Slope geology interpretation method based on drilling and geophysical exploration data fusion

Abstract

The invention discloses a side slope geology interpretation method based on drilling and geophysical exploration data fusion, and relates to the technical field of geological exploration. According to the interpretation method, a geophysical prospecting data interpretation result is regarded as soft data (with relatively low credibility), and a drilling data interpretation result is regarded as hard data (with relatively high credibility); a corresponding prior probability distribution calculation method, a likelihood function calculation method and a non-horizontal global posteriori updating mechanism considering drilling data influence are provided, and two kinds of exploration data are effectively fused at a decision-making layer; high-reliability and accurate interpretation of data information capable of being used for geological modeling of the two-dimensional layer slope stratum is realized; the method has the characteristics of high accuracy, good universality and strong operability, is suitable for large-scale interpretation of the slope rock mass quality, and has a positive effect on guaranteeing the safety of the road slope.

Description

Technical Field

[0001] The invention relates to the technical field of geological survey, and in particular to a slope geological interpretation method integrating drilling geophysical prospecting data. Background Art

[0002] In the field of highway engineering, high roadbed slopes are subject to landslides, collapses and other geological disasters caused by external factors such as rainfall and load, which is an important target for engineering safety risk prevention and control. Therefore, it is of great significance to identify the distribution of slope structures, rock structure characteristics, and carry out accurate slope stratigraphic geological modeling.

[0003] Reasonable geological modeling depends on accurate geological exploration data. Geological exploration is currently divided into two types: geophysical exploration and drilling. Among them: drilling directly drills into the stratum to obtain cores, and the data obtained from the exploration has the highest credibility. However, due to the high cost limit, the number of drill holes is often small, and as a one-dimensional exploration data, it is easy to have "one-hole views"; geophysical exploration is mainly based on methods such as ground penetrating radar, high-density electrical method and seismic waves. Its characteristics are low cost and wide range (depth). It can quickly obtain geological structure information under two-dimensional profiles, but due to the multi-solution nature of geophysical signals, the credibility of the exploration results obtained is usually much lower than that of drilling. It can be seen that drilling and geophysical exploration have their own advantages and disadvantages. In theory, the fusion of the two data can meet the needs of two-dimensional slope stratum geological modeling with high credibility and accuracy. However, due to the dimensional differences in the results of the two exploration data and the complex limitations of slope working conditions, there is still a lack of effective technical means to fuse the two data.

[0004] At present, the fusion of multi-source data can be roughly divided into three categories, namely data layer fusion, feature layer fusion and decision layer fusion. Data layer fusion refers to the direct fusion of the original data of the data source. This fusion method retains the most original information and is suitable for applications that require high precision and high real-time performance. Feature layer fusion refers to first extracting features from each data source and then merging these features together to improve the accuracy and robustness of the model. This fusion method can better balance the amount of data and computational complexity. Decision layer fusion refers to processing each data source independently and generating their own decisions, and then fusing these decision results. This fusion method improves the accuracy and reliability of the overall decision by integrating the results of each decision.

[0005] How to integrate drilling geophysical data at the decision-making level to form highly reliable and accurate information interpretation results that can be used for two-dimensional slope stratum geological modeling is a key technical issue that urgently needs to be solved in this field. Summary of the invention

[0006] The purpose of the present invention is to overcome the problem that the existing technology cannot fuse the drilling and geophysical data at the decision-making level and form a highly reliable and accurate information interpretation result, and proposes a slope geological interpretation method that fuses drilling and geophysical data.

[0007] In order to achieve the above-mentioned object of the invention, the present invention provides a slope geological interpretation method of drilling geophysical exploration data fusion, comprising the following steps:

[0008] S1. Obtaining the geophysical data interpretation results and drilling data interpretation results of the slope rock mass quality respectively;

[0009] S2, gridding and standardizing the geophysical data interpretation results and the drilling data interpretation results respectively to obtain a number of geophysical data grid units and drilling data grid units with the same interpretation space and label;

[0010] S3. Based on the geophysical data grid unit and the drilling data grid unit, a two-dimensional prior probability distribution function of the rock mass quality is constructed using a prior probability calculation formula; based on the grid length of the geophysical data grid unit, the drilling data grid unit is equidistantly divided and the secondary index is calculated and processed, and then the likelihood function of the rock mass quality is constructed using a confusion matrix;

[0011] S4. Based on the two-dimensional prior probability distribution function and likelihood function of the slope rock mass quality, the Bayesian probability update mechanism is used to perform fusion interpretation processing to obtain the fusion interpretation result of the slope rock mass quality at the drilling hole location;

[0012] S5. Based on the two-dimensional prior probability distribution function of rock mass quality and the fused interpretation results of rock mass quality at the drilling hole location, a fused interpretation process is performed using a non-horizontal global posterior update mechanism to obtain a fused interpretation result of the global rock mass quality of the slope.

