CN103412113B - Debris flow gully susceptibility method of discrimination and application thereof after a kind of shake - Google Patents

Abstract

The invention discloses a post-earthquake debris flow ditch sensitivity judgment method and its application. This method firstly determines the probability comprehensive discriminant value P of all landslides in the study area under any grading condition of the 6 evaluation factors; according to the P value, the original arbitrary grading conditions are integrated to obtain the sensitivity of the 6 evaluation factors Grading specific intervals and establishing corresponding scoring standards; then according to the distribution of a single ditch in the 6 evaluation factors, refer to the scoring standards to obtain the score of the ditch on the 6 evaluation factors; finally combine the gray relational method to determine the weight of the evaluation factors and establish Single ditch debris flow sensitivity discriminant model for debris flow prediction. Compared with the prior art, the present invention fully considers the regional characteristics of the research area where the single ditch is located, avoids the error of artificially dividing the evaluation factor sensitivity grading interval of the single ditch debris flow, and can quickly and accurately divide the evaluation factors into specific grades, In this way, the effective discrimination of the susceptibility to single ditch debris flow can be realized.

Description

A Sensitivity Judgment Method of Debris Flow Gullies after Earthquakes and Its Application

technical field

The invention relates to a probability-based method for discriminating the susceptibility of debris flow ditch after an earthquake, and the prediction and forecast of the susceptibility intensity of the debris flow outbreak in the post-earthquake area.

Background technique

After the "5.12" Wenchuan earthquake, the "4.14" Yushu earthquake and the "4.20" Lushan earthquake, a large number of debris flows broke out in the earthquake area. Due to the lack of an effective method for dividing the sensitivity interval of single-ditch evaluation factors, it is impossible to accurately evaluate the sensitivity of debris flow, resulting in a large number of casualties and property losses caused by sudden debris flow. For example, in 2010, the Dujiangyan mudslide in Sichuan caused Longchichang Town to be flooded; in 2010, the Zhouqu mudslide in Gansu caused 1,744 deaths and missing; in 2013, the Shimian Xiongjiagou mudslide in Sichuan caused 28 casualties. After the earthquake, a large number of debris flows in the western part of my country are in the stage of high susceptibility. Effective prediction of debris flow susceptibility is urgently needed to reduce and prevent debris flow disasters.

Debris flows that cause heavy losses and casualties after earthquakes are often closely related to the probability of landslides in the entire study area itself on the evaluation factor. However, the existing debris flow sensitivity discrimination methods do not combine the probability of landslides in the entire study area on the evaluation factors to determine the evaluation factor grading intervals, but only artificially classify the evaluation factors according to the characteristics of a single ditch, so it is impossible to determine the evaluation factors. The susceptibility of post-debris flow ditch can be effectively judged.

Contents of the invention

The purpose of the present invention is to address the deficiencies of the prior art, to provide a method and its application for the sensitivity judgment of the debris flow ditch after the earthquake, and to determine the scope of the research area by analyzing the probability distribution of the landslides in the research area on the evaluation factors In order to establish the sensitivity discrimination model of single ditch debris flow, the effective prediction of the possibility of debris flow outbreak can be realized.

For realizing the above object, technical scheme of the present invention is:

The present invention proposes a post-earthquake debris flow ditch sensitivity discrimination method, which uses the probability of all landslides in the entire research area on the 6 evaluation factors to divide the sensitivity grading intervals of each evaluation factor, and then conducts Sensitivity discrimination of a single debris flow ditch. First, starting from the distribution law of all landslides in the study area, fully combining the three major conditions of topography, hydrology and geology that lead to the formation of debris flows, determine the distribution of all landslides in topographic elements (including elevation factor, slope factor, slope aspect factor, gully bed longitudinal factor, etc.). Slope factor), geological elements (including stratum lithology factors) and hydrological elements (including gully density factors) under the arbitrary classification conditions of the probability comprehensive discriminant value; according to the size of the probability comprehensive discriminant value, the original arbitrary classification conditions are reintegrated , to obtain the intervals of the sensitivity levels under the six evaluation factors in the entire study area, that is, to divide the specific classification intervals corresponding to the sensitivity levels under each evaluation factor, so as to avoid artificially defining the sensitivity interval boundaries of the evaluation factors; Based on the determined sensitivity grading interval, establish the scoring standard for the sensitivity of all debris flows in the study area; then turn from the regional to single-ditch debris flow evaluation, and refer to the scoring based on the distribution of a single debris flow ditch in the study area in the three major elements of topography, hydrology and geology The scores of the ditch on the six evaluation factors are obtained according to the standard; finally, the weight of the evaluation factors is determined by combining the gray relational method, and two dimensionless factors are used to represent the score of the single ditch debris flow on each evaluation factor and the corresponding evaluation factor. The weight value is used to establish a single ditch debris flow sensitivity discriminant model to predict debris flow.

