CN-119398493-B - Advanced geological prediction method and system based on tunnel geological detection

CN119398493BCN 119398493 BCN119398493 BCN 119398493BCN-119398493-B

Abstract

The invention relates to the technical field of geological detection and discloses an advanced geological prediction method and system based on tunnel geological detection. The method comprises the steps of carrying out data denoising on a multisource data set which is acquired in advance in a tunnel construction area to obtain an original data set, carrying out preprocessing and multistage data fusion on the original data set to obtain a fusion data set, carrying out three-dimensional space modeling and feature extraction on the fusion data set to obtain an intelligent geological prediction result, carrying out multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel, carrying out construction scheme optimization on the geological risk distribution map to obtain a construction guiding scheme, and carrying out comprehensive analysis and dynamic updating on real-time monitoring data, the construction guiding scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. The method and the system overcome the defect of insufficient prediction precision, reliability and accuracy of the traditional geological prediction method, and reduce the safety risk.

Inventors

CHEN Jin
ZHANG XUEMIN
LI YUJUN
GAO HUIQING
DU HUIJUN
Ran Gonglang
ZHONG HAORAN
XIANG HAIYAN

Assignees

贵州省公路工程集团有限公司
贵州省建设投资集团公用建设有限公司

Dates

Publication Date: 20260512
Application Date: 20241009

Claims (6)

