Intelligent prediction and analysis method, system, equipment, storage medium and program product for underground cavern geology

Abstract

The present invention discloses a method, system, equipment, storage medium and program product for intelligent prediction and analysis of underground cavern geology. The method comprises: training an original recognition model based on sample data of geophysical exploration results obtained after geological interpretation to obtain a poor geological body prediction model; inputting current geophysical exploration results data of the tunnel section to be analyzed into the poor geological body prediction model for geological analysis to obtain poor geological body information; quantifying features of the poor geological body information, tunnel face surrounding rock category and excavation space extension information of the underground cavern; and performing advanced geological prediction and analysis of the underground cavern based on the poor geological body development level value, tunnel face surrounding rock category value and spatial extension feature value obtained after feature quantification. By predicting the type and distribution range of the poor geological body, accurate geological analysis of the underground cavern is achieved, effectively improving the accuracy of advanced geological prediction.

Description

Technical Field

[0001] The present invention relates to the field of geological prediction technology, and in particular to an intelligent prediction and analysis method, system, equipment, storage medium and program product for underground cavern geology. Background Art

[0002] With the development of the energy industry and the need for urban construction, the construction of underground caverns is crucial for fields such as water conservancy and hydropower, railway and highway transportation, energy mining, and municipal engineering. Advanced geological prediction is an effective method to understand the geological conditions in front of the tunnel face during underground cavern construction. It is an indispensable part of current underground cavern construction, plays an important role in safety assurance and disaster prevention, and is conducive to information-based construction. At present, the advanced geological prediction system for underground caverns in the water conservancy and hydropower industry is still imperfect. The use of a single detection method has limitations, and the reliability of geological analysis based on manual interpretation is highly dependent on the experience of the interpreter, resulting in limited accuracy of advanced geological prediction for underground caverns. Summary of the Invention

[0003] The main purpose of the present invention is to provide an intelligent prediction and analysis method, system, equipment, storage medium and program product for underground cavern geology, aiming to solve the technical problems that the existing technology has limitations in using a single detection method, and the reliability of geological analysis based on manual interpretation is highly dependent on the experience of the interpreter, resulting in limited accuracy of advanced geological prediction of underground caverns.

[0004] To achieve the above object, the present invention provides an intelligent prediction and analysis method for underground cavern geology, the method comprising the following steps:

[0005] Obtain historical geophysical exploration data of underground caverns;

[0006] Performing geological interpretation on the historical geophysical exploration results data to obtain geophysical exploration results sample data;

[0007] Constructing an original recognition model and training the original recognition model based on the sample data of the geophysical exploration results to obtain a poor geological body prediction model, wherein the original recognition model includes a U-Net network model;

[0008] Inputting the current geophysical exploration results data of the to-be-analyzed section of the underground cavern into the bad geological body prediction model for geological analysis to obtain bad geological body information of the to-be-analyzed section, wherein the bad geological body information includes bad geological body types and spatial distribution information of the bad geological body types;

[0009] Obtaining the type of surrounding rock and excavation space extension information of the tunnel face of the underground cavern;

[0010] Quantifying the characteristics of the unfavorable geological body information, the tunnel face surrounding rock category, and the excavation space extension information to obtain an unfavorable geological body development level value, a tunnel face surrounding rock category value, and a space extension characteristic value;

[0011] An advanced geological prediction analysis is performed on the underground cavern based on the unfavorable geological body development level value, the tunnel face surrounding rock category value and the spatial extension characteristic value.

[0012] Optionally, the historical geophysical exploration results data include seismic wave exploration results data and ground-penetrating radar exploration results data, the seismic wave exploration results data include wave velocity diagrams and seismic wave profile diagrams, and the ground-penetrating radar exploration results data include ground-penetrating radar profile diagrams; the acquisition of the historical geophysical exploration results data of underground caverns includes:

[0013] Obtain surrounding rock geological conditions and geological structural conditions based on historical geological analysis data of underground caverns;

[0014] Determining a cavern prediction strategy based on the surrounding rock geological conditions and the geological structural conditions;

[0015] Based on the cavern prediction strategy, seismic wave detection data and ground penetrating radar detection data are collected from the underground cavern to obtain seismic wave detection data and ground penetrating radar detection data;

[0016] The seismic wave detection data and the ground penetrating radar detection data are preprocessed to obtain historical geophysical detection results data, and the preprocessing includes static correction processing, denoising processing, dynamic correction processing, time-depth conversion processing and normalization processing.

[0017] Optionally, performing geological interpretation on the historical geophysical exploration results data to obtain geophysical exploration results sample data includes:

[0018] Performing geological interpretation feature analysis on the seismic wave detection result data and the ground-penetrating radar detection result data to obtain parameter change feature information of seismic wave parameters and ground-penetrating radar parameters, wherein the seismic wave parameters include seismic wave velocity, seismic wave frequency, and seismic wave amplitude, and the ground-penetrating radar parameters include ground-penetrating radar frequency and ground-penetrating radar amplitude;

[0019] Extracting abnormal features from parameter change characteristic information of the seismic wave parameters and the ground penetrating radar parameters to obtain abnormal seismic wave characteristic information and abnormal ground penetrating radar characteristic information;

[0020] Performing correlation analysis on the seismic wave abnormal characteristic information and the ground penetrating radar abnormal characteristic information to obtain the type of the sample bad geological body;

[0021] Geophysical exploration result sample data is constructed based on the sample poor geological body type and the historical geophysical exploration result data.

[0022] Optionally, the constructing of geophysical exploration achievement sample data based on the sample poor geological body type and the historical geophysical exploration achievement data includes:

[0023] determining a seismic wave abnormal segment and a ground penetrating radar abnormal segment based on the seismic wave abnormal characteristic information and the ground penetrating radar abnormal characteristic information;

[0024] Performing spatial superposition analysis on the seismic wave abnormal segment and the ground penetrating radar abnormal segment to obtain abnormal overlapping areas and abnormal non-overlapping areas;

[0025] Generating geometric constraints based on the geological structural regularity information of the underground cavern and the type of the sample adverse geological body;

[0026] Determine the pile number segment covered by the bad geological body of the sample bad geological body type according to the geometric constraint condition, the abnormal overlapping area and the abnormal non-overlapping area;

[0027] Determine the sample spatial distribution information of the sample bad geological body type based on the spatial position of the pile number segment;

[0028] Geophysical exploration result sample data is constructed based on the historical geophysical exploration result data, the sample poor geological body type and the sample spatial distribution information.

[0029] Optionally, the constructing of the original recognition model and training the original recognition model based on the geophysical exploration results sample data to obtain a poor geological body prediction model includes:

[0030] Determine an encoder, a decoder, and a skip connection layer, and construct an original recognition model based on the encoder, the decoder, and the skip connection layer, and initialize weight parameters of the original recognition model;

[0031] Performing convolution and pooling processing on the sample data of the geophysical exploration results through the encoder to output global semantic feature information of the bad geological body type;

[0032] Performing transposition convolution on the global semantic feature information through the decoder to output spatial distribution information and type labels of the bad geological body types;

[0033] Performing spatial alignment and multi-scale feature fusion on the global semantic feature information, spatial distribution information and type label of the bad geological body type through the skip connection layer;

[0034] A model weight parameter is obtained based on the output results of the encoder, the decoder and the skip connection layer, and a poor geological body prediction model is constructed based on the model weight parameter.