[0013] The present invention discloses a slope geological interpretation method integrating drilling and geophysical data, which regards the interpretation result of geophysical data as soft data (with lower credibility) and the interpretation result of drilling data as hard data (with higher credibility). Based on the Bayesian probability updating principle, a corresponding prior probability distribution calculation method, a likelihood function calculation method and a non-horizontal global posterior updating mechanism considering the influence of drilling data are proposed, which effectively integrates the two types of exploration data at the decision-making level, and realizes a highly reliable and accurate interpretation of data information that can be used for two-dimensional slope stratum geological modeling. The method has the characteristics of high accuracy, good universality and strong operability, is suitable for large-scale application in geological exploration of slope rock mass quality, and plays a positive role in ensuring the safety of road slopes.

[0014] Wherein, in step S1, preferably, the geophysical prospecting includes one of electrical prospecting, ground penetrating radar and seismic prospecting; and the drilling is advance drilling.

[0015] Geophysical electromagnetic waves, elastic waves and other signals are affected by various interference factors during propagation, and usually there are various types of noise. Therefore, preprocessing is required to improve the signal-to-noise ratio to better highlight the characteristics of the disease; preferably, in the process of obtaining the interpretation results of geophysical data of rock mass quality, preprocessing of geophysical data is also included; the preprocessing includes at least one of zero point reset, high-pass and low-pass filtering, and exponential gain.

[0016] Among them, in step S2, preferably, the gridding processing of the geophysical interpretation results includes: selecting an appropriate square grid length (the denser the grid, the higher the accuracy of the interpretation results obtained, but the larger the calculation scale, which is reasonably set according to the overall exploration area scale and exploration needs), gridding the two-dimensional geophysical interpretation results, and obtaining a number of geophysical data grid units; the gridding processing of the drilling data interpretation results includes: selecting the same grid length as that of the geophysical interpretation results gridding processing, gridding the drilling data interpretation results of the borehole position, and obtaining a number of borehole data grid units; through gridding processing, it is ensured that the grid units at the borehole position correspond to the grid unit positions of the geophysical interpretation results at the same position.

[0017] Preferably, the standardization process includes: unifying the geophysical data grid unit and the borehole data grid unit, and using the same interpretation space and label to represent them. For example, the geophysical grid unit and the borehole grid unit can be represented as follows:

[0018] S=(label 1 , label 2 ...label n );

[0019] In the formula, label means label, S represents the grid unit interpretation space, the interpretation space is composed of interpretation labels, and there are n interpretation labels including label1, label2...labeln; the interpretation labels are generally numbers 0, 1, 2, etc., and each data represents a rock quality category. If the grid units have the same interpretation space and label, it means that the rock quality categories contained in the two data should be consistent.

[0020] Among them, in step S3, the prior probability calculation formula is:

[0021]

[0022] In the formula, represents the initial probability distribution of the interpretation labels in each geophysical data grid cell; represents the initial probability update value considering spatial correlation, where spatial correlation refers to the interpretation label situation within the neighborhood (eight-neighborhood system) of the current geophysical data grid unit. The specific value is equal to the frequency of each interpretation label, and finally the result of the addition of the two is renormalized; f x (X) represents the prior probability distribution function of rock mass quality information X.

[0023] Preferably, the equidistant segmentation includes: segmenting the drilling data according to the grid length of the geophysical data grid unit; the secondary index calculation includes: calculating the mean and variance of the drilling index parameters within the equidistant segmentation sections.

[0024] Preferably, the method of calculating the likelihood function of rock mass quality using a confusion matrix comprises the steps of:

[0025] (1) Using the optimized classifier model to train the drilling data grid cell data (drilling location) after isometric segmentation and secondary index calculation;

[0026] (2) Use the trained classifier to predict the drilling data grid cell data (undrilled locations);

[0027] (3) The likelihood function is constructed based on the confusion matrix of the predicted output.

