] S204: according to the target landslide movement path line segment, obtain a profile graph of the target landslide movement path;

[0021] S205: according to the profile graph of the target landslide movement path, obtain elevation data of the target landslide movement path.

[0022] The above further scheme has the beneficial effect that: the pre-landslide topography of the landslide and the topographic features of the adjacent mountain have certain similarity, so that the elevation data of the target landslide movement path can be approximately obtained in the absence of pre-landslide topography data of the landslide, thereby approximately reflecting the elevation change characteristics of the real landslide movement path.

[0023] Further, the specific steps of S3 are as follows:

[0024] S301: according to the elevation data of the target landslide movement path, establish a topographic trend function of the landslide movement path by using a fitting function;

[0025] S302: according to the topographic trend function of the landslide movement path, obtain the best fitting coefficient of the topographic trend function based on the least square method;

[0026] S303: according to the best fitting coefficient, obtain the fitted topographic trend function;

[0027] S304: according to the fitted topographic trend function, remove the large-scale trend in the target landslide movement path to obtain the elevation data of the micro-topography of the landslide movement path.

[0028] The beneficial effects of the above-mentioned further scheme are: by detrending, large-scale topographic features that interfere with subsequent analysis can be removed, allowing subsequent analysis to focus on small-scale local changes and improving the accuracy of quantitative characterization of roughness.

[0029] Furthermore: the expression for the fractal dimension of the target landslide movement path after detrending in S4 is as follows:

[0030] D = log₄ / log[2(1+costan)] -1 (2h / L))]

[0031] Where D is the fractal dimension of the target landslide movement path after detrending, h is the average undulation height of the micro-topography on the landslide path, and L is the average extension length of the micro-topography on the landslide path.

[0032] The beneficial effects of the above-mentioned further scheme are: by calculating the fractal dimension, the roughness of the landslide movement path can be quantified, eliminating the uncertainty it exhibits as the observation scale changes, and improving the accuracy of the quantitative characterization of the roughness of the landslide movement path.

[0033] Furthermore: the expression for the path roughness coefficient of the target landslide in S5 is as follows:

[0034] PRC = 85.2671(D-1) 0.5679

[0035] Where PRC is the path roughness coefficient of the target landslide, and D is the fractal dimension of the target landslide's motion path after detrending.

[0036] The beneficial effects of the above-mentioned further scheme are as follows: by referring to the empirical formula between the joint roughness coefficient (JRC) and the fractal dimension, the path roughness coefficient of the target landslide calculated by the above formula can more intuitively characterize the roughness of the landslide movement path and reflect the local undulations and detailed features on the landslide movement path. Attached Figure Description

[0037] Figure 1 A flowchart illustrating a quantitative characterization method for the roughness of a high-speed, long-distance landslide path;

[0038] Figure 2 This is a longitudinal profile of the landslide's movement path. Detailed Implementation

[0039] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0040] As shown in Figure 1 A flow chart of a method for quantitatively representing the roughness of a high-speed long-distance landslide movement path, comprising the following steps:

[0041] S1: Select a target landslide and its surrounding area, and obtain remote sensing image data of the target landslide, DEM digital elevation data of the target landslide, and landslide boundary data of the target landslide;

[0042] S2: Obtain the elevation data of the target landslide movement path according to the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide;

[0043] S3: Perform detrended processing on the elevation data of the target landslide movement path to obtain the elevation data of the microtopography of the landslide movement path;

[0044] S4: According to the elevation data of the microtopography of the landslide movement path, calculate the fractal dimension of the target landslide movement path based on the calculation method of joint profile line fractal dimension;

[0045] S5: Calculate the path roughness coefficient of the target landslide according to the detrended fractal dimension of the target landslide movement path, and complete the quantitatively representing the roughness of the high-speed long-distance landslide movement path.

[0046] The specific steps of S1 are as follows:

[0047] S101: In the satellite image, select a target landslide and its surrounding area, and obtain remote sensing image data of the target landslide according to a set spatial pixel resolution, and obtain DEM digital elevation data of the target landslide according to a set elevation data sampling interval;

[0048] S102: In the satellite image, use the polygon tool to label the landslide boundary of the target landslide to obtain the landslide boundary data of the target landslide.