The debris flow outbreak in the post-earthquake debris flow ditch is formed by the start-up of landslides caused by the earthquake, and the terrain is a relatively obvious valley-type debris flow. The sensitivity judgment should fully consider the regional characteristics of the study area. Specifically, the steps of the post-earthquake debris flow ditch sensitivity discrimination method are as follows:

A. Obtain the elevation factor, gully bed vertical slope factor, slope factor, slope aspect factor and gully density factor of the entire study area through the vectorization of the 1:50,000 topographic map; obtain the entire research area through the vectorization of the 1:200,000 topographic map The stratum lithology factor; use the ARCGIS software to perform arbitrary intervals on the six evaluation factors obtained (namely, the aforementioned elevation factor, gully bed vertical slope factor, slope factor, slope aspect factor, gully density factor, and stratum lithology factor). Grading, to get the different grading conditions of the 6 evaluation factors.

B. Obtain the total area of the entire research area through the 1:50,000 topographic map, and interpret the area of all landslides in the entire research area through remote sensing images; according to the different grading conditions of the 6 evaluation factors obtained in step A, through remote sensing The images interpreted the area of landslides in the entire study area under different grading conditions of the six evaluation factors, and obtained the area of the study area under the different grading conditions of the six evaluation factors through the 1:50,000 topographic map.

C. Calculate the probability comprehensive discriminant value P of the six evaluation factors under different grading conditions by the following formula:

$\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}& {\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\\ \frac{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}& {\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right.$

In the formula, P—the probability comprehensive discriminant value of the six evaluation factors under different grading conditions.

P ₁ —Conditional probability, representing the proportion of the area of avalanches and landslides in the entire study area under different grading conditions of the six evaluation factors to the area of the study area of this level; Body area, the area of the study area under different grading conditions for the six evaluation factors, is determined by step B.

P ₂ —prior probability, which represents the ratio of the area of all landslides in the entire study area to the total area of the entire study area; the area of all landslides in the entire study area and the total area of the entire study area are determined by step B.

D. According to the probability comprehensive discriminant value P of the 6 evaluation factors obtained in step C under different grading conditions, select the probability comprehensive discriminant value P of one of the evaluation factors under different grading conditions one by one to carry out the following steps a)-d), divide The range of high and low sensitivity grading, and the establishment of scoring standards:

a) Determine the number of intervals, subtract 1 from the number of intervals to obtain the interval of intervals; sort the probability comprehensive discriminant values P under different classification conditions according to size; the number of intervals is generally 4-6.

b) The grading condition corresponding to the minimum P value is regarded as the lowest sensitivity interval, and the grading condition corresponding to the maximum P value is regarded as the highest sensitivity interval.

c) Calculate the difference between the second largest value of P value and the second smallest value of P value, and then divide by the interval of the number of intervals to obtain the incremental value of the classification interval; starting from the second smallest value of P value, determine the interval according to the incremental value of the classification interval The combined range of , get the remaining grading intervals except the lowest sensitivity interval and the highest sensitivity interval. That is: the number of the remaining grading intervals is the number of intervals minus 2 (that is, the lowest sensitivity interval and the highest sensitivity interval), starting from the next smallest value of the P value, increasing the incremental value of the grading interval until the P value is greater than the P value The second largest value, divide the interval between the second smallest value of P value and the second largest value of P value into several segments (the number is the number of intervals minus 2, corresponding to the number of the remaining classification intervals), when the P value belongs to one of the segments , and its corresponding grading condition is the combined range of the corresponding interval.

d) Starting from the lowest sensitivity interval, according to the increasing order of P value, score the obtained intervals sequentially, from small to large, and establish the scoring standard for debris flow in the high and low sensitivity grading intervals of the selected evaluation factors.

