Geological attribute modeling method and system based on joint constraints of multi-source information

Abstract

The present invention discloses a geological attribute modeling method and system based on multi-source information joint constraints, relating to the field of geological engineering technology. The method comprises: obtaining geological exploration data, obtaining geological attribute data through preprocessing, classifying the geological attribute data into continuous and discontinuous types, and quantifying the geological attribute data; performing multi-source geological attribute correlation analysis based on the quantified geological attribute data to obtain the correlations between different geological attributes; constructing a geological structure model of the area to be modeled, dividing the model into unstructured networks based on the correlations between different geological attributes, and assigning geological unit grid attributes based on 1D+2D trend graph constraints for each grid area based on the different geological attribute data obtained, thereby constructing a geological unit attribute model. The present invention can achieve tunnel geological attribute modeling and geological attribute prediction with higher accuracy and reliability.

Description

Technical Field

[0001] The present invention relates to the field of geological engineering technology, and in particular to a geological attribute modeling method and system based on multi-source information joint constraints. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Tunnel construction usually faces complex geological conditions, such as different rock and soil types, crack conditions, groundwater distribution and seismic activity, which directly affect the stability of the tunnel and the difficulty of construction. Therefore, accurately evaluating and modeling the geological characteristics around the tunnel is the key to the successful implementation of tunnel engineering. In order to improve the accuracy and reliability of tunnel geological modeling, a method of geological modeling using multi-point geostatistical technology has been extracted, which can construct a geological model that is more in line with the actual geological conditions. However, multi-point geostatistical technology still faces challenges in data acquisition and processing, especially in the fusion and correlation analysis of multi-source attribute data. It is still insufficient and cannot guarantee the accuracy of the final geological modeling. The main problems are:

[0004] (1) Traditional tunnel geological modeling methods mainly rely on drilling data and geological surveys. Although this method can provide basic geological information, it is costly and time-consuming, and still has problems such as data acquisition limitations and insufficient single data sources. In other words, during the tunnel construction process, drilling data is often limited by the number and distribution of drilling points, the uneven distribution of geological exploration points, or the hidden nature of cracks and faults, which may lead to the inability to accurately obtain data in certain areas, resulting in missing spatial data. This makes it difficult to fully reflect the complexity and variability of geological characteristics, especially in complex geological environments, where obtaining sufficient geological data is even more difficult.

[0005] (2) Although geological surveys can obtain more geological information, they are limited by the professional knowledge and experience of investigators and the limitations of survey methods, and their accuracy and reliability are also insufficient. In other words, there is a risk that the exploration points may not fully match the actual geological characteristics.

[0006] (3) In addition, the information from a single data source is often insufficient to fully reflect the true situation of geological characteristics, which affects the accuracy and reliability of geological modeling. The quality of data from different sources is uneven, and there may also be differences in accuracy, coverage, timestamps, etc., which leads to inaccurate final modeling. Summary of the Invention

[0007] To address the deficiencies of the above-mentioned prior art, the present invention provides a geological attribute modeling method and system based on multi-source information joint constraints. By analyzing the correlation between multi-source geological attributes, the geological structure model is gridded according to the correlation, and then 1D+2D trend chart constraints are used to assign values ​​to the geological unit grid attributes to construct a geological unit attribute model. For this attribute model, the distribution and variability of geological attributes in space are analyzed through dynamic fuzzy neural networks and multi-attribute probabilistic neural networks to better describe and predict geological characteristics, optimize the tunnel geological attribute model, and effectively improve the accuracy and reliability of tunnel geological attribute modeling and geological attribute prediction.

[0008] In a first aspect, the present invention provides a geological attribute modeling method based on joint constraints of multi-source information.

[0009] A geological attribute modeling method based on joint constraints of multi-source information, comprising:

[0010] Obtain geological exploration data, obtain geological attribute data after preprocessing, and classify and quantify the geological attribute data into continuous and discontinuous types;

[0011] Based on the quantified geological attribute data, multi-source geological attribute correlation analysis is performed to obtain the correlation between different geological attributes;

[0012] A geological structure model of the area to be modeled is constructed. The model is divided into an unstructured network based on the correlation between different geological attributes. For each grid area, the geological unit grid attributes are assigned based on the 1D+2D trend map constraints according to the different geological attribute data obtained, and a geological unit attribute model is constructed.

[0013] Further technical solutions also include:

[0014] Based on the geological unit attribute model, the nonlinear relationship between different attributes in the model is extracted through a dynamic fuzzy neural network. Then, a multi-attribute probabilistic neural network modeling method is adopted to update and aggregate the geological attributes of each grid node. In this way, the predicted geological attribute value of each grid cell in the geological unit attribute model that changes with time and the tunneling process is output.

[0015] In a second aspect, the present invention provides a geological attribute modeling system based on joint constraints of multi-source information.

