CN117190921A - Method for realizing JRC estimation based on Haoskov distance - Google Patents
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Abstract
The invention discloses a method for realizing JRC estimation based on Haosdorf distance. Extracting coordinates of 10 structural surface standard contour lines of Barton with the sampling interval dx as precision, and calculating the accumulated slope of each structural surface standard contour line according to the extracted x and y coordinate data to form a standard curve accumulated slope data sequence; measuring a structural plane curve in the field, extracting the coordinates of the actual measurement curve with the sampling interval dx as precision, calculating the accumulated slope of the actual measurement curve, and forming a curve accumulated slope data sequence of actual measurement data; and calculating Hausdorff distance between the accumulated slope of the measured curve and the accumulated slope of the standard curve, finding out the standard contour line of the structural surface most similar to the measured curve, and endowing the corresponding JRC value to the measured curve to finally realize the estimation of the JRC value of the measured curve. The invention realizes the JRC value estimation of the self-similarity of the curve accumulated slope, and avoids the randomness and subjectivity of JRC value determination caused by the comparison of naked eye observation and a standard section.
Description
Technical Field
The invention belongs to the technical field of engineering geological exploration, and particularly relates to a method for realizing JRC estimation based on Haosdorff distance.
Background
Barton and Choubey in 1977 in document 1"BARTONN,CHOUBEYV.The shear strength ofrockjoints in theory and practice[J ]. Rock Mechanics,1977, 10 (1/2): 1-54' provides that the structural surface roughness coefficient JRC is adopted to quantitatively describe the structural surface shape difference of the structural surface roughness fluctuation, 10 structural surface standard contour lines are constructed, and each standard curve corresponds to a corresponding JRC value, as shown in figure 1. Standard profiles corresponding to the standard contour lines of the 10 structural faces have been incorporated into the ISRM specification.
In actual field work, a visual comparison method is generally adopted, that is, by obtaining an actual structural surface morphology chart and a Barton standard profile morphology for comparison, a JRC value of a standard profile most similar to the actual profile according to a visual difference is the JRC value of the actual structural surface. The method is visual and visual, does not need calculation, and is widely used in field determination of the structural surface roughness coefficient. However, the geometric forms of the rock mass structural surfaces are complex and changeable, the lengths of the rock mass structural surfaces are different, and when the rock mass structural surfaces are compared with the standard section by adopting a visual comparison method, the rock mass structural surfaces are similar to a plurality of curves, and the rock mass structural surfaces are possibly dissimilar to all the standard curves. Thus, the JRC assessment score of the method often depends on the experience of the user, is difficult to accurately grasp, has great randomness, and usually causes artificial estimation errors.
To reduce subjectivity and randomness of visual comparison methods, many researchers established JRC and slope root mean square Z 2 The quantitative calculation formula is shown in table 1.
TABLE 1JRC values and Z 2 Summarizing the calculation formulas among the two
However, the following disadvantages are also present with the above method:
1) The existing quantitative calculation formula of JRC is obtained based on a relation fitting formula between the gradient root mean square of 10 standard curves of Barton and the JRC value, and the fitting formula has only 10 data, so that the limitation of the use of the fitting formula is caused, and the situation that the JRC is smaller than 0 or the JRC value is larger than 20 can occur when the formula is adopted for calculation, as shown in tables 1-3. If the JRC is smaller than 0 and is not in line with the actual situation, if the JRC is larger than 20, the calculated shear strength of certain calculation formulas of the shear strength based on the JRC is higher, so that the engineering safety is not facilitated.
2) The JRC value calculated by any calculation formula for the same curve should have only one JRC value, or the calculation results are not much different. But the slope root mean square Z in the fitting formula of JRC value 2 Closely related to the sampling interval of the section line, if the sampling interval is different, a situation that one curve corresponds to a plurality of JRC values will often occur according to different calculation formulas, as shown in table 4.
According to the study of document 3, it is found that the root mean square Z 2 Closely related to the sampling pitch of the section line, therefore, when the JRC value of the measured curve is calculated using an empirical formula, the section line sampling pitch must be identical to the sampling pitch used when deriving the empirical formula, otherwise the error in calculating JRC may be as high as 100% (see document 4).
