CN-121978769-A - Subway structure safety association geological defect geophysical prospecting and mapping collaborative inspection method and system

CN121978769ACN 121978769 ACN121978769 ACN 121978769ACN-121978769-A

Abstract

The invention discloses a collaborative inspection method and a collaborative inspection system for the geophysical prospecting and mapping of subway structure safety-related geological defects, and particularly relates to the technical field of subway engineering safety inspection, wherein the method comprises five steps of preprocessing and detection area division, multisource collaborative detection, drawing of detection data fusion into a graph, collaborative inspection, dynamic updating and operation and maintenance adaptation in the earlier stage, taking gravity exploration as a core, fusing a plurality of detection means, combining with an improved BP neural network model to strengthen gravity data weight, guaranteeing reliable results through a triple inspection mechanism, and constructing a full-flow closed-loop flow; the system comprises a main control module and five functional modules, is precisely matched with the steps of the method, and realizes integrated collaborative operation. The invention improves the detection precision of geological defects and earthquake susceptibility structures, reduces the manual error and the operation and maintenance cost, is suitable for the whole-stage application of subways, provides accurate data support for the safety prevention and control of subways, and has remarkable practicality and application value.

Inventors

HOU ZHICHAO
WANG FENGWU
GAO HU
ZHAO YA
LI LONG
LEI YIMING
DONG YANWEI
Fu Yaoshuai
SHI KEKE

Assignees

北京城建勘测设计研究院有限责任公司

Dates

Publication Date: 20260505
Application Date: 20260225

Claims (10)