[0016] A geological attribute modeling system based on multi-source information joint constraints, comprising:

[0017] The data acquisition and preprocessing module is used to acquire geological exploration data, obtain geological attribute data after preprocessing, and classify and quantify the geological attribute data into continuous and discontinuous types;

[0018] The geological attribute analysis module is used to perform multi-source geological attribute correlation analysis based on the quantified geological attribute data to obtain the correlation between different geological attributes;

[0019] The geological attribute modeling module is used to construct a geological structure model of the area to be modeled. It divides the model into unstructured networks based on the correlation between different geological attributes. For each grid area, according to the different geological attribute data obtained, the geological unit grid attributes are assigned based on the 1D+2D trend map constraints to construct a geological unit attribute model.

[0020] Further technical solutions also include:

[0021] The geological attribute prediction module is used to extract the nonlinear relationship between different attributes in the geological unit body attribute model through dynamic fuzzy neural network, and then adopt the multi-attribute probabilistic neural network modeling method to update and aggregate the geological attributes of each grid node, so as to output the geological attribute prediction value of each grid unit in the geological unit body attribute model that changes with time and the excavation process.

[0022] In a third aspect, the present invention further provides an electronic device comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned geological attribute modeling method based on joint constraints of multi-source information when executing the executable instructions stored in the memory.

[0023] In a fourth aspect, the present invention further provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned geological attribute modeling method based on joint constraints of multi-source information.

[0024] In a fifth aspect, the present invention also provides a computer program product, which includes executable instructions stored in a computer-readable storage medium; wherein, when the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned geological attribute modeling method based on joint constraints of multi-source information is implemented.

[0025] One or more of the above technical solutions have the following beneficial effects:

[0026] 1. The present invention provides a geological attribute modeling method and system based on multi-source information joint constraints. By analyzing the correlation between multi-source geological attributes, the geological structure model is gridded according to the correlation, and then 1D+2D trend chart constraints are used to assign values ​​to the geological unit grid attributes to construct a geological unit attribute model. For this attribute model, the distribution and variability of geological attributes in space are analyzed through dynamic fuzzy neural networks and multi-attribute probabilistic neural networks to better describe and predict geological characteristics, realize the optimization of tunnel geological attribute models, and effectively improve the accuracy and reliability of tunnel geological attribute modeling and geological attribute prediction.

[0027] 2. By accurately quantifying continuous and discontinuous attributes, the present invention can clearly distinguish different types of geological data, avoiding the data confusion that may occur in traditional methods. By using statistical methods such as the Spearman rank coefficient to perform correlation analysis on multi-source attributes, the present invention can reveal the inherent relationships and interactions between attributes, helping to discover the mutual influence between different geological features, providing multi-dimensional data support for tunnel modeling, and providing a quantitative basis for risk assessment. Through comprehensive attribute analysis, potential geological risks such as fracture distribution zones, areas with high water content, and areas with high degree of fragmentation can be accurately identified, thereby providing more targeted early warnings for tunnel construction.

[0028] 3. The present invention can effectively overcome the discretization problem that may occur in traditional modeling methods through attribute coarsening, grid assignment and removal of sample deviation, and ensure the rationality and consistency of attribute value assignment through fine spatial constraint means; multi-attribute modeling can simultaneously consider multiple key attributes such as crack distribution, water content, degree of fragmentation, etc., to form a more refined attribute body model, which provides rich multi-dimensional support for subsequent tunnel construction design, risk assessment, resource exploration and other work; by introducing deterministic crack parameters and combining point constraint and surface constraint modeling methods, the spatial distribution and structural characteristics of cracks can be more accurately reproduced in the geological model. Compared with traditional local modeling methods, the present invention can make full use of spatial statistical data and can capture the spatial variability and trend of geological characteristics in a larger range. Compared with traditional statistical modeling methods, this method is more intuitive and clear, and can effectively improve the physical interpretability of the model. In addition, the present invention can significantly improve the model accuracy and reduce the error of the geological model through fine grid subdivision and constraint assignment based on 1D+2D trend graphs.

[0029] 4. The present invention enhances the application and control capabilities of crack parameters, which not only helps to accurately model the geometric characteristics of cracks, but also enables effective control during tunnel construction. Through deterministic modeling of cracks, it can provide a specific basis for grouting, support design, etc., and provide effective protection for engineering safety.

[0030] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0032] Figure 1 This is a flow chart of a geological attribute modeling method based on multi-source information joint constraints according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] It should be noted that the following detailed descriptions are exemplary only and are intended to describe specific embodiments and provide further explanation of the present invention, and are not intended to limit the exemplary embodiments according to the present invention. Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0034] Example 1

[0035] This embodiment provides a geological attribute modeling method based on multi-source information joint constraints, such as Figure 1 As shown, the specific steps include:

[0036] Step S1: Acquire geological exploration data, obtain geological attribute data after preprocessing, and classify and quantify the geological attribute data into continuous and discontinuous types;

[0037] Step S2: Based on the quantified geological attribute data, a multi-source geological attribute correlation analysis is performed to obtain the correlation between different geological attributes;

[0038] Step S3: construct a geological structure model of the area to be modeled, divide the model into an unstructured network based on the correlation between different geological attributes, and for each grid area, assign geological unit grid attributes based on 1D+2D trend map constraints according to the different geological attribute data obtained, and construct a geological unit attribute model.