Finally, the sensitivity grading intervals of the six evaluation factors and the corresponding scoring standards were obtained.

E. For the post-earthquake debris flow gullies in the study area to be sensitively judged, the elevation factor, slope factor, slope factor, aspect factor, and gully of the debris flow gully were obtained by vectorizing the 1:50,000 topographic map Density factor, the formation lithology factor of this debris flow ditch was obtained by vectorizing the 1:200,000 topographic map.

F. According to the distribution of the groove on the 6 evaluation factors obtained in step E, referring to the sensitivity grading intervals and corresponding scoring standards of the 6 evaluation factors obtained in step D, the distribution of the groove on the 6 evaluation factors is obtained score.

G. According to the basic formula of the gray relational method, calculate the weight values of the 6 evaluation factors.

H. Determine the sensitivity of the post-earthquake debris flow ditch to be judged by the following formula:

$\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 6</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

In the formula, x _i (k) is the score of the post-earthquake debris flow ditch to be judged on the six evaluation factors, which is determined by step F; w _i is the weight value of the six evaluation factors, which is determined by step G; R is the discrimination value of sensitivity; when R is less than or equal to 2.0, the post-earthquake debris flow ditch to be subjected to sensitivity discrimination is a low-sensitivity debris flow basin; The gully is a moderately sensitive debris flow basin; when R is greater than or equal to 2.6, the post-earthquake debris flow gully to be judged for sensitivity is a highly sensitive debris flow basin.

The post-earthquake debris flow ditch sensitivity discrimination method is applicable to the prediction and prediction of the debris flow ditch sensitivity of a large number of debris flow avalanches after the earthquake, and the evaluation factor is graded according to the distribution probability of the regional avalanche bodies, so as to obtain the single ditch debris flow outbreak The sensitivity of debris flow is high; the sensitivity of debris flow is high, that is, the possibility of debris flow outbreak is high; the sensitivity of debris flow is low, that is, the possibility of debris flow outbreak is small. In this way, the sensitivity of debris flow can be quickly and accurately judged, and countermeasures for debris flow outbreaks can be prepared in advance.

Compared with the prior art, the beneficial effect of the present invention is: fully considering the regional characteristics of the research area where the single debris flow ditch is located, starting from a large research area, and arbitrarily grading all landslides in the research area on 6 evaluation factors The probability distribution of each evaluation factor is used to obtain the high and low sensitivity grading intervals under each evaluation factor, which avoids the error of artificially dividing the evaluation factor sensitivity grading interval of a single ditch debris flow, and can quickly and accurately divide the evaluation factors into specific grades, so as to realize the single ditch The effective discrimination of debris flow sensitivity can more accurately predict the possibility of debris flow that produces a large number of loose objects after an earthquake, and then can timely control highly sensitive debris flow to meet the needs of disaster prevention and mitigation.

Detailed ways

The preferred embodiments of the present invention will be further described below.

Dujiangyan City, Sichuan Province is located in the "5.12" Wenchuan Earthquake hardest-hit area, and belongs to the area with strong post-earthquake debris flow activities. The area of Longxi River Basin in Longchi Town in the study area is about 52.05km ² , which was seriously affected by the "5.12" Wenchuan Earthquake, and many landslides and other geological hazards occurred on the surface of the terrain, resulting in a large amount of loose materials accumulated in the foothills and ditches. In the road, it provides a good provenance condition for the formation of debris flow. On August 13, 2010, mass debris flows occurred in 48 debris flow ditches in the Longxi River Basin. This debris flow disaster caused a certain blockage of the Longxi River and raised the riverbed by 5m, which had a negative impact on the post-disaster restoration and reconstruction within the study area. It is planned to use the probability-based method for determining the sensitivity of debris flow ditches after earthquakes to discriminate the sensitivity of these 48 debris flow ditches.

