Carbonate rock slope deterioration evaluation method based on hyperspectral imaging and surface hardness

Abstract

The application provides a carbonate rock slope deterioration evaluation method based on hyperspectral imaging and surface hardness, comprising: obtaining a hyperspectral image and corresponding rock rebound data to obtain a corresponding spectral curve; calculating a correlation coefficient according to the spectral curve to obtain an optimal spectral index; performing characteristic wavelength screening on the optimal spectral index through a screening algorithm; constructing a Schmidt rebound hardness prediction model to obtain hardness data with the characteristic wavelength as input; establishing a fracture opening sensing spectral index according to the spectral index to obtain a rock mass discontinuous surface spectral index, grading the rock discontinuous surface to obtain microcracks and opening areas; obtaining water level data to obtain water level fluctuation data according to the water level data; and performing carbonate rock slope deterioration evaluation according to the rock rebound data, the hardness data, the microcracks and the opening areas and the water level fluctuation data. The application realizes quantitative identification of the characteristics of the rock discontinuous surface by constructing spectral indexes such as NDSI, FPSI and YGSI.

Description

Technical Field

[0001] This invention relates to the field of rock mass structural surface feature assessment technology, and in particular to a method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness. Background Technology

[0002] The periodic rise and fall of reservoir water levels easily create water level fluctuation zones along the river. Due to the complex interactions of various factors such as structure, erosion, weathering, and gravity, the rock mass in HFB (High-Pressure Water Bodies) exhibits progressive deterioration behavior. Many studies have explored the deterioration characteristics of rock masses from the perspectives of water-rock interactions and slope stability. However, the evolution of carbonate rock discontinuities, as an important manifestation of deterioration in water level fluctuation zones, is influenced by physical, chemical, and mechanical changes, and further research is needed. Accurately assessing the deterioration differences in water level fluctuation zones requires a detailed understanding of the characteristics of rock discontinuities; however, existing research has limitations in this area. Therefore, developing an effective method for predicting carbonate rock slope instability is of significant practical importance. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness to address the aforementioned technical problems.

[0004] A method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness includes the following steps:

[0005] Step S1: Obtain hyperspectral images of rock samples from the area to be evaluated and rock rebound data from the Schmidt test area. Based on the hyperspectral images, extract the spectral curves corresponding to the Schmidt hammer test area using the regional averaging method.

[0006] Step S2: Calculate the correlation coefficient between the spectral index composed of different bands and the rock rebound data based on the spectral curve to obtain the optimal spectral index;

[0007] Step S3: Select the characteristic wavelengths by using competitive adaptive reweighted sampling, continuous projection algorithm and random frog jumping algorithm to select the optimal spectral index;

[0008] Step S4: Construct a Schmidt springback hardness prediction model by inputting the characteristic wavelength into the Schmidt springback hardness prediction model to obtain hardness data;

[0009] Step S5: Establish an opening sensing spectral index based on the spectral index to obtain the spectral index of the rock discontinuity surface, and classify the rock discontinuity surface to obtain microcracks and opening areas.

[0010] Step S6: Obtain water level data, and obtain water level fluctuation data based on the water level data;

[0011] Step S7: Assess the deterioration of the carbonate rock slope based on the rock rebound data, the hardness data, the microcrack and opening area, and the water level fluctuation data.

[0012] In one embodiment, step S1 includes:

[0013] The hyperspectral image is preprocessed to obtain a preprocessed hyperspectral image;

[0014] Based on the preprocessed hyperspectral image, the spectral curve corresponding to the Schmidt hammer test area is extracted using the region averaging method.

[0015] In one embodiment, step S2 includes:

[0016] Calculate the spectral indices composed of different spectral bands based on the spectral curves;

[0017] The optimal spectral index is obtained by calculating the correlation coefficient between the spectral index and the rock rebound data.

[0018] In one embodiment, calculating the spectral index composed of different bands based on the spectral curve includes:

[0019] The spectral index is calculated using the following formula:

[0020]

[0021] Wherein, NDSI represents the Normalized Difference Spectral Index. Indicates the sth p Band reflectivity, Indicates the sth q Band reflectance, DSI represents the difference spectral index, and RSI represents the ratio spectral index.

[0022] In one embodiment, the correlation coefficient between the spectral index and the rock rebound data is calculated based on the spectral index to obtain the optimal spectral index, including:

[0023] The correlation coefficient is calculated using the following formula:

[0024]

[0025] Where r represents the correlation coefficient, n represents the sample size, i represents the i-th spectral index, and x i Indicates the spectral index value. y represents the average value of the spectral index. i This indicates rock rebound data. This represents the average value of the Schmidt hammer springback.

[0026] The correlation coefficients are compared, and the spectral index formed by the band combination with the highest correlation coefficient is taken as the optimal spectral index.

[0027] In one embodiment, step S4 includes:

[0028] The constructed Schmidt rebound hardness prediction model is the SG-CARS-PLS model.

[0029] In one embodiment, step S5 includes:

[0030] Establish the opening-sensing spectral index:

[0031]

[0032] Wherein, FPSI represents the aperture sensing spectral index, D 700 D represents the spectral reflectance at a wavelength of 700 nm. 450 This represents the spectral reflectance at a wavelength of 450 nm.