In addition, patent CN105678786a, patent CN105716545A, and patent CN105737768A disclose structural plane roughness coefficient evaluation methods based on Jaccard similarity measure, dice similarity measure, and Cosine similarity measure, respectively. The method mainly comprises the following steps: respectively extracting coordinates of 10 standard contour curves of Barton, calculating adjacent relief angles, respectively counting the distribution frequency of the voltage rising angles in each statistical interval, and reconstructing the relief angle feature vector of the standard contour curve; and then calculating the similarity between the fluctuation angle feature vector of the test curve and the fluctuation angle feature vector of each standard curve according to the Jaccard similarity measure, the Dice similarity measure or the sine similarity measure, and selecting the roughness coefficient of the corresponding curve when the similarity is 1 as the JRC value of the test curve.
The above method also has a series of disadvantages:
1) The similarity calculation of the method needs to count the fluctuation angle distribution interval, constructs a new fluctuation angle characteristic vector, carries out vector operation, and has complex calculation method and calculation process.
2) The structural surface roughness coefficient evaluation method for the similarity measurement of the method not only needs to ensure that the sampling interval of the test curve is the same as the curve interval of the standard curve, but also needs to ensure that the statistical interval of the voltage rising angle is the same, and has more severe requirements on calculation conditions.
3) The accuracy of the evaluation result is low.
Taking the test curve case published in the embodiment of patent CN105737768A as an example, according to the calculation method, the JRC value of the test curve is 2-4, but through visual comparison with 10 standard curves of Barton, the JRC value of the test curve is intuitively judged to belong to 18-20 according to engineering experience.
Taking the test curve case published in the embodiment of patent CN105678786a as an example, according to the calculation method, the JRC value of the test curve is 10-12, but through visual comparison with the 10 standard curves of Barton, the JRC value of the test curve should also belong to 18-20 according to engineering experience.
Taking the test curve case published in the embodiment of patent CN105716545a as an example, according to the calculation method, the JRC value of the test curve is 10-12, but through visual comparison with the 10 standard curves of Barton, the JRC value of the test curve should also belong to 18-20 according to engineering experience.
Further, in order to verify the error of the calculation result based on the similarity of the characteristic vector of the curve relief angle in the above method, further verification is performed by taking the test curve case published in the CN105737768A embodiment as an example, and the verification method is as follows: the coordinates of the test curve in CN105737768A (sampling interval dx=0.5 mm) were extracted, and the root mean square (Z) of the slope was calculated 2 ) Then, JRC values thereof were calculated according to the methods of documents 3, 4, and 5, as shown in Table 1The calculation results show that the JRC values of the test curves are all greater than 20. According to the same way, the JRC values in the other two published patents are calculated, see tables 2 and 3, also greater than 20. According to the 10 standard curves of Barton, the JRC value range is between 0 and 20, and for calculating that the JRC is larger than 20, the value should be 20. It can be seen that the JRC value calculated according to the methods disclosed in the above three patents and the slope root mean square Z are determined empirically 2 There is a large difference in the comparison of the calculated JRC values.
The engineering experience verification and the slope root mean square verification are combined to obtain that: the structural surface roughness coefficient evaluation method based on the curve relief angle feature vector similarity measurement in the three patent publications is inaccurate and has larger error.
JRC value calculation of test curves in Table 1CN105737768A
Table 2 JRC value calculation of test curve in CN105678786a
Table 3 JRC value calculation of test curve in CN105716545a
TABLE 4 calculation of different JRC values for the same Curve with different sampling intervals
Note that: root mean square (Z) of standard curve 2 ) Data are derived from publicly published document 6: li Rui New formula of rock joint JRC calculation based on Barton standard section line fine digital processingGo [ J]Rock mechanics and engineering report, 2018, 37 (S1): 3515-3522, calculation formula from document 3.