[0049] In an embodiment of the present application, data download can be performed using the 91 satellite assistant platform. After entering the 91 satellite assistant, the target landslide and its surrounding area are selected on the satellite image, and the required spatial pixel resolution of the image data, such as 5 meters or higher, is set. High-resolution remote sensing image data of the area is downloaded; the required sampling interval of the elevation data, such as 8 meters or higher, is set, and high-precision elevation DEM data of the area is downloaded; the polygon tool is used in Google Earth to mark the landslide boundary of the target landslide, and the target landslide boundary is accurately drawn, ensuring that the boundary range can accurately cover the main body and possible landslide area of the landslide. After drawing, the landslide boundary image of the target landslide is obtained, and saved as a kml file format.

[0050] Since the landslide and the adjacent mountain have the same or similar internal and external dynamic geological action background, the pre-landslide topography of the landslide and the topographic features of the adjacent mountain have certain similarity. Based on the similarity, the present application proposes a method for approximately extracting the motion path of the landslide, that is, according to the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide, the elevation data of the motion path of the target landslide is approximately obtained; the specific steps of S2 are as follows:

[0051] S201: The remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide are overlapped and kept consistent in the coordinate system to obtain a map view of the target landslide;

[0052] S202: According to the map view of the target landslide, the longitudinal profile line segment of the target landslide is obtained by extending along the main sliding direction of the landslide from the landslide shear outlet of the target landslide;

[0053] S203: The longitudinal profile line segment of the target landslide is translated to the mountain area adjacent to the target landslide to approximately obtain a motion path line segment of the target landslide;

[0054] S204: According to the motion path line segment of the target landslide, a profile map of the motion path of the target landslide is obtained;

[0055] S205: According to the profile map of the motion path of the target landslide, the elevation data of the motion path of the target landslide is obtained.

[0056] In an embodiment of the present application, the landslide boundary image of the target landslide can be imported into the Map Assistant platform 91, and the target landslide boundary data is converted, the appropriate coordinate system is selected to convert into shp file format, and it is ensured that the spatial information of the data is retained; the remote sensing image data (tiff format) of the target landslide, the DEM digital elevation data (tiff format) of the target landslide and the target landslide boundary data (shp format) are imported into ArcGIS, it is ensured that the coordinate systems of these data are consistent, the coordinate offset is avoided, the spatial information of the landslide boundary is correctly mapped to the map view in ArcGIS, and the map view of the target landslide is obtained;

[0057] The landslide movement path generally refers to the topographic state below the shear outlet before the landslide occurs, and the landslide movement path in the present application is a two-dimensional movement path, that is, the landslide changes in elevation along the main sliding direction, but the reconstruction of the topography before the landslide is complex and may be affected by different geological actions; in order to avoid this difficulty, another method for approximately obtaining the landslide movement path is provided in the present application, first, the 3D Analyst toolbar is enabled in ArcGIS, the "insert line" command is selected, and a longitudinal profile line segment extending from the shear outlet of the landslide along the main sliding direction of the landslide is drawn, then the longitudinal profile line segment is translated to the adjacent mountain area of the landslide to approximately obtain a target landslide path movement line segment, which is similar to the actual movement path of the landslide in space, and the elevation change of the line segment can approximately reflect the elevation change characteristics of the actual landslide movement path.

[0058] In the 3D Analyst toolbar, the "profile graph" command is used for the approximately obtained target landslide path movement line segment, and ArcGIS will automatically generate an elevation change graph along the path to show the elevation change of each position in the landslide movement process, and a longitudinal profile graph of the landslide movement path is obtained, as shown in Figure 2 In the generated longitudinal profile graph window of the landslide movement path, the "export data" option is selected to save the elevation data along the path in the.csv format, and the elevation data of the target landslide movement path is obtained.

