CN-122018028-A - Microgravity identification method and system for underground space

CN122018028ACN 122018028 ACN122018028 ACN 122018028ACN-122018028-A

Abstract

The application provides a microgravity identification method and system for an underground space. The method comprises the steps of performing forward computation on each group of geometric parameter combination by utilizing a gravity anomaly analysis formula to generate a synthetic sample data set composed of a gravity anomaly sequence and corresponding geometric parameters, performing tide correction and zero drift correction on actual measured microgravity original data to obtain a basic gravity anomaly value, performing terrain correction, altitude correction and middle layer correction on the basic gravity anomaly value, computing a Bragg gravity anomaly after correction, stripping a shallow disturbance field after determining an influence range of shallow disturbance to obtain target gravity anomaly data for inversion, training a deep learning inversion model by utilizing the synthetic sample data set, inputting the target gravity anomaly data for inversion into the trained deep learning inversion model, and outputting a prediction result of geometric parameters and spatial positions of an underground target body. The method can improve microgravity recognition accuracy under the urban shallow complex environment.

Inventors

ZHANG ZHIHOU
LI XIANG
HUANG LIMIN
CHEN XINYUAN
SHU YONGZHENG
HUANG JIANING
Huang shining
WU YANXIA

Assignees

西南交通大学

Dates

Publication Date: 20260512
Application Date: 20260413

Claims (10)