Disclosure of Invention
The purpose of the invention is that: a method for implementing JRC estimation based on Haoskov distance is provided. The invention realizes the JRC value estimation of the self-similarity of the curve accumulated slope based on the Haosdorf distance, and avoids the randomness and subjectivity of the JRC value determination caused by the comparison of naked eye observation and a standard section.
The technical scheme of the invention is as follows: the method for realizing JRC estimation based on Haoskov distance comprises the steps of extracting coordinates of 10 structural surface standard contour lines of Barton with sampling interval dx as precision, and calculating accumulated slope of each structural surface standard contour line according to the extracted x and y coordinate data to form a standard curve accumulated slope data sequence; measuring a structural plane curve in the field, extracting the coordinates of the actual measurement curve with the sampling interval dx as precision, calculating the accumulated slope of the actual measurement curve, and forming a curve accumulated slope data sequence of actual measurement data; and calculating Hausdorff distance between the accumulated slope of the measured curve and the accumulated slope of the standard curve, finding out the standard contour line of the structural surface most similar to the measured curve, and endowing the corresponding JRC value to the measured curve to finally realize the estimation of the JRC value of the measured curve.
In the method for realizing JRC estimation based on Haoskov distance, the calculation method of the Haoskov distance is as follows:
setting: curve coordinate data sequence C= [ x ] 1 y 1 ，x 2 y 2 ，x 3 y 3 ，···，x n y n ]Wherein x is n y n Is the abscissa of the nth sampling point on the curve at the sampling interval dx;
setting: (x) i ，y i )，(x i+1 ，y i+1 ) Coordinates of two adjacent points on the curve;
then: absolute value k of slope of curve i The calculation formula is as follows:
the cumulative slope of the curve Sumki is calculated as follows:
the cumulative slope data sequence is calculated as follows:
C_sumk＝[x 1 sumk 1 ，x 2 sumk 2 ，x 3 sumk 3 ，···，x n-1 sumk n-1 ]；
based on the above formula, the accumulated slope data sequence of the measured curve is calculated under the same sampling interval dxCumulative slope data sequence S of standard contour line of each structural surface 1_sumk ，S 2_sumk ，S 3_sumk ，···，S 10_sumk ；
Setting: the curve a consists of n points, the curve B is also provided with n points,
the point set a= { a 1 ,a 2 ,…,a n Point set b= { B } 1 ,b 2 ,…,b n And the Hausdorff distance between the point sets A and B is:
H(A,B)＝max[h(A,B),h(B,A)]
wherein: h (A, B) and h (B, A) are referred to as unidirectional Haosduo of point sets A through B, respectivelyA f distance and a unidirectional hausdorff distance from B to a; h (a, B) is referred to as the hausdorff distance between point sets a and B; i a i －b j I and b j －a i The I is the Euclidean distance between two points;
will beAnd S is equal to 1_sumk 、/>And S is equal to 2_sumk 、…、/>And S is equal to 10_sumk Respectively substituting the above formula to obtain +.>Respectively with S 1_sumk ，S 2_sumk ，S 3_sumk ，···，S 10_sumk Haoskov distance Hd of (H) 1 ，Hd 2 ，Hd 3 ，···，Hd 10 。
In the method for realizing JRC estimation based on Haoskov distance, when H d ＝H dmin ＝min[Hd 1 ，Hd 2 ，Hd 3 ，···，Hd 10 ]When H is d The corresponding standard contour line of the structural surface is most similar to the measured curve.
In the method for realizing JRC estimation based on Haoskov distance, the standard contour line of the structural surface is subjected to fine digital processing of removing the impurity points and repairing the fracture by adopting PHOTOSHOP and MATLAB before the calculation of the accumulated slope.
In the method for realizing JRC estimation based on Haoskov distance, the sampling interval is 1mm.
In the method for realizing JRC estimation based on Haoskov distance, the actually measured curve is acquired by acquiring the contour and coordinate data of the structural surface through a shape finder, a contour instrument or a scanner in a field environment.