[0033] Calculate the spectral index of the rock mass discontinuity surface based on the opening-sensing spectral index:

[0034]

[0035] Where YGSI represents the rock mass discontinuity spectral index, NDSI represents the current normalized difference spectral index, and NDSI represents the current normalized difference spectral index. min NDSI represents the minimum normalized difference spectral index. max The normalized difference spectral index (FPSI) represents the maximum value of the normalized difference spectral index, while the FPSI represents the fracture opening sensing spectral index. min The FPSI represents the minimum value of the spectral index for sensing fracture opening. max This indicates the maximum value of the spectral index for sensing the crack opening;

[0036] The rock discontinuities are classified according to the spectral index of the rock mass discontinuities to obtain microcrack and opening data.

[0037] The microcrack and opening areas are calculated based on the microcrack and opening data.

[0038] A carbonate rock slope degradation assessment system based on hyperspectral imaging and surface hardness is used to implement the carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness as described above, including:

[0039] The rebound acquisition module is used to acquire hyperspectral images of rock samples from the area to be evaluated and rock rebound data from the Schmidt test area. Based on the hyperspectral images, the spectral curves corresponding to the Schmidt hammer test area are extracted using the regional averaging method.

[0040] The index acquisition module is used to calculate the correlation coefficient between the spectral index composed of different bands and the Schmidt bounce value based on the spectral curve, so as to obtain the optimal spectral index.

[0041] The wavelength selection module is used to filter the characteristic wavelengths of the optimal spectral index through competitive adaptive reweighted sampling, continuous projection algorithm and random frog jumping algorithm to obtain the characteristic wavelengths.

[0042] The hardness acquisition module is used to construct a Schmidt springback hardness prediction model. The characteristic wavelength is input into the Schmidt springback hardness prediction model to obtain hardness data.

[0043] The degradation calculation module is used to establish an opening sensing spectral index based on the spectral index, obtain the spectral index of the rock discontinuity surface, and classify the rock discontinuity surface to obtain microcracks and opening areas.

[0044] The flooding acquisition module is used to acquire water level data and obtain water level fluctuation data based on the water level data;

[0045] The degradation assessment module is used to assess the degradation of carbonate rock slopes based on the rock rebound data, the hardness data, the microcrack and opening area, and the water level fluctuation data.

[0046] An apparatus includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the carbonate rock slope deterioration assessment method based on hyperspectral imaging and surface hardness described in the various embodiments above.

[0047] A storage medium storing a computer program that, when executed by a processor, implements the steps of the carbonate rock slope deterioration assessment method based on hyperspectral imaging and surface hardness described in the above embodiments.

[0048] Compared to existing technologies, the advantages and beneficial effects of this invention are as follows: This invention integrates hyperspectral imaging and Schmidt rebound hardness testing techniques to comprehensively and accurately characterize changes in carbonate rocks under water-rock interaction, providing a reliable basis for assessing the deterioration of water level drawdown zones. By constructing multiple spectral indices such as the Normalized Difference Spectral Index (NDSI), the Fracture Opening Sensing Spectral Index (FPSI), and the YGSI (Yellow Gas Discontinuity Spectral Index), it achieves quantitative identification of rock discontinuity characteristics. It can effectively distinguish different types of discontinuities such as high hardness, low hardness, microcracks, and openings, overcoming the limitations of general methods in quantitatively describing rock deterioration characteristics. It reveals the influence of water level fluctuation frequency on fracture development and the relationship between deterioration and rock strata structure, providing a basis for understanding deterioration mechanisms and assisting in geological disaster early warning and prevention. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness in one embodiment.

[0050] Figure 2 This is a schematic diagram of YGSI segmentation in one embodiment;

[0051] Figure 3 This is a schematic diagram of a carbonate rock slope degradation assessment system based on hyperspectral imaging and surface hardness in one embodiment.

[0052] Figure 4 This is a schematic diagram of the internal structure of the device in one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0054] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0055] For ease of understanding, the terms used in the embodiments of this invention are explained below:

[0056] CARS: Competitive Adaptive Reweighted Sampling.

[0057] SPA: Continuous Projection Algorithm.

[0058] RF: Random Frog Jumping Algorithm.

[0059] PLS: Partial Least Squares.

[0060] FPSI: Spectral Index for Sensing Crack Opening.

[0061] NDSI: Normalized Difference Spectral Index.

[0062] YGSI: Spectral index of rock mass discontinuities.

[0063] DSI: Spectral Difference Index.

[0064] RSI: Ratio Spectral Index.

[0065] SG: Savitzky-Golay smoothing filter.

[0066] In one embodiment, such as Figure 1 As shown, a method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness is provided, including the following steps:

[0067] Step S1: Obtain hyperspectral images of rock samples from the area to be evaluated and rock rebound data from the Schmidt test area. Based on the hyperspectral images, extract the spectral curves corresponding to the Schmidt hammer test area using the regional averaging method.

[0068] Specifically, a portable hyperspectral imaging system was used to collect images of rock samples from the area to be evaluated on-site, resulting in hyperspectral images.

[0069] In one embodiment, a portable hyperspectral imager with a wavelength range of 400-1000 nm is used to acquire hyperspectral data from rock samples. Initial acquisition parameters are set as follows: exposure value 80, target grayscale value 100, and channel spacing 5 nm. In each sampling area, at least five spectral images are acquired according to the set parameters, with a certain degree of overlap between adjacent images to ensure data integrity.

[0070] Schmidt tests were conducted in the rock mass area where hyperspectral images were acquired using a Schmidt hammer. After selecting appropriate test points and cleaning the surface, the rock rebound data was obtained and relevant information was recorded.

