Dynamic geologic model updating and predicting method based on tunnel face information

Abstract

The invention provides a dynamic geologic model updating and predicting method based on tunnel face information, which constructs and dynamically updates a geologic model by combining face grid point data acquired in a tunneling process and adopting Kriging interpolation and variation function fitting technologies. The method comprises the steps of collecting tunnel face data, cleaning standard data, and extracting a starting end face and a terminating end face of a tunnel; generating an initial geologic model by using the three-dimensional variation function model and Kriging interpolation; newly added tunnel face data are integrated, model parameters are dynamically adjusted, and geological distribution is updated; and finally, displaying the dynamically updated geologic model through a three-dimensional visualization technology. The problem that a traditional static model is difficult to adapt to dynamic changes of geological conditions in the tunneling process is solved, the geological model can be optimized in real time, accurate and reliable geological information support is provided for tunnel construction, and construction efficiency and safety are remarkably improved.

Description

Technical Field

[0001] The invention relates to a tunnel engineering geological modeling and prediction technology, in particular to a dynamic geological model updating and prediction method based on tunnel face information. Background Art

[0002] During tunnel construction, the dynamic changes in geological conditions have an extremely important impact on construction safety and design optimization. However, most current studies on 3D geological modeling focus on overall regional geological modeling based on geological survey information. These models are often static and difficult to dynamically adapt to the complex geological conditions actually encountered during tunneling. For example, during tunneling, the distribution of strata may change locally, the lithology may change suddenly, and the groundwater infiltration conditions may change dynamically. These factors directly affect construction efficiency and may bring serious safety hazards.

[0003] Traditional geological modeling methods usually rely on initial exploration data and construct static geological models through limited drilling data or cross-section measurements. This method has the problem of high uncertainty in prediction results, especially during tunnel excavation. Due to the inability to dynamically integrate newly added face geological data, model updates lag and it is difficult to timely reflect changes in actual geological conditions. This lag not only reduces the prediction accuracy of the geological model, but may also lead to a lack of scientific basis for the adjustment of the construction plan, thereby significantly increasing construction risks and engineering costs. Therefore, there is an urgent need for a modeling method that can combine dynamic data and reflect the newly discovered geological information in the face advancement process in real time, so as to achieve more accurate geological predictions and provide scientific and reliable technical support for tunnel construction. Summary of the invention

[0004] The present invention provides a dynamic geological model update and prediction method based on tunnel face information. By combining tunnel face data, Kriging interpolation modeling and dynamic update, high-precision prediction of the tunnel and surrounding geological areas is achieved, and the model results are displayed in real time using three-dimensional visualization technology. The advantage of this method is that it can dynamically integrate the newly added face data and optimize the geological model in real time, solving the problem that the traditional static model cannot adapt to the dynamic changes of geological conditions during the excavation process, and continuously iterates and updates the prediction results as the tunnel is excavated, providing more reliable geological information support for the construction process.

[0005] The dynamic geological model updating and prediction method based on tunnel face information includes the following steps:

[0006] The first step is to import the initial face grid point data from the tunnel excavation process, specifically:

[0007] Open the file storing the initial tunnel face data;

[0008] The file contains the three-dimensional coordinates (X, Y, Z) of the grid points and the geological attribute values ​​(D 1 , D 2 ,…D n );

[0009] Since formation attributes are usually discrete categories (e.g., formation is D 1 , D 2 , D 3 The traditional Kriging interpolation method is mainly applicable to continuous data, so the geological attribute value of each grid point is represented by a binary variable 0 or 1, that is, the value of each attribute is either 1 (indicating that the point belongs to the stratum) or 0 (indicating that the point does not belong to the stratum).

[0010] For example: If a point belongs to layer D 3 , then set D 1 =0,D 2 =0,D 3 =1;

[0011] Check whether the file format is correct (such as EXCEL format) to ensure that the data can be parsed correctly.

[0012] Extract the initial data of the tunnel face grid points:

[0013] Read each grid point data in the file, including coordinate information and attribute values;

[0014] Extract the grid point with the minimum Z value as the data of the tunnel start end face;

[0015] The grid points with the maximum Z values ​​are extracted as the data of the tunnel termination end face.

[0016] The second step is to preprocess the imported data to ensure data quality, specifically:

[0017] Clean the initial tunnel face data:

[0018] Check the duplicate grid points in the data and determine whether there are redundant points based on the coordinate information;

[0019] Remove duplicate points to ensure that each grid point appears only once in the data set;

[0020] After data preprocessing, a grid point dataset of the tunnel face is generated. This dataset is used as input to directly fit the three-dimensional variogram and construct the initial geological model.