The first step is to use ARCGIS software to obtain the elevation factor, gully bed vertical slope factor, slope factor, slope aspect factor and gully density factor of the entire Longchi area through vectorization of the 1:50,000 topographic map; through the 1:200,000 topographic map The stratum lithology factors of the entire Longchi area were obtained by map vectorization; the six evaluation factors obtained were graded in arbitrary intervals using ARCGIS software, and the different classification conditions of the six evaluation factors were obtained. See the second column of Table 1 for details.

In the second step, the total area of the entire Longchi area is 52.05km ² obtained through the 1:50,000 topographic map, and the area of all landslides in the entire Longchi area is 7.65km ² through interpretation of remote sensing images. According to the different grading conditions of the 6 evaluation factors obtained in step A, the area of the landslide body under the different grading conditions of the 6 evaluation factors in the entire Longchi area was respectively interpreted through the remote sensing images. The specific values are shown in the third column of Table 1 below. ; and through the 1:50,000 topographic map, the area of the study area under different grading conditions of the 6 evaluation factors was respectively obtained, and the specific values are shown in the fourth column of Table 1 below.

The third step, through the formula $P=\left\{\begin{array}{cc}\frac{p_1-p_2}{p_1*(1-p_2)}& p_1\&Greater\; Equal;p_2\\ \frac{p_1-p_2}{p_2*(1-p_1)}& p_1<p_2\end{array}\right.$ The comprehensive discriminant value P of the probability under different grading conditions of the 6 evaluation factors is calculated separately, and the specific values are shown in the seventh column of Table 1 below; in the calculation formula, P ₁ is the landslide of the entire Longchi area under the different grading conditions of the 6 evaluation factors The proportion of the body area to the area of the research area at this level, see the fifth column of Table 1 below for specific values, that is, the value in the fifth column of each row in Table 1 is the value in the third column of the row divided by the value in the fourth column; in the calculation formula, P ₂ is the ratio of the area of all landslides in the entire Longchi area to the total area of the entire Longchi area, that is, 7.65/52.05 = 0.15, see the sixth column of Table 1 below for specific values.

Table 1 The comprehensive discriminant value of the probability of all landslides in the Longchi area under arbitrary classification conditions on the 6 evaluation factors

In the fourth step, according to the probability comprehensive discrimination value P of the 6 evaluation factors obtained in the third step under different classification conditions, the probability comprehensive discrimination value P of one of the evaluation factors under different classification conditions is selected one by one as follows a)-d) Steps to divide the intervals of high and low sensitivity levels and establish scoring standards:

a) Determine the number of intervals as 4, subtract 1 from the number of intervals, and obtain the interval of the number of intervals as 3. The comprehensive discriminant value P of probability under different grading conditions is sorted by size; taking the slope factor as an example, the P values under the six grading conditions of the slope factor are 0.47 (48°-80°), 0.34 (40°-48 °), 0.14(32°～40°), -0.31(24°～32°), -0.6(0°～12°), -0.6(12°～24°).

b) The grading condition corresponding to the minimum P value is regarded as the lowest sensitivity interval, and the grading condition corresponding to the maximum P value is regarded as the highest sensitivity interval. Taking the slope factor as an example, the grading conditions (0°～12°) and (12°～24°) corresponding to the minimum P value of -0.6 are the lowest sensitivity intervals, and the grading conditions corresponding to the maximum P value of 0.47 (48°～ 80°) as the highest sensitivity interval.

c) Calculate the difference between the second largest value of P value and the second smallest value of P value, and then divide by the interval of the number of intervals to obtain the incremental value of the classification interval; starting from the second smallest value of P value, determine the interval according to the incremental value of the classification interval The combined range of , get the remaining grading intervals except the lowest sensitivity interval and the highest sensitivity interval. Taking the slope factor as an example, the difference between the second largest value of P value 0.34 and the second smallest value of P value -0.31 is calculated, and then divided by the interval 3 of the number of intervals, the incremental value of the classification interval is 0.22; from the second smallest value of P value Starting from -0.31, increase the incremental value of the grading interval by 0.22, and divide the interval between the second smallest value of P value -0.31 and the second largest value of P value 0.34 into two segments, namely (-0.31～-0.09) and (-0.09～0.34), When the P value belongs to one of the segments, the corresponding grading condition is the combination range of the corresponding interval, that is, the combination range of the second-lowest sensitivity interval is (24°～32°), and the combination range of the second-highest sensitivity interval is (32°～40°), (40°～48°).