1. The collaborative detection method for the geophysical prospecting and mapping of the subway structure safety association geological defect is characterized by comprising the following steps of: S1, collecting basic data or information along a subway line, defining a core detection area and an extension detection area, performing detection environment calibration and establishing an error correction model; S2, taking gravity exploration as a core, fusing micro-motion detection, geological radar detection and elastic wave CT detection, collecting and preprocessing multi-source detection data, and primarily identifying geological defect matters and seismic vulnerability geological structures; S3, integrating the preprocessed detection data through a multisource data fusion model, drawing a distribution diagram of the layered geological defect and the seismic vulnerability geological structure, and performing primary verification on drawing accuracy; s4, establishing association relation between geological defects, seismic vulnerability geological structures and subway structure safety through a triple inspection mechanism, evaluating inspection results and forming feedback; and S5, periodically carrying out detection rechecking, updating the drawing result and the three-dimensional model, butting the collaborative inspection result with the subway operation and maintenance management system, outputting a targeted operation and maintenance suggestion, and further feeding the updated result back to the error correction model of the step S1 and the multi-source collaborative detection scheme formulation of the step S2 as input so as to realize iterative tracking of dynamic evolution of the geological defect and self-adaptive optimization of the geological model.
2. The collaborative inspection method for geophysical prospecting and mapping of subway structure security association geological defects according to claim 1, wherein in step S1, the basic data or information includes subway line along-line geological prospecting report, subway structure design parameters, existing geophysical prospecting data and regional seismic geological data; The core detection area is within 50m of the periphery of the subway tunnel and is used for checking shallow geological defects; The extended detection area is in the range of 100-200m outside the core detection area and is used for checking the geological structure of the susceptibility of deep earthquake; The construction and use method of the error correction model comprises the steps of collecting historical data, including basic geological parameter samples and corresponding multi-source detection error data, taking the basic geological parameter samples as input, taking the corresponding detection error data as expected output, performing supervised training on a BP neural network, enabling the network to learn to obtain a mapping relation from geological parameters to errors, and enabling the trained model to automatically predict and output the corresponding error correction parameters aiming at new basic geological parameters.
3. The collaborative inspection method for geophysical prospecting and mapping of subway structure safety association geological defects according to claim 1, wherein in step S3, the multi-source data fusion model is an improved BP neural network fusion model, and the improvement is that higher initial connection weight is distributed for gravity exploration data at an input layer, and normalization processing independent of other data sources is adopted for the gravity data; The method for drawing the distribution map of the layered geological defect and the seismic vulnerability geological structure specifically comprises the steps of generating a distribution map of the shallow geological defect, a distribution map of the deep seismic vulnerability geological structure and a three-dimensional comprehensive distribution map, wherein the initial verification error of drawing precision is less than or equal to 5%.
4. The method for collaborative detection of geological fault geophysical prospecting and mapping for subway structure safety association according to claim 1, wherein in step S4, the triple detection mechanism comprises detection-plotting comparison detection, plotting-structure safety association detection and field real operation detection, wherein the qualification standard of the detection-plotting comparison detection is equal to or more than 95% of the consistency of the detection-plotting comparison detection and the plotting-structure safety association detection, and the qualification standard of the plotting-structure safety association detection is equal to or less than 5% of the prediction error of the association model; In the step S5, the dynamic updating period is every 6-12 months, and gravity exploration and micro-motion detection rechecking are carried out regularly.
5. A co-detection system for geophysical prospecting and mapping of geological defects of a subway structure safety association, for implementing the method as claimed in any one of claims 1 to 4, characterized by comprising: The main control module (1) is electrically connected with and controlled by the main control module (1) respectively: The preprocessing and region dividing module (2) is used for executing the step S1, and comprises the steps of collecting basic data or information along a subway line, defining a core detection region and an extension detection region, and establishing an error correction model based on a BP neural network; The multi-source cooperative detection module (3) is used for executing the step S2, and comprises the steps of taking gravity exploration as a core, fusing micro-motion detection, geological radar detection and elastic wave CT detection for data acquisition, and preprocessing the acquired multi-source detection data; The data fusion and drawing module (4) is used for executing the step S3, and comprises the steps of adopting an improved BP neural network fusion model to fuse the preprocessed multi-source detection data and drawing a shallow geological defect distribution map, a deep earthquake incident geological structure distribution map and a three-dimensional comprehensive distribution map according to fusion results; The collaborative inspection module (5) is used for executing the step S4, including implementing detection-drawing comparison inspection, drawing-structure safety association inspection and field real operation inspection, and establishing association relation and forming feedback according to inspection results; And the dynamic updating and operation and maintenance adapting module (6) is used for executing the step S5, and comprises triggering detection rechecking according to a preset period to update the drawing and the model, and docking the result to the subway operation and maintenance management system.
6. The system according to claim 5, wherein the multi-source cooperative detection module (3) comprises a gravity exploration unit (301), a micro-motion detection unit (302), a geological radar detection unit (303), an elastic wave CT detection unit (304) and a data preprocessing unit (305); the gravity exploration unit (301) is configured to adopt a high-precision gravimeter, and arrange detection points in a grid shape so as to acquire gravity anomaly data and gravity gradient data; the data preprocessing unit (305) is configured to perform noise reduction, correction and normalization processing on the data from each detection unit.
7. The system of claim 6, wherein the data preprocessing unit (305) performs correction operations on data acquired by the gravity survey unit (301), including terrain correction, grid correction, and equalization correction.
8. The system according to claim 5, wherein the data fusion and mapping module (4) comprises: the data fusion unit (401) is configured to run an improved BP neural network fusion model, integrate multi-source data by distributing higher initial connection weight to gravity exploration data and performing independent normalization processing, and output fused geologic body parameters; And the drawing unit (402) is configured to generate a shallow geological defect distribution map, a deep earthquake susceptibility geologic structure distribution map and a three-dimensional comprehensive distribution map according to the geologic body parameters output by the data fusion unit (401).
9. The system according to claim 5, wherein the co-verification module (5) is configured to: comparing the drawing result output by the data fusion and drawing module (4) with the original detection data of the multi-source collaborative detection module (3), and calculating consistency; Combining with subway structure design parameters, calling a correlation model to calculate the risk probability of the geological structure marked in the drawing result on the safety of the subway structure; and receiving input of field actual operation inspection, comprehensively evaluating the inspection result and generating feedback information containing operation and maintenance suggestions.
10. The system according to claim 5, wherein the dynamic update and operation and maintenance adaptation module (6) is configured to: sending an instruction to the multi-source cooperative detection module (3) to start gravity exploration and micro-motion detection rechecking with each 6-12 months as a period; updating the drawing result and the three-dimensional model in the data fusion and drawing module (4) according to the rechecking data; pushing the updated model, the inspection result and the operation and maintenance suggestion to a subway operation and maintenance management system; And feeding back the updated drawing result to the error correction model of the preprocessing and region dividing module (2) and the detection scheme configuration of the multi-source collaborative detection module (3).