[0028] Preferably, in step (1), the classifier model is XGBoost; the model constructs multiple weak evaluators on the data one by one, and gradually accumulates and summarizes the modeling results of multiple weak evaluators through multiple iterations to obtain better regression or classification performance than a single model. This superposition strategy using a single decision tree as a weak evaluator can be expressed in an additive form, that is, the XGBoost classifier model can be expressed as:

[0029]

[0030] In the formula, represents the prediction result of the entire model on this sample, k represents the total number of weak evaluators (decision trees), and f k represents the kth decision tree, x i Represents the eigenvector corresponding to sample i.

[0031] Among them, the optimized XGBoost introduces model complexity to measure the computational efficiency of the algorithm. Therefore, the optimized XGBoost model consists of two parts: the traditional loss function and the model complexity. The specific formula is:

[0032]

[0033] In the formula, O bjrepresents the objective function of the model, n represents the total amount of data imported into the kth tree, and y i represents the true label, represents the predicted value, f k represents the kth decision tree, Ω represents the transformation value of the tree model, Represents the traditional loss function, measuring the true label y i and predicted values The difference between Represents the complexity of the model.

[0034] Among them, in step (2), the expression of constructing the likelihood function based on the confusion matrix is:

[0035]

[0036] Where TP and FN represent the number of true positive and false negative samples in the prediction confusion matrix, respectively.

[0037] Among them, in step S4, the Bayesian probability update mechanism includes: based on the two-dimensional prior probability distribution function and the likelihood function, using the Bayesian probability estimation formula to obtain the posterior probability distribution function, and then obtaining the fusion interpretation result of the rock mass quality at the drilling hole location through the posterior probability distribution function; the Bayesian probability estimation formula is:

[0038] f x ′(X)=aL(X)f x (X);

[0039] In the formula, f x ′(X) represents the posterior probability distribution function of rock mass quality information X; f x (X) represents the prior probability distribution function of rock mass quality information X; X is the original space random input vector, X = (X 1 , X 2 , ..., X n ) T , n is the number of random parameters; L(X) represents the likelihood function; a is a constant; T is the matrix transposition operator.

[0040] Among them, in step S5, the non-horizontal global posterior update mechanism includes: based on the two-dimensional prior probability distribution function of rock mass quality and the fusion interpretation result of rock mass quality at the drilling hole position, using the global update formula, obtaining the fusion probability distribution function, and then obtaining the fusion interpretation result of the global rock mass quality by fusing the probability distribution function; the global update formula is:

[0041] f x ″(X)=f x (X)+d x (X);

[0042] In the formula, f x ″(X) represents the posterior probability distribution function of the rock mass quality information X that needs to be globally updated in addition to the Bayesian probability update position; d x (X) represents the probability update value calculated based on the posterior update probability of the drilling data grid unit that has the greatest impact on the current grid unit and taking into account the distance between the two; f x (X) represents the prior probability distribution function of rock mass quality information X.

[0043] Preferably, d x The calculation formula of (X) is:

[0044] dx(X)=f x ′(X)ω d ;

[0045] In the formula, ω d represents the weight, which is related to the distance from the drilling data grid unit. The closer the position is to the drilling data grid unit, the greater the influence of the drilling data is, that is, the greater the weight is; the farther the position is from the drilling data grid unit, the less the influence of the drilling data is, that is, the smaller the weight is; f x ′(X) represents the posterior probability distribution function of rock mass quality information X.

[0046] Preferably, ω d The calculation formula is as follows: d =e -bd ; Where d is the distance from the drilling grid unit, b is a constant term, and e is a constant.

[0047] Preferably, the method for locating the drilling grid unit that has the greatest impact on the current grid includes:

[0048] a. Determine the interface of the slope strata based on the results of geophysical interpretation; fit the interface using a polynomial equation to obtain a quantitative curve equation (g(X)) and map it to a two-dimensional grid cell;

[0049] b. Calculate the current grid cell A(X A ,Y A ) and the vertical distance d between the grid unit of the quantitative curve equation A , that is, d A =g(X A )-Y A ;

[0050] c. Let the vertical intersection point B(X B ,Y B ), then the drilling grid unit that has the greatest impact on the current grid is C(X B ,Y B-d A ).