[0035] Optionally, the constructing of the original recognition model and training the original recognition model based on the geophysical exploration results sample data to obtain a poor geological body prediction model includes:

[0036] Constructing an original recognition model, wherein the original recognition model includes a ReLU activation function and a Sigmoid activation function, wherein the ReLU activation function is used for a convolutional layer of the original recognition model, and the Sigmoid function is used for an output layer of the original recognition model;

[0037] Training the original recognition model based on the geophysical exploration results sample data to obtain a candidate recognition model;

[0038] Construct a loss function and an optimizer, wherein the optimizer includes an AdaDelta adaptive learning rate optimizer, and the loss function includes a multi-classification cross entropy loss function, wherein the multi-classification cross entropy loss function includes:

[0039]

[0040] in, Indicates the The actual bad geological body type label of samples, Represents the candidate recognition model for the The sample labels predicted by samples, represents the total number of samples, Indicates the number of bad geological body types, represents the total penalty parameter for prediction error;

[0041] The candidate identification model is optimized and converged based on the loss function and the optimizer to obtain a poor geological body prediction model.

[0042] In addition, to achieve the above-mentioned purpose, the present invention also proposes an underground cavern geological intelligent prediction and analysis system, which includes:

[0043] Detection data acquisition module, used to obtain historical geophysical detection results data of underground caverns;

[0044] A geological interpretation module is used to perform geological interpretation on the historical geophysical exploration results data to obtain geophysical exploration results sample data;

[0045] A model building module is used to build an original recognition model and train the original recognition model based on the sample data of the geophysical exploration results to obtain a poor geological body prediction model, wherein the original recognition model includes a U-Net network model;

[0046] a geological analysis module, configured to input the current geophysical exploration results data of the to-be-analyzed section of the underground cavern into the unfavorable geological body prediction model for geological analysis, and obtain unfavorable geological body information of the to-be-analyzed section, wherein the unfavorable geological body information includes the type of unfavorable geological body and the spatial distribution information of the type of unfavorable geological body;

[0047] A cavern information acquisition module, configured to acquire the type of surrounding rock and excavation space extension information of the tunnel face of the underground cavern;

[0048] A feature quantification module is used to perform feature quantification on the unfavorable geological body information, the tunnel face surrounding rock category, and the excavation space extension information to obtain an unfavorable geological body development level value, a tunnel face surrounding rock category value, and a space extension feature value;

[0049] The advanced geological prediction and analysis module is used to perform advanced geological prediction and analysis on the underground cavern based on the unfavorable geological body development level value, the tunnel face surrounding rock category value and the spatial extension characteristic value.

[0050] In addition, to achieve the above-mentioned purpose, the present application also proposes an intelligent prediction and analysis device for underground cavern geology, which includes: a memory, a processor, and a computer program stored on the memory and runnable on the processor, and the computer program is configured to implement the steps of the intelligent prediction and analysis method for underground cavern geology as described above.

[0051] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the underground cavern geological intelligent prediction and analysis method as described above are implemented.

[0052] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the underground cavern geological intelligent prediction and analysis method as described above.

[0053] The present invention obtains historical geophysical exploration results data of underground caverns; performs geological interpretation on the historical geophysical exploration results data to obtain geophysical exploration results sample data; constructs an original recognition model, and trains the original recognition model based on the geophysical exploration results sample data to obtain a bad geological body prediction model, wherein the original recognition model includes a U-Net network model; inputs the current geophysical exploration results data of the to-be-analyzed cave section of the underground cavern into the bad geological body prediction model for geological analysis to obtain bad geological body information of the to-be-analyzed cave section, wherein the bad geological body information includes bad geological body types and spatial distribution information of the bad geological body types; obtains the surrounding rock type and excavation space extension information of the tunnel face of the underground cavern; performs feature quantification on the bad geological body information, the surrounding rock type of the tunnel face and the excavation space extension information to obtain the development of bad geological bodies. Level value, face surrounding rock category value and spatial extension characteristic value; based on the development level value of the adverse geological body, the category value of the face surrounding rock and the spatial extension characteristic value, the underground cavern is subjected to advanced geological prediction analysis; since the present invention performs geological interpretation on historical geophysical exploration results data and constructs geophysical exploration results sample data based on the geological interpretation results, the sample quality and model training efficiency are effectively improved, and the adverse geological body prediction model obtained after training is used to perform geological analysis on the current geophysical exploration results data of the tunnel section to be analyzed, thereby accurately obtaining the adverse geological body type and spatial distribution information of the tunnel section to be analyzed, and by quantifying the characteristics of the adverse geological body information, the category of the face surrounding rock, the excavation progress information and the spatial extension information, it is possible to accurately evaluate the changes in the geological conditions in front of the underground cavern, thereby realizing accurate geological analysis of the underground cavern and effectively improving the accuracy of advanced geological prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 It is a schematic diagram of the structure of the underground cavern geological intelligent prediction and analysis device in the hardware operating environment involved in the embodiment of the present invention;

[0056] Figure 2 This is a flow chart of the first embodiment of the intelligent prediction and analysis method for underground cavern geology according to the present invention;

[0057] Figure 3 This is a flow chart of a second embodiment of the intelligent prediction and analysis method for underground cavern geology according to the present invention;

[0058] Figure 4 This is a structural block diagram of the first embodiment of the underground cavern geology intelligent prediction and analysis system of the present invention.

[0059] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0060] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of the underground cavern geological intelligent prediction and analysis equipment in the hardware operating environment involved in the embodiment of the present invention.

[0062] like Figure 1 As shown, the intelligent prediction and analysis device for underground cavern geology may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display and an input unit, such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may be a storage system independent of the processor 1001.

[0063] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the underground cavern geological intelligent prediction and analysis equipment, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0064] like Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and an underground cavern geological intelligent prediction and analysis program.

[0065] exist Figure 1In the underground cavern geology intelligent prediction and analysis device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the underground cavern geology intelligent prediction and analysis device of the present invention can be set in the underground cavern geology intelligent prediction and analysis device, and the underground cavern geology intelligent prediction and analysis device calls the underground cavern geology intelligent prediction and analysis program stored in the memory 1005 through the processor 1001, and executes the underground cavern geology intelligent prediction and analysis method provided by the embodiment of the present invention.

[0066] The embodiment of the present invention provides an intelligent prediction and analysis method for underground cavern geology, referring to Figure 2 , Figure 2 Schematic diagram of the process of the first embodiment of the intelligent prediction and analysis method for underground cavern geology of the present invention.

[0067] In this embodiment, the underground cavern geological intelligent prediction and analysis method includes the following steps:

[0068] Step S10: Acquire historical geophysical exploration data of underground caverns.

[0069] It should be noted that this embodiment can be applied to the advanced geological prediction of underground caverns. The advanced geological prediction can promote the rapid development of digitalization and refinement of geological analysis and geological prediction technologies in underground cavern construction. Among them, the advanced geological prediction can be an advanced prediction of the surrounding rock and stratum conditions in front of and around the tunnel face when the underground cavern is excavated.

[0070] In some embodiments, advanced geological prediction of underground caverns can be achieved using three main methods: engineering geological analysis, geophysical exploration, and geological exploration. Engineering geological analysis includes surface geological body projection and tunnel face cataloging. Geological exploration includes advanced pilot tunneling and advanced drilling. Engineering geological analysis and geological exploration analyze geological conditions to identify key high-risk areas, classify surrounding rock types, and provide guidance for underground cavern excavation. However, these methods require professional geologists, have short prediction ranges, and their accuracy is closely related to the experience of the forecaster.