1. An advanced geological prediction method based on tunnel geological detection is characterized by comprising the following steps: Carrying out data denoising on a multisource data set acquired in advance in a tunnel construction area to obtain an original data set, wherein the multisource data set comprises historical geological data, real-time monitoring data, geological radar detection data, seismic wave method detection data, advanced drilling data and infrared detection data; preprocessing the original data set and fusing multi-level data to obtain a fused data set; performing three-dimensional spatial modeling and feature extraction on the fusion data set to obtain an intelligent geological prediction result, wherein the intelligent geological prediction result comprises the steps of performing spatial interpolation processing on the fusion data set to obtain a continuously distributed geological attribute field, performing grid division on the continuously distributed geological attribute field to obtain a discretized three-dimensional geological distribution field, performing geological unit classification on the discretized three-dimensional geological distribution field to obtain a geological unit partition map, performing boundary refinement on the geological unit partition map to obtain a target geological structure twin, performing geological parameter assignment on the target geological structure twin to obtain a parameterized geological twin, performing uncertainty quantization on the parameterized geological twin to obtain a parameter probability density function, performing random field generation on the parameter probability density function to obtain a plurality of groups of geological parameter realization, and performing statistical analysis on the geological parameter realization to obtain a geological parameter statistical feature; Performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of a tunnel, wherein the multi-factor risk assessment comprises the steps of performing geological factor decomposition on the intelligent geological prediction result to obtain multi-factor geological factor data, performing weight distribution on the multi-factor geological factor data to obtain a factor weight matrix, performing hierarchical analysis on the factor weight matrix to obtain factor importance ranking, performing threshold screening on the factor importance ranking to obtain a key influence factor set, performing relevance analysis on the key influence factor set to obtain a factor correlation coefficient matrix, performing principal component extraction on the factor correlation coefficient matrix to obtain a main control factor combination, performing fuzzy comprehensive evaluation on the main control factor combination to obtain a risk grade score, performing normalization processing on the risk grade score to obtain a standardized risk index, performing spatial interpolation on the standardized risk index to obtain a continuous risk distribution field, performing region division on the continuous risk distribution field to obtain a risk partition map, performing boundary optimization on the risk partition map to obtain a refined risk region, and performing risk grade extraction on the refined risk region to obtain a geological risk grade in front of the distribution map; optimizing the construction scheme of the geological risk distribution map to obtain a construction guiding scheme; And comprehensively analyzing and dynamically updating the collected real-time monitoring data, the construction guiding scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result.
2. The advanced geological prediction method based on tunnel geological detection according to claim 1, wherein the step of performing data denoising on a multi-source data set acquired in advance in a tunnel construction area to obtain an original data set, wherein the multi-source data set includes historical geological data, real-time monitoring data, geological radar detection data, seismic detection data, advanced drilling data and infrared detection data, comprises the following steps: Performing digital processing on the historical geological data to obtain digital geological data, and performing hierarchical classification on the digital geological data to obtain hierarchical geological information; Denoising and filtering the real-time monitoring data to obtain cleaned monitoring data, and performing time sequence analysis on the cleaned monitoring data to obtain monitoring trend data; performing signal enhancement processing on geological radar detection data to obtain enhanced radar data, and performing image segmentation on the enhanced radar data to obtain segmented stratum interface data; Performing spectrum analysis on the seismic wave method detection data to obtain spectrum characteristic data, and performing inversion calculation on the spectrum characteristic data to obtain stratum speed structure data; performing core analysis on the advanced drilling data to obtain lithology distribution data, and performing spatial interpolation on the lithology distribution data to obtain three-dimensional lithology distribution data; Performing temperature gradient analysis on the infrared detection data to obtain temperature abnormal region data, and performing cluster analysis on the temperature abnormal region data to obtain position information of potential geological abnormal points; And merging the layered geological information, the monitoring trend data, the segmented stratum interface data, the stratum speed structure data, the three-dimensional lithology distribution data and the potential geological abnormal point position information into the original data set.
3. The advanced geological prediction method based on tunnel geological exploration according to claim 2, wherein said step of preprocessing and multi-level data fusion of said original data set to obtain a fused data set comprises: performing spatial registration processing on the layered geological information to obtain registered geological data, and performing scale normalization on the registered geological data to obtain standardized geological data; Performing outlier detection on the monitoring trend data to obtain outlier marking data, and performing interpolation processing on the outlier marking data to obtain corrected monitoring data; performing stratum interface extraction on the segmented stratum interface data to obtain interface feature data, and performing geometric correction on the interface feature data to obtain corrected stratum structure data; Waveform inversion is carried out on the stratum velocity structure data to obtain velocity field data, and geological parameter conversion is carried out on the velocity field data to obtain lithology attribute data; Carrying out space statistical analysis on the three-dimensional lithology distribution data to obtain a lithology probability distribution map, and carrying out threshold segmentation on the lithology probability distribution map to obtain a candidate geologic body boundary; morphological processing is carried out on the potential geological abnormal point position information to obtain an abnormal region outline, and feature matching is carried out on the abnormal region outline to obtain target geological structure information; and carrying out multi-source data fusion on the standardized geological data, the corrected monitoring data, the corrected stratum structure data, the lithology attribute data, the candidate geologic body boundary and the target geological structure information to obtain the fusion data set.
4. The advanced geological prediction method based on tunnel geological exploration according to claim 1, wherein the step of optimizing the construction scheme to obtain the construction guidance scheme includes: Performing regional division on the geological risk distribution map to obtain risk level partitions, and performing excavation method matching on the risk level partitions to obtain a preliminary excavation scheme; carrying out mechanical analysis on the preliminary excavation scheme to obtain stress distribution data, and carrying out safety coefficient calculation on the stress distribution data to obtain an excavation stability evaluation result; analyzing the support requirement of the excavation stability evaluation result to obtain support type suggestions, and performing parameter optimization on the support type suggestions to obtain a preliminary support scheme; performing numerical simulation on the preliminary supporting scheme to obtain supporting effect prediction data, and performing security check on the supporting effect prediction data to obtain optimized supporting parameters; Comprehensively analyzing the optimized support parameters and the preliminary excavation scheme to obtain Shi Gongbu-order arrangement, and performing process optimization on the construction step arrangement to obtain a preliminary construction procedure; and estimating the construction period of the preliminary construction procedure to obtain a construction progress plan, and optimizing the resource allocation of the construction progress plan to obtain the construction guidance scheme.
5. The advanced geological forecast method based on tunnel geological exploration according to claim 4, wherein the step of comprehensively analyzing and dynamically updating the collected real-time monitoring data, the construction guidance scheme, the intelligent geological forecast result and the geological risk distribution map to obtain the advanced geological forecast result comprises the following steps: Performing data cleaning on the real-time monitoring data to obtain effective monitoring data, and performing trend analysis on the effective monitoring data to obtain monitoring parameter variation trend; Comparing and analyzing the variation trend of the monitoring parameter with the intelligent geological prediction result to obtain prediction deviation data, and judging a threshold value of the prediction deviation data to obtain a prediction correction index; Performing weight distribution on the prediction correction index to obtain a correction weight matrix, and performing weighted fusion on the correction weight matrix and the intelligent geological prediction result to obtain an updated geological prediction result; Performing superposition analysis on the updated geological prediction result and the geological risk distribution map to obtain a dynamic risk assessment result, and grading the dynamic risk assessment result to obtain an updated risk distribution map; carrying out adaptability analysis on the updated risk distribution map and the construction guidance scheme to obtain a scheme adjustment suggestion, and carrying out feasibility assessment on the scheme adjustment suggestion to obtain an optimized construction scheme; and carrying out data fusion on the optimized construction scheme, the updated geological prediction result and the updated risk distribution map to obtain the advanced geological prediction result.
6. A tunnel geological detection-based advanced geological prediction system for performing the tunnel geological detection-based advanced geological prediction method according to any one of claims 1 to 5, comprising: The denoising module is used for denoising the multi-source data set acquired in advance in the tunnel construction area to obtain an original data set, wherein the multi-source data set comprises historical geological data, real-time monitoring data, geological radar detection data, seismic wave method detection data, advanced drilling data and infrared detection data; The fusion module is used for preprocessing the original data set and fusing the multi-level data to obtain a fusion data set; The extraction module is used for carrying out three-dimensional spatial modeling and feature extraction on the fusion data set to obtain an intelligent geological prediction result, and comprises the steps of carrying out spatial interpolation processing on the fusion data set to obtain a continuously distributed geological attribute field, carrying out grid division on the continuously distributed geological attribute field to obtain a discretized three-dimensional geological distribution field, carrying out geological unit classification on the discretized three-dimensional geological distribution field to obtain a geological unit partition map, carrying out boundary refinement on the geological unit partition map to obtain a target geological structure twin body, carrying out geological parameter assignment on the target geological structure twin body to obtain a parameterized geological twin body, carrying out uncertainty quantization on the parameterized geological twin body to obtain a parameter probability density function, carrying out random field generation on the parameter probability density function to obtain a plurality of groups of geological parameter realization, and carrying out statistical analysis on the plurality of groups of geological parameter realization to obtain a geological parameter statistical feature; The evaluation module is used for carrying out multi-factor risk evaluation on the intelligent geological prediction result to obtain a geological risk distribution map in front of a tunnel, and comprises carrying out geological factor decomposition on the intelligent geological prediction result to obtain multi-dimensional geological factor data, carrying out weight distribution on the multi-dimensional geological factor data to obtain a factor weight matrix, carrying out hierarchical analysis on the factor weight matrix to obtain factor importance ranking, carrying out threshold screening on the factor importance ranking to obtain a key influence factor set, carrying out correlation analysis on the key influence factor set to obtain a factor correlation coefficient matrix, carrying out principal component extraction on the factor correlation coefficient matrix to obtain a principal component combination, carrying out fuzzy comprehensive evaluation on the principal component combination to obtain a risk grade score, carrying out normalization treatment on the risk grade score to obtain a normalized risk index, carrying out spatial interpolation on the normalized risk index to obtain a continuous risk distribution field, carrying out region division on the continuous risk distribution field to obtain a risk partition map, carrying out boundary optimization on the partition map to obtain a refined risk region, and carrying out front grade of the refined risk region to obtain a geological risk distribution map; The optimizing module is used for optimizing the construction scheme of the geological risk distribution map to obtain a construction guiding scheme; and the updating module is used for comprehensively analyzing and dynamically updating the collected real-time monitoring data, the construction guiding scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result.