[0071] In one embodiment, 160 test points are arranged in each test area, and each test lasts approximately 5 minutes. The final rebound data is the average of 5 tests to improve the reliability and representativeness of the data. For sub-areas containing cracks and openings, rebound data from a smooth surface in the same sub-area are used instead to avoid interference from factors such as cracks on the test results.

[0072] The spectral image was divided into 160 small regions in 10 rows and 16 columns, each corresponding to a rock rebound value. The spectral data in each test region was averaged using the regional averaging method, and the spectral curve corresponding to the Schmidt hammer test region was extracted.

[0073] In this embodiment, the Schmidt hammer has advantages such as speed, economy, and minimal destructiveness, and is widely used for testing the surface hardness of rock masses. The measured values ​​can reflect the relationship between rock strength and hardness.

[0074] In terms of hardness, Schmidt rebound data directly reflects the hardness of the rock surface. Rock surfaces with higher hardness show relatively larger rebound values ​​under Schmidt hammer impact; conversely, rock surfaces with lower hardness show smaller rebound values. By measuring the Schmidt rebound values ​​of different areas (such as high-hardness, low-hardness, microcracked, and open areas), the differences in hardness between different areas can be distinguished, thus providing a quantitative basis for assessing the hardness of rock surfaces.

[0075] Regarding strength, there is a certain positive correlation between Schmidt data and the compressive strength of rocks. The magnitude of rebound data can, to some extent, indicate the rock's ability to resist pressure. By conducting Schmidt rebound tests on rocks at different elevations and combining this with an empirical conversion formula for rocks in the region, the compressive strength of the rocks can be estimated. The formula is as follows:

[0076] UCS = 6.97 × e (0.014×RN×ρ)

[0077] Where UCS is the uniaxial compressive strength, RN is the Schmidt rebound value, and ρ is the rock density.

[0078] Based on this, step S1 includes:

[0079] The hyperspectral image is preprocessed to obtain a preprocessed hyperspectral image;

[0080] Based on the preprocessed hyperspectral image, the spectral curve corresponding to the Schmidt hammer test area is extracted using the region averaging method.

[0081] Specifically, preprocessing includes black and white correction, SG smoothing, and arithmetic averaging.

[0082] In this embodiment, black-and-white correction is a correction method in digital image processing that can eliminate background noise and non-uniformity in an image. The Savitzky-Golay smoothing filter is a commonly used digital signal processing method for smoothing data; the local polynomial fitting method in SG smoothing can preserve the characteristics of spectral data.

[0083] Step S2: Calculate the correlation coefficient between the spectral index composed of different bands and the rock rebound data based on the spectral curve to obtain the optimal spectral index.

[0084] Specifically, the Normalized Difference Spectral Index (NDSI), Difference Spectral Index (DSI), and Ratio Spectral Index (RSI) are calculated. The correlation between each index and the Schmidt rebound value at all preprocessing wavelengths is determined by an ergodic method. Based on this, the distribution of rock surface strength can be indirectly reflected through spectral characteristics, thereby more comprehensively evaluating the strength characteristics of different parts of the slope.

[0085] Based on this, step S2 includes:

[0086] Calculate the spectral indices composed of different spectral bands based on the spectral curves;

[0087] The optimal spectral index is obtained by calculating the correlation coefficient between the spectral index and the rock rebound data.

[0088] Specifically, the optimal spectral index is selected by calculating the PEN correlation between spectral indices composed of different bands and rock rebound data.

[0089] Based on this, the calculation of spectral indices composed of different spectral bands according to the spectral curve includes:

[0090] The spectral index is calculated using the following formula:

[0091]

[0092] Wherein, NDSI represents the Normalized Difference Spectral Index. Indicates the sth p Band reflectivity, Indicates the sth q Band reflectance, DSI represents the difference spectral index, and RSI represents the ratio spectral index.

[0093] Specifically, several spectral indices are calculated, including the Normalized Difference Spectral Index (NDSI), the Difference Spectral Index (DSI), and the Ratio Spectral Index (RSI). p The band is the visible light band, s q The band is the shortwave infrared band.

[0094] Based on this, the correlation coefficient between the spectral indices and the rock rebound data is calculated, and the optimal spectral indices are obtained, including:

[0095] The correlation coefficient is calculated using the following formula:

[0096]

[0097] Where r represents the correlation coefficient, n represents the sample size, i represents the i-th spectral index, and x i Indicates the spectral index value. y represents the average value of the spectral index. i This indicates rock rebound data. This represents the average value of the Schmidt hammer springback.

[0098] The correlation coefficients are compared, and the spectral index formed by the band combination with the highest correlation coefficient is taken as the optimal spectral index.

[0099] Specifically, by iterating through all wavelength combinations within the 400-1000 nm wavelength range, the correlation coefficient between the spectral index and the Schmidt rebound value at each wavelength point within the 400-1000 nm wavelength range for different wavelength combinations is calculated to determine the strength of the correlation between spectral characteristics and the Schmidt rebound value. The calculation formula is as follows:

[0100]

[0101] Where r is the correlation coefficient, x i This is the spectral index value. y is the average value of the spectral index. i This represents the Schmidt hammer springback value. is the average value of the Schmidt hammer rebound, and n is the number of samples.

[0102] The correlation coefficients of all combinations are compared, and the spectral index formed by the band combination with the highest correlation coefficient is taken as the optimal spectral index.

[0103] Step S3: Select the characteristic wavelengths by using competitive adaptive reweighted sampling, continuous projection algorithm and random frog jumping algorithm to select the optimal spectral index.