[0071] This embodiment aims at the advanced geological prediction work based on the geophysical detection method, and uses artificial intelligence technology to realize intelligent prediction analysis. By combining seismic wave detection and ground penetrating radar detection to analyze unfavorable geological bodies, the unfavorable geological body analysis is performed from the detection results of two dimensions, which effectively improves the prediction accuracy. The advanced geological prediction is performed based on the unfavorable geological body information, the type of surrounding rock of the tunnel face and the extension information of the excavation space, thereby effectively improving the accuracy of the advanced geological prediction and providing reliable data support for the construction of underground caverns.

[0072] It should be understood that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution capabilities, such as a tablet computer, personal computer, mobile phone, etc., or a terminal electronic device capable of performing the aforementioned functions. This embodiment and the following embodiments will be described below using an intelligent prediction and analysis device for underground cavern geology (hereinafter referred to as the analysis device) as an example.

[0073] It should be noted that the historical geophysical exploration data may be geophysical exploration data of other sections of the underground cavern that have been historically explored. In some embodiments, the historical geophysical exploration data may be seismic wave detection data and ground penetrating radar detection data that have been verified through excavation.

[0074] It can be understood that this embodiment obtains seismic wave detection data and ground-penetrating radar detection data through seismic wave detection and ground-penetrating radar detection, and obtains historical geophysical detection results data of underground caverns based on the seismic wave detection data and ground-penetrating radar detection data.

[0075] Furthermore, in order to improve the data accuracy and quality of geophysical exploration data, thereby improving the accuracy of geological analysis and prediction, in some embodiments, the historical geophysical exploration data includes seismic wave exploration data and ground-penetrating radar exploration data, the seismic wave exploration data includes a wave velocity map and a seismic wave profile map, and the ground-penetrating radar exploration data includes a ground-penetrating radar profile map; the above-mentioned step S10 may include:

[0076] Step S101: Obtaining surrounding rock geological conditions and geological structural conditions based on historical geological analysis data of the underground cavern;

[0077] Step S102: determining a cavern prediction strategy based on the surrounding rock geological conditions and the geological structural conditions;

[0078] Step S103: collecting seismic wave detection data and ground penetrating radar detection data from the underground cavern based on the cavern prediction strategy to obtain seismic wave detection data and ground penetrating radar detection data;

[0079] Step S104: pre-processing the seismic wave detection data and the ground penetrating radar detection data to obtain historical geophysical detection results data.

[0080] It should be noted that the analysis equipment can obtain historical geophysical exploration data through geophysical exploration methods. The geophysical exploration methods used in this embodiment include seismic wave detection (a long-distance advanced detection method) and ground-penetrating radar (a short-distance advanced detection method). Therefore, the geophysical exploration results involved include seismic wave detection results and ground-penetrating radar detection results. Seismic wave detection results include: P-wave velocity maps, S-wave velocity maps, and seismic profiles (two-dimensional time-distance images); ground-penetrating radar detection results include: radar profiles (two-dimensional time-distance images).

[0081] It should be noted that the preprocessing includes static correction processing, denoising processing, dynamic correction processing, time-depth conversion processing and normalization processing; among them, static correction is used to eliminate the time difference caused by terrain and velocity differences; dynamic correction and superposition processing are used to eliminate the propagation time difference and enhance the effective wave energy; depth conversion processing can be time-depth conversion, which is used to calculate the depth based on the electromagnetic wave velocity.

[0082] In some embodiments, the analysis equipment selects appropriate observation methods and related observation parameters according to the surrounding rock geological conditions and structural conditions; data is collected according to the observation system design plan; due to the complexity of the on-site construction environment and the different types, formats, and time dimensions of the collected data, the collected data is pre-processed by static correction, denoising, dynamic correction, time-depth conversion, and normalization to obtain geophysical exploration results data.

[0083] Step S20: Performing geological interpretation on the historical geophysical exploration results data to obtain geophysical exploration results sample data.

[0084] It should be noted that the geophysical exploration result sample data may be sample data used to train the bad geological body network model. The geophysical exploration result sample data may include geophysical exploration results and the bad geological body type and spatial distribution information corresponding to the geophysical exploration results.

[0085] In a specific implementation, the analysis equipment can perform geological interpretation on the geophysical exploration results data to obtain the type and spatial distribution information of unfavorable geological bodies, and construct geophysical exploration results sample data based on the geophysical exploration results, the type and spatial distribution information of unfavorable geological bodies.

[0086] In some embodiments, the analysis equipment marks the types and spatial distribution information of unfavorable geological bodies verified by excavation on the detection results, forming one or more samples for machine learning, so that the latter can realize the function of intelligent identification of unfavorable geological bodies by learning sample data.

[0087] Furthermore, in order to accurately perform geological interpretation on historical geophysical exploration results data and construct high-quality geophysical exploration results sample data, thereby improving model training efficiency and performance, in some embodiments, the above step S20 may include:

[0088] Step S201: performing geological interpretation feature analysis on the seismic wave detection result data and the ground penetrating radar detection result data to obtain parameter change feature information of seismic wave parameters and ground penetrating radar parameters;

[0089] Step S202: extracting abnormal features from the parameter change characteristic information of the seismic wave parameters and the ground penetrating radar parameters to obtain abnormal seismic wave characteristic information and abnormal ground penetrating radar characteristic information;

[0090] Step S203: performing correlation analysis on the seismic wave abnormal characteristic information and the ground penetrating radar abnormal characteristic information to obtain the type of sample adverse geological body;

[0091] Step S204: constructing geophysical exploration result sample data based on the sample poor geological body type and the historical geophysical exploration result data.

[0092] It should be noted that the seismic wave parameters include seismic wave velocity, seismic wave frequency, and seismic wave amplitude, and the ground-penetrating radar parameters include ground-penetrating radar frequency and ground-penetrating radar amplitude. The ground-penetrating radar abnormality feature information may include abnormal frequency features and abnormal amplitude features within the ground-penetrating radar features; the seismic wave abnormality feature information may include abnormal wave velocity features, abnormal frequency features, and abnormal amplitude features within the seismic wave features.

[0093] It is understandable that the analysis equipment can analyze the signal characteristics of geophysical exploration data and explain the changes in various parameters. For seismic exploration results, the focus is on: wave velocity, frequency, amplitude, etc., and for ground penetrating radar detection results, the focus is on: frequency, amplitude, etc.; secondly, combined with the signal propagation law, the geological body characteristics that may lead to the above-mentioned signal characteristics are analyzed, including: whether there are unfavorable geological bodies, the types of unfavorable geological bodies, the spatial distribution of unfavorable geological bodies, and the geological conditions in front of the tunnel face are interpreted; finally, the surrounding rock classification results are given.

[0094] Taking the seismic wave method as an example: the longitudinal wave velocity of the seismic waves in the section between pile numbers J0+400 and J0+500 ranges from 2600 m / s to 3200 m / s, indicating that the overall surrounding rock conditions are comparable to those at the current tunnel face. The reflection signal from pile numbers J0+438 to J0+463 is strong, with low frequency and large amplitude, suggesting the presence of abundant water in this area. The rock mass quality in the section between pile numbers J0+400 and J0+500 is Class III, while that in the section between pile numbers J0+438 and J0+463 is Class IV, requiring careful attention during excavation.