1. A method for microgravity identification of an underground space, comprising the steps of: s1, acquiring actual measurement microgravity original data of a target area and engineering geology priori information containing the geometric type of a target body, wherein the engineering geology priori information comprises geometric parameters; S2, defining key geometric parameters of the target body based on the geometric type of the target body, setting the variation range of each geometric parameter, and generating a plurality of groups of geometric parameter combinations through traversing sampling; s3, carrying out tide correction and zero drift correction on the actually measured microgravity original data to obtain a basic gravity abnormal value, carrying out terrain correction after determining a terrain correction range of the basic gravity abnormal value, carrying out height correction and middle layer correction on the basic gravity abnormal value, and calculating out the Bragg gravity abnormal value after correction; S4, determining an influence range of the shallow interference, and stripping the shallow interference field from the Bragg gravity anomaly based on the influence range of the shallow interference to obtain target gravity anomaly data for inversion; And S5, constructing a deep learning inversion model, training the deep learning inversion model by using the synthetic sample data set, inputting the target gravity anomaly data for inversion into the trained deep learning inversion model, and outputting a prediction result of geometric parameters and spatial positions of an underground target body.
2. The method of microgravity identification of a subterranean space of claim 1, further comprising: S6, substituting the geometric parameters predicted by the deep learning inversion model into a forward model to calculate theoretical gravity anomaly, comparing the theoretical gravity anomaly with the gravity anomaly after actual measurement or processing to calculate residual error, and carrying out consistency verification on the predicted geometric parameters and engineering investigation data to comprehensively evaluate the precision and the confidence coefficient of the predicted result.
3. The method of claim 1, wherein the geometric parameters include radius, depth of burial, spacing and horizontal position of the target.
4. A method for identifying microgravity in a subterranean space according to claim 3 wherein the step of forward computing each set of geometric parameter combinations using a gravity anomaly resolution formula comprises: Forward modeling is performed by adopting a gravity anomaly analysis formula; ; Wherein, the A gravity abnormal value, G is a universal gravitation constant, H is the center burial depth of an underground cylinder, sigma is the density difference between the cylinder and surrounding rock stratum, R is the radius of the cylinder, dx 1 is the horizontal distance between the center of the underground left cylinder and a surface measuring point, dx 2 is the horizontal distance between the center of the underground right cylinder and the surface measuring point, and the gravity anomaly values of a plurality of observation points are calculated along a preset measuring line to form the gravity anomaly sequence.
5. The method of claim 4, wherein generating a composite sample dataset of gravity anomaly sequences and corresponding geometric parameters comprises superimposing random Gaussian noise on the gravity anomaly sequences of the partial samples to promote robustness of the trained model.
6. The microgravity identification method for the underground space according to claim 1, wherein the topography correction comprises building correction and natural topography correction, the building and the surface topography are discretized into a plurality of rectangular prism units, each rectangular prism unit adopts a gravity anomaly calculation formula to calculate the gravity anomaly generated by each topography unit on a measuring point in a region; gravity anomaly calculation formula: ; ; ; ; ; Wherein G is a universal gravitation constant, ; The density difference between the building and the air, and the coordinate of the observation point is The boundary of a rectangular prism being in global coordinates X 1 is the relative horizontal distance from the observation point to the left side surface of the prism, x 2 is the relative horizontal distance from the observation point to the right side surface of the prism, y 1 is the relative distance from the observation point to the front edge surface of the prism, y 2 is the relative distance from the observation point to the rear edge surface of the prism, z 1 is the height difference from the observation point to the top of the prism, and z 2 is the height difference from the observation point to the bottom of the prism; the distance between the observation points and 8 vertexes of the rectangular prism is respectively; and finally, integrating to obtain the terrain correction value TC through the gravity anomaly generated by all rectangular prism units on the measuring points in the intercepting range of the terrain correction.
7. The method for microgravity recognition of a subterranean space according to claim 6, wherein the altitude correction is performed by Wherein, the method comprises the steps of, As the height correction value, a correction value for the height, Is the height difference between the observation surface and the reference surface; Intermediate layer correction Wherein, the method comprises the steps of, For the intermediate layer correction value, G is the gravitational constant, Ρ is the average density of the intermediate layer, t is the thickness of the intermediate layer); calculating the adoption of the gravity anomaly of the Bragg Wherein, the method comprises the steps of, As a result of the basic gravity anomaly value, For the terrain correction value, As the height correction value, a correction value for the height, An intermediate layer correction value.
8. The method for identifying microgravity of a subsurface space according to claim 1, wherein determining the influence range of the superficial interference, stripping the superficial interference field from the bragg gravity anomaly based on the influence range of the superficial interference, and obtaining the target gravity anomaly data for inversion comprises: analyzing the influence rule of the fluctuation density interface under different burial depths and amplitude conditions on the surface gravity anomaly, so as to determine the potential field separation range as the influence range of the superficial interference; in the case of abnormal gravity of the Bragg Based on the above, stripping the influence range of the shallow interference to obtain the target gravity anomaly for removing the regional field anomaly of the shallow interference and for inversion, which comprises the following steps: Abnormal setting field From the field of area And local field The composition is as follows: ; Is provided with Representative and point Distance from each other An average value of a 4-point bit field of (a): In the formula, Is the cutting radius, the field is And Weighted average of (a), i.e. Wherein, the And Is a weighting coefficient, and ; ; ; ; ; ; ; ; ; Wherein, the Is a horizontal gradient; Is a vertical gradient; An amount of deviation between the center point Z (x, y) and half of the difference in the horizontal direction; an amount of deviation between the center point Z (x, y) and half of the vertical direction difference; The calculated field is called the 1 st cut field, using Representation of Repeating the calculation to obtain the 2 nd cutting area Sequentially iterate, finally have ; Thus there is And iterating for a plurality of times until the change of the regional field tends to be stable, and finally obtaining the target gravity anomaly data for inversion.
9. A method of microgravity recognition of a subterranean space according to any one of claims 1-8, wherein the target is one or more of a tunnel, void or pipeline corridor.
10. A microgravity recognition system for a subterranean space, comprising: The acquisition module is used for acquiring actual measurement microgravity original data of the target area and engineering geological priori information containing the geometric type of the target body, wherein the engineering geological priori information comprises geometric parameters; The forward model is used for defining key geometric parameters based on the geometric type of the target body, setting the variation range of each geometric parameter, and generating a plurality of groups of geometric parameter combinations through traversing sampling; the gravity anomaly calculating module is used for carrying out tide correction and zero drift correction on the actually measured microgravity original data to obtain a basic gravity anomaly value, carrying out terrain correction after determining a terrain correction range of the basic gravity anomaly value, carrying out height correction and middle layer correction on the basic gravity anomaly value, and calculating out Bragg gravity anomaly after correction; the stripping module is used for determining the influence range of the shallow interference, stripping the shallow interference field from the Bragg gravity anomaly based on the influence range of the shallow interference, and obtaining target gravity anomaly data for inversion; the prediction module is used for constructing a deep learning inversion model, training the deep learning inversion model by utilizing the synthesized sample data set, inputting the target gravity anomaly data for inversion into the trained deep learning inversion model, and outputting a prediction result of geometric parameters and spatial positions of an underground target body.