Advantageous effects
Compared with the prior art, the method has the defects of randomness and subjectivity in a JRC value estimation method determined by a field vision comparison method, and the method comprises the steps of carrying out refinement treatment on10 standard structural surface curves of Barton, extracting coordinates of the standard curves with dx as precision, calculating accumulated slopes of the curves according to the extracted x and y coordinate data, and forming a standard curve accumulated slope data sequence; measuring a structural plane curve in the field, extracting the coordinates of the actual measured curve with dx as precision, calculating the accumulated slope of the actual measured curve, and forming a curve accumulated slope data sequence of actual measured data; the Hausdorff distance between the accumulated slope of the measured curve and the accumulated slope of the standard curve is calculated to find out the standard section most similar to the measured curve, so that the accurate estimation of the JRC value based on the similarity of the accumulated slope of the curve is realized, and the randomness and subjectivity of manually determining the JRC value are avoided.
The invention adopts the slope root mean square Z 2 Compared with the calculation method of the method, the method has the following advantages:
1) The defect that the calculation result in the JRC calculation formula exceeds the JRC boundary is overcome. The JRC value calculated by the Haosdorf distance-based JRC value estimation method is between 0 and 20, thereby avoiding adopting the slope root mean square Z 2 The formula computes JRC values beyond 20.
2) The condition that a plurality of JRC value calculation results appear on the same curve due to different sampling intervals is overcome. The invention is known by analysis: although the sampling interval has a larger influence on the JRC calculation result, the gradient root mean square Z is changed no matter how the sampling interval is changed 2 Are positively correlated with JRC values. Based on the method, in order to overcome the influence of the sampling interval on the JRC calculation error, the invention provides a JRC estimation method based on the curve accumulated slope similarity, and the JRC value of the estimated actual measurement curve can be obtained only by ensuring that the sampling interval of the actual measurement curve is consistent with the sampling interval of the standard curve. See tables 5 and 6.
In addition, compared with a calculation method based on the similarity of the relief angle feature vector, the method has the following advantages:
1) The requirement on the calculation condition is simple. The invention can obtain the corresponding estimation result only by ensuring that the sampling interval of the test curve is the same as the sampling interval of the standard curve.
2) The accuracy of the evaluation result is high. Taking the test curve case published by the CN105737768A embodiment as an example, the Hausdorff distance (Hausdorff distance) of the cumulative slope of the test curve and the standard curve is calculated, and the JRC value of the test curve at the sampling interval of 0.5mm and the sampling interval of 1mm is calculated to be 18-20. The specific calculation results are shown in tables 5 and 6.
TABLE 5 JRC values (sampling spacing 0.5 mm) for the test curves in CN105737768A were calculated according to the invention
TABLE 6 JRC values (sampling spacing 1 mm) for the test curves in CN105737768A were calculated according to the invention
Through comparison, the calculation result based on the accumulated slope similarity provided by the invention is more accurate than the calculation result based on the fluctuation angle feature vector similarity provided by the patent CN 105737768A.
In conclusion, the method realizes JRC value estimation based on the similarity of curve accumulated slopes, avoids randomness and subjectivity of JRC value determination caused by comparison between naked eye observation and standard section, and has higher calculation accuracy.
Drawings
FIG. 1 is a Barton10 structural plane standard curve and corresponding JRC values;
FIG. 2 is a cross section of a standard Barton curve obtained after refinement;
FIG. 3 is a schematic view of the in situ shear failure plane of mudstone, where (a) is τ 1-2 A picture of the shear failure surface of the test block, (b) is tau 1-3 Cutting the broken surface photo by the test block;
FIG. 4 is a graph of the profile of the extracted fracture surface, where (a) is τ 1-2 Splitting on the breaking surfaceA plane curve (b) is τ 1-3 Breaking the profile curve on the surface;
fig. 5 is a test curve fitted at dx=1 mm sampling intervals.
Fig. 6 shows the profile of a curve of a structural surface in field measurement.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not intended to be limiting.
Example 1. A method for realizing JRC estimation based on Haoskov distance comprises the following steps:
step one: the Barton standard section line was obtained. Barton and Choubey 1997 published "The Shear Strength ofRock Joints in Theory and Practice" in the J.rock Mechanics "from which corresponding PDF versions could be downloaded. From this article, an original standard section line image can be obtained (fig. 1).