[0095] In some embodiments, the analysis device can analyze whether there are any areas in the result data where the signal characteristics differ from the surrounding rock signal characteristics; if so, the possible causes of the signal change are analyzed to determine the type of unfavorable geological body causing the signal change; the unfavorable geological body contours are delineated: for seismic wave result data, reduced wave velocity, reflection wave event offset, and amplitude mutation areas may indicate the boundaries of unfavorable geological bodies; for ground penetrating radar result data, reflection wave event offset and amplitude mutation areas may indicate the boundaries of unfavorable geological bodies. Finally, based on the geological interpretation results of the geophysical exploration result data, preliminary conclusions of the geophysical exploration analysis are drawn, such as the possible types and spatial distribution information of unfavorable geological bodies and the classification of surrounding rocks analyzed by geophysical exploration.

[0096] Step S30: constructing an original recognition model, and training the original recognition model based on the geophysical exploration result sample data to obtain a poor geological body prediction model.

[0097] It should be noted that the original recognition model includes a U-Net network model.

[0098] In some embodiments, the analysis device can determine the encoder, decoder and skip connection layers of the U-Net network model, construct an original recognition model based on the encoder, decoder and skip connection layers, generate a training set, a test set and a validation set based on the sample data of geophysical exploration results, train, test and validate the original recognition model based on the sample data of geophysical exploration results, and obtain a poor geological body prediction model.

[0099] Furthermore, in order to improve the model prediction efficiency and accuracy, in some embodiments, the above step S30 may include:

[0100] Step S301: determining an encoder, a decoder, and a skip connection layer, and constructing an original recognition model based on the encoder, the decoder, and the skip connection layer, and initializing weight parameters of the original recognition model;

[0101] Step S302: performing convolution and pooling processing on the geophysical exploration result sample data through the encoder to output global semantic feature information of the bad geological body type;

[0102] Step S303: performing transposition convolution on the global semantic feature information through the decoder to output spatial distribution information and type labels of the bad geological body types;

[0103] Step S304: performing spatial alignment and multi-scale feature fusion on the global semantic feature information, spatial distribution information and type label of the bad geological body type through the skip connection layer;

[0104] Step S305: obtaining model weight parameters based on the output results of the encoder, the decoder and the skip connection layer, and constructing a poor geological body prediction model based on the model weight parameters.

[0105] It should be noted that the encoder includes five stages of convolutional layers and four stages of downsampling layers, wherein the first convolutional layer of the first stage includes a 3×3 convolution kernel and 1 input channel, the second convolutional layer and the third convolutional layer both include a 3×3 convolution kernel and 32 input channels, the first convolutional layer and the second convolutional layer of the second stage both include a 3×3 convolution kernel and 64 input channels, the first convolutional layer and the second convolutional layer of the third stage both include a 3×3 convolution kernel and 128 input channels, the first convolutional layer and the second convolutional layer of the fourth stage both include a 3×3 convolution kernel and 256 input channels, the first convolutional layer and the second convolutional layer of the fifth stage both include a 3×3 convolution kernel and 512 input channels, and the downsampling layers of the four stages all include a 2×2 maximum pooling layer.

[0106] The decoder includes five stages of convolutional layers and four stages of upsampling layers, wherein the first convolutional layer and the second convolutional layer of the first stage both include 3×3 convolution kernels and 256 input channels, the first convolutional layer and the second convolutional layer of the second stage both include 3×3 convolution kernels and 128 input channels, the first convolutional layer and the second convolutional layer of the third stage both include 3×3 convolution kernels and 64 input channels, the first convolutional layer and the second convolutional layer of the fourth stage both include 3×3 convolution kernels and 32 input channels, the first convolutional layer of the fifth stage includes a 1×1 convolution kernel and 1 input channel, and the upsampling layers of the four stages all include 2×2 transposed convolution layers.

[0107] The jump connection layer includes four connection layers, wherein the first connection layer connects the 32-channel feature map obtained by the first-stage convolutional layer in the decoding module with the 32-channel feature map obtained by the fifth-stage convolutional layer in the encoding module to obtain a 64-channel fusion feature map, the second connection layer connects the 64-channel feature map obtained by the second-stage convolutional layer in the decoding module with the 64-channel feature map obtained by the fourth-stage convolutional layer in the encoding module to obtain a 128-channel fusion feature map, the third connection layer connects the 128-channel feature map obtained by the third-stage convolutional layer in the decoding module with the 128-channel feature map obtained by the third-stage convolutional layer in the encoding module to obtain a 256-channel fusion feature map, and the fourth connection layer connects the 256-channel feature map obtained by the fourth-stage convolutional layer in the decoding module with the 256-channel feature map obtained by the second-stage convolutional layer in the encoding module to obtain a 512-channel fusion feature map.

[0108] It should be noted that the encoder is used to abstract geological features, and the processing operation is to perform convolution and pooling on the sample data of geophysical exploration results; the output data is the global semantics of the output of the poor geological body type, for example: water-rich confidence level 90%.

[0109] The decoder is used to restore spatial details. The processing operation is to perform transposed convolution on the encoder output features. The output data is the spatial distribution information and type label of the output poor geological body, for example: pile number xx~xx segment, rich water development, confidence level 90%.

[0110] The skip connection layer is used for multi-scale information fusion. The processing operation is to connect the signal features extracted by the encoder with the signal features extracted by the decoder. The processing operation includes spatial alignment. The output data is the output water content estimate to improve the forecast accuracy.

[0111] Furthermore, in order to improve the optimization efficiency and convergence speed of the model, effectively avoid the problems of local optimum and overfitting, and thus improve the prediction accuracy of unfavorable geological bodies, in some embodiments, the above step S30 may include:

[0112] Step S3001: constructing an original recognition model;

[0113] Step S3002: training the original recognition model based on the geophysical exploration result sample data to obtain a candidate recognition model;

[0114] Step S3003: constructing a loss function and an optimizer;

[0115] Step S3004: Optimizing and converging the candidate identification model based on the loss function and the optimizer to obtain a poor geological body prediction model.

[0116] It should be noted that the original recognition model includes a ReLU activation function and a Sigmoid activation function. The ReLU activation function is used after the convolution layer of the original recognition model, and the Sigmoid function is used for the output layer of the original recognition model.

[0117] It should be noted that the optimizer includes the AdaDelta adaptive learning rate optimizer. The AdaDelta adaptive learning rate optimizer can effectively solve the problem of too rapid decrease in learning rate by adaptively adjusting the learning rate based on the second-order matrix estimation of gradient update.

[0118] The loss function includes a multi-classification cross entropy loss function, and the multi-classification cross entropy loss function includes:

[0119]

[0120] in, Indicates the The actual bad geological body type label of samples, Represents the candidate recognition model for the The sample labels predicted by samples, represents the total number of samples, Indicates the number of bad geological body types, represents the total penalty parameter for prediction error.

[0121] In a specific implementation, the analysis device uses the geophysical exploration data to perform model training on the U-Net network model, optimizes and converges the model using the loss function and the AdaDelta adaptive learning rate optimizer, and obtains the poor geological body network model.

[0122] In some embodiments, the training and optimization convergence process of the bad geological body prediction model may include:

[0123] Step A1: Data construction, for example, using a measured dataset of an underground cavern project of a pumped storage power station, including:

[0124] Seismic longitudinal wave velocity maps (50 images), shear wave velocity maps (50 images), and seismic profiles (50 images) were collected using the TGS360Pro system. Ground-penetrating radar profiles were collected using the CO730 system. The collected seismic wave data and ground-penetrating radar data were jointly annotated using pre-trained expert models and manual work, including four types of semantic labels (fault fracture zone, water-rich, soft rock, and cave) and pixel-level spatial coordinates.