[0021] The third step is to build a preliminary geological model of the tunnel based on the initial data, specifically:

[0022] Based on the preprocessed data set, calculate the maximum distance between grid points:

[0023] Calculate the Euclidean distance between each two grid points one by one, the formula is:

[0024] Distance = [(X 2 -X 1 ) 2 +(Y 2 -Y 1 ) 2 +(Z 2 -Z 1 ) 2 ] 1 / 2

[0025] The distances between all point pairs are recorded, the maximum distance between grid points is determined, and then the variation function interval is divided to ensure the accuracy of the interpolation model.

[0026] Estimate the semivariance:

[0027] Divide the distance between point pairs into several intervals;

[0028] In each interval, the average square of the attribute value differences of the corresponding point pairs is calculated, and the formula is:

[0029]

[0030] in

[0031] N is the number of point pairs,

[0032] Z i and Z j is the point pair attribute value.

[0033] Fit an initial three-dimensional variogram model:

[0034] In the fitting process, the error is calculated by the least square method, and a suitable variance function, such as an exponential model or a spherical model, is selected to fit the initial semivariance value curve;

[0035] The selection of the variogram model is based on the actual distribution characteristics of the semivariance: when the semivariance increases exponentially with distance, the exponential model is selected; when the semivariance tends to be stable after increasing to a certain distance, the spherical model is selected;

[0036] The exponential model can be used to fit the variogram, the formula is as follows:

[0037]

[0038] in

[0039] C 0 is the base value, which indicates the degree of variation at a very short distance (usually zero distance), reflecting measurement errors or irregularities at very small scales.

[0040] C is the nugget value, which indicates the maximum value of the variation of geological attributes in a larger range.

[0041] a is the range, which represents the distance from the variation function to the nugget value C.

[0042] The fitting results provide a description of the spatial correlation of geological attributes for Kriging interpolation.

[0043] Using the fitted variogram, the pre-processed grid point data of the tunnel face is input into the Kriging interpolation model as conditional data;

[0044] The interpolation process is based on the following formula:

[0045] in,

[0046] Z * (X) is the predicted value,

[0047] λ i is the weight,

[0048] Z(X i ) is the attribute value of the known data point.

[0049] Generate distribution prediction results of initial tunnel geological properties.

[0050] The fourth step is to import the newly added face data during tunnel excavation and update the geological model, specifically:

[0051] Open the newly added tunnel face grid point data file:

[0052] Whenever the tunnel is excavated for a certain distance, the three-dimensional coordinates and geological attribute values ​​of the newly added face grid points are recorded to ensure that the newly added data covers the construction area;

[0053] Check the data file to ensure that the format is consistent with the original data to avoid data reading errors.

[0054] Integrate the new data into the original dataset:

[0055] Merge the newly added face grid point data with the original data set to ensure the integrity of coordinate information and attribute values;

[0056] Remove duplicate grid points and renormalize data ranges to ensure uniformity.

[0057] Calculate the updated preprocessed data semivariance, fit the variogram, and adjust the Kriging interpolation model parameters:

[0058] Recalculate the point pair distance based on the newly added data and update the semivariance value;

[0059] After updating the semivariance value, refit the weight matrix of the Kriging interpolation model to predict the area affected by the new data:

[0060] Use the newly added face grid point data to input the updated Kriging interpolation model;

[0061] According to the three-dimensional coordinates (X, Y, Z) of the newly added data and the corresponding geological attribute values;

[0062] Perform point-by-point interpolation calculations on the newly added area and output the predicted attribute value for each grid point.

[0063] Interpolate the grid points in the surrounding area to generate distribution predictions of geological attributes to ensure the integrity of geological information in the surrounding area;

[0064] Generate updated geological attribute distribution data covering the area affected by the newly added face data.

[0065] The fifth step is to normalize the prediction results and generate geological attribute probability values, specifically:

[0066] Generate geological property probability values:

[0067] In the prediction model, the geological attribute value of each grid point is represented by a binary variable 0 or 1. After Kriging interpolation is performed on the attribute value of each stratum (D1, D2, D3), the interpolation result is a continuous value, indicating the "possibility" or "probability" that the prediction point belongs to a certain stratum;

[0068] For example, the interpolation result of a prediction point is D 1 =0.7, D2=0.2, D3=0.1, indicating that there is a 70% probability that this point belongs to formation D 1 ; 20% probability belongs to stratum D 2 ; 10% probability belongs to stratum D 3 .

