A method and system for estimating the boundary of a gravity anomaly target body

Abstract

The application belongs to the field of gravity anomaly detection, and in particular to a gravity anomaly target body boundary estimation method and system. Based on the comparison and calculation of boundary functions and forward data sets, the characteristic line segment length vector is obtained from the extreme points of the boundary functions, a reference model is set in the observation range, the characteristic line segment length of each reference model is calculated, the boundary length and the characteristic line segment vector forward data set are established, the target to be measured is matched and compared with the data set, and the boundary estimation of the target body to be measured in the medium field gravity environment is realized. The problems of data attenuation, divergence and low resolution in gravity detection in the medium field environment are solved.

Description

Technical Field

[0001] This application belongs to the field of gravity anomaly detection, specifically a method and system for estimating the boundary of a gravity anomaly target. Background Technology

[0002] Gravity methods are among the earliest geophysical techniques developed for studying and characterizing the Earth's structure and internal processes. Modeling and analyzing gravity anomaly data plays a crucial role in interpreting subsurface geological structures. One major aspect is using boundary functions to depict the physical boundaries of known gravity anomalies. In recent years, many boundary identification functions have been designed to explore subsurface structures. Because boundary identification functions can highlight inconspicuous information in raw gravity data and enable rapid, real-time display of subsurface anomaly boundaries, they have been widely applied in the analysis and interpretation of target anomaly boundary problems.

[0003] In the field of gravity detection, the mid-field environment is a transitional observation region between the near-field and far-field, with an observation distance typically comparable to the size of the target (generally 0.5-3 times the maximum size of the target). Within this observation distance, the gravity field / gravity gradient field differs from contact measurements in the near-field and does not satisfy the point-source approximation of the far-field. Unlike the near-field, gravity data observed in the mid-field exhibits significant attenuation and divergence; unlike the far-field, the geometry and density distribution of the target still significantly influence the signal in the mid-field. Mid-field detection frequently occurs in fields such as underground structure exploration, airborne gravity gradient measurement, and precise location of deep ore bodies. With the continuous increase in underground observation depth and the increasing refinement of observation targets, the application of mid-field environment gravity observation in polar environment exploration and deep-sea underwater exploration will be inevitable. However, due to the long observation distance in the mid-field environment, the corresponding gravity anomaly data suffers from attenuation, divergence, and low resolution, making mainstream boundary identification functions unsuitable for boundary estimation in the mid-field environment. Summary of the Invention

[0004] This application provides a method and system for estimating the boundary of a gravity anomaly target, which solves the problems of data attenuation, divergence, and low resolution in gravity detection in a medium-field environment.

[0005] A method for estimating the boundary of a gravity anomaly target body according to the first aspect of this application includes: acquiring observation data of the target body under a mid-field gravity gradient environment;

[0006] A boundary function is constructed to describe the boundary of the target body to be measured, and the extreme points of the boundary function are calculated to obtain the vector of the feature line segments to be measured.

[0007] Within a preset observation range, set up multiple reference models with different parameters, and calculate the feature line segment vector corresponding to each reference model;

[0008] Based on the physical parameters of the reference model and the corresponding feature line segment vectors, a forward modeling dataset between the physical parameters and the feature line segment vectors is established.

[0009] The feature line segment vectors to be measured, obtained from the observation data of the target body, are matched and compared with the feature line segment vectors in the forward modeling dataset.

[0010] Based on the matching comparison results, the optimal boundary estimate of the target body to be tested is determined.

[0011] Furthermore, within a preset observation range, multiple reference models with different parameters are set up, and the feature line segment vector corresponding to each reference model is calculated, including:

[0012] set up Reference models with different combinations of physical parameters;

[0013] Calculate the horizontal component of the gravity gradient of the reference model;

[0014] The boundary function of the reference model is calculated based on the horizontal component of the gravity gradient;

[0015] On the boundary function, identify and extract extreme points;

[0016] For each reference model, among the extracted extreme points, a pair of extreme points that are symmetrically distributed about the boundary function axis are selected;

[0017] Calculate the Euclidean distance between each pair of symmetrical extreme points and define it as the length of the feature line segment corresponding to the reference model;

[0018] Arrange all feature segment lengths corresponding to each reference model according to a predetermined rule to construct a feature segment vector consisting of M feature segment lengths as elements.

[0019] Furthermore, the physical parameters include: length parameter, width parameter, height parameter, burial depth parameter, and residual density.

