Geological disaster susceptibility evaluation method

Abstract

The invention relates to the field of geological disaster assessment, and discloses a geological disaster susceptibility assessment method, which comprises the following steps: based on a known regional geological disaster susceptibility assessment information amount model, acquiring various factor data influencing the occurrence of geological disasters, including DEM data, geological structure data, surface coverage data, river data and road data; the method comprises the following steps: performing data processing and spatial analysis on various factor data by utilizing ArcGIS software, performing information amount calculation according to an information amount calculation model to obtain information amount data of various factors, then performing superposition and calculation to obtain comprehensive factor information amount data, drawing a cumulative frequency distribution curve, performing classification according to feature points of the cumulative frequency curve, and performing classification according to feature points of the cumulative frequency distribution curve. An evaluation partition map of the geological disaster susceptibility is obtained, and geological disaster susceptibility evaluation and disaster risk evaluation are achieved.

Description

Technical Field

[0001] The invention belongs to the field of geological disaster assessment, and in particular relates to a method for assessing the susceptibility of geological disasters. Background Art

[0002] The Qinghai-Tibet Plateau has strong tectonic uplift, complex topography, and sensitive and changeable climate. It is a high-risk area for geological disasters such as earthquakes, debris flows, collapses, and landslides. Against the backdrop of global warming and active crustal movement, the danger of geological disasters on the Qinghai-Tibet Plateau is gradually increasing, and the disaster risk is also increasing with the population and social and economic development level.

[0003] The occurrence of geological disasters is the result of the combined effects of many factors, including topography, geological structure, rock and soil structure type, vegetation development, rainfall, earthquakes, river erosion, and human engineering activities. Among them, topography, geological structure, stratum lithology, and rock and soil structure type are the basic conditions for the occurrence of landslide disasters, while rainfall, earthquakes, and human engineering activities are the inducing factors for the formation of geological disasters. For geological disasters in a specific space, their geographical and geological environmental characteristics have certain continuity and similarity characteristics; due to the changes in inducing factors, their occurrence often has a large degree of randomness.

[0004] Therefore, conducting geological disaster investigations and risk assessments are important tasks in disaster prevention and mitigation, assessing the susceptibility and risk of geological disasters so as to prevent them before they occur. Summary of the invention

[0005] The present invention provides a method for assessing the susceptibility of geological disasters, which is used to assess the susceptibility and risk of geological disasters.

[0006] In order to solve the above technical problems, the technical solutions included in the present invention and the beneficial effects corresponding to the technical solutions are as follows:

[0007] The present invention provides a method for assessing the susceptibility of geological disasters, which includes obtaining DEM data, performing slope, terrain relief, and slope aspect analysis and calculation, performing grading according to a predetermined grading standard, and converting the result into raster data; obtaining geological map data and fault zone data in the map according to geological structure data, vectorizing the geological map data, extracting engineering geological rock groups, and converting the data into raster data; vectorizing the fault zone data in the geological map, performing buffer zone analysis according to a predetermined impact range, and converting the buffer zone result into raster data; obtaining surface cover data, performing reclassification, and obtaining the vegetation cover degree of different areas, And convert it into raster data; obtain river data, perform buffer zone analysis on the impact range of river lateral erosion according to the predetermined classification standard, and convert the buffer zone results into raster data; obtain road data, perform buffer zone analysis according to the impact range of road slope cutting, and convert the buffer zone results into raster data; calculate the information volume of the raster data of all factors according to the information volume calculation model to obtain the information volume data of various factors, and then overlay and calculate to obtain the information volume data of comprehensive factors, and draw the cumulative frequency distribution curve, and classify according to the characteristic points of the cumulative frequency curve to obtain the evaluation zoning map of geological disaster susceptibility.

[0008] The beneficial effects of the above technical solution are: based on the known regional geological disaster susceptibility evaluation information volume model, various factor data affecting the occurrence of geological disasters are obtained, including DEM data, geological structure data, surface cover data, river data, and road data. ArcGIS software is used to process and spatially analyze the various factor data, and the information volume is calculated according to the information volume calculation model to obtain the information volume data of various factors, which are then superimposed and calculated to obtain the comprehensive factor information volume data, and a cumulative frequency distribution curve is drawn. Classification is performed according to the characteristic points of the cumulative frequency curve to obtain an evaluation zoning map of geological disaster susceptibility, thereby realizing geological disaster susceptibility evaluation and disaster risk assessment.