[0039] Further technical solutions also include:

[0040] Step S4: Based on the geological unit body attribute model, the nonlinear relationship between different attributes in the model is extracted through a dynamic fuzzy neural network, and then the geological attributes of each grid node are updated and aggregated using a multi-attribute probabilistic neural network modeling method, so as to output the predicted geological attribute value of each grid cell in the geological unit body attribute model that changes with time and the excavation process.

[0041] The following content introduces the geological attribute modeling method based on multi-source information joint constraints proposed in this embodiment in more detail.

[0042] In step S1, geological exploration data is acquired, and geological attribute data is obtained after preprocessing. The geological attribute data is classified into continuous and discontinuous types and quantified.

[0043] In this embodiment, during the tunnel geological modeling process, the measured geological exploration data of the tunnel area, such as cracks, resistivity, seismic waves, etc., are first obtained, and then the acquired data are inverted, extrapolated or estimated using inversion technology (i.e., preprocessing) to obtain corresponding key geological attribute data, such as water content based on resistivity inversion and degree of fragmentation based on seismic wave inversion. These attribute data are continuous or discrete in space, so these data are appropriately quantified and processed for subsequent analysis and modeling.

[0044] Specifically, geological exploration data (including crack width, crack distribution, resistivity, and seismic waves) is acquired from the tunnel area. This data is then inverted to obtain three attribute data types: cracks, water content, and degree of fragmentation. This attribute data is then classified into continuous and discrete types, and different data quantification preprocessing is performed based on the attribute data type. Water content is obtained through resistivity inversion. Typically, in a tunnel, areas with high groundwater content have high resistivity, while areas with low water content have low resistivity. The degree of fragmentation is inverted through seismic wave propagation velocity, which is related to the degree of rock fragmentation. Generally, seismic wave propagation velocity is slower in fragmented areas.

[0045] Furthermore, continuous attribute data are standardized or normalized to eliminate dimensional differences and numerical ranges; non-continuous attribute data are encoded, such as using one-hot encoding or label encoding, so that they can be used for subsequent numerical calculations and association rule mining.

[0046] In this embodiment, for the three types of attribute data, namely fissures, water content, and degree of fragmentation, the width of fissures and the water content inverted based on resistivity belong to typical continuous attributes, and numerical expressions can be obtained through quantification. Specifically, the width of fissures usually has a continuous numerical range. To quantify the width of fissures, standardization or normalization can be performed on it to analyze it on a unified scale; the water content inverted based on resistivity usually shows continuous changes related to groundwater saturation and porosity, and there is usually an inverse relationship between the magnitude of the resistivity value and the water content. Normalization or standardization of the resistivity value is performed.

[0047] The size of fissures and the degree of fragmentation inverted based on the seismic wave propagation velocity, etc. all belong to typical discontinuous attributes. Among them, the seismic wave propagation velocity is negatively correlated with the degree of fragmentation of the medium. Therefore, during the process of inverting the degree of fragmentation, the seismic wave propagation velocity data can be standardized and normalized, or the inverse ratio of the seismic wave propagation velocity data can be transformed to obtain continuous numerical values of fragmentation. Then, for these continuous numerical values of fragmentation, they can be converted into different grades, such as mild, moderate, severe, etc., and used as discontinuous attributes for calculation. Specifically, for discrete attributes (i.e., discontinuous attributes) such as the presence of fissures, rock type, degree of fragmentation category, etc., for such discrete attributes, according to characteristics such as the size and shape of fissures, they are divided into different categories. For example, according to the size of fissures, they can be divided into "large fissures" (assigned a value of 3), "medium fissures" (assigned a value of 2), "small fissures" (assigned a value of 1), and analyzed with unified numerical values; according to the degree of fragmentation in geology, it can be divided into multiple grades, such as "no fragmentation", "mild fragmentation", "moderate fragmentation", "severe fragmentation", etc. These types of fragmentation degrees can be quantitatively analyzed through water content information and further quantitatively assigned through the water inflow. For example, "no fragmentation" (weak water-richness, q ≤ 0.1 L / m.s) is encoded as 0, "mild fragmentation" is encoded as 1 (medium water-richness, 0.1 < q ≤ 1.0 L / m.s), "moderate fragmentation" is encoded as 2 (strong water-richness, 1 < q ≤ 5.0 L / m.s), "severe fragmentation" is encoded as 3 (extremely strong water-richness, q > 5.0 L / m.s), thus completing the assignment.

[0048] In step S2, based on the quantified geological attribute data, multi-source geological attribute correlation analysis is performed to obtain the correlations between different geological attributes.

[0049] Specifically, by calculating the Spearman rank coefficient correlation coefficient between multi-source geological attributes, the correlation relationships between different geological attributes (such as fissures, water content, degree of fragmentation) are evaluated. The specific steps are as follows:

[0050] Step S2.1: Through the above step S1, obtain geological attribute data such as cracks, water content and degree of fragmentation in the tunnel area, and preprocess the obtained data, including: checking missing values, outliers and duplicate values ​​in the data, and performing appropriate processing, such as filling missing values, deleting outliers or duplicate values, etc.