[0059] In an embodiment of the present application, the specific steps of S3 are as follows:

[0060] S301: According to the elevation data of the target landslide movement path, a topographic trend function of the landslide movement path is established by using a fitting function; wherein, the topographic trend function is established to remove the large-scale topographic undulations with greater influence, such as slope and slope direction, and leave small-scale topographic features, such as micro-topographic undulations or local irregular elevation fluctuations, and the object of detrended is one-dimensional elevation data on the target landslide movement path, i.e. the elevation change along the main landslide direction; therefore, the skilled in the art can select different detrended methods according to specific needs, and the linear fitting detrended method and the quadratic polynomial fitting detrended method are selected for illustration in the embodiment, and the expressions of the corresponding topographic trend functions are respectively:

[0061] T1(x) = a0 + a1x

[0062] T2(x) = a2 + a3x + a4x 2

[0063] Wherein, T1(x) is a linear trend function, T2(x) is a quadratic polynomial trend function, a0, a1, a2, a3 and a4 are coefficients to be solved, and x is the distance along the main sliding direction of the landslide.

[0064] S302: According to the topographic trend function of the landslide movement path, the best fitting coefficients of the topographic trend function are obtained based on the least square method; the specific fitting method is as follows:

[0065] The linear fitting expression is as follows:

[0066]

[0067] The quadratic polynomial fitting expression is as follows:

[0068]

[0069] Wherein, x i is the horizontal coordinate of the i th data in the DEM digital elevation data of the target landslide, and Z(x i ) is the vertical coordinate of the i th data in the DEM digital elevation data of the target landslide.

[0070] By minimizing the sum of squares of errors between the fitting function and the observation data, the best fitting coefficients a0, a1, a2, a3 and a4 are solved.

[0071] S303: According to the best fitting coefficients, the fitted topographic trend function is obtained;

[0072] S304: According to the fitted terrain trend function, the large-scale trend in the target landslide movement path is removed to obtain the elevation data of the landslide movement path microtopography, which can reflect the small-scale change of the terrain characteristics, and the specific way to remove the large-scale trend is:

[0073] The expression of linear fitting detrending is as follows:

[0074] E1(x)=Z(x)-T1(x)=Z(x)-(a0+a1x)

[0075] The expression of quadratic polynomial fitting detrending is as follows:

[0076] E2(x)=Z(x)-T2(x)=Z(x)-(a2+a3x+a4x 2 )

[0077] Wherein, E1(x) is the landslide movement path microtopography by linear fitting detrending, E2(x) is the landslide movement path microtopography by quadratic polynomial fitting detrending, and Z(x) is the DEM digital elevation data of the target landslide movement path.

[0078] In one embodiment of the present application, based on the joint roughness coefficient (JRC, Joint Roughness Coefficient), the present application proposes a "path roughness coefficient" (PRC, Path Roughness Coefficient), which is a parameter for quantitatively describing the geometric roughness of rock joint surface. Based on the idea of using JRC to represent the roughness of joint profile line, the present application proposes to use path roughness coefficient to quantitatively represent the roughness of target landslide movement path, in the following way:

[0079] According to the landslide movement path microtopography, the calculation method of the fractal dimension of the joint profile line is referred to for calculation to obtain the fractal dimension of the target landslide movement path after detrending;

[0080] According to the fractal dimension of the target landslide movement path after detrending, the path roughness coefficient of the target landslide is calculated to complete the quantitative representation of the roughness of the high-speed long-range landslide movement path.

[0081] The expression of the fractal dimension of the target landslide movement path after detrending is as follows:

[0082] D=log4 / log[2(1+cos tan -1 (2h / L))]

[0083] Wherein, D is the fractal dimension of the target landslide movement path after detrending, h is the average height of the microtopography on the landslide path, and L is the average extension length of the microtopography on the landslide path.

[0084] The expression of the path roughness coefficient of the target landslide is as follows:

[0085] PRC=85.2671(D-1) 0.5679

[0086] Wherein, PRC is the path roughness coefficient of the target landslide, and D is the fractal dimension of the de-trended path of the target landslide; the roughness degree of the landslide movement path is more directly characterized by the path roughness coefficient.