[0020] Furthermore, the boundary functions of the reference model are calculated based on the horizontal components of the gravity gradient, including:

[0021] intermediate variables are constructed based on the gravity gradient tensor components. Its expression is:

[0022] ,

[0023] in, For the first The gravity gradient tensor of a reference model in each direction, with the horizontal components of the gravity gradient. These are preset adjustment parameters used to control the filtering characteristics and response intensity of the boundary function;

[0024] For the intermediate variables, calculate the values ​​with respect to the horizontal direction. and The first-order partial derivative is obtained. Directional intermediate quantity and Directional intermediate quantity ;

[0025] Calculate the first based on two intermediate values. The boundary function of the reference model is calculated using the following formula:

[0026] Furthermore, the extreme points satisfy:

[0027] The first-order partial derivatives of the boundary function in both horizontal directions are zero:

[0028] , ,

[0029] Furthermore, the discriminant formed by the second derivatives of the boundary function in the two horizontal directions is greater than zero, where, No. The boundary function corresponding to each reference model For the first The boundary function corresponding to the reference model is the th... The x and y coordinates of the extreme points in the coordinate system.

[0030] Furthermore, the discriminant is:

[0031] ,

[0032] in, and For the first The boundary functions corresponding to the reference models are respectively in , The second derivative of the direction, For the first The boundary function corresponding to each reference model is Mixed second derivative function in mixed directions.

[0033] Further, the feature segment vectors obtained from the observation data of the target object are matched and compared with the feature segment vectors in the forward modeling dataset, including:

[0034] Calculate the correlation distance between the feature line segment vector of the target under test and the feature line segment vector of each reference model in the forward modeling dataset in turn.

[0035] Select the smallest value Each correlation distance, and The physical parameters of the feature line segment vectors corresponding to each correlation distance in the forward modeling dataset;

[0036] The boundary lengths of the physical parameters in the x-direction and y-direction are extracted respectively and used as the optimal reference boundary lengths;

[0037] Calculate the mean values ​​of the boundary lengths in the x and y directions respectively, and use these mean values ​​as the optimal boundary estimates of the target body in the x and y directions.

[0038] Further, calculating the boundary coordinates of the target object based on the optimal boundary estimate includes:

[0039] Obtain the boundary function that characterizes the boundary features of the underground density anomaly, and determine the geometric center coordinates of the target body in the horizontal plane based on the boundary function;

[0040] Based on the aforementioned geometric center coordinates, combined with The optimal boundary estimate of the direction and The optimal boundary estimate of the direction is used to construct the vertex coordinate range of the horizontal rectangular boundary of the target body.

[0041] A gravity anomaly target body boundary estimation system according to the first aspect of this application includes:

[0042] The data acquisition module is used to acquire observation data of the target body under test in a mid-field gravity gradient environment;

[0043] The feature extraction module is used to construct a boundary function describing the boundary of the target body to be tested, and to obtain the feature line segment vector to be tested by calculating the extreme points of the boundary function.

[0044] The reference model construction module is used to set up multiple reference models with different physical parameters within a preset observation range and to calculate the feature line segment vector corresponding to each reference model.

[0045] The dataset construction module is used to establish a forward modeling dataset between the physical parameters and the feature line segment vectors based on the physical parameters of the reference model and their corresponding feature line segment vectors.

[0046] The matching calculation module is used to match and compare the feature line segment vectors obtained from the observation data of the target body to be measured with the feature line segment vectors in the forward modeling dataset.

[0047] The boundary estimation module is used to determine the optimal boundary estimate of the target body based on the matching comparison results.

[0048] Compared with the prior art, the advantages of this application are as follows:

[0049] This application achieves the characterization of gravity anomalies using extreme points through boundary functions, and obtains the boundary length through matching of forward modeling datasets and calculation of correlation distances. It overcomes the problems of gravity data attenuation, divergence, and low resolution in mid-range environments, enabling accurate boundary estimation of gravity anomalies in mid-range environments, thus providing a solution for boundary estimation of gravity anomalies.