[0051] Step S2.2: Use the Spearman rank correlation coefficient to evaluate the linear correlation between different geological attributes and measure the degree of linear correlation between two variables. The Spearman rank correlation coefficient is calculated between any two attributes in the attribute matrix to obtain a correlation matrix. Considering the correlation between the three geological attributes A (e.g., fracture width), B (e.g., water content), and C (e.g., degree of fragmentation), the following correlation matrix is ​​constructed:

[0052]

[0053] Among them, r AB Represents the Spearman rank correlation coefficient between attributes A and B; r AC represents the Spearman rank correlation coefficient between attributes A and C; r BC Represents the Spearman rank correlation coefficient between attributes B and C.

[0054] By constructing the above correlation matrix, the correlation between multi-source geological attributes can be further clarified, laying the foundation for the subsequent uneven grid division of the address model.

[0055] In step S3, a multi-point geostatistical method is used to simulate the spatial distribution and variability of geological features to create a geological unit attribute model. Specifically, the following steps are included:

[0056] Step S3.1: Construct a geological structure model of the area to be modeled, and perform unstructured network division on the model based on the correlation between different geological attributes to describe the three-dimensional geological spatial structure of the tunnel area.

[0057] Specifically, when constructing the geological structure model, the inversion constraint method is used to ensure that the spatial boundary of the geological unit body can accurately reflect the actual geological structure, and the surface constraint conditions of the geological unit body are determined through exploration data, seismic inversion results and geological knowledge. On the basis of the surface constraint conditions, according to the distribution of fractures or tunnel faces, the geological unit body grid is further unevenly divided through unstructured grid division to obtain more small units.

[0058] The determination of the above-mentioned surface constraints includes:

[0059] (1) Based on the exploration data (such as drilling and logging data), the spatial distribution information of the boundary of the geological unit body is determined. For example, the distribution and depth of the underground rock layer can be identified by the location and depth of the drill hole, and then the surface boundary function of the geological body can be determined, which can be expressed as:

[0060] f(x,y,z)=0;

[0061] In the above formula, f(x,y,z) is a function that describes the shape of the boundary surface of the geological unit, and (x,y,z) represents the spatial coordinates.

[0062] (2) Changes in seismic wave velocity can reflect the boundaries of different geological units. For example, changes in fracture zones, faults, and rock layers will affect the propagation of seismic waves. The constraint surface can be determined by this, which can be expressed as:

[0063] v1(x,y,z)≠v2(x,y,z);

[0064] In the above formula, v1 and v2 represent the wave velocities of geological body 1 and geological body 2 respectively.

[0065] (3) Surface constraints of geological units also include:

[0066]

[0067] In the above formula, f i (x, y, z) represents the surface boundary function of the i-th geological unit obtained by inversion, g i The actual measurement data.

[0068] Furthermore, the geological unit model is gridded unevenly. That is, the tunnel face is divided according to the specific needs of the tunnel area, based on factors such as fracture distribution and fracture friction. During the division process, considering the complex geological environment around the tunnel, a single grid size may not be able to effectively express the spatial variability of geological properties. Therefore, a multi-level grid division method is adopted. The areas with complex geological conditions of the tunnel face are divided with finer grids, while the peripheral areas are divided with coarser grids.

[0069] In this embodiment, based on the correlation between multiple geological attributes, a multi-level grid division method is used to perform uneven grid division on the grid, thereby achieving uneven grid division of the geological unit model, including:

[0070] (1) Based on the correlation between fracture data and water content, a finer mesh is used for areas with dense fractures during mesh generation.

[0071] (2) Areas with a high degree of fragmentation usually have more cracks and larger crack widths. When dividing the grid, these areas can be divided into smaller grid units;

[0072] (3) Areas with severe fragmentation usually have higher water content. By introducing the correlation between water content and fragmentation into grid division, the density of the grid can be reasonably adjusted in the complex geological environment around the tunnel.

[0073] Step S3.2: For each grid area, according to the different geological attribute data obtained, the geological unit grid attribute assignment based on the 1D+2D trend map constraints is performed, the multi-attribute and multi-dimensional numerical analysis is expanded, and the geological unit attribute model is preliminarily constructed.

[0074] Specifically, 1D trend graphs are used to describe the changes in a certain attribute over time or sequence. Through deep learning methods, trends and patterns can be learned from time series data, and time dependencies can be captured. 2D trend graphs are used to describe attribute changes in the spatial dimension. By combining 1D (time) and 2D (space) trend graphs, deep learning models can simultaneously learn the relationship between time and space, thereby enabling spatial and temporal attribute predictions, achieving more accurate modeling and prediction of geological attributes, and more accurately estimating the geological characteristics within the tunnel area.

[0075] The construction of the above geological unit attribute model specifically includes the following steps:

[0076] Step S3.2.1. Coarsening of geological attribute data. First, the acquired geological attribute data (fractures, water content, and fragmentation) are spatially downscaled. This means that the geological attribute data are grouped or divided according to a certain spatial resolution. Through gridding, the original high-resolution geological attribute data (such as fracture width and water content) are mapped to a larger, coarser grid, converting it into a low-resolution form. This spatial downscaling process reduces the complexity of the data. By mapping the data to a larger grid, the number of data points contained in each grid cell can be reduced.