[0104] Specifically, competitive adaptive reweighted sampling (CARS), continuous projection algorithm (SPA), and random frog jumping algorithm (RF) are used to screen the spectral data for characteristic wavelengths, and sensitive wavelengths related to the surface hardness of carbonate rocks are selected. The extracted characteristic variables (characteristic wavelengths) can be used as inputs to the model.

[0105] When selecting feature wavelengths, the three algorithms identify the wavelength combinations most correlated with target properties such as rock surface hardness. Through multiple iterations and optimizations, the algorithms gradually select the feature wavelengths that contribute most to the model performance, and these are used as the final selection results. To reduce the impact of randomness in the algorithms, each algorithm is run multiple times, and the feature wavelengths that appear most frequently are selected as the final selection results.

[0106] In this embodiment, CARS uses adaptive reweighted sampling technology to effectively filter out feature wavelengths that contribute significantly to the model.

[0107] The SPA algorithm is based on vector space projection. It projects high-dimensional spectral data into a low-dimensional space and selects mutually orthogonal vectors as feature wavelengths during the projection process.

[0108] The RF algorithm lies in random selection and iterative optimization. By utilizing the characteristics of random selection, it can explore the potential combination of features in the data more comprehensively.

[0109] Step S4: Construct a Schmidt springback hardness prediction model by inputting the characteristic wavelength into the Schmidt springback hardness prediction model to obtain hardness data.

[0110] Specifically, the optimal Schmidt rebound hardness prediction model is constructed by combining characteristic wavelengths and the partial least squares (PLS) method. The characteristic wavelengths of unknown rocks are input into the trained PSL model to calculate the corresponding predicted hardness values ​​and obtain hardness data.

[0111] In the process of establishing the PLS model, the input data is first standardized, the covariance matrix of the characteristic wavelength data is calculated, the eigenvalues ​​and eigenvectors are solved, and the number of principal components is determined based on the cumulative contribution rate. The formula for calculating the principal component score is: t i =p1x i1 +p2x i2 +…+p m x im (where t) i p is the principal component score of the i-th sample. j Let x be the loading vector of the j-th principal component. ij Let m be the spectral value of the i-th sample at the j-th wavelength, and m be the number of characteristic wavelengths. By establishing a linear regression relationship between the principal components and the Schmidt hammer rebound hardness, a prediction model is obtained.

[0112] Based on this, step S4 includes:

[0113] The constructed Schmidt rebound hardness prediction model is the SG-CARS-PLS model.

[0114] Specifically, this is measured using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). 2 To assess model accuracy, the smaller the RMSE and MAE, the better the accuracy. 2 The closer the value is to 1, the higher the model's prediction accuracy. For the three models SG-CARS-PLS, SG-RF-PLS, and SG-SPA-PLS, their coefficients of determination (R²) are calculated respectively. 2 The performance of the model was evaluated using root mean square error (RMSE) and mean absolute error (MAE). After calculation and comparison, it was found that the R² of the SG-CARS-PLS model was superior. 2 The R-value reached 0.96, RMSE was 2.29, and MAE was 1.74; the R-value of the SG-RF-PLS model was... 2 The R-value is 0.95, RMSE is 2.36, and MAE is 1.76; the R-value of the SG-SPA-PLS model is... 2 The accuracy was 0.93, RMSE was 2.31, and MAE was 1.78. Considering all factors, the SG-CARS-PLS model exhibited high accuracy, therefore it was determined to be the optimal model.

[0115] Step S5: Establish a fracture opening sensing spectral index based on the spectral index to obtain the spectral index of the rock discontinuity surface, and classify the rock discontinuity surface to obtain microcracks and opening areas.

[0116] Specifically, feature extraction and preprocessing are performed on the spectral data of microcracks and openings to establish the crack opening sensing spectral index (FPSI).

[0117] Previous analyses of rock discontinuities using methods such as the Normalized Difference Spectral Index (NDSI) have certain limitations (surface roughness, mineral composition, and lighting conditions all affect the spectral response). The development of cracks and openings is a significant indicator of slope degradation, and the FPSI can better quantify the extent of cracks and openings in rocks.

[0118] NDSI is related to surface hardness and can reflect changes in the mechanical properties of rocks; FPSI can identify microcracks and opening regions. Compared with a single evaluation index, the rock discontinuity surface spectral index (YGSI) constructed by combining the two can comprehensively consider the physical and structural characteristics of rocks and evaluate the deterioration of rock discontinuities from multiple perspectives.

[0119] Based on this, step S5 includes:

[0120] Establish spectral indices for sensing fracture openings:

[0121]

[0122] Wherein, FPSI represents the fracture opening sensing spectral index, D 700 D represents the spectral reflectance at a wavelength of 700 nm. 450 This represents the spectral reflectance at a wavelength of 450 nm.

[0123] Calculate the spectral index of the rock mass discontinuity surface based on the spectral index of the fracture opening:

[0124]

[0125] Where YGSI represents the rock mass discontinuity spectral index, NDSI represents the current normalized difference spectral index, and NDSI represents the current normalized difference spectral index. min NDSI represents the minimum normalized difference spectral index. max The normalized difference spectral index (FPSI) represents the maximum value of the normalized difference spectral index, while the FPSI represents the fracture opening sensing spectral index. min The FPSI represents the minimum value of the spectral index for sensing fracture opening. max This indicates the maximum value of the spectral index for sensing the crack opening;

[0126] The rock discontinuities are classified according to the spectral index of the rock mass discontinuities to obtain microcrack and opening data.

[0127] The microcrack and opening areas are calculated based on the microcrack and opening data.