[0051] Furthermore, the present invention also proposes an interpretation device, comprising at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned slope geological interpretation method integrating drilling geophysical exploration data.

[0052] Furthermore, the present invention also proposes a computer-readable medium on which instructions executable by a processor are stored. When the instructions are executed by the processor, the processor executes the above-mentioned slope geological interpretation method integrating drilling geophysical exploration data.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. The present invention provides a slope geological interpretation method for drilling geophysical data fusion. For the geophysical data results at the two-dimensional level, a priori probability distribution calculation method considering spatial correlation is proposed, which can effectively and reasonably convert the label interpretation results into the priori probability distribution required for the Bayesian probability update calculation, so that the data can be fused and updated through the Bayesian probability update principle, overcoming the shortcomings of traditional Kriging interpolation in terms of data distribution form and the inability to consider the credibility of data from different sources, and significantly improving the accuracy of the interpretation results of rock mass quality geological prediction data.

[0055] 2. The present invention provides a method for interpreting slope geology by integrating drilling and geophysical data. For the drilling data results at the one-dimensional level, it proposes to train the model and obtain the confusion matrix for each interpretation label by machine learning, so as to reasonably calculate the likelihood function required for the Bayesian probability update calculation, and proposes a global update mechanism that takes into account the influence of drilling results and non-horizontal influence mechanisms. By calculating the distance between the current grid unit and the drilling grid unit that has the greatest influence on the current grid and converting it into a weight, the final fusion of the two data is achieved, which significantly improves the accuracy and credibility of the interpretation results of the global rock quality geological forecast data of the slope, and is conducive to the construction of the slope geological condition model. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the flow chart of the slope geological interpretation method of drilling geophysical data fusion in Example 1 of the present invention;

[0057] Figure 2 It is a graph showing the grid standardization, prior probability distribution calculation and interface curve equation fitting result of the geophysical interpretation result in Example 1 of the slope geological interpretation method for drilling geophysical data fusion of the present invention;

[0058] Figure 3 It is a local Bayesian probability update result diagram of the drilling interpretation result in Example 1 of the slope geological interpretation method for drilling geophysical data fusion of the present invention;

[0059] Figure 4 This is an effect diagram of the global posterior probability update mechanism of non-horizontal influence in Example 1 of the slope geological interpretation method for drilling geophysical data fusion of the present invention;

[0060] Figure 5 This is a diagram showing the effect of the global posterior probability update mechanism of the slope geological interpretation method fused with drilling geophysical data in the present invention compared with the traditional horizontal influence. DETAILED DESCRIPTION

[0061] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. However, it should not be understood that the scope of the above subject matter of the present invention is limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention.

[0062] Embodiment 1:

[0063] A slope geological interpretation method based on drilling geophysical data fusion (flow chart as shown in the figure) Figure 1 As shown), comprising the following steps:

[0064] S1. Obtain the interpretation results of geophysical exploration data (taking geological radar as an example) of a slope project and the interpretation results of drilling (advance drilling) data under the corresponding profile (there are three types of interpretation result labels, namely air, strongly weathered sandstone and moderately weathered sandstone, which are represented by green, yellow and purple respectively in the subsequent attached figures).

[0065] S2. Select an appropriate square grid length (grid length l is 0.5m, grid number is 20×60), grid the two-dimensional geophysical interpretation results, and obtain a number of geophysical data grid units; select the same grid length as that used in the gridding of the geophysical interpretation results (grid length l is 0.5m), grid the interpretation results of the advance drilling data at the borehole position, and obtain a number of borehole data grid units; then characterize the geophysical data grid unit and the borehole data grid unit using the same interpretation space and label (characterized as the following formula: S = (label 1 , label 2 ...label n )).

