A high-precision inversion method for gravity data considering the influence of polar ice cover

Abstract

The present invention discloses a high-precision inversion method for gravity data that takes into account the influence of polar ice cover, the method comprising: obtaining geological data of a study area, constructing a three-dimensional ice-rock thickness matrix based on the geological data; dividing the underground space of the study area into discrete grids, and preliminarily calibrating the physical properties of the discrete grids according to the three-dimensional ice-rock thickness matrix and the ice layer density to obtain an initial density distribution model; constructing a high-precision physical property inversion objective function based on the discrete grids and the initial density distribution model; solving the high-precision physical property inversion objective function and outputting the inversion result. The high-precision inversion method and system for gravity data proposed by the present invention can directly invert the rock layer under the ice surface, and the inversion result is stable, which is suitable for detection inversion in polar regions covered with ice.

Description

Technical Field

[0001] The present invention relates to the field of geophysics, and in particular to a high-precision inversion method for gravity data taking into account the influence of polar ice cover. Background Art

[0002] The Antarctic continent is almost entirely covered by glaciers, accounting for more than 80% of the world's modern ice cover. The existence of the ice shields the space below it, making it impossible to directly observe and study the material composition and geological structure of the underground space, further hindering related research on the evolution of polar geotectonic structures.

[0003] As an indirect perspective method, gravity method can be applied to the study of underground medium structure distribution. For the inversion of gravity data in polar regions, the conventional method requires first peeling off the ice layer and then extending the observation data on the undulating ice surface downward to the undulating bedrock surface. This method is cumbersome and the downward extension is unstable, which increases the uncertainty of the inversion results. Therefore, it is urgent to propose a new high-precision inversion method and system for gravity data that considers the influence of polar ice cover. Summary of the invention

[0004] The present invention provides a high-precision inversion method for gravity data taking into account the influence of polar ice cover, which is used to solve the problem that the traditional inversion method in the prior art has a complicated process and uncertain inversion results.

[0005] A high-precision inversion method for gravity data considering the influence of polar ice cover, the method comprising:

[0006] Acquire geological data of a study area, and construct a three-dimensional ice-rock thickness matrix based on the geological data;

[0007] Dividing the underground space of the study area into discrete grids, and preliminarily calibrating the physical properties of the discrete grids according to the three-dimensional ice-rock thickness matrix and the ice layer density to obtain an initial density distribution model;

[0008] Constructing a high-precision physical property inversion objective function based on the discrete grid and the initial density distribution model;

[0009] Solve the high-precision physical property inversion objective function and output the inversion result.

[0010] Optionally, constructing a three-dimensional ice-rock thickness matrix based on the geoscientific data includes:

[0011] Obtaining the areal ice thickness distribution of the study area based on the geological data; the geological data at least includes: well logging data, seismic data, geological data and ice radar data;

[0012] A three-dimensional ice-rock thickness matrix is ​​constructed based on the areal ice layer thickness distribution.

[0013] Optionally, the method further includes: using irregular polyhedral grids to divide the underground space of the study area into discrete grids.

[0014] Optionally, the high-precision physical property inversion objective function is:

[0015] φ=φ d +μφ m +B 新

[0016] In the formula, φ d is the data fitting matrix; φ m is the regularization term; μ is the regularization parameter; B 新 is the physical property constraint of ice layer-rock layer.

[0017] Optionally, the data fitting matrix is ​​obtained by the following formula:

[0018]

[0019] Where G∈R a×b is the kernel matrix, a is the number of observation data, b is the number of discrete grids; m∈R b is the model parameter to be determined, that is, the density value of each discrete grid; d obs ∈R a is the observed data vector; W d is the data weighting matrix, which is in the following form:

[0020] W d =diag(1 / σ)

[0021] The i-th element of σ is the noise standard deviation σ of the i-th data. i ;

[0022] The regularization term is obtained by:

[0023]

[0024] Where m apr is the initial density distribution model of underground space in the study area; W a is the model weight matrix, which is as follows:

[0025] W a =W m W b Z

[0026] Where W m is the focusing constraint matrix; W b is the prior weighting matrix; Z is the depth weighting matrix;

[0027] Focus constraint matrix Wm for:

[0028]

[0029] is the focusing weight factor associated with the jth grid volume The focusing weight vector composed of

[0030] The depth weighting matrix Z is as follows:

[0031]

[0032] Where z is the discrete grid center depth matrix, and z0 is a positive value.

[0033] Optionally, the priori weighting matrix is ​​obtained by the following formula:

[0034] W b =diag(ζ)

[0035] Where ζ is the model weight coefficient ζ corresponding to the jth grid j Vector composed of.

[0036] Optionally, the ice layer-rock layer physical property constraint item is:

[0037] B 新 =B 冰 +B 岩

[0038] Where B 冰 is the ice layer physical property constraint; B 岩 is the rock formation physical property constraint;

[0039] Ice layer properties range:

[0040] Rock physical property range:

[0041] Ice layer physical property constraints:

[0042]

[0043] Where λ is the barrier parameter; k is the number of grid cells in the ice layer segmentation model; Z i is the depth weighting coefficient; m 冰 To model the ice layer;

[0044] Rock physical property constraints:

[0045] Where p is the number of grid cells in the rock formation segmentation model; m 岩 It is a rock formation segmentation model.

[0046] Optionally, the regularization parameter is determined by:

[0047]

[0048] Where D is a random matrix with 1 and -1 accounting for 50% each; GCV(μ) is the calculated value of the GCV formula corresponding to the input regularization parameter μ.