[0128] Specifically, spectral reflectance data for high-hardness, low-hardness, microcrack, and opening regions were extracted from hyperspectral images, and these regions were accurately delineated using image features and field observation information. Statistical analysis was performed on the spectral reflectance data of each region, comparing the characteristics of the spectral curves of different regions. It was found that the spectral curves of the high-hardness and low-hardness regions were similar, but the reflectance of the low-hardness region was slightly lower. The spectral curves of the microcrack and opening regions showed similar trends, with the reflectance of the opening region being even lower.

[0129] In this step, by comparing the sensitivity of NDSI, DSI, and RSI to cracks and openings, the wavelengths with the highest sensitivity to cracks and openings were determined to be 700 nm and 450 nm, respectively. The Crack Opening Sensing Spectral Index (FPSI) was then established, and the calculation formula is as follows: Based on the actual situation of the study area, the FPSI threshold was determined to be 1.95. The FPSI value was calculated pixel by pixel in the hyperspectral image to generate a binary image of the crack opening region.

[0130] The rock quality discontinuity surface spectral index (YGSI) was constructed by combining FPSI and NDSI. The NDSI and FPSI were normalized, and the calculation formula was as follows: Rock discontinuities are classified based on YGSI values ​​to accurately distinguish between open areas, microcracks, high-hardness areas, and low-hardness areas. Specific classification criteria are as follows: Figure 2 As shown:

[0131] Low-hardness region (0.0≤YGSI<0.3): The NDSI value is relatively low, and the FPSI value is also low (below 1.95). The rock surface roughness in the low-hardness region is relatively large, resulting in a lower spectral reflectance than the high-hardness region. However, no obvious cracks or openings have yet formed, and its physical properties are between those of the high-hardness region and the crack-opening region.

[0132] High-hardness region (0.3≤YGSI<0.5): corresponds to a higher NDSI value and a relatively lower FPSI value (below 1.95). This means that the rock surface in this region is relatively smooth, hard, and has spectral reflectance characteristics similar to intact rock, with fewer cracks and openings.

[0133] Microcracked regions (0.5 ≤ YGSI < 0.8): The characteristics of microcracked regions are manifested in the YGSI as a change in NDSI value, possibly falling between high and low hardness regions, while the FPSI value begins to increase, but has not yet reached 1.95. This indicates that microcracks are beginning to appear on the rock surface, affecting the spectral reflectance characteristics and distinguishing this region from intact rock regions.

[0134] Opening regions (0.8 ≤ YGSI < 1.0): Opening regions have higher FPSI values ​​(greater than 1.95), and their NDSI values ​​also change due to the presence of openings, showing significant differences from other regions. The spectral reflectance of opening regions differs from intact rock and other regions due to the influence of cracks and openings; this region can be accurately identified and distinguished using YGSI.

[0135] Based on the constructed YGSI, an image segmentation algorithm was used to process the hyperspectral image. The image was divided into different regions according to the YGSI threshold, and the number of pixels in each region was counted using a pixel counting method. This was combined with the image resolution (2048×2046 pixels corresponds to 1.44m). 2 Calculate the actual area of ​​microcracks and openings on rock discontinuities at different elevations.

[0136] Step S6: Obtain water level data and obtain water level fluctuation data based on the water level data.

[0137] Specifically, water level data is acquired, including multi-year water level monitoring data for the area to be assessed. The frequency of water inundation is statistically analyzed based on this data, calculating the number of times rocks at different elevations are submerged per unit time. Microcracks and openings on rock discontinuities are identified using a YGSI (Yellow Gamma-Spot Indicator) model, and the changes in the proportion of microcracks and openings in the images are analyzed. The correlation between submersion frequency and deterioration rate is analyzed to determine the relationship between crack development degree and water level submersion frequency.

[0138] Collect water level monitoring data for the area to be assessed over many years, and count the number of times rocks at different elevations are submerged by water levels within a unit of time (e.g., one year), i.e., the water level submersion frequency.

[0139] Step S7: Assess the deterioration of the carbonate rock slope based on the rock rebound data, the hardness data, the microcrack and opening area, and the water level fluctuation data.

[0140] Specifically, a comprehensive slope stability assessment index system is established by combining rock surface strength data (such as rock rebound data and hardness data) with other parameters (such as crack distribution, microcrack and opening area, and water level fluctuation data). By analyzing the changes of these indicators over time and in the environment, the stability status of the slope can be determined, and potential geological hazard risks can be predicted. For example, if a significant decrease in rock surface hardness is found in a certain area, while the area of ​​microcracks and openings increases (or exceeds a predetermined threshold), and this area is located in an elevation range with frequent water level fluctuations, it can be determined that the slope stability in this area is poor, and corresponding reinforcement or monitoring measures are required.

[0141] This invention provides a method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness. It innovatively integrates hyperspectral imaging with Schmidt rebound hardness testing technology to comprehensively and accurately characterize changes in carbonate rocks under water-rock interaction, providing a reliable basis for assessing the deterioration of water level drawdown zones. By constructing multiple spectral indices such as the Normalized Difference Spectral Index (NDSI), the Fracture-Opening Sensing Spectral Index (FPSI), and the YGSI (Yellow Gas Discontinuity Spectral Index), it achieves quantitative identification of rock discontinuity characteristics. It can effectively distinguish different types of discontinuities, such as high hardness, low hardness, microcracks, and openings, overcoming the limitations of general methods in quantitatively describing rock deterioration characteristics. It reveals the influence of water level fluctuation frequency on fracture development and the relationship between deterioration and rock structure, providing a basis for understanding deterioration mechanisms and contributing to geological disaster early warning and prevention.