[0077] Step S3.2.2: Grid mapping of geological attribute data. Using refined gridding techniques, select an appropriate grid size based on the actual needs of the region, divide the region into multiple grid cells, each representing a specific spatial range, and assign a geological attribute value to each grid cell. To remove bias caused by uneven data samples or noise, sample bias removal is performed. By de-averaging the sample data, the values ​​of all data points are adjusted to be consistent with the global mean, thereby reducing the impact of local sample imbalance on the results.

[0078] Step S3.2.3: For each assigned geological attribute value in each grid cell, generate a 1D temporal trend graph of the geological attribute to show how the geological attribute changes over time. Using the generated 1D trend graph as a constraint, the grid cell assignment results are corrected. Simultaneously, using a two-dimensional interpolation method, generate a 2D spatial distribution trend graph of the geological attribute on a cross section or other two-dimensional plane to show how the geological attribute changes with spatial position. Using the generated 2D trend graph as a constraint, the grid cell assignment results are corrected. For example, using the 2D trend graph as a constraint for correction, if the attribute value of a grid cell does not match the spatial variation shown in the trend graph, the attribute value of the grid cell can be adjusted based on the guidance of the 2D trend graph to ensure that it conforms to the actual spatial variation pattern.

[0079] Step S3.2.4: Based on the 2D trend graph, depth from the surface or tunnel entrance is introduced to construct a 3D trend graph. This graph displays the vertically varying trends of fractures, resistivity inversion, and water content in different strata at different depths. Based on this, a complete 3D spatial distribution trend graph is generated through spatial interpolation based on the three-dimensional data. The attribute values ​​of the grid cells are adjusted to ensure more consistent attribute changes in three dimensions. For example, if the attribute values ​​of a grid cell deviate from the actual three-dimensional distribution trend in the geological environment, these values ​​can be corrected to maintain consistency across depths and lateral distributions.

[0080] Based on the joint application of the above 1D+2D trend map constraints, by combining the 1D trend map, 2D trend map, and 3D trend map constraints, each grid cell is ultimately assigned an attribute value that conforms to the actual geological conditions, ensuring the spatial consistency of the assignment results.

[0081] The interpolation method and trend graph correction described above ensure that attribute values ​​across grid cells maintain spatial consistency. For example, the spatial distribution of attributes such as fractures and water content often exhibits certain patterns (e.g., fracture width increases with depth, or certain areas have higher water content). The correction process, through interpolation and trend graph constraints, ensures that the attribute values ​​of each grid cell conform to these spatial variations. This effectively constrains and guides grid cell assignments, accommodating the varying trends of attributes such as fractures, resistivity, and water content in different strata, and enables fracture attribute modeling based on deterministic fracture parameters.

[0082] In step S4, considering that the use of deep learning technology can automatically learn the complex relationships between data and perform accurate prediction and classification by constructing a deep neural network model, this embodiment introduces deep learning technology to fully utilize the nonlinear relationship between multi-source attribute data, improve the accuracy and reliability of geological modeling, and achieve accurate prediction of address unit attributes.

[0083] Specifically, based on the geological unit attribute model constructed above, a multi-source attribute probability fusion method based on a deep learning model is adopted to construct a dynamic fuzzy neural network and a multi-attribute probabilistic neural network. The nonlinear relationship between different attributes in the model is extracted through the dynamic fuzzy neural network. Then, a multi-attribute probabilistic neural network modeling method is adopted to update and aggregate the geological attributes of each grid node, and output the predicted geological attribute value of each grid unit in the geological unit attribute model that changes with time and the excavation process. Specifically, the following steps are included:

[0084] Step S4.1: Construct a dynamic fuzzy neural network (DFNN) to mine and integrate nonlinear relationships among multiple attributes in the modeling of the tunnel area geological unit, so as to adaptively adjust the change value of the model attributes.

[0085] Since the three attributes of cracks, resistivity inversion water content, and seismic wave inversion fragmentation degree usually have strong spatial dependence and exist in multi-level nonlinear relationships, for example, there is a complex interactive effect between the distribution of cracks and water content. At the same time, considering that in long-term projects such as tunnel construction, the changes in geological environment are very significant, this embodiment introduces a dynamic fuzzy neural network DFNN, which can effectively mine these nonlinear relationships through neural networks, so that DFNN can adaptively adjust the change values ​​of model attributes.

[0086] First, each grid node in the three-dimensional space is used as a data point to perform numerical interpolation of geological attributes. For missing values, DFNN fills the missing data by interpolating the attribute values ​​of nearby three-dimensional coordinate points. The interpolated data set contains not only the numerical value of each attribute, but also its corresponding spatial coordinates (x, y, z).

[0087] Secondly, the spatial coordinates and attribute values ​​of each data point obtained after interpolation serve as input data for the DFNN input layer and are fed into the DFNN network for training. The network will automatically capture the complex nonlinear relationships between multi-source attributes. Specifically, for the interpolated attribute dataset, DFNN defines the correlation between attributes based on the repetitiveness of attribute values. This is the interaction between multiple geological attributes, such as fractures, water content (from resistivity inversion), and fragmentation (from seismic wave inversion). DFNN models these interactions through activation functions, ultimately outputting the predicted geological attribute value for each grid cell, taking into account the values ​​of other attributes.