[0050] In a mid-range environment, the differences between local information of the target are not obvious, and its internal dissimilarity is diluted in gravity data. The main objective of gravity boundary detection is to identify the horizontal boundary length of the target, that is, the length of the target body in the mid-range gravity boundary. , The dimensions and range coordinates in the direction (length and width). This application treats the horizontal boundary of the target under test as a rectangle, uses a rectangular bounding box to accurately locate the horizontal boundary of the target body, and calculates the optimal length of the target under test. and optimal width This is to estimate its boundary length. Attached Figure Description

[0051] Figure 1 A flowchart illustrating a method for estimating the boundary of a gravity anomaly target body, as provided in this application embodiment;

[0052] Figure 2 A schematic diagram of the structure of a gravity anomaly target body boundary estimation system provided in this application embodiment;

[0053] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0054] Figure 4 A schematic diagram illustrating a boundary function example provided in this application embodiment;

[0055] Figure 5 The embodiment provided in this application provides a distribution map of the extreme points of the boundary function;

[0056] Figure 6 This is a schematic diagram of the boundary function feature point extraction scheme provided in the embodiments of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] Due to the long observation distance in the mid-field environment, the corresponding gravity anomaly data suffers from attenuation, divergence, and low resolution. This makes the mainstream boundary identification function unsuitable for boundary estimation in the mid-field environment, resulting in problems such as data attenuation, divergence, and low resolution in gravity detection in the mid-field environment.

[0060] This application proposes a method and system for estimating the boundary of a gravity anomaly target. Based on a comparative calculation of the boundary function and a forward modeling dataset, the characteristic line segment length vector is obtained from the extreme points of the boundary function. Reference models are set within the observation range, and the characteristic line segment lengths of each reference model are calculated. A forward modeling dataset of boundary lengths and characteristic line segment vectors is established. The target to be measured is matched and compared with the dataset to achieve boundary estimation of the target under a mid-gravity environment.

[0061] See Figure 1 As shown in the embodiment of this application, a method for estimating the boundary of a gravity anomaly target body is provided, including: S101, acquiring observation data of the target body under a mid-field gravity gradient environment;

[0062] S102, construct a boundary function to describe the boundary of the target body to be measured, calculate the extreme points of the boundary function, and obtain the vector of the feature line segments to be measured;

[0063] S103, Set up multiple reference models with different parameters within the preset observation range, and calculate the feature line segment vector corresponding to each reference model;

[0064] S104, Based on the physical parameters of the reference model and the corresponding feature line segment vectors, establish a forward modeling dataset between the physical parameters and the feature line segment vectors;

[0065] S105, the feature line segment vector to be measured obtained based on the observation data of the target body to be measured is matched and compared with the feature line segment vector in the forward modeling dataset;

[0066] S106. Based on the matching comparison results, determine the optimal boundary estimate of the target body to be tested.

[0067] This application's embodiments utilize boundary functions to characterize gravity anomalies using extreme points, and obtain the boundary length through matching forward datasets and calculating correlation distances. This method overcomes the characteristics of gravity data attenuation, divergence, and low resolution in mid-range environments, enabling accurate boundary estimation of gravity anomalies even in mid-range conditions.

[0068] In step S102, a boundary function is constructed to describe the boundary of the target body to be measured. The extreme points of the boundary function are calculated to obtain the vector of the feature line segments to be measured. The specific steps are as follows:

[0069] Within the observation range, the gravity distribution on the observation surface is obtained using a gravimeter, and the gravity anomaly data of the target object is obtained through data preprocessing (noise reduction, continuation, zero-point drift error compensation, etc.). .

[0070] Based on gravity anomaly data Calculate the horizontal component of the second-order gravitational gradient: , , , , , The above six components are the gravity anomaly tensor ( , , The partial derivative of ) in , The component in the direction. Its transformation formula according to Fourier transform theory is:

[0071] ,

[0072] ,

[0073] ,

[0074] ,

[0075] ,

[0076] in, Gravity anomaly tensor Fourier transform in the wavenumber domain; , They are respectively exist , Wave number in both directions, For wave number, .

[0077] Calculate the feature line segment vector of the target object:

[0078] The boundary function corresponding to the gravity anomaly to be estimated is calculated based on the horizontal component of the gravity gradient of the target object. Specifically: Boundary function The calculation formula is as follows:

[0079] ,

[0080] in, These are intermediate quantities of the boundary function, and their calculation formulas are as follows:

[0081] ,

[0082] ,

[0083] in, for System parameters, parameters Its function is to adjust the filtering efficiency of the boundary function. The value can be adjusted between -5 and 5 depending on the actual situation.

[0084] The extreme points of the boundary function are calculated to obtain the vector of the feature line segment to be measured:

[0085] In the embodiments of this application, It is a generic representation of the proposed boundary function.