[0009] Furthermore, the information volume calculation model is:

[0010]

[0011] Among them, P(Y|X i ) indicates that in factor X i The probability of geological disaster Y occurring under the following conditions; P(Y) is the probability of geological disaster Y occurring; I Xi→Y For geological disaster Y in factor X i The amount of information under i It indicates different levels of slope gradients, terrain undulations and slope directions, or different engineering geological rock groups, or the degree of vegetation coverage, or different river lateral erosion impact ranges, or different road slope cutting impact ranges.

[0012] Furthermore, during the calculation of the amount of information, the probability thereof is calculated using the sample frequency.

[0013] Furthermore, in factor X i The probability of geological disaster Y occurring under i ) Using the geological disaster number sample frequency N i / N is used to estimate the probability of geological disasters P(Y) using the factor sample frequency S i / S is estimated; where S is the total number of grids for all factors in the study area; S i X is the factor in the study area i The number of grids; N is the number of geological disasters in the study area; N i X is the internal factor of the study area i The number of geological disasters that occurred.

[0014] Furthermore, the comprehensive factor information amount I z The calculation formula is:

[0015]

[0016] Here, n is the total number of factors.

[0017] Furthermore, the engineering geological rock formation includes a hard intrusive rock formation, a weak metamorphic rock formation, a weak interlayered clastic rock formation, and a hard clastic rock formation.

[0018] Furthermore, the vegetation coverage degree includes rock exposed area, sparse vegetation area, relatively sparse vegetation area, generally dense vegetation area, relatively dense vegetation area, and dense vegetation area.

[0019] Furthermore, the evaluation zones include high-risk zones, medium-risk zones, low-risk zones, and weak-risk zones. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the data processing and analysis calculation technology process in the present invention;

[0021] Figure 2 It is the frequency distribution curve diagram of geological disaster information in Zhuoni County in the present invention. DETAILED DESCRIPTION

[0022] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings.

[0023] Method Example:

[0024] The basic concept of the present invention is that for a specific continuous space, a relatively quantitative geological disaster susceptibility assessment process is feasible, and there are many application case studies in this regard. Among them, the information volume model is one of the commonly used methods.

[0025] According to the assumption of geological disaster susceptibility assessment: areas with topography, geological background and other factors similar to those of areas where geological disasters have occurred are likely to experience geological disasters in the future. Then, the information evaluation model is a comprehensive information evaluation model based on the probability statistics of the relationship between geological disasters that have occurred and various influencing factors. The occurrence of geological disasters is affected by many factors. The comprehensive information evaluation model considers the combination of the best geological disaster occurrence factors under a certain geological environment, including not only the number of basic factors but also the status of each factor. For a specific slope, the comprehensive information evaluation model considers the quantity and status of all information related to geological disasters contained in a certain area. It is expressed as:

[0026]

[0027] Among them, P(Y|X i ) indicates that in factor X i The probability of geological disaster Y occurring under the following conditions; P(Y) is the probability of geological disaster Y occurring; I Xi→Y For geological disaster Y in factor X i The amount of information under i It indicates different levels of slope gradients, terrain undulations and slope directions, or different engineering geological rock groups, or the degree of vegetation coverage, or different river lateral erosion impact ranges, or different road slope cutting impact ranges.

[0028] In the specific calculation process, sample frequency is usually used for estimation for convenience:

[0029]

[0030] Among them, S is the total number of grids of all factors in the study area; S i X is the factor in the study area i The number of grids; N is the number of geological disasters in the study area; N i X is the internal factor of the study area i The number of geological disasters that occurred.

[0031] Calculate the comprehensive factor information I provided by n factors in a certain evaluation unit on the occurrence of geological disasters z :

[0032]

[0033] Among them, I zis the comprehensive factor information; n is the total number of factors. The comprehensive factor information I calculated according to this formula z It is a comprehensive indicator that affects the occurrence of geological disasters in this unit. The larger its value, the more favorable it is for the occurrence of geological disasters, that is, the higher the susceptibility of geological disasters in this unit.