[0066] S3. Based on the interpretation space and labels of the geophysical data grid unit, a two-dimensional prior probability distribution function of rock mass quality is constructed using the prior probability calculation formula;

[0067] Among them, the prior probability calculation formula is:

[0068] In the formula, Represents the initial probability distribution of each grid cell label in the geophysical interpretation results; represents the initial probability update value considering spatial correlation, where spatial correlation refers to the interpretation label situation in the neighborhood of the current grid unit (eight-neighborhood system). The specific value is equal to the frequency of each interpretation label, and finally the result of the addition of the two is renormalized; f x (X) represents the prior probability distribution function of rock mass quality information X;

[0069] Based on the grid length of the geophysical data grid unit, the interpretation space and labels of the drilling data grid unit are equally divided and the secondary index is calculated and processed, and then the likelihood function of the rock mass quality is constructed using the confusion matrix.

[0070] Among them, the specific methods of equidistant segmentation and secondary index calculation and processing can refer to the relevant content in patent ZL202110674747.5);

[0071] Among them, the method of using confusion matrix to calculate the likelihood function of rock mass quality includes:

[0072] (1) Using the optimized classifier model

[0073] In the formula, O bj represents the objective function of the model, n represents the total amount of data imported into the kth tree, and y i represents the true label, represents the predicted value, f k represents the kth decision tree, Ω represents the transformation value of the tree model, Represents the traditional loss function, measuring the true label y i and predicted values The difference between The complexity of the representative model) is trained on the grid cell data (drilling location) of the advance drilling data that has been processed by equidistant segmentation and secondary index calculation;

[0074] (2) Using the trained classifier to predict the grid cell data of the advance drilling data (undrilled locations);

[0075] (3) According to the confusion matrix of the predicted output (

[0076] Where TP and FN represent the number of true positive and false negative samples in the prediction confusion matrix, respectively) to construct the likelihood function (① air (1, 0, 0), ② strongly weathered sandstone (0, 0.95, 0.05), ③ moderately weathered sandstone (0, 0.07, 0.93)).

[0077] S4. Based on the two-dimensional prior probability distribution function and likelihood function, the posterior probability distribution function is obtained using the Bayesian probability estimation formula, and then the fusion interpretation result of the rock mass quality at the drilling hole location is obtained through the posterior probability distribution function ( Figure 3 ).

[0078] Among them, the Bayesian probability estimation formula is: x ′(X)=aL(X)f x (X);

[0079] In the formula, f x ′(X) represents the posterior probability distribution function of rock mass quality information X; f x (X) represents the prior probability distribution function of rock mass quality information X; X is the original space random input vector, X = (X 1 , X 2 , ..., X n ) T , n is the number of random parameters; L(X) represents the likelihood function; a is a constant; T is the matrix transposition operator.

[0080] S5. Based on the two-dimensional prior probability distribution function of rock mass quality and the fusion interpretation result of rock mass quality at the drilling hole location, the fusion probability distribution function is obtained by using the global update formula, and then the fusion interpretation result of the global rock mass quality is obtained by fusion probability distribution function ( Figure 4 ).

[0081] Among them, the global update formula is: x ″(X)=f x (X)+d x (X);

[0082] In the formula, f x ″(X) represents the posterior probability distribution function of the rock mass quality information X that needs to be globally updated in addition to the Bayesian probability update position; d x (X) represents the probability update value calculated based on the distance from the drilling grid cell that has the greatest impact on the current grid; f x (X) represents the prior probability distribution function of rock mass quality information X.

[0083] Among them, d x The calculation formula of (X) is: dx(X) = f x ′(X)ω d ;

[0084] In the formula, ω d represents the weight, which is related to the distance from the drilling grid unit. The closer the position is to the drilling grid unit, the greater the influence of the drilling data is, that is, the greater the weight is; the farther the position is from the drilling grid unit, the less the influence of the drilling data is, that is, the smaller the weight is; fx ′(X) represents the posterior probability distribution function of rock mass quality information X;

[0085] Among them, ω d The calculation formula is as follows: d =e -bd ; Where d is the distance from the drilling grid unit, b is a constant term, and e is a constant.

[0086] Among them, the positioning method of the drilling grid unit that has the greatest impact on the current grid includes:

[0087] a. Determine the interface of the slope strata based on the results of geophysical interpretation; use polynomial equations to fit the interface ( Figure 2 ), the quantitative curve equation (g(X)) is obtained and mapped to the two-dimensional grid cells;

[0088] b. Calculate the current grid cell A(X A ,Y A ) and the vertical distance d between the grid unit of the quantitative curve equation A , that is, d A =g(X A )-Y A ;

[0089] c. Let the vertical intersection point B(X B ,Y B ); then the drilling grid unit that has the greatest impact on the current grid is C(X B ,Y B -d A );

[0090] If the drilling grid cell with the greatest impact on the current grid is located beyond the slope stratum range (due to the inclined slope, when d A When the value is too large, there is a possibility that the coordinates of point C exceed the top and bottom layers of the slope. In this case, the top or bottom drilling grid unit is regarded as the grid unit to be found.