[0069] After interpolation, the stratigraphic attribute values ​​of some points may not meet the requirements of the probability distribution (for example, the sum may be greater than or less than 1). In order to ensure the comparability of different stratigraphic attribute values, the geological attribute values ​​of each grid point are normalized. After normalization, the attribute values ​​are standardized to probability values, the value range is limited to between 0 and 1, and the sum of all geological attribute values ​​is 1;

[0070] For the prediction results of each grid point, the geological attribute values ​​are dynamically normalized, and the formula is as follows:

[0071]

[0072] P i The geological attribute value for each grid point

[0073] Generate a probability list to clarify the attribution of grid points in different geological layers;

[0074] The correctness of the processing results is ensured by checking whether the sum of the normalized probability values ​​of each grid point is 1.

[0075] The sixth step is to perform a three-dimensional visualization of the dynamically predicted geological model, specifically:

[0076] Open the forecast data result file:

[0077] Read the three-dimensional grid points and their corresponding geological attribute distribution data after the prediction is completed.

[0078] Construct 3D geometric point cloud:

[0079] Based on the geometric mapping theory, the 3D grid point data is converted into a point cloud model using ParaView or other software with similar functions;

[0080] Assign attribute values ​​to each grid point as color labels for the point cloud.

[0081] Plot the distribution of attributes:

[0082] Draw a point cloud to show the distribution of geological attributes of the tunnel and surrounding area;

[0083] Set up a color mapping scheme to assign different colors to different geological attribute values;

[0084] Use color mapping schemes to clarify the correspondence between geological attribute values ​​and colors;

[0085] Add color bar description to facilitate identification of geological attribute probability distribution in each area. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A schematic diagram of the overall process of a dynamic geological model updating and prediction method based on tunnel face information provided by an embodiment of the present invention;

[0087] Figure 2 A dynamic geological model updating and prediction method based on tunnel face information provided by an embodiment of the present invention; a tunnel face information representation schematic diagram;

[0088] Figure 3 A schematic diagram of a face grid point of a dynamic geological model updating and prediction method based on tunnel face information provided by an embodiment of the present invention;

[0089] Figure 4 A schematic diagram of a preliminary fitting semi-variance of a dynamic geological model updating and prediction method based on tunnel face information provided by an embodiment of the present invention;

[0090] Figure 5 A preliminary geological model rendering of a dynamic geological model updating and prediction method based on tunnel face information provided by an embodiment of the present invention;

[0091] Figure 6 A schematic diagram of the updated fitting semi-variance of a dynamic geological model updating and prediction method based on tunnel face information provided by an embodiment of the present invention;

[0092] Figure 7 A geological distribution probability map of a dynamic geological model updating and prediction method based on tunnel face information provided by an embodiment of the present invention;

[0093] Figure 8 A visualization effect diagram of a dynamically updated geological model of a dynamic geological model updating and prediction method based on tunnel face information provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0095] See also Figures 1 to 8 The embodiment of the present invention provides a dynamic geological model updating and prediction method based on tunnel face information, comprising the following steps:

[0096] In order to construct the geological model, the grid point information is first extracted from the file storing the tunnel face data. The extracted content includes the three-dimensional coordinates (X, Y, Z) and geological attribute values ​​(D 1 , D 2 , D 3 ). The geological attribute value of each grid point is represented by a binary variable 0 or 1. If a grid point completely belongs to a certain stratum (such as D 1 ), then set D 1 =1, while other strata (such as D 2 ,D 3 ) attribute value is 0. For example: if a point belongs to stratum D 3 , then set D 1 =0,D 2 =0,D 3 =1, such as Figure 2 shown.

[0097] After extraction, the grid point data is preprocessed. During the data preprocessing process, duplicate points are deleted to ensure the uniqueness of the data. The cleaned data will be used for interpolation modeling and dynamic update. Then, the starting end face and the ending end face of the tunnel are extracted according to the Z value, which correspond to the grid point sets with the minimum and maximum Z values, respectively, to provide boundary conditions for the initial construction of the model, such as Figure 3 shown.

[0098] After completing the data preprocessing, the maximum distance of the grid points is obtained by calculating the Euclidean distance between the grid points. Based on this, the distance of the point pair is divided into several intervals, and the semivariance value in each interval is calculated as the geological attribute value of the point pair. The semivariance value increases rapidly with the increase of distance and then tends to be stable after reaching a certain value, but fluctuates in the long-distance part, so the exponential model is used to fit the semivariance curve, such as Figure 4 After the fitting is completed, the Kriging interpolation method is used to generate the initial geological model and predict the distribution of geological attributes of the tunnel and surrounding areas, as shown in Figure 5 shown.

[0099] Every 25m of tunnel excavation, new face grid point data is collected, and the extracted content includes three-dimensional coordinates (X, Y, Z) and geological attribute values ​​(D1, D2, D3). The new data is integrated with the original data set. After the new data enters the system, it needs to be cleaned and deduplicated to ensure data accuracy.