[0088] Among them, repeatability refers to the frequency of occurrence of certain attribute values ​​between different data points in the input data, that is, the frequency or distribution density of a certain attribute value (such as crack width, water content from resistivity inversion, and fragmentation degree from seismic wave inversion) in the data set. The repeatability can be calculated by counting the frequency of occurrence of a certain attribute or attribute combination in the data set. After calculating the repeatability of the obtained attributes, the nonlinear activation function of the DFNN hidden layer is used to consider the mutual influence and superposition effect of the three attributes. In the hidden layer of the neural network, the relationship between the attributes is learned through nonlinear activation functions and multi-layer networks. The input of each attribute is mapped to a nonlinear space, thereby simulating the interaction effect between the attributes. Finally, the correlation between the attributes is clarified, and the geological attribute value of each grid cell is predicted and output based on this correlation.

[0089] The specific structure of the above dynamic fuzzy neural network (DFNN) is:

[0090] (1) Input layer: The input data is a data set after numerical interpolation, which includes geological attribute values ​​such as fractures, water content from resistivity inversion, and fragmentation degree from seismic wave inversion, as well as the corresponding spatial coordinates (x, y, z). Different geological attribute values ​​are input to different nodes of the network respectively;

[0091] (2) Hidden layer: The hidden layer considers the mutual influence and superposition effect of the three attributes (cracks, water content from resistivity inversion, and fragmentation degree from seismic wave inversion) through a nonlinear activation function;

[0092] (3) Fuzzy membership function parameter adjustment mechanism: DFNN dynamically adjusts the parameters of the fuzzy membership function according to the characteristics of different attribute data to adapt to the processing requirements of different attribute data.

[0093] During the DFNN model training process, DFNN dynamically adjusts the degree of fuzzification and flexibly adjusts the parameters of the fuzzy membership function according to the characteristics of different attribute data. In addition, the DFNN training process is adaptive and usually does not require a fixed training set. Usually, a data set processed by numerical interpolation is used as the training set, which contains the numerical values ​​of attributes such as cracks, resistivity inversion water content, and seismic wave inversion fragmentation degree, as well as their corresponding spatial coordinates (x, y, z). The model initializes the network parameters by selecting different initial data and optimizes them through a dynamic training process. Different attribute data such as cracks, resistivity inversion water content, and seismic wave inversion fragmentation degree are input into different nodes of the network for initial training of the model.

[0094] Preferably, during model training, DFNN dynamically adjusts network parameters, gradually optimizing the model's predictive capabilities based on the characteristics of the data. DFNN is capable of handling complex nonlinear relationships in tunnel geology, and its application in tunnel geological modeling can improve the project's predictive accuracy, especially under complex geological conditions. DFNN can establish precise nonlinear relationships between multiple attributes, such as fracture distribution, water content derived from resistivity inversion, and fragmentation degree derived from seismic wave inversion, providing reliable technical support for the design and construction of tunnel projects. As training progresses, the network automatically learns the relationships between attributes and gradually eliminates noise or bias in the data, improving prediction accuracy.

[0095] Step S4.2: Using the multi-attribute probabilistic neural network (MPNN) modeling method, the geological attribute data of the grid nodes are transmitted and updated to achieve the fusion and integration of the multi-attribute data, and the geological attribute prediction value of each grid cell in the geological unit body attribute model is output.

[0096] The complex nonlinear relationships between different attributes mined by the aforementioned DFNN provide a basis for the intrinsic connections between attributes during the MPNN node information transmission and fusion process. For example, the specific nonlinear relationship between fractures and water content analyzed by the DFNN enables the MPNN to more rationally consider the spatial distribution and mutual influence of these two attributes when processing node features. Based on this nonlinear relationship, the MPNN further fuses and integrates multi-attribute data. By defining a graph structure (nodes represent grid cells, and edges represent spatial relationships), the initial feature vector of each node (including fractures, water content values ​​from resistivity inversion, and fragmentation values ​​from seismic wave inversion) is fused between nodes through message passing and updates. This allows each node to integrate all information from its neighborhood. The final aggregated node feature vector can be used for subsequent prediction tasks (such as grouting probability prediction).

[0097] Specifically, to construct the MPNN model, a graph structure is first defined, consisting of nodes and edges. Each node represents a grid cell in the tunnel geological model, and grid cells are connected by edges to represent the spatial relationship between nodes. Each node has an initial feature vector, which includes numerical values ​​of cracks, water content (resistivity inversion), and fragmentation (seismic wave inversion). For each grid node, the corresponding numerical values ​​of cracks, water content (resistivity inversion), and fragmentation are extracted and input into the neural network as the node's initial features. A message passing mechanism is set up in the MPNN. Through several rounds of message passing, nodes exchange information and weights and update their own feature vectors. Each node exchanges information with neighboring nodes, transfers the feature vectors of neighboring nodes, and combines its own features to update its own feature vector through aggregation operations. After several rounds of message passing, each node in the network integrates all information from its neighborhood. After the final round of message passing, the feature vectors of each node are aggregated to obtain the aggregated node feature vector, which can be used for subsequent prediction tasks. For example, the final node feature representation can be used as a basis for predicting grouting probability.