[0086] Find the boundary function All extreme points ,in Represents the first boundary function For the x and y coordinates of the extreme points in the coordinate system, where , For the boundary function The logarithm of the extreme points of axisymmetric points. Conditions met:

[0087] ,

[0088] Among them, discriminant The calculation formula is:

[0089] ,

[0090] in, , , Boundary function In respectively , and The second derivative function in the y-mixing direction.

[0091] The vector of the feature line segment to be measured is calculated as follows:

[0092] Based on the extraction of extreme points, select a pair of extreme points that are symmetric about the boundary function axis, denoted as: and Calculate the length of the characteristic line segment with these two extreme points as endpoints. , .

[0093] .

[0094] Lengths of all feature segments The vector of the feature line segments to be tested is formed.

[0095] Step S103 is independent of steps S101 and S102, and is one of the steps used to build the forward dataset. Step S103 includes:

[0096] set up Reference models with different combinations of physical parameters;

[0097] Calculate the horizontal component of the gravity gradient of the reference model;

[0098] The boundary function of the reference model is calculated based on the horizontal component of the gravity gradient;

[0099] On the boundary function, identify and extract extreme points;

[0100] For each reference model, among the extracted extreme points, a pair of extreme points that are symmetrically distributed about the boundary function axis are selected;

[0101] Calculate the Euclidean distance between each pair of symmetrical extreme points and define it as the length of the feature line segment corresponding to the reference model;

[0102] Arrange all feature segment lengths corresponding to each reference model according to a predetermined rule to construct a feature segment vector consisting of M feature segment lengths as elements.

[0103] The forward modeling dataset includes the model parameters (including boundary lengths) of the reference model and the corresponding feature segment vectors. The specific steps are as follows:

[0104] Setting reference model parameters: Analyzing the characteristics of gravity data under field observation conditions, and considering the accuracy and versatility of gravity forward modeling, the reference model is set as a cube. This allows the proposed method to adapt to different types of target bodies, including geometric shapes with fixed aspect ratios such as cuboids, cylinders, and ellipsoids. In the method proposed in this application, forward modeling calculations are performed using the physical parameters of different cubes to complete the construction of the forward modeling dataset.

[0105] The required estimation accuracy is the interval step size. Set the reference model's own size parameters; the reference model is in... , , Length parameter in direction Wide parameter High parameters and burial depth parameters And set the residual density of the reference model. Its parameters are expressed as follows:

[0106] ,

[0107] ,

[0108] ,

[0109] ,

[0110] ,

[0111] in: For long parameters The parameter setting sequence number, ; For wide parameters The parameter setting sequence number, ; As a reference model Orientation dimension high parameter The parameter setting sequence number, ; Burial depth parameter The parameter setting sequence number, ; Residual density The parameter setting sequence number, ; , , , and These represent the reference model at different levels. Directional dimensions and their own dimensions, horizontal Direction itself and its dimensions, perpendicular The number of parameters to be set regarding the orientation, dimensional accuracy, burial depth, and residual density. The number of these settings depends on the precision requirements of the actual project. The base depth indicates the burial depth range of the gravity detection target below this depth; It is the smallest density unit.

[0112] By combining the set physical parameters in sequence, we obtain Group( ( ) Reference models with different physical parameters.

[0113] exist In the reference model group, the calculation of the first The integral formula for the horizontal component of the gravity gradient of the reference model is:

[0114] ,

[0115] ,

[0116] ,

[0117] ,

[0118] ,

[0119] in, It is the gravitational constant; The residual density of the reference model; The coordinates of the gravity detection point on the observation surface; The coordinates of any point within the reference model cube; For the first The volume of a reference model; For the first The gravity gradient tensor of a reference model consists of the horizontal components of the gravity gradient in each direction. These horizontal components are the main computational elements of the boundary functions.

[0120] Furthermore, by solving the integral, we obtain the corresponding analytical expression:

[0121] ,

[0122] ,

[0123] ,

[0124] ,

[0125] ,

[0126] in, The observation distance is the distance from the probe point to the centroid of the cube. and For the first The coordinates of the two points with the minimum and maximum coordinate values ​​of the reference model cube are given. Based on the above analytical expression, these two coordinate values ​​are substituted as the upper and lower limits of integration into the corresponding... By calculating the position, the analytical values ​​of the horizontal components of each gravity gradient can be obtained.