[0034] In this embodiment, taking Zhuoni County as an example, the technical solution of the present invention is used to conduct a geological disaster susceptibility and risk assessment on Zhuoni County.

[0035] Zhuoni County is located in the eastern part of the Qinghai-Tibet Plateau, southern Gansu, and southeastern Gannan Prefecture. The county belongs to the western part of the Qinling East-West Complex Tectonic Zone, also known as the West Qinling (Ling). Most of the landforms are medium and low mountains, with high terrain in the southwest and low terrain in the northeast. The highest point is the Zhayikega Peak in the south, with an altitude of 4,920 meters, and the lowest is the Zangbawa area in the northeast, with an altitude of 2,000 meters, a height difference of 2,920 meters. The south of Zhuoni County is the Dieshan Mountains running east-west, forming the ridge of the county; north of Dieshan is the Zhongdai terrain cut by the Taohe River system. Except for the part above 3,600 meters above sea level, which is exposed rock, the rest is dense forests and mountain grasslands, and many alluvial beaches are formed in the river valley area; the central part is dominated by plateau hills with open river valleys; the northern part is formed by many peaks such as Wudang Mountain, forming a northern barrier, presenting a broken alpine landform.

[0036] Zhuoni County's geology belongs to the southern part of the Northern Qinling Fold Belt, and its structure is complex. The main structures are divided into two categories. First, the strata in Taoyan, Zangbawa, Qiagai, and Kangduo Townships are mainly composed of Carboniferous limestone, peatstone, Permian sandstone, Permian limestone, and shale. Because the river has been cutting down for a long time, canyons have been formed. In addition, the local villagers have disorderly destroyed forests and reclaimed land for farmland. When encountering heavy rainfall, landslides, mud-rock flows and other disasters are easily formed; second, the strata in Liulin Town, Muer Township, Zangbawa Township, Taoyan Township, Zhagulu Town, Nalang and other townships where the Tao River passes are composed of loose sedimentary layers of the Quaternary system, loess-like sub-sand, alluvial sand and pebbles. Due to long-term soil erosion, the strata are severely wrinkled, the wrinkling phenomenon is common, and the average stratum inclination is about 30 degrees, which is prone to landslides, mud-rock flows, collapses and other geological disasters.

[0037] Related research shows that the factors that affect the occurrence of geological disasters include topography, engineering geological rock formations (stratum lithology), geological structures, river lateral erosion, etc. Based on this, an evaluation factor system is constructed as shown in Table 1:

[0038] Table 1 Evaluation factors of geological disaster susceptibility

[0039]

[0040]

[0041] As shown in Table 2, the data and sources of the survey collected in this embodiment are as follows:

[0042] Table 2 Data sources and description

[0043]

[0044] Based on the regional geological disaster susceptibility assessment information volume model established earlier, ArcGIS software (a comprehensive system for processing maps and geographic information) was used to process and spatially analyze the collected DEM data (Digital Elevation Model, Digital Elevation Model, abbreviated as DEM, is a data set of plane coordinates (X, Y) and elevation (Z) of regular grid points within a certain range, mainly describing the spatial distribution of regional landforms), geological structure data, surface cover data, river data, road traffic data, geological disaster survey data, and residential data to achieve geological disaster susceptibility evaluation and disaster risk assessment in Zhuoni County.

[0045] The obtained DEM data are analyzed and calculated for slope, terrain relief and slope direction, and the grades are divided according to the predetermined classification standards, and the results are converted into raster data; the geological map data and the fault zone data in the map are obtained according to the geological structure data, the geological map data are vectorized, the engineering geological rock group is extracted and converted into raster data, the fault zone data in the geological map is vectorized, the buffer zone analysis is performed according to the predetermined influence range, and the buffer zone results are converted into raster data; the obtained surface cover data are reclassified to obtain the vegetation coverage degree of different regions and converted into raster data; the obtained river data are analyzed for the buffer zone of the river lateral erosion influence range according to the predetermined classification standards, and the buffer zone results are converted into raster data; the obtained road data are analyzed for the buffer zone according to the influence range of the road slope cutting, and the buffer zone results are converted and output into raster data; all the factor raster data are calculated according to the information volume calculation model to obtain the information volume data of various factors, and then superimposed and calculated to obtain the information volume data of comprehensive factors, and the cumulative frequency distribution curve is drawn, and the classification is performed according to the characteristic points of the cumulative frequency curve to obtain the evaluation zoning map of geological disaster susceptibility.