[0091] Example 2

[0092] An interpretation device includes at least one processor and a memory connected to the at least one processor in communication; the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a rock mass quality interpretation method for multi-source geological prediction data fusion in Example 1.

[0093] Example 3

[0094] A computer-readable medium stores instructions executable by a processor, and when the instructions are executed by the processor, the processor executes a rock mass quality interpretation method for multi-source geological prediction data fusion in Example 1.

[0095] Comparative Example:

[0096] A slope geological interpretation method for drilling geophysical exploration data fusion includes the following steps:

[0097] S1-S4 are exactly the same as in Example 1;

[0098] S5. Based on the two-dimensional prior probability distribution function of rock mass quality and the fusion interpretation result of rock mass quality at the drilling hole location, the fusion probability distribution function is obtained by using the global update formula, and then the fusion interpretation result of the global rock mass quality is obtained by fusion probability distribution function ( Figure 5 ).

[0099] Among them, the global update formula is: x ″(X)=f x (X)+d x (X);

[0100] In the formula, f x ″(X) represents the posterior probability distribution function of the rock mass quality information X that needs to be globally updated in addition to the Bayesian probability update position; d x (X) represents the probability update value calculated based on the distance from the drilling location; f x (X) represents the prior probability distribution function of rock mass quality information X.

[0101] Among them, d x The calculation formula of (X) is: dx(X) = f x ′(X)ω d ;

[0102] In the formula, ω d Represents the weight, which is related to the distance from the drilling location. The closer the distance to the drilling location, the greater the impact of the drilling data, that is, the greater the weight; the farther the distance to the drilling location, the less impact of the drilling data, that is, the smaller the weight; f x ′(X) represents the posterior probability distribution function of rock mass quality information X;

[0103] Among them, ω d The calculation formula is as follows: d =e -bd ; In the formula, d is the distance from the drilling position, b is the constant term, and e is a constant.

[0104] Finally, it should be noted that the embodiments described in detail above are only the best practices of the invention and cannot be used to limit the scope of rights of the invention. Equivalent replacement of the technical solutions recorded in the aforementioned embodiments does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the invention, and they should all be included in the scope of the claims and description of the invention.

Claims

1. A slope geological interpretation method based on drilling geophysical data fusion, characterized in that: The following steps are involved: S1. Obtaining the geophysical data interpretation results and drilling data interpretation results of the slope rock mass quality respectively; S2, gridding and standardizing the geophysical data interpretation results and the drilling data interpretation results respectively, to obtain a number of geophysical data grid units and drilling data grid units with the same interpretation space and label; S3. Based on the geophysical data grid unit and the drilling data grid unit, a two-dimensional prior probability distribution function of the rock mass quality is constructed using a prior probability calculation formula; based on the grid length of the geophysical data grid unit, the drilling data grid unit is equidistantly divided and the secondary index is calculated and processed, and then the likelihood function of the rock mass quality is constructed using a confusion matrix; S4. Based on the two-dimensional prior probability distribution function and likelihood function of the slope rock mass quality, the Bayesian probability update mechanism is used to perform fusion interpretation processing to obtain the fusion interpretation result of the slope rock mass quality at the drilling hole location; S5. Based on the two-dimensional prior probability distribution function of rock mass quality and the fused interpretation results of rock mass quality at the drilling hole location, a fused interpretation process is performed using a non-horizontal global posterior update mechanism to obtain a fused interpretation result of the global rock mass quality of the slope.

2. The slope geological interpretation method according to claim 1, characterized in that: In step S2, the gridding processing of the geophysical interpretation result includes: selecting a suitable square grid length, gridding the two-dimensional geophysical interpretation result, and obtaining a plurality of geophysical data grid units; The gridding processing of the drilling data interpretation result includes: selecting the same grid length as that of the geophysical exploration interpretation result gridding processing, gridding the drilling data interpretation result of the borehole position, and obtaining a plurality of borehole data grid units; The standardization process includes: unifying the geophysical data grid units and the drilling data grid units, and using the same interpretation space and labels for representation.