[0098] Preferably, after preliminary data processing, attribute values ​​related to each grid cell, such as fractures, water content from resistivity inversion, and fragmentation from seismic wave inversion, are extracted from the geological exploration data to form a training dataset for the MPNN, which is then used to train the MPNN network. By fusing and synthesizing multi-source attribute data through the MPNN, node feature vectors are continuously updated and aggregated, optimizing the actual characteristics of the geological body and outputting predicted geological attribute values ​​for each grid cell in the geological unit body attribute model that change over time and during tunneling.

[0099] Example 2

[0100] This embodiment provides a geological attribute modeling system based on multi-source information joint constraints, specifically including:

[0101] The data acquisition and preprocessing module is used to acquire geological exploration data, obtain geological attribute data after preprocessing, and classify and quantify the geological attribute data into continuous and discontinuous types;

[0102] The geological attribute analysis module is used to perform multi-source geological attribute correlation analysis based on the quantified geological attribute data to obtain the correlation between different geological attributes;

[0103] The geological attribute modeling module is used to construct a geological structure model of the area to be modeled. It divides the model into unstructured networks based on the correlation between different geological attributes. For each grid area, according to the different geological attribute data obtained, the geological unit grid attributes are assigned based on the 1D+2D trend map constraints to construct a geological unit attribute model.

[0104] Further technical solutions also include:

[0105] The geological attribute prediction module is used to extract the nonlinear relationship between different attributes in the geological unit body attribute model through dynamic fuzzy neural network, and then adopt the multi-attribute probabilistic neural network modeling method to update and aggregate the geological attributes of each grid node, so as to output the geological attribute prediction value of each grid unit in the geological unit body attribute model that changes with time and the excavation process.

[0106] Example 3

[0107] This embodiment provides an electronic device, including: a memory for storing executable instructions; and a processor for implementing the above method provided in this embodiment when executing the executable instructions stored in the memory.

[0108] Example 4

[0109] This embodiment further provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by a processor, the processor will be caused to execute the above method provided in this embodiment.

[0110] Example 5

[0111] This embodiment provides a computer program product including executable instructions, which are computer instructions stored in a computer-readable storage medium. When a processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the electronic device performs the method provided in this embodiment.

[0112] The steps involved in the above embodiments 2 to 5 correspond to those in embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media that includes one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and cause the processor to perform any method of the present invention.

[0113] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0114] The above description is only a preferred embodiment of the present invention. Although the specific implementation of the present invention is described in conjunction with the accompanying drawings, it does not limit the scope of protection of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present invention.

Claims

1. A geological attribute modeling method based on joint constraints of multi-source information, characterized in that: include: Obtain geological exploration data, obtain geological attribute data after preprocessing, and classify and quantify the geological attribute data into continuous and discontinuous types; Based on the quantified geological attribute data, multi-source geological attribute correlation analysis is performed to obtain the correlation between different geological attributes; Construct a geological structure model of the area to be modeled, divide the model into unstructured networks based on the correlation between different geological attributes, and assign geological unit grid attributes based on 1D+2D trend map constraints to each grid area according to the different geological attribute data obtained, and construct a geological unit attribute model; When constructing the geological structure model, the inversion constraint method is used to make the spatial boundary of the geological unit reflect the actual geological structure. The surface constraint conditions of the geological unit are determined through exploration data and seismic inversion results. Based on the surface constraint conditions, several small units are obtained through unstructured grid division according to the distribution of fractures or tunnel faces. Determination of the surface constraint conditions includes: Based on the survey data, the spatial distribution information of the geological unit boundary is determined, including: the distribution and depth of the underground rock layer can be identified by the location and depth of the drill hole, and the surface boundary function of the geological body is determined, which is expressed as: ； In the above formula, is a function that describes the shape of the boundary surface of the geological unit. Represents spatial coordinates; The change in seismic wave velocity is used to reflect the boundaries of different geological units, thereby determining the constraint surface, which is expressed as: ； In the above formula, and represent the wave velocities of geological body 1 and geological body 2 respectively; Determine the surface constraints of the geological unit volume, including: ； In the above formula, Indicates the inverted i The surface boundary function of a geological unit volume, is the actual measurement data; The construction of the geological unit attribute model includes: Coarsening of geological attribute data; that is, converting the acquired geological attribute data into a low-resolution form by spatially downscaling the data; Grid mapping of geological attribute data; that is, using refined grid subdivision technology, the grid size is selected according to the actual needs of the region, the region is divided into multiple grid cells, each grid cell represents a certain spatial range, and each grid cell is assigned a geological attribute value; For the geological attribute values ​​assigned to each grid cell, a 1D time-varying trend graph of the geological attribute is generated to show how the geological attribute changes over time. The generated 1D trend graph is used as a constraint to correct the grid cell assignment results. At the same time, a 2D spatial distribution trend graph of the geological attribute is generated on a cross section or other 2D plane using a 2D interpolation method to show how the geological attribute changes with spatial position. The generated 2D trend graph is used as a constraint to correct the grid cell assignment results. Based on the 2D trend graph, the depth from the surface or tunnel entrance is introduced to form a 3D trend graph, which is used to show the changing trends of different geological attributes at different depths under vertical changes. Based on the data of three dimensions in space, a complete 3D spatial distribution trend graph is generated through spatial interpolation to adjust the attribute values ​​of grid cells. Based on the geological unit attribute model, the nonlinear relationship between different attributes in the model is extracted through a dynamic fuzzy neural network. Then, a multi-attribute probabilistic neural network modeling method is adopted to update and aggregate the geological attributes of each grid node. In this way, the predicted geological attribute value of each grid cell in the geological unit attribute model that changes with time and the tunneling process is output.