d) Starting from the lowest sensitivity interval, according to the increasing order of P value, score the obtained intervals sequentially, from small to large, and establish the scoring standard for debris flow in the high and low sensitivity grading intervals of the selected evaluation factors. Taking the slope factor as an example, the lowest sensitivity interval (0°-24°) is 1 point, the second-lowest sensitivity interval (24°-32°) is 2 points, and the second-highest sensitivity interval (32°-48°) is 1 point. 3 points, the highest sensitivity interval (48°～80°) is 4 points. A score of 4 indicates that debris flow is very likely to occur within the grading interval of a certain evaluation factor, a score of 3 indicates that debris flow is more likely to occur within the grading interval of a certain evaluation factor, a score of 2 indicates that debris flow may occur within the grading interval of a certain evaluation factor, and a score of 1 indicates that a certain evaluation Debris flow is less likely to occur within the factor classification interval.

Finally, the sensitivity grading intervals of the six evaluation factors and the corresponding scoring standards were obtained. The specific values are shown in Table 2 below.

Table 2 Sensitivity grading intervals and corresponding scoring standards for debris flow in Longchi area on 6 evaluation factors

In the fifth step, for the 48 debris flow ditches in the Longchi area, the elevation factor, the vertical slope factor of the gully bed, the slope factor, the aspect factor and the gully density factor of the 48 debris flow ditches were respectively obtained through the vectorization of the 1:50,000 topographic map. The stratum lithology factors of 48 debris flow ditches were obtained by vectorization of 1:200,000 topographic map.

In the sixth step, according to the distribution of the 48 grooves on the 6 evaluation factors obtained in the fifth step, referring to the sensitivity grading intervals and corresponding scoring standards of the 6 evaluation factors obtained in the fourth step, 48 grooves are obtained The scores on the 6 evaluation factors, the specific values are shown in Table 3 below.

serial number mudslide ditch name slope Aspect elevation vertical slope of ditch bed Formation lithology Ravine Density DF01 wangjiagou 2 4 3 1 2 2 DF02 modaogou 2 4 3 1 2 2 DF03 Sanshen Gonggou 2 4 2 1 2 2 DF04 Tea Horse Gudaogou 1 4 2 4 2 2 DF05 swallow's nest ditch 1 4 2 3 2 1 DF06 Gongjiagou 1 4 2 1 2 2 DF07 bayi ditch 3 4 4 1 4 3 DF08 Coal Pinggou 1 3 2 1 2 2 DF09 dustpan ditch 3 3 2 1 2 1 DF10 caojialinggou 1 3 3 1 2 2 DF11 Lizipinggou 2 3 3 4 2 2 DF12 maliugou 3 3 3 4 4 2 DF13 Wells and trenches 2 4 2 2 4 2 DF14 huangyanggou 3 4 4 4 4 2 DF15 water ditch 3 4 3 4 4 2 DF16 baiguotanggou 1 3 2 4 4 2 DF17 hemp willow ditch 2 4 4 3 4 2 DF18 shuijiupinggou 2 3 3 1 4 2