3. The slope geological interpretation method according to claim 1, characterized in that: In step S3, the prior probability calculation formula is: In the formula, f x i (X) represents the initial probability distribution of the interpreted labels in each geophysical data grid cell; s i x (X) represents the initial probability update value considering spatial correlation; f x (X) represents the prior probability distribution function of rock mass quality information X.

4. The slope geological interpretation method according to claim 1, characterized in that: In step S3, the method of calculating the likelihood function of rock mass quality using the confusion matrix includes: (1) Using an optimized classifier model to train the drilling data grid unit data after isometric segmentation and secondary index calculation; (2) Using the trained classifier to predict the drilling data grid cell data; (3) The likelihood function is constructed based on the confusion matrix of the predicted output.

5. The rock mass quality interpretation method according to claim 4, characterized in that: The optimized classifier model formula in step (1) is: In the formula, O bj represents the objective function of the model, n represents the total amount of data imported into the kth tree, and y i represents the true label, represents the predicted value, f k represents the kth decision tree, and Ω represents the transformation value of the tree model.

6. The slope geological interpretation method according to claim 5, characterized in that: In step (2), the expression of the likelihood function constructed based on the confusion matrix is: Where TP and FN represent the number of true positive and false negative samples in the prediction confusion matrix, respectively.

7. The slope geological interpretation method according to claim 1, characterized in that: In step S4, the Bayesian probability update mechanism includes: based on the two-dimensional prior probability distribution function and the likelihood function, using the Bayesian probability estimation formula to obtain the posterior probability distribution function, and then obtaining the fusion interpretation result of the rock mass quality at the drilling hole location through the posterior probability distribution function; the Bayesian probability estimation formula is: f x ′(X)=aL(X)f x (X); In the formula, f x ′(X) represents the posterior probability distribution function of rock mass quality information X; f x (X) represents the prior probability distribution function of rock mass quality information X; X is the original spatial random input vector, X = (X1, X2, ..., X n ) T , n is the number of random parameters; L(X) represents the likelihood function; a is a constant; T is the matrix transposition operator.

8. The slope geological interpretation method according to claim 1, characterized in that: In step S5, the non-horizontal global a posteriori update mechanism includes: based on the two-dimensional prior probability distribution function of rock mass quality and the fused interpretation result of the rock mass quality at the drilling hole position, using the global update formula, obtaining the fused probability distribution function, and then obtaining the fused interpretation result of the global rock mass quality by fusing the probability distribution function; the global update formula is: f x ″(X)=f x (X)+d x (X); In the formula, f x ″(X) represents the posterior probability distribution function of the rock mass quality information X that needs to be globally updated in addition to the Bayesian probability update position; d x (X) represents the probability update value calculated based on the posterior update probability of the drilling data grid unit that has the greatest impact on the current grid unit and taking into account the distance between the two; f x (X) represents the prior probability distribution function of rock mass quality information X; The d x The calculation formula of (X) is: dx(X)=f x ′(X)ω d ; In the formula, ω d represents weight; f x ′(X) represents the posterior probability distribution function of rock mass quality information X; The ω d The calculation formula is as follows: d =e -bd ; Where d is the distance from the drilling grid unit, b is a constant term, and e is a constant; The methods for locating the drilling grid cells that have the greatest impact on the current grid include: a. Determine the interface of the slope strata based on the results of geophysical interpretation; fit the interface using a polynomial equation to obtain a quantitative curve equation and map it to a two-dimensional grid unit; b. Calculate the current grid cell A(X A ,Y A ) and the vertical distance d between the grid unit of the quantitative curve equation A , that is, d A =g(X A )-Y A ; c. Let the vertical intersection point B(X B ,Y B ), then the drilling grid unit that has the greatest impact on the current grid is C(X B ,Y B -d A ).

9. A slope geological interpretation device, characterized in that: It includes at least one processor and a memory that is communicatively connected to the at least one processor; the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the slope geological interpretation method described in any one of claims 1-8.

10. A computer-readable medium, characterized in that Instructions executable by a processor are stored thereon, and when the instructions are executed by the processor, the processor executes the slope geological interpretation method according to any one of claims 1-8.

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