[0142] It should be noted that the method of this embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this embodiment, and the multiple devices will interact with each other to complete the method described.

[0143] It should be noted that the above description describes some embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0144] Based on the same inventive concept, and corresponding to any of the above embodiments, this invention also provides a carbonate rock slope deterioration assessment system based on hyperspectral imaging and surface hardness.

[0145] refer to Figure 3 The carbonate rock slope degradation assessment system based on hyperspectral imaging and surface hardness includes:

[0146] The rebound acquisition module 301 is used to acquire hyperspectral images of rock samples in the area to be evaluated and rock rebound data in the Schmidt test area, and extract the spectral curve corresponding to the Schmidt hammer test area based on the hyperspectral images using the regional averaging method.

[0147] The index acquisition module 302 is used to calculate the correlation coefficient between the spectral index composed of different bands and the rock rebound data based on the spectral curve, so as to obtain the optimal spectral index.

[0148] The wavelength selection module 303 is used to select characteristic wavelengths from the optimal spectral index through competitive adaptive reweighted sampling, continuous projection algorithm and random frog jumping algorithm to obtain characteristic wavelengths;

[0149] The hardness acquisition module 304 is used to construct a Schmidt springback hardness prediction model, and inputs the characteristic wavelength into the Schmidt springback hardness prediction model to obtain hardness data.

[0150] The degradation calculation module 305 is used to establish a crack opening sensing spectral index based on the spectral index, obtain degradation data of the rock discontinuity, and classify the rock discontinuity to obtain microcracks and opening areas.

[0151] The flooding acquisition module 306 is used to acquire water level data and obtain water level fluctuation data based on the water level data.

[0152] The degradation assessment module 307 is used to assess the degradation of carbonate rock slopes based on the rock rebound data, the hardness data, the microcrack and opening area, and the water level fluctuation data.

[0153] For ease of description, the above system is described by dividing it into various modules based on their functions. Of course, in implementing this invention, the functions of each module can be implemented in one or more software and / or hardware components.

[0154] The system described in the above embodiments is used to implement the corresponding carbonate rock slope deterioration assessment method based on hyperspectral imaging and surface hardness in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0155] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the carbonate rock slope deterioration assessment method based on hyperspectral imaging and surface hardness as described in any of the above embodiments.

[0156] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0157] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0158] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0159] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0160] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0161] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0162] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0163] The electronic devices described in the above embodiments are used to implement the corresponding carbonate rock slope deterioration assessment method based on hyperspectral imaging and surface hardness in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0164] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the carbonate rock slope deterioration assessment method based on hyperspectral imaging and surface hardness as described in any of the above embodiments.

[0165] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0166] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the carbonate rock slope deterioration assessment method based on hyperspectral imaging and surface hardness as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0167] The technical solutions of this invention will be clearly and completely described below with reference to the embodiments thereof. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0168] Example 1

[0169] An example of assessing a carbonate slope:

[0170] Data acquisition was performed using a portable hyperspectral imaging system. The camera and computer were connected, the spectral processing software was opened, and the acquisition parameters were set: exposure value was set to 80, target grayscale value to 100, and channel spacing to 5nm. First, a standard grayscale plate was placed in front of the rock sample in the area to be evaluated, and data was acquired from the white plate. Then, a light shield was used to cover the camera lens to acquire dark current data for subsequent light intensity correction. After removing the white plate, the camera position remained unchanged, and hyperspectral images of the rock sample surface were acquired. The acquired image resolution was 2048×2046. The above steps were repeated five times within the same area to ensure the reliability and representativeness of the data.

[0171] Since the raw data may be affected by factors such as air disturbance, unstable light source, and uneven sensor preheating, a Savitzky-Golay (SG) smoothing filter is used to process the spectral curves. This filter, through a local polynomial fitting method, effectively preserves the characteristics of the spectral data while reducing the interference of random noise. The smoothed spectral curves are then averaged to obtain representative spectral curves for each test region.

[0172] Select rock areas with good integrity and relatively flat surfaces as Schmidt hammer test points. Before testing, carefully clean the rock surface of dust, debris, and other impurities with a clean brush to ensure the test surface is clean and tidy. Hold an N-type HD-225S Schmidt hammer (impact energy 2.207J) perpendicular to the rock surface and slowly apply pressure, causing the hammer head to impact the rock surface, and record the rebound value. In each test area, 160 test points are set up at equal intervals of 16×10, with each test area being 1.44m². 2 Each test lasted approximately 5 minutes. For each test point, five Schmidt hammer tests were repeated, and the final rebound value was the average of these five test results. When testing sub-regions containing cracks and openings, if cracks or openings affected the rebound data measurement, rebound data from a smooth surface within the same sub-region was used instead. The location information of the test points and the corresponding rock surface features were recorded in detail to ensure accurate correspondence between the rebound data and the spectral images.

[0173] The Normalized Difference Spectral Index (NDSI), Difference Spectral Index (DSI), and Ratio Spectral Index (RSI) were calculated using the following formulas. During the calculation, all wavelength combinations within the 400-1000 nm wavelength range were examined, and the spectral indices for different wavelength combinations were calculated. The correlation coefficients between the spectral indices and the rock rebound data were calculated to obtain the optimal spectral indices.