Step two: standard section line fine digital processing. Reference 2 discloses a method for performing fine digital processing on10 standard section lines by using photohop and MATLAB graphic processing professional software to remove miscellaneous points, repair breaks and the like, so as to obtain 10 Barton standard section lines (as shown in fig. 2). The coordinates of each curve are extracted from MATLAB for analysis and calculation at a sampling interval of dx=1 mm with the left end point of each curve as the (0, 0) point.
Step three: and obtaining the actually measured curve outline and coordinate data of the structural surface. The outline and coordinate data of the structural surface can be obtained in the field by adopting a shape taking device, a profiler, a scanner or the like.
Step four: at dx sampling interval, acquiring coordinate data series of curve as C, respectively calculating slope k of curve i Cumulative slope Sum ki Data sequence c_sumk reconstructed by cumulative slope i 。
Let a curve data coordinate c= [ x ] 1 y 1 ，x 2 y 2 ，x 3 y 3 ，···，x n y n ]Wherein the method comprises the steps of
The absolute value calculation formula of the slope of the curve is shown in formula 1:
cumulative slope of curve:
the data sequence of the cumulative slope reconstruction is
C_sumk＝[x 1 sumk 1 ，x 2 sumk 2 ，x 3 sumk 3 ，···，x n-1 sumk n-1 ]
Step five: under the same sampling interval, respectively acquiring accumulated slope data sequences of the test curves according to the fourth stepAnd cumulative slope data sequence for each standard curve +.>Wherein the cumulative slope data sequence of the 10 standard curves is S respectively 1_sumk ，S 2_sumk ，S 3_sumk ，···，S 10_sumk 。
Step six: and calculating the Hausdorff distance between the cumulative slope of the curve of the actually measured structural surface and the Barton standard section by adopting a Hausdorff distance method. Respectively calculating Hausdorff distances between the measured structural plane curve accumulated slope sequence and 10 standard Batobn curve accumulated slope sequences, which are Hd respectively 1 ，Hd 2 ，Hd 3 ，···，Hd 10 。
The hausdorff distance calculation formula is as follows:
set point set a= { a 1 ,a 2 ,…,a n }，B＝{b 1 ,b 2 ,…,b n And the Hausdorff distance between the point sets A and B is:
H(A,B)＝max[h(A,B),h(B,A)] (11)
wherein: h (A, B) and h (B, A) are referred to as the one-way Hausdorff distance of point sets A to B and the one-way Hausdorff distance of B to A, respectively; h (A, B) is called Hausdorff distance between point sets A and B; i a i －b j I and b j －a i And I is the Euclidean distance between two points.
Step seven: comparing the Hausdorff distance of the curves, wherein the smaller the Hd (Hausdorff distance) value is, the higher the similarity degree between the test curve and the standard curve is, and selecting the minimum Hausdorff distance (Hd min ) The JRC value of the corresponding standard curve is the JRC value of the measured curve.
Hd＝Hd min ＝min[Hd 1 ，Hd 2 ，Hd 3 ，···，Hd 10 ]
JRC Measuring ＝JRC Hd 。
Example 2. Taking Barton standard section line data as an example, the cumulative slope Hausdorff (Hausdorff) distance similarity of each curve and other 9 curves is calculated respectively, the sampling interval is dx=1 mm, and the calculation results are shown in table 7 below.