[0125] Step A2: Model training, including:

[0126] (1) Network parameter initialization:

[0127] Convolutional layer initialization: use He normal initialization;

[0128] Upsampling layer initialization: bilinear interpolation initialization;

[0129] Skip connection layer initialization: Xavier uniform initialization is used;

[0130] Output layer initialization: The semantic segmentation head is initialized with zero-mean Gaussian, and the spatial regression head is initialized with LeCun normal;

[0131] (2) Construct multi-classification cross entropy loss as the loss function.

[0132] The input data is geophysical exploration results data. Based on the dual output requirements of classification and positioning tasks, a weighted composite loss function is constructed to calculate the loss. Backpropagation and parameter update are performed according to the following formula:

[0133]

[0134] in, and is the weight coefficient, .

[0135] (3) The AdaDelta adaptive learning rate optimizer is used to optimize the model convergence.

[0136] Due to the complex data collection environment in the field, geophysical exploration data is often noisy. AdaDelta dynamically adjusts the learning rate to achieve greater stability in the noisy gradient generation process. Furthermore, to meet task output requirements, which require simultaneous prediction of both the type and spatial location of unfavorable geological bodies, AdaDelta optimizes the learning rate for each parameter individually, eliminating manual tuning. AdaDelta automatically performs the following operations: squared gradient accumulation, parameter update step size, and update cumulative variables.

[0137] Step A3: Convergence process analysis, including:

[0138] By tracking the convergence index, parameters can be adjusted. The convergence index can be the following three types:

[0139] (1) Classification: Comprehensively evaluate the recognition accuracy of four types of unfavorable geological bodies, referring to the following formula:

[0140]

[0141] Among them, TP represents the number of samples of this class correctly predicted by the model, FP represents the number of samples of this class that are not correctly predicted by the model, FN represents the number of samples of this class that are not predicted in the annotation, and mIoU represents the mean of the intersection-over-union ratio of each category.

[0142] (2) Positioning, used to measure the mean absolute error of spatial distribution information prediction, refer to the following formula:

[0143]

[0144] in, N represents the number of samples; y true Indicates the i The actual pile number segment and spatial distribution position of each sample, where the pile number segment parameter is the sum of the starting pile number and the ending pile number, and the spatial distribution position is the horizontal development position (relative position from the axis of the underground cavern, cross-section diagram). x Axis mapping) and longitudinal developmental position (burial depth, profile y axis mapping); y pred Indicates the iThe predicted pile number segment and spatial distribution position of each sample; MAE represents the spatial position error of the adverse geological body.

[0145] (3) Loss reduction rate, used to determine whether the model has entered the stable convergence stage, refer to the following formula:

[0146]

[0147] in, L represents the total penalty for prediction error; L t Indicates the loss at the current stage; L t-10 Represents the loss in the previous period; DR represents the loss decline rate.

[0148] Step A4: Use the test dataset to verify the prediction accuracy. The model's prediction accuracy for fault fracture zones, water-rich areas, soft rocks, and caves is between 85% and 90%. Compared with manual interpretation, the accuracy is improved by 5 to 10 percentage points, and the interpretation rate is increased by 70%.

[0149] Step S40: inputting the current geophysical exploration result data of the to-be-analyzed section of the underground cavern into the unfavorable geological body prediction model for geological analysis to obtain unfavorable geological body information of the to-be-analyzed section.

[0150] It should be noted that the unfavorable geological body information includes the unfavorable geological body type and its spatial distribution information. For example, unfavorable geological body types may include fault fracture zones, water-rich areas, soft rock, and karst caves. The tunnel section to be analyzed may be the unexplored (unknown) tunnel section ahead of the tunnel face.

[0151] It should be noted that the above-mentioned spatial distribution information can be the distribution range of the unfavorable geological body, specifically including the pile number segment and spatial orientation. Spatial orientation mainly refers to when the unfavorable geological body is only locally developed, in which case not only the pile number segment but also the spatial position should be indicated, such as the upper / lower / left / right side of pile number xx to pile number xx.

[0152] In some embodiments, advanced geological prediction of underground caverns can be applied to water conservancy and hydropower projects. Based on the characteristics and requirements of these projects, the analysis equipment focuses on the following types of unfavorable geological bodies: fault fracture zones, water-rich areas, soft rock, and karst caves. These four types represent the desired unfavorable geological body types. During actual exploration, the input data for the unfavorable geological body prediction model includes geophysical exploration data.

[0153] Step S50: Obtaining the surrounding rock type and excavation space extension information of the tunnel face of the underground cavern.

[0154] It should be noted that the tunnel face surrounding rock type can refer to the unexcavated rock or soil surrounding and in front of the working face of the underground cavern. The tunnel face surrounding rock type primarily describes the properties of this geological body, such as its solidity, stability, presence of cracks, and presence of water.

[0155] It should be noted that the above-mentioned excavation spatial extension information can be the spatial extension information of the structures predicted and revealed by the previous excavation. The excavation spatial extension information can include the excavated length, the time point when a specific geological layer is reached, the spatial distribution information of the unfavorable geological bodies revealed by the excavation, etc.

[0156] In some embodiments, the analysis equipment can classify the surrounding rock within a certain distance in front of the tunnel face based on the preliminary conclusions of the engineering geological analysis, the results of the geophysical advanced detection analysis, and the results of the geological exploration analysis to obtain the tunnel face surrounding rock category.

[0157] Step S60: quantifying the characteristics of the unfavorable geological body information, the tunnel face surrounding rock type and the excavation space extension information to obtain an unfavorable geological body development level value, a tunnel face surrounding rock type value and a space extension characteristic value.

[0158] In some embodiments, the analysis equipment can convert the surrounding rock categories of the tunnel face obtained from the engineering geological analysis into corresponding numerical values as the input layer values of the neural network. The input layer values of the neural network corresponding to surrounding rock categories I, II, III1, III2, IV1, IV2, and V are: 1, 2, 3, 3.5, 4, 4.5, and 5, respectively.

[0159] In some embodiments, the development level value of the poor geological body is divided into the following categories according to the prediction confidence: 0-25% corresponds to the non-existence of the poor geological body, 25-50% (including 25%) corresponds to the low possibility of the existence of the poor geological body, 50-75% (including 50%) corresponds to the high possibility of the existence of the poor geological body, and 75-100% (including 75%) corresponds to the existence of the poor geological body. The development level value of the poor geological body is taken with reference to the following Table 1, which is a quantitative table of the development level of the poor geological body.

[0160] Table 1. Quantitative table of development levels of unfavorable geological bodies

[0161]

[0162] In some embodiments, the spatial extension characteristic value can be the spatial extension characteristic value of the structure speculated in the early excavation and revealed by the excavation. The analysis equipment can determine the spatial extension characteristic value by analyzing the spatial extension characteristics of the structure speculated in the early excavation and revealed by the excavation, and comparing it with the following Table 2, which is a quantification table of spatial extension characteristics.

[0163] Table 2, Quantization table of spatial extension features

[0164]

[0165] Step S70: performing advanced geological prediction analysis on the underground cavern based on the unfavorable geological body development level value, the tunnel face surrounding rock category value, and the spatial extension characteristic value.