[0127] Then for the first The boundary function of each reference model is expressed as: The calculation formula is as follows:

[0128] ,

[0129] in, yes Directional intermediate quantity and Directional intermediate quantity The calculation formulas are as follows:

[0130] ,

[0131] ,

[0132] Similarly, For the first The gravity gradient tensor of a reference model in each direction, with the horizontal components of the gravity gradient. These are preset adjustment parameters used to control the filtering characteristics and response intensity of the boundary function. The value can be adjusted between -5 and 5 depending on the actual situation.

[0133] Selecting boundary functions for each reference model Upper extreme point ,

[0134] In this embodiment, It is a generic representation of the proposed boundary function. Then it means the first The boundary function corresponding to each reference model.

[0135] Find the first Boundary functions corresponding to each reference model All extreme points ,in Indicates the first The boundary function corresponding to the reference model is the th... For the x and y coordinates of the extreme points in the coordinate system, where , For about The logarithm of the extreme points of axisymmetric points. Conditions met:

[0136] ,

[0137] Among them, discriminant The calculation formula is:

[0138] ,

[0139] in, , , For the first Boundary function of each model exist , and Second derivative in the y-mixing direction:

[0140] .

[0141] Calculate the feature segment vectors corresponding to each reference model:

[0142] For the The reference model, based on the already extracted extreme points, selects the one about the... Boundary functions of a reference model A pair of extreme points symmetric to the function axis are denoted as: and Calculate the length of the characteristic line segment with these two extreme points as endpoints. , .

[0143] ,

[0144] Based on this reference model The length of each feature segment is the feature segment vector that makes up the reference model, i.e. , .

[0145] In the example, see the steps for calculating the feature line segment. Figure 4 , Figure 5 and Figure 6 .in, Figure 4 For the first The boundary function simulation graph of the first reference model, then the first... The distribution of extreme points on the boundary functions of the reference models can be found in [reference]. Figure 5 As shown. Furthermore, the selection results for the eigenvalue points used to characterize the boundary function are as follows: Figure 6 As shown, the specific operation involves setting the boundary function in... , Characteristic extreme points (points B1~B2) on the central axes (denoted as line1 and line2 respectively) in the direction. 12 These are the eigenvalues ​​of line 1, points C1~C1. 12The feature extreme points (A1~A4) and four global maximum points (A1~A4) on line 2 are extracted, resulting in a total of 28 extreme points. Then, based on symmetry, 14 feature line segments are obtained (…). ). Calculate the lengths of the 14 characteristic line segments, which are as follows:

[0146] , , , , , , , , , , , , , ,

[0147] Obtain the feature line segment vector .

[0148] The physical parameters and corresponding feature line segment vectors of the reference model will be used to construct the forward modeling dataset. Let the i-th reference model be... A reference model in , The horizontal boundary length of the direction is expressed as and To facilitate the representation and recording of forward datasets, where: ; ; .

[0149] In this embodiment, the forward dataset includes , Two areas: The region represents the physical parameters of the reference model, specifically including the physical parameters of the reference model in... , , The boundary length of the direction is the length parameter. Wide parameter High parameters Burial depth parameters and residual density Five parameters; The region represents the feature segment vector of the corresponding reference model. .but A forward model is constructed from several reference models, and then... This represents a forward dataset, which is expressed as a two-dimensional matrix:

[0150] .

[0151] In step S105, the correlation distance between the feature line segment vector of the target to be tested and the feature line segment vector of each reference model in the forward modeling dataset is calculated sequentially; the distance with the smallest value is selected. Each correlation distance, and The physical parameters of the feature line segment vectors corresponding to the correlation distances in the forward modeling dataset are obtained; the boundary lengths in the x and y directions of the physical parameters are extracted respectively as the optimal reference boundary lengths; the mean values ​​of the boundary lengths in the x and y directions are calculated respectively, and the mean values ​​are used as the optimal boundary estimates of the target body in the x and y directions.

[0152] Specifically, the feature line segment vectors to be tested obtained in steps S101 and S102 are matched and compared with the feature line segment vectors in the forward dataset obtained in step S104.

[0153] By iterating through matching and optimizing, the optimal boundary estimate is calculated, and the correlation distance between the feature segment vector to be tested and the corresponding feature segment vector of each reference model in the feature segment vector of the forward dataset is calculated sequentially. :

[0154] ,

[0155] in, The reference model number, i.e., the [number]th A reference model, ; Indicates the first in the forward dataset Feature line segment vectors in a reference model The One element; express of The mean of the elements. Represents the vector of the feature line segment to be measured; express of The mean of the elements.