[0046] like Figure 1 As shown, the steps of data processing and analysis calculation technology are as follows:

[0047] Step 1: Analyze and calculate the slope, terrain relief and slope aspect of the DEM data, and classify them according to the predetermined classification standards;

[0048] Step 2: Vectorize the geological map data, extract and group them into hard intrusive rock group, soft metamorphic rock group, soft interlayer clastic rock group, and hard clastic rock group, and convert them into raster data;

[0049] Step 3: Vectorize the fault zones in the geological map, perform buffer zone analysis according to the predetermined impact range, and convert the buffer zone results into raster data;

[0050] Step 4: Reclassify the land cover data into rock bare area, sparse vegetation area, relatively sparse vegetation area, generally dense vegetation area, relatively dense vegetation area, and dense vegetation area;

[0051] Step 5: Perform a buffer analysis of the river lateral erosion impact range on the river data according to the predetermined classification standard, and convert the buffer results into raster data;

[0052] Step 6: Convert the geological hazard point data into vector point data to extract the types of factors involved in the calculation;

[0053] Step 7: Perform buffer analysis on the road data according to the road slope cutting influence range, convert the buffer results into raster data, calculate the information volume according to the information volume calculation model, and obtain the information volume data of the road factors;

[0054] Step 8: Calculate the information amount of each type of single factor grid data generated in the above steps 1 to 7 according to the information amount calculation model to obtain the information amount data of each type of factor;

[0055] Step 9: Overlay and calculate the information data of various factors to obtain the information data of comprehensive factors, classify them according to the characteristic points of the cumulative frequency curve, and obtain the evaluation zoning map of geological disaster susceptibility.

[0056] Step 10: Overlay analysis of basic condition factor information and population distribution data to achieve geological disaster risk assessment.

[0057] The details are as follows:

[0058] 1. Topographic data processing

[0059] (1) DEM data preprocessing

[0060] The DEM data downloaded from the geospatial data cloud was projected using the Project Raster tool; the resampling tool was used to resample the resolution of the projected DEM data to a resolution of 30*30m.

[0061] (2) Slope analysis

[0062] Use the Slope tool, set the output measurement units to degrees, and perform a DEM slope analysis.

[0063] (3) Terrain undulation analysis

[0064] Use the focal statistics tool, set the neighborhood analysis height and width to 3 pixels, and the statistical type to the difference between the maximum and minimum values ​​to perform terrain relief analysis.

[0065] (4) Slope analysis

[0066] Use the aspect tool to calculate the aspect of DEM data.

[0067] (5) Reclassification

[0068] Take terrain relief as an example for reclassification. Select the grid value Value as the reclassification field and reclassify according to the preset terrain relief.

[0069] 2. Geological structure data processing

[0070] 3. River data processing

[0071] (1) Filling

[0072] First, use the Depression Filling tool to fill depressions in the DEM data.

[0073] (2) Flow direction

[0074] Use the filled data to perform flow analysis.

[0075] (3) Flow

[0076] Use the generated flow direction data for traffic analysis and set the output type to floating point.

[0077] (4) Grid computing

[0078] Perform raster calculation on the flow data and set the threshold to 1200. The smaller the threshold, the more tributaries are generated.

[0079] (5) River network classification

[0080] After raster calculation, use the river network classification tool to classify the river network, and set the river network classification method to STRAHLER.

[0081] (6) Raster River Network Vectorization

[0082] Use the River Network Vectorization tool to vectorize the classified river network.

[0083] (7) Buffer Analysis

[0084] According to the predetermined level, the buffer zone analysis is carried out for 100m and 400m respectively.

[0085] (8) Erase, merge and convert faces to raster

[0086] Use the Erase tool to erase the 100m buffer from the 400m buffer of the river and the 400m buffer from the administrative boundary of Zhuoni County, merge the results, and then convert the merged results into raster.