[0174] Three algorithms—Competitive Adaptive Reweighted Sampling (CARS), Continuous Projection Algorithm (SPA), and Random Frog Jumping (RF)—were used to select feature wavelengths from the preprocessed optimal spectral indices. The 160 optimal spectral indices were randomly divided into training and test sets. 112 data points were used as the training set for model training and feature wavelength selection; the remaining 48 data points were used as the test set to verify the model's accuracy and generalization ability. The training set data was analyzed using these three algorithms. Based on the algorithm principles and calculation rules, the importance weight of each wavelength in characterizing rock surface hardness was determined, and feature wavelengths sensitive to hardness were selected.

[0175] Using the selected characteristic wavelengths as input variables, and combining them with the partial least squares (PLS) method, the following Schmidt springback hardness prediction model is constructed to calculate the hardness data.

[0176] Normalized Dissimilarity Index (NDSI), Dissimilarity Index (DSI), and Ratio Index (RSI) of the spectral data were calculated. By traversing all possible combinations within the 400-1000 nm wavelength range, the correlation coefficients between NDSI, DSI, and RSI and the Schmidt rebound value were calculated for each combination. The results showed that NDSI achieved a maximum absolute correlation coefficient of 0.6928 across 121 wavelength combinations, with the highest correlation wavelengths concentrated between 550 nm and 650 nm. Further optimization determined the optimal estimation parameters to be 560 nm and 645 nm. In contrast, the absolute correlation coefficients of DSI and RSI were 0.1148 and 0.3319, respectively, significantly lower than those of NDSI. Therefore, NDSI was selected as the optimal spectral index for subsequent characterization and analysis of rock surface hardness and discontinuity features.

[0177] Based on the correlation established between Schmidt rebound values ​​(hardness data) and spectral characteristics, a smart algorithm is used to select characteristic wavelengths on the spectral curves after SG smoothing to identify discontinuities in carbonate rocks. Detailed analysis of the spectral reflectance curves of regions with different hardness levels (high and low) and different fracture states (microcracks and openings) reveals that the spectral curves of high and low hardness regions exhibit certain similarities in overall shape and reflectance values, while the spectral curves of microcracks and openings show similar characteristics and relatively low reflectance. This is because factors such as surface roughness, mineral composition, and illumination conditions affect the spectral response of rocks. In microcracks and openings, increased surface roughness leads to decreased spectral reflectance, and the larger shadow area in the opening region further reduces reflectance.

[0178] Based on the above analysis, to more accurately distinguish between microcracks and opening regions, the Ratio Spectral Index (RSI) was chosen to establish the Fractured Opening Sensing Spectral Index (FPSI) by comparing NDSI, DSI, and RSI. After feature extraction and analysis of the spectral data of microcracks and opening regions, it was found that wavelengths of 700 nm and 450 nm are most sensitive to cracks and openings; therefore, the FPSI calculation formula was determined to be FPSI = D... 700 / D 450 Based on extensive experimental data and statistical analysis, regions with an FPSI greater than 1.95 were identified as crack and opening areas. Using this FPSI, the acquired spectral images were processed to obtain the identification results of crack and opening areas, providing important evidence for further analysis of discontinuity degradation.

[0179] A rock discontinuity surface spectral index (YGSI) was constructed by combining NDSI and FPSI to more accurately classify and identify rock discontinuities. Normalization of the YGSI improved its ability to better reflect the characteristics of discontinuities. Analysis of rock discontinuities at different elevations within the study area using the constructed YGSI revealed a close correlation between the development of microcracks and opening regions and reservoir water level fluctuations. For example, at elevations of 161.2m and 162.2m, cracks were observed to develop primarily along bedding planes, forming deep trough-type deterioration. This is because bedding planes are more susceptible to mechanical erosion, chemical dissolution, and wet-dry cycles under water-rock interaction. At elevation 169.5m, small trough-type deterioration appeared, which is speculated to be a pre-stage of opening formation. At elevation 174.2m, the rock surface mainly showed a small number of cracks caused by weathering and rainfall, without obvious opening characteristics. Statistical analysis of water level data over many years revealed a regular change in the proportion of microcracks and opening areas as the frequency of water inundation increased, gradually decreasing from 12.84% at an elevation of 161.2m to 6.03% at an elevation of 174.5m. Furthermore, the crack development trend was positively correlated with the frequency of water inundation; that is, the longer the inundation time and the higher the frequency, the more pronounced the crack development. This indicates that water-rock interaction has a significant impact on the deterioration of rock discontinuities, and the deterioration trend is largely controlled by the bedding plane structure of the rock strata. As the reservoir water level increases, the rock mass gradually becomes saturated under submerged conditions. The compressive and tensile strengths of the rock mass, as well as the shear strength of the discontinuities, are greatly reduced, leading to certain deformation of the rock mass.

[0180] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of the invention as described above, which are not provided in the details for the sake of brevity.

[0181] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0182] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of the invention, the well-known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. Furthermore, systems may be illustrated in block diagram form to avoid obscuring the embodiments of the invention, and this also takes into account the fact that the details of implementation of these block diagram systems are highly dependent on the platform on which the embodiments of the invention will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of the invention, it will be apparent to those skilled in the art that the embodiments of the invention may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0183] Although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0184] The embodiments of this invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this invention should be included within the protection scope of this invention.