[0156] Take the correlation distance Center front Each minimum value corresponds to a physical or spatial parameter of the feature line segment vector in the forward modeling dataset, serving as the target body in the model. , The optimal reference boundary lengths in the direction are denoted as follows: and The average value was taken as the value of the target body in the test. , The optimal boundary estimate of the direction is denoted as and :

[0157] .

[0158] Calculate the boundary coordinates of the target object: These boundary coordinates represent the range of vertex coordinates of the horizontal rectangular boundary of the target object. , and , ,

[0159] ,

[0160] in, express and , express , , , The geometric center coordinates of the anomaly, i.e., the boundary function. The geometric center coordinates.

[0161] ,

[0162] in, , Boundary function The two extreme points of the upper central symmetry and The coordinates.

[0163] On the other hand, see Figure 2 The block diagram shown illustrates a gravity anomaly target body boundary estimation system. This application provides a gravity anomaly target body boundary estimation system for implementing the above-mentioned method, comprising:

[0164] The data acquisition module is used to acquire observation data of the target body under test in a mid-field gravity gradient environment;

[0165] The feature extraction module is used to construct a boundary function describing the boundary of the target body to be tested, and to obtain the feature line segment vector to be tested by calculating the extreme points of the boundary function.

[0166] The reference model construction module is used to set up multiple reference models with different physical parameters within a preset observation range and to calculate the feature line segment vector corresponding to each reference model.

[0167] The dataset construction module is used to establish a forward modeling dataset between the physical parameters and the feature line segment vectors based on the physical parameters of the reference model and their corresponding feature line segment vectors.

[0168] The matching calculation module is used to match and compare the feature line segment vectors obtained from the observation data of the target body to be measured with the feature line segment vectors in the forward modeling dataset.

[0169] The boundary estimation module is used to determine the optimal boundary estimate of the target body based on the matching comparison results.

[0170] This disclosure also provides an electronic device. Figure 3 An exemplary structural diagram of the electronic device is shown. See also Figure 3 The electronic device includes a processor 301 and a memory 302. The memory 302 stores a computer program, which, when executed by the processor 301, causes the processor to perform certain actions. Figure 1 The method for estimating the boundary of a gravity anomaly target body is shown.

[0171] The processor 301 may be a central processing unit (CPU), a graphics processing unit (GPU), or other form of processing unit with data processing capabilities and / or instruction execution capabilities.

[0172] The memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may execute the program to implement the following... Figure 1 The method for estimating the boundary of a gravity anomaly target body.

[0173] Depending on the specific application, the electronic device may also include any other suitable components. For example, the electronic device may also include a communication component 303, and the present disclosure does not limit the composition and implementation of the communication component.

[0174] In addition to the methods and systems, embodiments of this disclosure also provide a computer program product comprising a computer program that, when executed by a processor, causes the processor to perform embodiments of this disclosure. Figure 1The steps in the gravity anomaly target body boundary estimation method are shown. The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. These programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0175] Furthermore, embodiments of this disclosure also provide a computer-readable storage medium having a computer program stored thereon, the computer program causing the processor to execute when run by a processor. Figure 1 The steps in the gravity anomaly target body boundary estimation method are shown. The computer-readable storage medium can be any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0176] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for estimating the boundary of a gravity anomaly target body, characterized in that, include: Acquire observational data of the target body under a mid-field gravity gradient environment; A boundary function is constructed to describe the boundary of the target body to be measured, and the extreme points of the boundary function are calculated to obtain the vector of the feature line segments to be measured. Within a preset observation range, set up multiple reference models with different parameters, and calculate the feature line segment vector corresponding to each reference model; Based on the physical parameters of the reference model and the corresponding feature line segment vectors, a forward modeling dataset between the physical parameters and the feature line segment vectors is established. The feature line segment vectors to be measured, obtained from the observation data of the target body, are matched and compared with the feature line segment vectors in the forward modeling dataset. Based on the matching comparison results, the optimal boundary estimate of the target body to be tested is determined.