[0166] In some embodiments, the analysis device can conduct comprehensive advanced geological forecast analysis based on multi-information fusion, building upon advanced geological forecast data collection, data processing, and data analysis. This analysis integrates the analytical conclusions of different forecasting methods, cross-validates the type and spatial distribution of adverse geological bodies, and the classification of surrounding rock masses, and ultimately, arrives at a comprehensive advanced geological forecast conclusion (including the type and spatial distribution of adverse geological bodies, the classification of surrounding rock masses, and the spatial extension characteristics of excavation).

[0167] In some embodiments, the analysis device may input the unfavorable geological body development level, the tunnel face surrounding rock type, and the spatial extension characteristic value into a pre-established geological prediction model. This model, constructed by combining historical data and expert knowledge, can output a risk level for the geological conditions ahead. For example, if the unfavorable geological body development level is high and the tunnel face surrounding rock type value indicates that the surrounding rock is very fragile, and the spatial extension characteristic indicates the presence of a fault extending toward the currently predicted tunnel section, then a high landslide risk is predicted ahead.

[0168] This embodiment obtains historical geophysical exploration results data of underground caverns; performs geological interpretation on the historical geophysical exploration results data to obtain geophysical exploration results sample data; constructs an original recognition model, and trains the original recognition model based on the geophysical exploration results sample data to obtain a bad geological body prediction model, wherein the original recognition model includes a U-Net network model; inputs the current geophysical exploration results data of the to-be-analyzed tunnel section of the underground cavern into the bad geological body prediction model for geological analysis to obtain bad geological body information of the to-be-analyzed tunnel section, wherein the bad geological body information includes bad geological body types and spatial distribution information of the bad geological body types; obtains the tunnel face surrounding rock type and excavation space extension information of the underground cavern; performs feature quantification on the bad geological body information, the tunnel face surrounding rock type and the excavation space extension information to obtain bad geological body development information. level value, face surrounding rock category value and spatial extension characteristic value; based on the development level value of the adverse geological body, the face surrounding rock category value and the spatial extension characteristic value, the underground cavern is subjected to advanced geological prediction analysis; since this embodiment performs geological interpretation on historical geophysical exploration results data and constructs geophysical exploration results sample data based on the geological interpretation results, the sample quality and model training efficiency are effectively improved, and the adverse geological body prediction model obtained after training is used to perform geological analysis on the current geophysical exploration results data of the tunnel section to be analyzed, thereby accurately obtaining the adverse geological body type and spatial distribution information of the tunnel section to be analyzed, and by quantifying the characteristics of the adverse geological body information, the face surrounding rock category, the excavation progress information and the spatial extension information, it is possible to accurately evaluate the changes in the geological conditions in front of the underground cavern, thereby realizing accurate geological analysis of the underground cavern and effectively improving the accuracy of advanced geological prediction.

[0169] refer to Figure 3 , Figure 3 This is a flow chart of the second embodiment of the intelligent prediction and analysis method for underground cavern geology of the present invention.

[0170] Based on the first embodiment above, in this embodiment, step S204 further includes:

[0171] Step S2041: determining a seismic wave abnormal segment and a ground penetrating radar abnormal segment based on the seismic wave abnormal characteristic information and the ground penetrating radar abnormal characteristic information.

[0172] It should be noted that analytical equipment can analyze seismic wave profiles and identify abnormal areas in parameters such as seismic wave velocity and amplitude. These anomalies may indicate the presence of underground geological structures such as caves, faults, or aquifers. Signal processing techniques (such as filtering and deconvolution) are used to identify abnormal areas in characteristics such as reflected signal intensity and phase changes, which may indicate the presence of fractures, cavities, or other geological anomalies.

[0173] Step S2042: performing spatial overlay analysis on the seismic wave abnormal segment and the ground penetrating radar abnormal segment to obtain abnormal overlapping areas and abnormal non-overlapping areas.

[0174] In some embodiments, the analysis equipment can perform spatial overlay analysis on the GIS platform for seismic wave anomaly segments and ground penetrating radar anomaly segments, clarifying the overlapping areas between the two and the independent anomaly areas. This helps to accurately locate the location of potential adverse geological bodies and their impact range.

[0175] Step S2043: Generate geometric constraints based on the geological structure regularity information of the underground cavern and the type of the sample unfavorable geological body.

[0176] In some embodiments, the analysis equipment can analyze underground caverns, collect and analyze face data under similar geological structures in past projects, understand their geological structural laws (such as fault strikes and rock layer dips), and set corresponding geometric constraints based on the above laws and the types of unfavorable geological bodies in the samples (such as fracture zones and water-rich areas) (for example, the width of the fracture zone does not exceed 1 meter and the dip is 30°-60° northwest).

[0177] Step S2044: determining the pile number segments covered by the bad geological body of the sample bad geological body type according to the geometric constraint conditions, the abnormal overlapping area and the abnormal non-overlapping area.

[0178] In some embodiments, the analysis equipment can further narrow down the specific pile number segments where the undesirable geological bodies may exist based on geometric constraints and the specific conditions of abnormal overlapping areas and non-overlapping areas; use drilling verification to refine the initially delineated pile number segments and ultimately determine the exact coverage range of the undesirable geological bodies.

[0179] Step S2045: Determine the sample spatial distribution information of the sample bad geological body type based on the spatial position of the pile number segment.

[0180] It should be understood that the analysis equipment can draw a spatial distribution map of the adverse geological body, including information such as its length, width, and depth, based on the finally confirmed pile number segment and its corresponding geological structure characteristics.

[0181] Step S2046: constructing geophysical exploration result sample data based on the historical geophysical exploration result data, the sample poor geological body type and the sample spatial distribution information.

[0182] It should be noted that the analysis equipment can summarize historical geophysical exploration results data (such as previous seismic waves, ground penetrating radar detection reports), sample adverse geological body types and sample spatial distribution information, standardize all data to ensure format consistency and readability, and create a geophysical exploration results sample database, including but not limited to geological descriptions, detection methods, abnormal characteristics, sample spatial distribution and other contents. Based on the geophysical exploration results sample database, geophysical exploration results sample data are obtained to train, test and verify the adverse geological body analysis model of the analyzed hole section in a targeted manner.

[0183] This embodiment determines seismic wave anomaly segments and ground penetrating radar anomaly segments based on the seismic wave anomaly characteristic information and the ground penetrating radar anomaly characteristic information, performs spatial superposition analysis on the seismic wave anomaly segments and the ground penetrating radar anomaly segments, obtains anomaly overlapping areas and anomaly non-overlapping areas, generates geometric constraints based on the geological structure regularity information of the underground cavern and the sample poor geological body type, determines the pile number segments covered by the poor geological body of the sample poor geological body type based on the geometric constraints, the anomaly overlapping areas and the anomaly non-overlapping areas, determines the sample spatial distribution information of the sample poor geological body type based on the spatial position of the pile number segments, constructs geophysical exploration results sample data based on the historical geophysical exploration results data, the sample poor geological body type and the sample spatial distribution information, thereby accurately combining the ground penetrating radar characteristics and the seismic wave characteristics for analysis, comprehensively analyzing the poor geological body information from two dimensions, and improving the accuracy of poor geological analysis.

[0184] In addition, an embodiment of the present invention also proposes a computer-readable storage medium, on which an underground cavern geology intelligent prediction and analysis program is stored. When the underground cavern geology intelligent prediction and analysis program is executed by a processor, the steps of the underground cavern geology intelligent prediction and analysis method described above are implemented.