[0087] The present application has the beneficial effects that: the present application quantitatively characterizes the roughness degree of the high-speed long-range landslide movement path based on the path roughness coefficient, only needs to count the average relief height and average extension length of the micro-terrain on the landslide movement path, and calculates the fractal dimension of the de-trended path of the high-speed long-range landslide movement path and the path roughness coefficient of the landslide based on the joint fractal model h-L method of the joint profile roughness degree characterization method in the rock mass mechanics field, the calculation process is simple, the application range is wide, the roughness of the high-speed long-range landslide movement path can be revealed under different spatial scales, the influence of the micro-terrain in the landslide movement process is fully revealed, and more comprehensive roughness characterization is provided, and the present application has strong theoretical significance and practical application value.

Claims

1. A method for quantitatively characterizing the roughness of a high-speed long-range landslide motion path, characterized in that, The method comprises the following steps: S1: selecting a target landslide and its surrounding area, and obtaining remote sensing image data of the target landslide, DEM digital elevation data of the target landslide, and landslide boundary data of the target landslide; S2: obtaining elevation data of the target landslide movement path according to the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide; S3: performing detrended processing on the elevation data of the target landslide movement path to obtain the elevation data of the microtopography of the landslide movement path; S4: calculating the detrended fractal dimension of the target landslide movement path based on the calculation method of the joint profile line fractal dimension according to the elevation data of the microtopography of the target landslide movement path; S5: calculating the path roughness coefficient of the target landslide according to the detrended fractal dimension of the target landslide movement path to complete the quantitative characterization of the roughness of the high-speed long-distance landslide movement path.

2. The method for quantitatively characterizing the roughness of a high-speed long-range landslide motion path according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: selecting a target landslide and its surrounding area on a satellite image, and obtaining remote sensing image data of the target landslide according to a set spatial pixel resolution and obtaining DEM digital elevation data of the target landslide according to a set elevation data sampling interval; S102: labeling the landslide boundary of the target landslide in the satellite image by using a polygon tool to obtain the landslide boundary data of the target landslide.

3. The method for quantitatively characterizing the roughness of a high-speed long-range landslide motion path according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: superimposing the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide, and keeping the coordinate system consistent to obtain a map view of the target landslide; S202: extending along the main sliding direction of the landslide from the landslide shear outlet of the target landslide to obtain a longitudinal profile line segment of the target landslide; S203: translating the longitudinal profile line segment of the target landslide to the adjacent mountain area of the target landslide to approximately obtain a target landslide movement path line segment; S204: obtaining a profile graph of the target landslide movement path according to the target landslide movement path line segment; S205: obtaining the elevation data of the target landslide movement path according to the profile graph of the target landslide movement path.

4. The method for quantitatively characterizing the roughness of a high-speed long-range landslide motion path according to claim 1, characterized in that, The specific steps of S3 are as follows: S301: establishing a terrain trend function of the landslide movement path by using a fitting function according to the elevation data of the target landslide movement path; S302: obtaining the best fitting coefficient of the terrain trend function based on the least square method according to the terrain trend function of the landslide movement path; S303: obtaining the fitted terrain trend function according to the best fitting coefficient; S304: removing large-scale trends in the target landslide movement path to obtain the elevation data of the microtopography of the landslide movement path according to the fitted terrain trend function.

5. The method for quantitatively characterizing the roughness of a high-speed long-range landslide motion path according to claim 1, characterized in that, The expression of the detrended fractal dimension of the target landslide movement path in S4 is as follows: D = log4 / log[2(1 + costan -1 (2h / L))] Wherein, D is the detrended fractal dimension of the target landslide movement path, h is the average relief height of the microtopography on the landslide path, and L is the average extension length of the microtopography on the landslide path.

6. The method of quantitatively characterizing the roughness of a high-speed long runout path according to claim 1, wherein, The expression of the path roughness coefficient of the target landslide in S5 is as follows: PRC = 85.2671(D - 1) 0.5679 Wherein, PRC is the path roughness coefficient of the target landslide, D is the fractal dimension of the de-trended path of the target landslide.

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