Description

Microgravity identification method and system for underground space Technical Field The application relates to the field of gravity identification, in particular to a microgravity identification method and system for an underground space. Background Gravity exploration is a classical geophysical method whose basic principle is to detect gravity anomalies caused by maldistribution of subsurface material density by measuring subtle changes in the earth's gravitational field. Based on the gravity exploration, the core application of the gravity exploration can be divided into two aspects, namely, directly utilizing gravity anomaly to identify underground anomaly bodies and quantitatively solving density differences, geometric forms and spatial positions of underground media through gravity inversion to further infer underground structural characteristics. Gravity inversion has evolved many sophisticated methods (such as regularized based three-dimensional inversion, parker-Oldenburg density interface inversion, etc.) as a key step in deriving subsurface properties from observed data. These methods have remarkable effects in processing areas or large-scale structures, however, when facing small-scale and shallow targets (such as shallow tunnels and underground holes), the methods often face double dilemmas that the resolution is insufficient, the target body is difficult to finely describe, and the inversion result is seriously non-unique (i.e. multiple underground models may generate similar gravity anomalies), so that the uncertainty of the interpretation result is increased. Accordingly, microgravity surveys aimed at detecting such locally small targets place higher demands on data accuracy and processing methods. Particularly, in environments with dense human activities such as cities, interference signals generated by surface topography, buildings, shallow bedrock interfaces and the like can seriously mask or confuse weak gravity anomalies from a target body, and further restrict the identification accuracy. Therefore, how to effectively suppress interference and improve the gravity recognition precision of shallow small targets becomes a key technical problem to be broken through in the microgravity exploration field. Disclosure of Invention The application aims to provide a microgravity identification method and a microgravity identification system for an underground space, which can improve the precision of microgravity identification in a shallow complex environment of a city. The application is realized in the following way: In a first aspect, the present application provides a microgravity identification method for an underground space, comprising the steps of: s1, acquiring actual measurement microgravity original data of a target area and engineering geology priori information containing the geometric type of the target body, wherein the engineering geology priori information comprises geometric parameters; S2, defining key geometric parameters based on the geometric type of the target body, setting the variation range of each geometric parameter, and generating a plurality of groups of geometric parameter combinations through traversing sampling; S3, carrying out tide correction and zero drift correction on the actually measured microgravity original data to obtain a basic gravity abnormal value, carrying out terrain correction after determining a terrain correction range of the basic gravity abnormal value, carrying out height correction and middle layer correction on the basic gravity abnormal value, and calculating out the Bragg gravity abnormal value after correction; S4, determining an influence range of the shallow interference, and stripping the shallow interference field from the Bragg gravity anomaly based on the influence range of the shallow interference to obtain target gravity anomaly data for inversion; S5, constructing a deep learning inversion model, training the deep learning inversion model by using a synthetic sample data set, inputting the target gravity anomaly data for inversion into the trained deep learning inversion model, and outputting a prediction result of geometric parameters and spatial positions of an underground target body. Based on the first aspect, the method further comprises: S6, substituting the geometric parameters predicted by the deep learning inversion model into a forward model to calculate theoretical gravity anomaly, comparing the theoretical gravity anomaly with the gravity anomaly after actual measurement or processing to calculate residual error, and carrying out consistency verification on the predicted geometric parameters and engineering investigation data to comprehensively evaluate the precision and the confidence coefficient of the predicted result. Based on the first aspect, the geometric parameters include a radius, a burial depth, a spacing, and a horizontal position of the target. Based on the first aspect, the step of performing forward c