Claims

1. A method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness, characterized in that, include: Step S1: Obtain hyperspectral images of rock samples from the area to be evaluated and rock rebound data from the Schmidt test area. Based on the hyperspectral images, extract the spectral curves corresponding to the Schmidt hammer test area using the regional averaging method. Step S2: Calculate the correlation coefficient between the spectral index composed of different bands and the rock rebound data based on the spectral curve to obtain the optimal spectral index; Step S3: Select the characteristic wavelengths by using competitive adaptive reweighted sampling, continuous projection algorithm and random frog jumping algorithm to select the optimal spectral index; Step S4: Construct a Schmidt springback hardness prediction model by inputting the characteristic wavelength into the Schmidt springback hardness prediction model to obtain hardness data; Step S5: Establish the fracture opening sensing spectral index based on the spectral index to obtain the spectral index of the rock discontinuity surface, and classify the rock discontinuity surface to obtain the microcracks and opening area. Step S6: Obtain water level data, and obtain water level fluctuation data based on the water level data; Step S7: Assess the deterioration of carbonate rock slopes based on the rock rebound data, hardness data, microcrack and opening area, and water level fluctuation data. Step S5 includes: Establish spectral indices for sensing fracture openings: in, Indicates the spectral index for sensing the fracture opening. This represents the spectral reflectance at a wavelength of 700 nm. This represents the spectral reflectance at a wavelength of 450 nm. Calculate the spectral index of the rock mass discontinuity surface based on the spectral index of the fracture opening: in, Indicates the spectral index of the rock mass discontinuity surface. Indicates the current normalized difference spectral index. This represents the minimum value of the normalized difference spectral index. This represents the maximum value of the normalized difference spectral index. Indicates the spectral index for sensing the fracture opening. This indicates the minimum value of the spectral index for sensing the crack opening. This indicates the maximum value of the spectral index for sensing the crack opening; The rock discontinuities are classified according to the spectral index of the rock mass discontinuities to obtain microcrack and opening data. The microcrack and opening areas are calculated based on the microcrack and opening data.

2. The method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness according to claim 1, characterized in that, Step S1 includes: The hyperspectral image is preprocessed to obtain a preprocessed hyperspectral image; Based on the preprocessed hyperspectral image, the spectral curve corresponding to the Schmidt hammer test area is extracted using the region averaging method.

3. The method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness according to claim 1, characterized in that, Step S2 includes: Calculate the spectral indices composed of different spectral bands based on the spectral curves; The optimal spectral index is obtained by calculating the correlation coefficient between the spectral index and the rock rebound data.

4. The method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness according to claim 3, characterized in that, The calculation of the spectral index composed of different bands based on the spectral curve includes: The spectral index is calculated using the following formula: in, Represents the normalized difference spectral index. Indicates the sth p Band reflectivity, Indicates the sth q Band reflectivity, Indicates the difference spectral index, This indicates the ratio spectral index.

5. The method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness according to claim 3, characterized in that, The process of calculating the correlation coefficient with the rock rebound data based on the spectral index to obtain the optimal spectral index includes: The correlation coefficient is calculated using the following formula: in, Represents the correlation coefficient. Indicates sample size. Indicates the first Spectral indices, Indicates the spectral index value. This represents the average value of the spectral index. This indicates rock rebound data. This represents the average value of the Schmidt hammer springback. The correlation coefficients are compared, and the spectral index formed by the band combination with the highest correlation coefficient is taken as the optimal spectral index.

6. The method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness according to claim 1, characterized in that, Step S4 includes: The Schmidt springback hardness prediction model is the SG-CARS-PLS model.

7. A carbonate rock slope deterioration assessment system based on hyperspectral imaging and surface hardness, characterized in that, The method for assessing the deterioration of carbonate rock slopes based on hyperspectral imaging and surface hardness as described in any one of claims 1-6 includes: The rebound acquisition module is used to acquire hyperspectral images of rock samples from the area to be evaluated and rock rebound data from the Schmidt test area. Based on the hyperspectral images, the spectral curves corresponding to the Schmidt hammer test area are extracted using the regional averaging method. The index acquisition module is used to calculate the correlation coefficient between the spectral index composed of different bands and the rock rebound data based on the spectral curve, so as to obtain the optimal spectral index. The wavelength selection module is used to filter the characteristic wavelengths of the optimal spectral index through competitive adaptive reweighted sampling, continuous projection algorithm and random frog jumping algorithm to obtain the characteristic wavelengths. The hardness acquisition module is used to construct a Schmidt springback hardness prediction model. The characteristic wavelength is input into the Schmidt springback hardness prediction model to obtain hardness data. The degradation calculation module is used to establish a crack opening sensing spectral index based on the spectral index, obtain the spectral index of the rock discontinuity surface, and classify the rock discontinuity surface to obtain microcracks and opening areas. The flooding acquisition module is used to acquire water level data and obtain water level fluctuation data based on the water level data; The degradation assessment module is used to assess the degradation of carbonate rock slopes based on the rock rebound data, the hardness data, the microcrack and opening area, and the water level fluctuation data. The degradation calculation module includes: Establish spectral indices for sensing fracture openings: in, Indicates the spectral index for sensing the fracture opening. This represents the spectral reflectance at a wavelength of 700 nm. This represents the spectral reflectance at a wavelength of 450 nm. Calculate the spectral index of the rock mass discontinuity surface based on the spectral index of the fracture opening: in, Indicates the spectral index of the rock mass discontinuity surface. Indicates the current normalized difference spectral index. This represents the minimum value of the normalized difference spectral index. This represents the maximum value of the normalized difference spectral index. Indicates the spectral index for sensing the fracture opening. This indicates the minimum value of the spectral index for sensing the crack opening. This indicates the maximum value of the spectral index for sensing the crack opening; The rock discontinuities are classified according to the spectral index of the rock mass discontinuities to obtain microcrack and opening data. The microcrack and opening areas are calculated based on the microcrack and opening data.

8. An apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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