Description

Advanced geological prediction method and system based on tunnel geological detection Technical Field The invention relates to the technical field of geological detection, in particular to an advanced geological prediction method and system based on tunnel geological detection. Background Advanced geological forecast in tunnel construction is a key technology for guaranteeing construction safety and efficiency. The traditional advanced geological prediction method mainly relies on a single detection means, such as geological radar, seismic wave method and the like, and combines experience judgment to predict the forward geological condition. These methods can find abnormal geologic bodies in front to some extent, but have limited prediction accuracy and reliability. However, the information acquired by a single detection means is often monolithic, and complex geological conditions are difficult to comprehensively reflect. Meanwhile, the traditional method has insufficient comprehensive analysis capability on multi-source data, and the advantages of various detection data are difficult to fully utilize. In addition, the traditional method lacks a dynamic updating mechanism, and the prediction result cannot be corrected in time according to real-time monitoring data in the construction process. These deficiencies result in limited accuracy and timeliness of advanced geological predictions, increasing the security risk of tunnel construction. Disclosure of Invention In view of the above, the embodiment of the invention provides a method and a system for advanced geological prediction based on tunnel geological detection, which are used for solving the defects of insufficient prediction precision, reliability and accuracy and high safety risk of the traditional geological prediction method, and remarkably improving the comprehensiveness and accuracy of geological prediction. The invention provides an advanced geological prediction method based on tunnel geological detection, which comprises the steps of carrying out data denoising on a multisource data set acquired in advance in a tunnel construction area to obtain an original data set, wherein the multisource data set comprises historical geological data, real-time monitoring data, geological radar detection data, seismic wave method detection data, advanced drilling data and infrared detection data, carrying out preprocessing and multistage data fusion on the original data set to obtain a fusion data set, carrying out three-dimensional space modeling and feature extraction on the fusion data set to obtain an intelligent geological prediction result, carrying out multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of a tunnel, carrying out construction scheme optimization on the geological risk distribution map to obtain a construction guiding scheme, and carrying out comprehensive analysis and dynamic updating on the acquired real-time monitoring data, the construction guiding scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. The invention also provides an advanced geological forecast system based on tunnel geological detection, which comprises the following steps: The denoising module is used for denoising the multi-source data set acquired in advance in the tunnel construction area to obtain an original data set, wherein the multi-source data set comprises historical geological data, real-time monitoring data, geological radar detection data, seismic wave method detection data, advanced drilling data and infrared detection data; The fusion module is used for preprocessing the original data set and fusing the multi-level data to obtain a fusion data set; The extraction module is used for carrying out three-dimensional space modeling and feature extraction on the fusion data set to obtain an intelligent geological prediction result; the evaluation module is used for performing multi-factor risk evaluation on the intelligent geological prediction result to obtain a geological risk distribution diagram in front of the tunnel; The optimizing module is used for optimizing the construction scheme of the geological risk distribution map to obtain a construction guiding scheme; and the updating module is used for comprehensively analyzing and dynamically updating the collected real-time monitoring data, the construction guiding scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the technical scheme provided by the invention, through a multi-source data fusion technology, various information such as historical geological data, real-time monitoring data, geological radar detection data and the like is comprehensively utilized, and the comprehensiveness and accuracy of geological prediction