[0087] (9) Vectorization

[0088] Create geological structure and fault zone shp files, and vectorize geological structure data by rock groups and fault zones based on the 1:1.5 million geological map of the Qinghai-Tibet Plateau and adjacent areas.

[0089] (10) Buffer Analysis

[0090] According to the predetermined level, the buffer zone analysis is carried out for 500m and 1000m respectively.

[0091] (11) Vector Rasterization

[0092] Use the 500m buffer to erase the 1000m buffer, and use the 1000m buffer to erase the administrative divisions of Zhuoni County. Then, after erasing the 500m buffer, 1000m buffer, and administrative division 1000m buffer, merge them, and then convert them into raster according to the different buffer ranges.

[0093] 4. Land cover data processing

[0094] (1) Extract by mask

[0095] The global land use and land cover raster data were extracted using the Extract by Mask tool based on the administrative division data of Zhuoni County.

[0096] (2) Resampling

[0097] The extracted land cover data is resampled and the resolution is set to 30m*30m.

[0098] (3) Reclassification

[0099] Open the raster attribute table and reclassify.

[0100] 5. Road data processing

[0101] (1) Buffer Analysis

[0102] According to the predetermined level, the buffer zone analysis is carried out for 200m and 500m respectively.

[0103] (2) Erase, merge, and convert faces to raster

[0104] Use the Erase tool to erase the 200m buffer from the 500m buffer of the road network and the 500m buffer from the administrative boundary of Zhuoni County, merge the results, and then convert the merged results into raster.

[0105] 6. Disaster Survey Data Processing

[0106] Use the Add XY Data tool to specify the X and Y column fields, set the projection coordinate system, vectorize the geological hazard survey data, and export it as shp data.

[0107] 7. Population and settlement data processing

[0108] (1) Statistical Yearbook Data

[0109] According to the population and government seat location information of each township in the 2020 Statistical Yearbook, the coordinate picking system is used to perform coordinate picking conversion and vectorize the population data.

[0110] (2) Kernel density analysis

[0111] Use the kernel density analysis tool, set the output pixel size to 30m, set the search radius to 15000m, and conduct a kernel density analysis of the population data of Zhuoni County.

[0112] 8. Geological disaster susceptibility assessment

[0113] (1) Calculation of information volume

[0114] The reclassified slope, terrain relief, slope aspect, geological structure, fault zone, river, road network and geological disaster data were used to extract multi-valued particles to obtain the factor types of each geological point, and the number of geological disaster points, the total number of disaster points and the number of classified grids on each factor were counted respectively. The area of ​​each pixel was multiplied by the area of ​​the classified grids to obtain 30*30=900m 2 Get the area of ​​the graded grids, sum them up to get the total area of ​​the grids, and use the information calculation formula to calculate the amount of information.

[0115] (2) Information Assignment

[0116] Use the Raster to Polygon tool to convert the above raster into a polygon, create a new xxl field, and use the Select By Attribute tool to filter it. Use the Field Calculator tool to assign values ​​to the xxl field according to the amount of information calculated in the above table.

[0117] (3) Convert polygon to raster

[0118] Use the surface to raster tool to generate information raster of each factor according to the xxl field for the vector layer after the above assignment.

[0119] (4) Susceptibility evaluation

[0120] Use the raster calculator tool to sum the information rasters of the above factors to achieve susceptibility evaluation.

[0121] (5) Normalization

[0122] The mean-standard deviation normalization algorithm is used to normalize the geological hazard susceptibility assessment results.

[0123] (6) Information cumulative frequency distribution curve

[0124] like Figure 2 As shown in the figure, the normalized information value is used to plot the unit information cumulative frequency distribution curve with a step size of 0.04 (the percentage of the cumulative information of different step sizes in the total information). The results show that the information cumulative frequency distribution curve has obvious inflection points at around 0.20, 0.32, and 0.44.

[0125] (7) Classification

[0126] like Figure 2 As shown in the figure, the evaluation results are divided into four categories using the inflection points of the frequency distribution curve of 0.20, 0.32, and 0.44, and the regional geological disaster susceptibility is divided into four levels: high susceptibility area (0.44-1.00), medium susceptibility area (0.32-0.44), low susceptibility area (0.20-0.32), and weak susceptibility area (0.00-0.32), and a geological disaster susceptibility evaluation zoning map of Zhuoni County is generated.