DF19 jiangjiagou 1 2 3 4 4 1 DF20 chenjiapogou 1 2 2 4 4 2 DF21 liaojiagou 3 1 3 4 4 1 DF22 shazipinggou 2 4 3 3 4 2 DF23 Li Quan Taigou 3 4 3 4 3 2 DF24 horseshoe nest ditch 2 2 3 4 4 3 DF25 jianpinggou 3 3 4 4 3 2 DF26 papaya garden ditch 2 4 4 1 3 1 DF27 Walnut Tree Ditch 1 3 3 2 3 1 DF28 osmanthus tree ditch 1 4 3 1 3 2 DF29 Majiawu Jigou 1 2 3 1 3 2 DF30 Walnut Tree No. 2 Ditch 3 2 4 4 3 2 DF31 Walnut Tree No. 3 Ditch 2 2 3 4 3 1 DF32 paper factory ditch 2 3 4 1 3 2 DF33 Fengdongyan No. 1 Ditch 3 3 4 4 3 2 DF34 Fengdongyan No. 2 Ditch 2 4 3 3 3 2 DF35 sunjiagou 3 3 4 1 3 3 DF36 Shuangyangzigou 3 4 4 2 3 2 DF37 chunyashugou 2 4 4 4 3 2 DF38 douhongkougou 2 3 3 4 3 2 DF39 cold ditch 2 4 4 4 3 3 DF40 dashipinggou 3 3 4 4 3 2 DF41 qishupinggou 2 2 4 1 3 2 DF42 pig trough 3 4 3 1 3 2 DF43 baiyangou 1 2 3 1 2 1 DF44 Mud ditch 4 4 1 4 3 2 DF45 Banjiugang 1# ditch 3 3 3 1 3 2 DF46 Banjiugang 2# ditch 1 1 3 4 3 1 DF47 Banjiu Ganggou 1 2 4 1 3 1 DF48 changhebagou 3 4 3 3 3 2

Table 3 Scores of 48 debris flow ditches in Longchi area on 6 evaluation factors

In the seventh step, according to the basic formula of the gray relational method, the weight values of the six evaluation factors are calculated, and the specific values are shown in Table 4 below.

6 evaluation factors Weights slope 0.19 Aspect 0.24 elevation 0.05 vertical slope of ditch bed 0.10 Formation lithology 0.14 Ravine Density 0.29

Table 4 Weight values of 6 evaluation factors

The eighth step, through the formula Determine the sensitivity of 48 debris flow ditches respectively, and the specific results are shown in Table 5 below. In the calculation formula, x _i (k) is the score of the debris flow ditch on the six evaluation factors after the earthquake, which is determined by the sixth step; w _i is the weight value of the six evaluation factors, which is determined by the seventh step; R is the sensitivity size discriminant value.

Table 5 The sensitivity distribution table of 48 debris flow ditches in Longchi area

According to the debris flow susceptibility obtained by the above discrimination method, the Bayi Valley sensitivity value discriminant value R is 3.26, which belongs to the range of R≥2.6, and is a highly sensitive debris flow basin. Comparing the judgment results with the actual situation, Bayigou had five large debris flows on May 14, May 19, July 17, 2008, August 13, and August 18, 2010 and caused Significant property losses, especially on August 13, 2010, the huge debris flow silted the road below, washed away the Bayigou Bridge, damaged 6.67km ² of farmland, and destroyed all 10 sand dams under construction. Therefore, it is very important to judge the sensitivity of debris flow ditch in time to prevent debris flow hazards.

Claims (3)

1. debris flow gully susceptibility method of discrimination after a shake, after described shake, the debris flow occurrence of debris flow gully is started by seismic slumped mass to be formed, it is characterized in that: after described shake, the susceptibility of debris flow gully differentiates the provincial characteristics will considering study area, place, and concrete method of discrimination step is as follows:

A. the Elevation factor of whole study area, the ditch bed longitudinal river slope factor, slope factor, aspect factor and the gully density factor is obtained by 1:5 ten thousand Topographical Digitization; The formation lithology factor of whole study area is obtained by 1:20 ten thousand Topographical Digitization; Utilize ARCGIS software to carry out arbitrary interval classification respectively to obtain 6 evaluation points, obtain the different classification conditions of 6 evaluation points;

B. obtained the total area of whole study area by 1:5 ten thousand topomap, go out all slumped mass areas in whole study area by remote sensing image interpretation; For the different classification conditions of 6 evaluation points obtained in steps A, by remote sensing image respectively solution to translate in whole study area, slumped mass area under 6 different classification conditions of evaluation points, and obtain the study area area under the different classification condition of 6 evaluation points respectively by 1:5 ten thousand topomap;