2. A geological attribute modeling method based on multi-source information joint constraints according to claim 1, characterized in that: The geological attributes include fractures, water content based on resistivity inversion, and degree of fragmentation based on seismic wave inversion; Among them, the width of the crack and the water content based on resistivity inversion are continuous geological attributes, while the size of the crack and the degree of fragmentation based on seismic wave propagation velocity inversion are discontinuous geological attributes; the continuous geological attribute data are standardized or normalized, and the discontinuous geological attribute data are coded.

3. A geological attribute modeling system based on multi-source information joint constraints, characterized by: include: The data acquisition and preprocessing module is used to obtain geological exploration data of the tunnel area to be modeled, and obtain continuous attribute data and discontinuous attribute data after quantitative processing; The geological attribute analysis module is used to obtain the correlation between different geological attributes through multivariate attribute correlation analysis based on the quantified geological attribute data; The geological attribute modeling module is used to construct a geological structure model of the area to be modeled and perform cell network division. Based on the different geological attribute data obtained, the unit cell grid attribute values ​​are assigned based on 1D+2D trend graph constraints to preliminarily build a geological attribute model. The geological attribute model optimization module is used to extract the nonlinear relationship between different attributes in the model through dynamic fuzzy neural network based on the geological attribute model, and then use the multi-attribute probabilistic neural network modeling method to update and aggregate the geological characteristics of each grid node to obtain the final geological attribute model; When constructing the geological structure model, the inversion constraint method is used to make the spatial boundary of the geological unit reflect the actual geological structure. The surface constraint conditions of the geological unit are determined through exploration data and seismic inversion results. Based on the surface constraint conditions, several small units are obtained through unstructured grid division according to the distribution of fractures or tunnel faces. Determination of the surface constraint conditions includes: Based on the survey data, the spatial distribution information of the geological unit boundary is determined, including: the distribution and depth of the underground rock layer can be identified by the location and depth of the drill hole, and the surface boundary function of the geological body is determined, which is expressed as: ； In the above formula, is a function that describes the shape of the boundary surface of the geological unit. Represents spatial coordinates; The change in seismic wave velocity is used to reflect the boundaries of different geological units, thereby determining the constraint surface, which is expressed as: ； In the above formula, and represent the wave velocities of geological body 1 and geological body 2 respectively; Determine the surface constraints of the geological unit volume, including: ； In the above formula, Indicates the inverted i The surface boundary function of a geological unit volume, is the actual measurement data; The construction of the geological unit attribute model includes: Coarsening of geological attribute data; that is, converting the acquired geological attribute data into a low-resolution form by spatially downscaling the data; Grid mapping of geological attribute data; that is, using refined grid subdivision technology, the grid size is selected according to the actual needs of the region, the region is divided into multiple grid cells, each grid cell represents a certain spatial range, and each grid cell is assigned a geological attribute value; For the geological attribute values ​​assigned to each grid cell, a 1D time-varying trend graph of the geological attribute is generated to show how the geological attribute changes over time. The generated 1D trend graph is used as a constraint to correct the grid cell assignment results. At the same time, a 2D spatial distribution trend graph of the geological attribute is generated on a cross section or other 2D plane using a 2D interpolation method to show how the geological attribute changes with spatial position. The generated 2D trend graph is used as a constraint to correct the grid cell assignment results. Based on the 2D trend graph, the depth from the surface or tunnel entrance is introduced to form a 3D trend graph, which is used to show the changing trends of different geological attributes at different depths under vertical changes. Based on the data of three dimensions in space, a complete 3D spatial distribution trend graph is generated through spatial interpolation to adjust the attribute values ​​of grid cells. The geological attribute prediction module is used to extract the nonlinear relationship between different attributes in the geological unit body attribute model through dynamic fuzzy neural network, and then adopt the multi-attribute probabilistic neural network modeling method to update and aggregate the geological attributes of each grid node, so as to output the geological attribute prediction value of each grid unit in the geological unit body attribute model that changes with time and the excavation process.

4. An electronic device, characterized in that: include: a memory for storing executable instructions; The processor is configured to implement the geological attribute modeling method based on multi-source information joint constraints as described in any one of claims 1-2 when executing the executable instructions stored in the memory.

5. A computer-readable storage medium, characterized in that Executable instructions are stored, which are used to cause a processor to execute the executable instructions to implement the geological attribute modeling method based on multi-source information joint constraints as described in any one of claims 1-2.

6. A computer program product, characterized in that The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the geological attribute modeling method based on multi-source information joint constraints described in any one of claims 1-2 is implemented.

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