Table 7Barton standard section line data cumulative slope Hausdorff distance calculation result table (dx=1 mm)
| Hd | JRC 0-2 | JRC 2-4 | JRC 4-6 | JRC6-8 | JRC 8-10 | JRC 10-12 | JRC 12-14 | JRC 14-16 | JRC 16-18 | JRC 18-20 |
| JRC 0-2 | 0 | 3.06603 | 4.71936 | 6.99331 | 9.72853 | 10.78038 | 11.77530 | 16.72914 | 17.48315 | 21.98502 |
| JRC 2-4 | 3.06603 | 0 | 1.65487 | 3.92883 | 6.66266 | 7.71552 | 8.70938 | 13.66352 | 14.41866 | 18.92054 |
| JRC 4-6 | 4.71936 | 1.65487 | 0 | 2.27396 | 5.01692 | 6.06138 | 7.05850 | 12.01084 | 12.76379 | 17.26566 |
| JRC6-8 | 6.99331 | 3.92883 | 2.27396 | 0 | 2.74996 | 3.78792 | 4.78758 | 9.73776 | 10.48983 | 14.99171 |
| JRC 8-10 | 9.72853 | 6.66266 | 5.01692 | 2.74996 | 0 | 1.05960 | 2.05117 | 7.00626 | 7.76283 | 12.26470 |
| JRC 10-12 | 10.78038 | 7.71552 | 6.06138 | 3.78792 | 1.05960 | 0 | 1.40811 | 5.94990 | 6.70323 | 11.20510 |
| JRC 12-14 | 11.77530 | 8.70938 | 7.05850 | 4.78758 | 2.05117 | 1.40811 | 0 | 4.95509 | 5.71166 | 10.21353 |
| JRC 14-16 | 16.72914 | 13.66352 | 12.01084 | 9.73776 | 7.00626 | 5.94990 | 4.95509 | 0 | 1.30614 | 5.25844 |
| JRC 16-118 | 17.48315 | 14.41866 | 12.76379 | 10.48983 | 7.76283 | 6.70323 | 5.71166 | 1.30614 | 0 | 4.50187 |
| JRC 18-20 | 21.98502 | 18.92054 | 17.26566 | 14.99171 | 12.26470 | 11.20510 | 10.21353 | 5.25844 | 4.50187 | 0 |
As can be seen from table 7, the similarity distance between each curve and its own cumulative curve data sequence is 0, and as the JRC value increases or decreases, the similarity distance increases, which indicates that the similarity is worse, so that the accuracy and the accuracy of the method for estimating the structural surface roughness coefficient (JRC) based on the cumulative slope similarity are verified. The difference between the cumulative slope of each curve and the two curves adjacent to each other is the smallest, which indicates that the similarity between the two curves adjacent to each other is the best, and the estimation of the structural surface roughness coefficient JRC value can be realized based on the similarity of the cumulative slopes.
Example 3. A method for realizing JRC estimation based on Haoskov distance. Taking a set of mudstone on-site shear tests of a certain hydropower station of tansania as an example, photographs of shear failure surfaces are shown in (a) and (b) in fig. 3, and a surface profiler is used to obtain a 10cm structural surface section along the shear direction, as shown in (a) and (b) in fig. 4.
By means of tau 1-2 Profile curve (test 1) and τ on the fracture surface 1-3 The profile curve on the fracture surface (test 2) is extracted in matlab software at a sampling interval of dx=1 mm to its coordinates, and the fracture surface curve is re-fitted at a sampling interval of 1mm as shown in fig. 5. From the extracted curve coordinates, hausdorff distances of the cumulative slope data sequences of the test1 and test2 curves and the cumulative slope data sequences of the Barton10 profile curves are calculated as shown in tables 8 and 9.
TABLE 8 Hausdorff (Hausdorff) distance calculation Table of Test1 Curve from Standard Curve
TABLE 9 Hausdorff (Hausdorff) distance calculation Table of Test2 curve from standard curve
As can be seen from table 8, the test1 curve has the smallest distance from Hausdorff (Hausdorff) of the cumulative slope of the 4 th Barton standard section line curve, indicating that its JRC value is closest to the JRC value of the 4 th Barton standard section, so jrc=6 to 8 of the test1 curve.
As can be seen from table 9, the cumulative slope of the test2 curve and the 7 th Barton standard section line curve, hausdorff, is the smallest, indicating that the JRC value is closest to the JRC value of the 7 th Barton standard section, so that jrc=12 to 14 of the test2 curve.
Example 4. Taking a field actually measured structural plane curve as an example, referring to fig. 6, the hausdorff distance of the cumulative slope of the actually measured structural plane curve and 10 standard curves is calculated by using the present invention, and is shown in table 10. The Hastethodor distance between the measured curve and the accumulated slope of the 5 th Barton standard section line curve is the smallest, which indicates that the JRC value of the measured curve is closest to the JRC value of the 5 th Barton standard section, so that the JRC=8-10 of the measured curve.