[0100] The integrated data are used to recalculate the semivariance values ​​and to adjust the weight matrix of the Kriging interpolation model to reflect the spatial characteristics of the newly added data, such as Figure 6 Then, the updated Kriging interpolation model is used to re-predict the geological attribute distribution of the tunnel and surrounding areas to generate an updated geological model probability distribution, as shown in Figure 7 shown.

[0101] In order to intuitively display the prediction results of the geological model, the calculated grid point data is exported as a standardized Excel file, converted into CSV format, and then imported into ParaView software for three-dimensional visualization. Through geometric mapping technology, a three-dimensional point cloud model is generated, and the coordinate information (X, Y, Z) of each grid point is mapped into three-dimensional space. Based on the predicted geological attribute value, a corresponding color value is assigned to each grid point, and the depth of the color intuitively reflects the difference in the stratum attribute value. In the visualization model, by updating the calculated data file, the newly added face data can be loaded in real time, and the point cloud model can be incrementally adjusted to promptly reflect the impact of the newly added data on the geological model. At the same time, the color mapping function of ParaView is used to set a variety of color gradient schemes, assign clear colors to different geological attribute values, and intuitively display the spatial distribution of geological attributes. In addition, by adding color bars, the correspondence between geological attribute values ​​and colors is clearly marked to ensure that construction personnel can quickly identify the stratum type and its attribute distribution.

[0102] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A dynamic geological model updating and prediction method based on tunnel face information, characterized in that: The following steps are involved: The first step is to import the geological information of the face grid points during tunnel excavation to form the initial face data set; The second step is to pre-process the grid point information of the tunnel face, including removing duplicate point data formats; The third step is to construct a face geological model based on the preliminary face data obtained from the tunnel and use the Kriging interpolation method, combined with the fitted variogram model, to calculate the preliminary geological attribute distribution of the tunnel and the surrounding area; the fourth step is to recalculate the point pair distance, semi-variance value and variogram according to the face grid point data newly added during the tunnel excavation process, dynamically adjust the Kriging interpolation model parameters, and re-predict the geological attribute distribution of the tunnel range and its surrounding areas based on the dynamically updated model to further optimize the accuracy of the model; the fifth step is to normalize the geological attribute values ​​predicted by the model and generate probability values ​​of multiple geological attributes to ensure the consistency of the prediction results; the sixth step is to use three-dimensional point cloud rendering technology to perform real-time visualization of the dynamically updated geological model to intuitively display the distribution of tunnel geological attributes and their dynamic changes.

2. The dynamic geological model updating and prediction method based on tunnel face information according to claim 1 is characterized in that: When importing the face grid point data, it includes obtaining the three-dimensional coordinates (X, Y, Z) of the grid points and their corresponding geological attribute values ​​(D1, D2, ... n ) and store it in data files such as EXCEL.

3. The dynamic geological model updating and prediction method based on tunnel face information according to claim 1 is characterized in that: When preprocessing the face grid point data, duplicate point data is removed to ensure data integrity and consistency.

4. The dynamic geological model updating and prediction method based on tunnel face information according to claim 1 is characterized in that: Based on the preprocessed face data, the maximum distance between grid points is calculated, the semi-variance value is estimated in intervals, and a preliminary three-dimensional variogram model is fitted. The distribution of tunnel geology and surrounding area attributes is predicted based on the fitted preliminary variogram model.

5. The dynamic geological model updating and prediction method based on tunnel face information according to claim 1 is characterized in that: According to the newly added face grid point data during the tunnel excavation process, the parameters of the Kriging interpolation model are dynamically adjusted, including recalculating the point pair distance, semi-variance value and updated three-dimensional variation function curve, and re-predicting the geological attribute distribution of the tunnel range and its surrounding areas based on the updated interpolation model.

6. The method for updating and predicting a dynamic geological model based on tunnel face information according to any one of claims 1 to 5, characterized in that: When normalizing the model prediction results, the geological attribute value of each grid point is converted into a probability value. After normalization, the sum of the probabilities of each grid point is 1, ensuring the comparability of the geological attribute values.

7. The dynamic geological model updating and prediction method based on tunnel face information according to claim 1 is characterized in that: The dynamically updated geological model is displayed through 3D point cloud drawing technology, a color mapping scheme is used to distinguish geological attribute values, and the probability distribution of geological attributes in each area is clearly marked through color bars. The 3D point cloud drawing adopts geometric mapping technology, and the prediction results are imported into 3D visualization software, which intuitively displays the geological distribution through color depth, and supports incremental loading of dynamically updated data.

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