C. the Probabilistic Synthesis discriminant value P under the different classification condition of 6 evaluation points is calculated respectively by following formula:

$P=\left\{\begin{array}{cc}\frac{p_1-p_2}{p_1*(1-p_2)}& p_1\&GreaterEqual;p_2\\ \frac{p_1-p_2}{p_2*(1-p_1)}& p_1<p_2\end{array}\right.$

In formula, the Probabilistic Synthesis discriminant value under the different classification condition of P-6 evaluation points;

P ₁slumped mass area in-whole study area, under 6 different classification conditions of evaluation points accounts for the ratio of this rank study area area; Slumped mass area in whole study area, under 6 different classification conditions of evaluation points, the study area area under 6 different classification conditions of evaluation points, determines by step B;

P ₂in-whole study area, all slumped mass areas account for the ratio of the whole study area total area; All slumped mass areas in whole study area, the total area of whole study area, determines by step B;

D. for the Probabilistic Synthesis discriminant value P under the different classification condition of 6 evaluation points obtained in step C, the Probabilistic Synthesis discriminant value P chosen one by one under the different classification condition of wherein 1 evaluation points carries out following a)-d) step, divide the interval of susceptibility height classification, and set up scoring criterion:

A) determine interval number, interval number is subtracted 1, obtains the interval of interval number; Probabilistic Synthesis discriminant value P under different classification condition is sorted by size;

B) classification condition corresponding for P value minimum value is interval as minimum susceptibility, classification condition corresponding for P value maximal value is interval as most hypersensitivity;

C) P value second largest value and P value sub-minimum are carried out difference operation, then divided by the interval of interval number, obtain increment value between graded region; From P value sub-minimum, determine interval combination range successively according to increment value between graded region, obtain except minimum susceptibility is interval and between all the other graded regions except most hypersensitivity interval;

D) start from minimum susceptibility interval, according to P value incremental order, give a mark to the interval obtained successively, score value is ascending, set up the susceptibility of rubble flow at selected evaluation points just between graded region on scoring criterion;

Finally obtain the susceptibility of 6 evaluation points just between graded region, and corresponding scoring criterion;

E. for debris flow gully after the shake of susceptibility differentiation pending in study area, obtain the Elevation factor of this debris flow gully, the ditch bed longitudinal river slope factor, slope factor, aspect factor and the gully density factor by 1:5 ten thousand Topographical Digitization, obtained the formation lithology factor of this debris flow gully by 1:20 ten thousand Topographical Digitization;

F. according to the distribution situation of this ditch obtained in step e on 6 evaluation points, with reference to 6 evaluation points obtained in step D susceptibility height graded region between and corresponding scoring criterion, obtain the score value of this ditch on 6 evaluation points;

G. according to Grey Incidence fundamental formular, the weighted value of 6 evaluation points is calculated;

H. the susceptibility size of debris flow gully after the shake that pending susceptibility differentiates is determined by following formula:

$R=\underset{i=1}{\overset{6}{\mathrm{\&Sigma;}}}{x}_i\left(k\right)w_i$

In formula, x _ithe score value of debris flow gully on 6 evaluation points after k shake that () differentiates for pending susceptibility, is determined by step F; w _ibe the weighted value of 6 evaluation points, determined by step G; R is susceptibility size discriminant value; When R is less than or equal to 2.0, then after the shake of pending susceptibility differentiation, debris flow gully is hyposensitivity catchment basin of debris flow; Be less than 2.6 when R is greater than 2.0, then after the shake of pending susceptibility differentiation, debris flow gully is middle susceptibility catchment basin of debris flow simultaneously; When R is more than or equal to 2.6, then after the shake of pending susceptibility differentiation, debris flow gully is hypersensitivity catchment basin of debris flow.

2. debris flow gully susceptibility method of discrimination after shake according to claim 1, is characterized in that: step Da) in interval number be 4-6.

3. the application of debris flow gully susceptibility method of discrimination after shaking as claimed in claim 1, is characterized in that: be applicable to the prediction to debris flow gully susceptibility after shake.