Table 10 accumulated slope Haoskov distance calculating table for actual measurement curve and standard curve
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (6)
1. The method for realizing JRC estimation based on Haosdorf distance is characterized in that coordinates of 10 structural surface standard contour lines of Barton are extracted with the sampling interval dx as precision, and the accumulated slope of each structural surface standard contour line is calculated according to the extracted x and y coordinate data to form a standard curve accumulated slope data sequence; measuring a structural plane curve in the field, extracting the coordinates of the actual measurement curve with the sampling interval dx as precision, calculating the accumulated slope of the actual measurement curve, and forming a curve accumulated slope data sequence of actual measurement data; and calculating Hausdorff distance between the accumulated slope of the measured curve and the accumulated slope of the standard curve, finding out the standard contour line of the structural surface most similar to the measured curve, and endowing the corresponding JRC value to the measured curve to finally realize the estimation of the JRC value of the measured curve.
2. The method for realizing JRC estimation based on hausdorff distance according to claim 1, wherein the calculation method of hausdorff distance is as follows:
setting: curve coordinate data sequence C= [ x ] 1 y 1 ，x 2 y 2 ，x 3 y 3 ，···，x n y n ]Wherein x is n y n Is the abscissa of the nth sampling point on the curve at the sampling interval dx;
setting: (x) i ，y i )，(x i+1 ，y i+1 ) Coordinates of two adjacent points on the curve;
then: absolute value k of slope of curve i The calculation formula is as follows:
the cumulative slope of the curve Sumki is calculated as follows:
the cumulative slope data sequence is calculated as follows:
C_ sumk ＝[x 1 sumk 1 ，x 2 sumk 2 ，x 3 sumk 3 ，···，x n-1 sumk n-1 ]；
based on the above formula, the accumulated slope data sequence of the measured curve is calculated under the same sampling interval dxCumulative slope data sequence S of standard contour line of each structural surface 1_sumk ，S 2_sumk ，S 3_sumk ，···，S 10_sumk ；
Setting: the curve a consists of n points, the curve B is also provided with n points,
the point set a= { a 1 ,a 2 ,…,a n Point set b= { B } 1 ,b 2 ,…,b n And the Hausdorff distance between the point sets A and B is:
H(A,B)＝max[h(A,B),h(B,A)]
wherein: h (A, B) and h (B, A) are referred to as the one-way Haoskov distance of point sets A to B and the one-way Haoskov distance of B to A, respectively; h (a, B) is referred to as the hausdorff distance between point sets a and B; i a i －b j I and b j －a i The I is the Euclidean distance between two points;
will beAnd S is equal to 1_sumk 、/>And S is equal to 2_sumk 、…、/>And S is equal to 10_sumk Respectively substituting the above formula to obtain +.>Respectively with S 1_sumk ，S 2_sumk ，S 3_sumk ，···，S 10_sumk Haoskov distance Hd of (H) 1 ，Hd 2 ，Hd 3 ，···，Hd 10 。
3. The method for implementing JRC estimation based on Hausdorff distance according to claim 2, wherein when H d ＝H dmin ＝min[Hd 1 ，Hd 2 ，Hd 3 ，···，Hd 10 ]When H is d The corresponding standard contour line of the structural surface is most similar to the measured curve.
4. The method for realizing JRC estimation based on Haoskov distance according to claim 1, wherein the standard contour line of the structural surface is subjected to fine digital processing of removing the impurity points and repairing the fracture by using PHOTOSHOP and MATLAB before the calculation of the accumulated slope.
5. The method for implementing JRC estimation based on hausdorff distance according to claim 1, wherein the sampling interval is 1mm.
6. The method for realizing JRC estimation based on hausdorff distance according to claim 1, wherein the measured curve is acquired by acquiring profile and coordinate data of the structural surface by a profile extractor, a profile meter or a scanner in a field environment.
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