2. The method for estimating the boundary of a gravity anomaly target body according to claim 1, characterized in that, Within a preset observation range, multiple reference models with different parameters are set up, and the feature line segment vector corresponding to each reference model is calculated, including: set up Reference models with different combinations of physical parameters; Calculate the horizontal component of the gravity gradient of the reference model; The boundary function of the reference model is calculated based on the horizontal component of the gravity gradient; On the boundary function, identify and extract extreme points; For each reference model, among the extracted extreme points, a pair of extreme points that are symmetrically distributed about the boundary function axis are selected; Calculate the Euclidean distance between each pair of symmetrical extreme points and define it as the length of the feature line segment corresponding to the reference model; Arrange all feature segment lengths corresponding to each reference model according to a predetermined rule to construct a feature segment vector consisting of M feature segment lengths as elements.

3. The method for estimating the boundary of a gravity anomaly target body according to claim 1, characterized in that, The physical parameters include: length, width, height, burial depth, and residual density.

4. The method for estimating the boundary of a gravity anomaly target body according to claim 2, characterized in that, The boundary functions of the reference model are calculated based on the horizontal component of the gravity gradient, including: Intermediate variables are constructed based on the gravity gradient tensor components. Its expression is: ， in, For the first The gravity gradient tensor of a reference model in each direction, with horizontal components of the gravity gradient. These are preset adjustment parameters used to control the filtering characteristics and response intensity of the boundary function; For the intermediate variables, calculate the values ​​with respect to the horizontal direction. and The first-order partial derivative is obtained. Directional intermediate quantity and Directional intermediate quantity ; Calculate the first based on two intermediate values The boundary function of the reference model is calculated using the following formula: 。 5. The method for estimating the boundary of a gravity anomaly target body according to claim 2, characterized in that, The extreme points satisfy: The first-order partial derivatives of the boundary function in both horizontal directions are zero: ， ， Furthermore, the discriminant formed by the second derivatives of the boundary function in the two horizontal directions is greater than zero, where, No. The boundary function corresponding to each reference model For the first The boundary function corresponding to the reference model is the th reference model. The x and y coordinates of the extreme points in the coordinate system.

6. The method for estimating the boundary of a gravity anomaly target body according to claim 5, characterized in that, The discriminant is: ， in, and For the first The boundary functions corresponding to the reference models are respectively in , The second derivative of the direction, For the first The boundary function corresponding to each reference model is Mixed second derivative function in mixed directions.

7. The method for estimating the boundary of a gravity anomaly target body according to claim 1, characterized in that, The matching and comparison calculation of the feature line segment vectors to be measured, obtained from the observation data of the target body, and the feature line segment vectors in the forward modeling dataset includes: Calculate the correlation distance between the feature line segment vector of the target under test and the feature line segment vector of each reference model in the forward modeling dataset in turn. Select the smallest value Each correlation distance, and The physical parameters of the feature line segment vectors corresponding to each correlation distance in the forward modeling dataset; The boundary lengths of the physical parameters in the x-direction and y-direction are extracted respectively and used as the optimal reference boundary lengths; Calculate the mean values ​​of the boundary lengths in the x and y directions respectively, and use these mean values ​​as the optimal boundary estimates of the target body in the x and y directions.

8. The method for estimating the boundary of a gravity anomaly target body according to claim 1, characterized in that, Calculating the boundary coordinates of the target object based on the optimal boundary estimate includes: Obtain the boundary function that characterizes the boundary features of the underground density anomaly, and determine the geometric center coordinates of the target body in the horizontal plane based on the boundary function; Based on the aforementioned geometric center coordinates, combined with The optimal boundary estimate of the direction and The optimal boundary estimate of the direction is used to construct the vertex coordinate range of the horizontal rectangular boundary of the target body.

9. A gravity anomaly target body boundary estimation system, used to execute the gravity anomaly target body boundary estimation method according to any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire observation data of the target body under test in a mid-field gravity gradient environment; The feature extraction module is used to construct a boundary function describing the boundary of the target body to be tested, and to obtain the feature line segment vector to be tested by calculating the extreme points of the boundary function. The reference model construction module is used to set up multiple reference models with different physical parameters within a preset observation range and to calculate the feature line segment vector corresponding to each reference model. The dataset construction module is used to establish a forward modeling dataset between the physical parameters and the feature line segment vectors based on the physical parameters of the reference model and their corresponding feature line segment vectors. The matching calculation module is used to match and compare the feature line segment vectors obtained from the observation data of the target body to be measured with the feature line segment vectors in the forward modeling dataset. The boundary estimation module is used to determine the optimal boundary estimate of the target body based on the matching comparison results.

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