[0185] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0186] The above-mentioned computer-readable storage medium may be included in the underground cavern geology intelligent prediction and analysis device; or it may exist independently without being assembled into the underground cavern geology intelligent prediction and analysis device.

[0187] In addition, an embodiment of the present invention also proposes a computer program product, including an underground cavern geology intelligent prediction and analysis program, which implements the steps of the underground cavern geology intelligent prediction and analysis method described above when executed by a processor.

[0188] The specific implementation of the computer program product of the present invention is basically the same as the various embodiments of the above-mentioned underground cavern geological intelligent prediction and analysis method, and will not be repeated here.

[0189] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the underground cavern geology intelligent prediction and analysis system of the present invention.

[0190] like Figure 4 As shown, the underground cavern geological intelligent prediction and analysis system proposed in the embodiment of the present invention includes:

[0191] The detection data acquisition module 10 is used to obtain historical geophysical detection results data of underground caverns;

[0192] A geological interpretation module 20 is used to perform geological interpretation on the historical geophysical exploration results data to obtain geophysical exploration results sample data;

[0193] A model building module 30 is used to build an original recognition model and train the original recognition model based on the geophysical exploration results sample data to obtain a poor geological body prediction model, wherein the original recognition model includes a U-Net network model;

[0194] The geological analysis module 40 is configured to input the current geophysical exploration results data of the section to be analyzed of the underground cavern into the unfavorable geological body prediction model for geological analysis, and obtain unfavorable geological body information of the section to be analyzed, wherein the unfavorable geological body information includes the type of unfavorable geological body and the spatial distribution information of the unfavorable geological body type;

[0195] The cavern information acquisition module 50 is used to obtain the type of surrounding rock and excavation space extension information of the tunnel face of the underground cavern;

[0196] A feature quantification module 60 is used to perform feature quantification on the unfavorable geological body information, the tunnel face surrounding rock type, and the excavation space extension information to obtain an unfavorable geological body development level value, a tunnel face surrounding rock type value, and a space extension feature value;

[0197] The advanced geological prediction and analysis module 70 is used to perform advanced geological prediction and analysis on the underground cavern based on the unfavorable geological body development level value, the tunnel face surrounding rock category value and the spatial extension characteristic value.

[0198] This embodiment obtains historical geophysical exploration results data of underground caverns; performs geological interpretation on the historical geophysical exploration results data to obtain geophysical exploration results sample data; constructs an original recognition model, and trains the original recognition model based on the geophysical exploration results sample data to obtain a bad geological body prediction model, wherein the original recognition model includes a U-Net network model; inputs the current geophysical exploration results data of the to-be-analyzed tunnel section of the underground cavern into the bad geological body prediction model for geological analysis to obtain bad geological body information of the to-be-analyzed tunnel section, wherein the bad geological body information includes bad geological body types and spatial distribution information of the bad geological body types; obtains the tunnel face surrounding rock type and excavation space extension information of the underground cavern; performs feature quantification on the bad geological body information, the tunnel face surrounding rock type and the excavation space extension information to obtain bad geological body development information. level value, face surrounding rock category value and spatial extension characteristic value; based on the development level value of the adverse geological body, the face surrounding rock category value and the spatial extension characteristic value, the underground cavern is subjected to advanced geological prediction analysis; since this embodiment performs geological interpretation on historical geophysical exploration results data and constructs geophysical exploration results sample data based on the geological interpretation results, the sample quality and model training efficiency are effectively improved, and the adverse geological body prediction model obtained after training is used to perform geological analysis on the current geophysical exploration results data of the tunnel section to be analyzed, thereby accurately obtaining the adverse geological body type and spatial distribution information of the tunnel section to be analyzed, and by quantifying the characteristics of the adverse geological body information, the face surrounding rock category, the excavation progress information and the spatial extension information, it is possible to accurately evaluate the changes in the geological conditions in front of the underground cavern, thereby realizing accurate geological analysis of the underground cavern and effectively improving the accuracy of advanced geological prediction.

[0199] The intelligent underground cavern geology prediction and analysis system provided in this application utilizes the intelligent underground cavern geology prediction and analysis method described in the aforementioned embodiments, and is capable of resolving the technical issues associated with intelligent underground cavern geology prediction and analysis. Compared to the prior art, the intelligent underground cavern geology prediction and analysis system provided in this application offers the same beneficial effects as the intelligent underground cavern geology prediction and analysis method described in the aforementioned embodiments. Other technical features of the intelligent underground cavern geology prediction and analysis system are the same as those disclosed in the aforementioned embodiments and are not further elaborated upon here.

[0200] It should be understood that the above is only an example and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any limitation on this.

[0201] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the embodiment scheme, and no limitation is made here.

[0202] In addition, for technical details not fully described in this embodiment, please refer to the intelligent prediction and analysis method for underground cavern geology provided in any embodiment of the present invention, and will not be repeated here.

[0203] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0204] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0205] Through the above description of the embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented by means of software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0206] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent prediction and analysis method for underground cavern geology, characterized in that: The underground cavern geological intelligent prediction and analysis method comprises: Obtain historical geophysical exploration data of underground caverns; Conduct geological interpretation on the historical geophysical exploration results data to construct geophysical exploration results sample data; Constructing an original recognition model and training the original recognition model based on the sample data of the geophysical exploration results to obtain a poor geological body prediction model, wherein the original recognition model includes a U-Net network model; Inputting the current geophysical exploration results data of the to-be-analyzed section of the underground cavern into the bad geological body prediction model for geological analysis, and obtaining bad geological body information of the to-be-analyzed section, wherein the bad geological body information includes the bad geological body type and spatial distribution information of the bad geological body type, wherein the spatial distribution information is the distribution range of the bad geological body, including the pile number segment and spatial position of the bad geological body; Obtaining the type of surrounding rock of the tunnel face and excavation space extension information of the underground cavern, wherein the excavation space extension information includes the excavated length and the spatial distribution information of the unfavorable geological body revealed by the excavation; Quantifying the characteristics of the unfavorable geological body information, the tunnel face surrounding rock category, and the excavation space extension information to obtain an unfavorable geological body development level value, a tunnel face surrounding rock category value, and a space extension characteristic value; Performing advanced geological prediction analysis on the underground cavern based on the unfavorable geological body development level value, the tunnel face surrounding rock category value, and the spatial extension characteristic value; The geological interpretation of the historical geophysical exploration results data to construct geophysical exploration results sample data includes: Performing geological interpretation on the historical geophysical exploration results data to obtain seismic wave anomaly characteristic information, ground penetrating radar anomaly characteristic information, sample adverse geological body types and historical geophysical exploration results data; determining a seismic wave abnormal segment and a ground penetrating radar abnormal segment based on the seismic wave abnormal characteristic information and the ground penetrating radar abnormal characteristic information; Performing spatial superposition analysis on the seismic wave abnormal segment and the ground penetrating radar abnormal segment to obtain abnormal overlapping areas and abnormal non-overlapping areas; Generating geometric constraints based on the geological structural regularity information of the underground cavern and the type of the sample adverse geological body; Determine the pile number segment covered by the bad geological body of the sample bad geological body type according to the geometric constraint condition, the abnormal overlapping area and the abnormal non-overlapping area; Determine the sample spatial distribution information of the sample bad geological body type based on the spatial position of the pile number segment; Geophysical exploration result sample data is constructed based on the historical geophysical exploration result data, the sample poor geological body type and the sample spatial distribution information.