[0127] The assessment of the susceptibility of geological disasters in Zhuoni County is as follows: areas with high susceptibility to geological disasters are mainly distributed on both sides of rivers and roads. These places are also places with intensive human activities and are more susceptible to human interference. Coupled with the influence of factors such as topography and geology, they are more susceptible to geological disasters.

[0128] The specific implementation methods of the present invention are given above, but the present invention is not limited to the described implementation methods. Under the idea given by the present invention, the technical means in the above-mentioned embodiments are transformed, replaced, and modified in a way that is easy for a person skilled in the art to think of, and the effect is basically the same as the corresponding technical means in the present invention, and the purpose of the invention is basically the same. The technical solution formed in this way is formed by fine-tuning the above-mentioned embodiments, and this technical solution still falls within the protection scope of the present invention.

Claims

1. A method for assessing the susceptibility of geological disasters, characterized in that: DEM data is obtained, and slope, terrain relief and slope direction are analyzed and calculated. The data is classified according to the predetermined classification standard, and the results are converted into raster data. The geological map data and the fault zone data in the map are obtained according to the geological structure data, and the geological map data is vectorized. The engineering geological rock group is extracted and converted into raster data. The fault zone data in the geological map is vectorized, and the buffer zone analysis is performed according to the predetermined influence range, and the buffer zone results are converted into raster data. The surface cover data is obtained, and reclassified to obtain the vegetation coverage degree of different areas, and converted into raster data. The river data is obtained, and the buffer zone analysis of the river lateral erosion influence range is performed according to the predetermined classification standard, and the buffer zone results are converted into raster data. The road data is obtained, and the buffer zone analysis is performed according to the influence range of the road slope cutting, and the buffer zone results are converted and output into raster data. The raster data of all factors are calculated according to the information volume calculation model to obtain the information volume data of various factors, and then the information volume data of the comprehensive factors are obtained by superposition and calculation, and the cumulative frequency distribution curve is drawn, and the classification is performed according to the characteristic points of the cumulative frequency curve to obtain the evaluation zoning map of the susceptibility of geological disasters.

2. The method for assessing geological hazards according to claim 1, characterized in that: The information volume calculation model is: Among them, P(Y|X i ) indicates that in factor X i The probability of geological disaster Y occurring under the condition of ; P(Y) is the probability of geological disaster Y occurring; For geological disaster Y in factor X i The amount of information under i It indicates different levels of slope gradients, terrain undulations and slope directions, or different engineering geological rock groups, or the degree of vegetation coverage, or different river lateral erosion impact ranges, or different road slope cutting impact ranges.

3. The method for assessing geological disaster susceptibility according to claim 2, characterized in that: During the calculation of the information quantity, the probability thereof is calculated using the sample frequency.

4. The method for assessing geological disaster susceptibility according to claim 3, characterized in that: In factor X i The probability of geological disaster Y occurring under i ) Using the geological disaster number sample frequency N i / N is used to estimate the probability of geological disasters P(Y) using the factor sample frequency S i / S is used for estimation; where S is the total number of grids for all factors in the study area; S i X is the factor in the study area o The number of grids; N is the number of geological disasters in the study area; N i X is the internal factor of the study area i The number of geological disasters that occurred.

5. The method for assessing geological disaster susceptibility according to claim 4, characterized in that: The comprehensive factor information amount I z The calculation formula is: Here, n is the total number of factors.

6. The method for assessing geological disaster susceptibility according to claim 1, characterized in that: The engineering geological rock formations include hard intrusive rock formations, weak metamorphic rock formations, weak interlayered clastic rock formations, and hard clastic rock formations.

7. The method for assessing geological disaster susceptibility according to claim 1, characterized in that: The vegetation coverage levels include bare rock areas, sparse vegetation areas, relatively sparse vegetation areas, generally dense vegetation areas, relatively dense vegetation areas, and dense vegetation areas.

8. The method for assessing geological disaster susceptibility according to claim 1, characterized in that: The evaluation zones include high-risk zones, medium-risk zones, low-risk zones, and weak-risk zones.

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