Description

Subway structure safety association geological defect geophysical prospecting and mapping collaborative inspection method and system Technical Field The invention relates to the technical field of subway engineering safety detection, in particular to a collaborative detection method and system for geophysical prospecting and mapping of subway structure safety association geological defects. Background The geological conditions along the subway engineering are complex, geological defects such as shallow karst cavities, underground water body anomalies and the like, and seismic vulnerability geological structures such as deep movable fracture zones, sandy soil liquefaction layers and the like can generate long-term potential threats to the safety of the subway structure, particularly in the subway operation and maintenance stage, the evolution of the geological defects and the activity change of the geological structures are easy to cause potential safety hazards such as tunnel settlement and lining cracking, so that the method is very important to the accurate detection, mapping and collaborative inspection of the geological defects and the seismic vulnerability geological structures. At present, the subway geological defect detection mostly adopts a single geophysical detection means, the problems of insufficient detection precision and identification and disconnection of shallow and deep geological information exist, detection, drawing and inspection links are mutually independent, a cooperative verification mechanism is lacked, the deviation between a detection result and an actual geological condition is easy to cause, meanwhile, the existing detection technology mostly lacks depth adaptation with subway operation and maintenance management, does not have a dynamic updating mechanism, is difficult to track geological defect evolution rules in real time, and cannot provide accurate and continuous data support for safety prevention and control of a subway structure. In addition, the existing detection system has the defects of single detection function, poor suitability with a detection method, difficulty in completing the whole-flow operation of detection, drawing, inspection and feedback, multiple manual intervention links, low inspection efficiency, easiness in introducing human errors and incapability of meeting the requirements of high-precision and high-efficiency collaborative detection and drawing inspection of subway engineering investigation, construction and operation and maintenance at all stages. Therefore, there is a need to develop a method and a system for collaborative detection of subway geological defects by multi-means collaboration, integration of detection and mapping, and adaptation to operation and maintenance requirements, so as to solve the defects in the prior art. Disclosure of Invention In order to overcome the defects in the prior art, the invention provides a collaborative detection method and a collaborative detection system for geophysical prospecting and mapping of subway structure safety-related geological defects, which are used for solving the problems in the background art. In order to achieve the above purpose, the present invention provides the following technical solutions: On the one hand, the invention provides a collaborative detection method for geophysical prospecting and mapping of subway structure safety association geological defects, which comprises the following steps: S1, collecting basic data or information along a subway line, defining a core detection area and an extension detection area, performing detection environment calibration and establishing an error correction model; S2, taking gravity exploration as a core, fusing micro-motion detection, geological radar detection and elastic wave CT detection, collecting and preprocessing multi-source detection data, and primarily identifying geological defect matters and seismic vulnerability geological structures; S3, integrating the preprocessed detection data through a multisource data fusion model, drawing a distribution diagram of the layered geological defect and the seismic vulnerability geological structure, and performing primary verification on drawing accuracy; s4, establishing association relation between geological defects, seismic vulnerability geological structures and subway structure safety through a triple inspection mechanism, evaluating inspection results and forming feedback; and S5, periodically carrying out detection rechecking, updating the drawing result and the three-dimensional model, butting the collaborative inspection result with the subway operation and maintenance management system, outputting a targeted operation and maintenance suggestion, and further feeding the updated result back to the error correction model of the step S1 and the multi-source collaborative detection scheme formulation of the step S2 as input so as to realize iterative tracking of dynamic evolution of t