2. The intelligent prediction and analysis method for underground cavern geology according to claim 1, characterized in that: The historical geophysical exploration results data include seismic wave exploration results data and ground-penetrating radar exploration results data, the seismic wave exploration results data include wave velocity diagrams and seismic wave profile diagrams, and the ground-penetrating radar exploration results data include ground-penetrating radar profile diagrams; The acquisition of historical geophysical exploration data of underground caverns includes: Obtain surrounding rock geological conditions and geological structural conditions based on historical geological analysis data of underground caverns; Determining a cavern prediction strategy based on the surrounding rock geological conditions and the geological structural conditions; Based on the cavern prediction strategy, seismic wave detection data and ground penetrating radar detection data are collected from the underground cavern to obtain seismic wave detection data and ground penetrating radar detection data; The seismic wave detection data and the ground penetrating radar detection data are preprocessed to obtain historical geophysical detection results data, and the preprocessing includes static correction processing, denoising processing, dynamic correction processing, time-depth conversion processing and normalization processing.

3. The intelligent prediction and analysis method for underground cavern geology according to claim 2, characterized in that: The geological interpretation of the historical geophysical exploration results data to construct geophysical exploration results sample data includes: Performing geological interpretation feature analysis on the seismic wave detection result data and the ground-penetrating radar detection result data to obtain parameter change feature information of seismic wave parameters and ground-penetrating radar parameters, wherein the seismic wave parameters include seismic wave velocity, seismic wave frequency, and seismic wave amplitude, and the ground-penetrating radar parameters include ground-penetrating radar frequency and ground-penetrating radar amplitude; Extracting abnormal features from parameter change characteristic information of the seismic wave parameters and the ground penetrating radar parameters to obtain abnormal seismic wave characteristic information and abnormal ground penetrating radar characteristic information; Performing correlation analysis on the seismic wave abnormal characteristic information and the ground penetrating radar abnormal characteristic information to obtain the type of the sample bad geological body; Geophysical exploration result sample data is constructed based on the sample poor geological body type and the historical geophysical exploration result data.

4. The intelligent prediction and analysis method for underground cavern geology according to any one of claims 1 to 3, characterized in that: The method of constructing an original recognition model and training the original recognition model based on the geophysical exploration result sample data to obtain a poor geological body prediction model includes: Determine an encoder, a decoder, and a skip connection layer, and construct an original recognition model based on the encoder, the decoder, and the skip connection layer, and initialize weight parameters of the original recognition model; Performing convolution and pooling processing on the sample data of the geophysical exploration results through the encoder to output global semantic feature information of the bad geological body type; Performing transposition convolution on the global semantic feature information through the decoder to output spatial distribution information and type labels of the bad geological body types; Performing spatial alignment and multi-scale feature fusion on the global semantic feature information, spatial distribution information and type label of the bad geological body type through the skip connection layer; A model weight parameter is obtained based on the output results of the encoder, the decoder and the skip connection layer, and a poor geological body prediction model is constructed based on the model weight parameter.

5. The intelligent prediction and analysis method for underground cavern geology according to claim 4, characterized in that: The method of constructing an original recognition model and training the original recognition model based on the geophysical exploration result sample data to obtain a poor geological body prediction model includes: Constructing an original recognition model, wherein the original recognition model includes a ReLU activation function and a Sigmoid activation function, wherein the ReLU activation function is used for a convolutional layer of the original recognition model, and the Sigmoid activation function is used for an output layer of the original recognition model; Training the original recognition model based on the geophysical exploration results sample data to obtain a candidate recognition model; Construct a loss function and an optimizer, wherein the optimizer includes an AdaDelta adaptive learning rate optimizer, and the loss function includes a multi-classification cross entropy loss function, wherein the multi-classification cross entropy loss function includes: in, Indicates the The true bad geological body type label of samples, Represents the candidate recognition model for the The sample labels predicted by samples, represents the total number of samples, Indicates the number of bad geological body types, represents the total penalty parameter for prediction error; The candidate identification model is optimized and converged based on the loss function and the optimizer to obtain a poor geological body prediction model.

6. An intelligent prediction and analysis system for underground cavern geology, characterized in that: The underground cavern geological intelligent prediction and analysis system includes: Detection data acquisition module, used to obtain historical geophysical detection results data of underground caverns; A geological interpretation module is used to perform geological interpretation on the historical geophysical exploration results data and construct geophysical exploration results sample data; A model building module is used to build an original recognition model and train the original recognition model based on the sample data of the geophysical exploration results to obtain a poor geological body prediction model, wherein the original recognition model includes a U-Net network model; a geological analysis module, configured to input the current geophysical exploration results data of the to-be-analyzed section of the underground cavern into the unfavorable geological body prediction model for geological analysis, and obtain unfavorable geological body information of the to-be-analyzed section, wherein the unfavorable geological body information includes the type of unfavorable geological body and spatial distribution information of the type of unfavorable geological body, wherein the spatial distribution information is the distribution range of the unfavorable geological body, including the pile number segment and spatial position of the unfavorable geological body; a cavern information acquisition module, configured to acquire the type of surrounding rock of the tunnel face and excavation space extension information of the underground cavern, wherein the excavation space extension information includes the excavated length and the spatial distribution information of the unfavorable geological bodies revealed by the excavation; A feature quantification module is used to perform feature quantification on the unfavorable geological body information, the tunnel face surrounding rock category, and the excavation space extension information to obtain an unfavorable geological body development level value, a tunnel face surrounding rock category value, and a space extension feature value; An advanced geological prediction and analysis module is used to perform advanced geological prediction and analysis on the underground cavern based on the unfavorable geological body development level value, the tunnel face surrounding rock category value and the spatial extension characteristic value; The geological interpretation module is also used to perform geological interpretation on the historical geophysical exploration results data to obtain seismic wave anomaly characteristic information, ground penetrating radar anomaly characteristic information, sample poor geological body type and historical geophysical exploration results data; determine the seismic wave anomaly segment and the ground penetrating radar anomaly segment based on the seismic wave anomaly characteristic information and the ground penetrating radar anomaly characteristic information; perform spatial superposition analysis on the seismic wave anomaly segment and the ground penetrating radar anomaly segment to obtain anomaly overlapping areas and anomaly non-overlapping areas; generate geometric constraints based on the geological structure law information of the underground cavern and the sample poor geological body type; determine the pile number segment covered by the poor geological body of the sample poor geological body type based on the geometric constraints, the abnormal overlapping areas and the abnormal non-overlapping areas; determine the sample spatial distribution information of the sample poor geological body type based on the spatial position of the pile number segment; and construct geophysical exploration results sample data based on the historical geophysical exploration results data, the sample poor geological body type and the sample spatial distribution information.

7. An intelligent prediction and analysis device for underground cavern geology, characterized in that: The underground cavern geology intelligent prediction and analysis device includes: a memory, a processor, and an underground cavern geology intelligent prediction and analysis program stored in the memory and runnable on the processor. The underground cavern geology intelligent prediction and analysis program is configured to implement the underground cavern geology intelligent prediction and analysis method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores an underground cavern geology intelligent prediction and analysis program, and when the underground cavern geology intelligent prediction and analysis program is executed by the processor, the underground cavern geology intelligent prediction and analysis method according to any one of claims 1 to 5 is implemented.

9. A computer program product, characterized in that The computer program product includes an underground cavern geology intelligent prediction and analysis program, which, when executed by a processor, implements the steps of the underground cavern geology intelligent prediction and analysis method according to any one of claims 1 to 5.

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