[0049] A high-precision inversion method for gravity data considering the influence of polar ice cover in the present invention, the system comprises:

[0050] A data acquisition unit, used to acquire geological data of a research area and construct a three-dimensional ice-rock thickness matrix based on the geological data;

[0051] A model acquisition unit, used for dividing the underground space of the study area into discrete grids, and preliminarily calibrating the physical properties of the discrete grids according to the three-dimensional ice-rock thickness matrix and the ice layer density to obtain an initial density distribution model;

[0052] A function construction unit, used for constructing a high-precision physical property inversion objective function based on the discrete grid and the initial density distribution model;

[0053] The function solving unit is used to solve the high-precision physical property inversion objective function and output the inversion result.

[0054] A computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any of the methods described above.

[0055] The high-precision inversion method and system for gravity data taking into account the influence of polar ice cover proposed in the present invention can directly invert the strata under the ice surface, and the inversion result is stable, and is suitable for detection inversion in ice-covered polar regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flow chart of a high-precision inversion method for gravity data taking into account the influence of polar ice cover in an embodiment of the present invention;

[0057] Figure 2 is a schematic cross-sectional diagram of the Antarctic geological structure according to an embodiment of the present invention;

[0058] Figure 3 It is a schematic diagram of the structure of a high-precision inversion system for gravity data taking into account the influence of polar ice cover in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0060] It should be understood that in the various embodiments of this document, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0061] The embodiment of the present invention provides a high-precision inversion method for gravity data taking into account the influence of polar ice cover, such as Figure 1 As shown, the method includes:

[0062] Step 100, obtain the geological data of the study area, and construct a three-dimensional ice-rock thickness matrix based on the geological data. Specifically, the geological data is multi-source geological data, and the acquired geological data may mainly include well logging data, seismic data, geological data, and ice radar data, etc. In other embodiments of the present invention, other known geological data may also be acquired according to different inversion requirements. Based on the acquisition of multi-source geological data of the study area, a three-dimensional ice-rock thickness matrix may be constructed.

[0063] Step 200, the underground space of the study area is divided into discrete grids, and the physical properties of the discrete grids are preliminarily calibrated according to the three-dimensional ice-rock thickness matrix and the ice layer density to obtain an initial density distribution model. Specifically, the underground of the study area is a three-dimensional space, and the three-dimensional space is divided into discrete grids to facilitate subsequent calculations. Based on the three-dimensional ice edge thickness matrix and the ice layer density, the physical properties of each of the above discrete grids are preliminarily calibrated to obtain an initial density distribution model, wherein the ice layer density is a known quantity, and the physical property information of the ice layer can be accurately assigned, and the physical property information of the rock layer can be focused on in subsequent calculations.

[0064] Step 300: constructing a high-precision physical property inversion objective function based on the discrete grid and the initial density distribution model. Specifically, based on the discrete grid and the initial density distribution model in step 200, a high-precision physical property inversion objective function can be constructed.

[0065] Step 400, solving the high-precision physical property inversion objective function and outputting the inversion result.

[0066] The method described in the specific embodiment of the present invention can directly explore the address structure covered by the ice layer, the method is simple, and the inversion result is specific and certain.

[0067] The high-precision inversion method and system for gravity data considering the influence of polar ice cover described in the specific embodiment of the present invention preferably constructs a three-dimensional ice rock thickness matrix based on the geoscience data, including:

[0068] A three-dimensional ice-rock thickness matrix is ​​constructed based on the area ice layer thickness distribution. Specifically, Figure 2 As shown, the areal ice thickness distribution h in the study area is obtained according to the geological data, and a three-dimensional ice rock thickness matrix A(x, y, h) is constructed based on this, where x, y are the coordinates of the observation point, and h is the areal ice thickness corresponding to the observation point x, y.

[0069] The horizontal and vertical coordinates of the observation point and the corresponding area ice sheet thickness are used as three-dimensional coordinates.

[0070] The high-precision inversion method and system for gravity data taking into account the influence of polar ice cover described in the specific embodiment of the present invention preferably further comprises: using irregular polyhedral grids to divide the underground space of the study area into discrete grids, which can better fit the undulating ice bottom and ice top (ice surface).

[0071] In the method and system for high-precision inversion of gravity data considering the influence of polar ice cover described in the specific embodiment of the present invention, preferably, the high-precision physical property inversion objective function is:

[0072] φ=φ d +μφ m +B 新

[0073] In the formula, φ d is the data fitting matrix; φ m is the regularization term; μ is the regularization parameter; B 新 is the physical property constraint of ice layer-rock layer.

[0074] In the high-precision inversion method and system for gravity data considering the influence of polar ice cover described in the specific embodiment of the present invention, preferably, the data fitting matrix is ​​obtained by the following formula:

[0075]

[0076] Where G∈R a×b is the kernel matrix, a is the number of observation data, b is the number of discrete grids; m∈R b is the model parameter to be determined, that is, the density value of each discrete grid; d obs ∈R a is the observed data vector; W d is the data weighting matrix, which is in the following form:

[0077] W d =diag(1 / σ)

[0078] The i-th element of σ is the noise standard deviation σ of the i-th data. i ;

[0079] The regularization term is obtained by:

[0080]

[0081] Where m apr is the initial density distribution model; W a is the model weight matrix, which is as follows:

[0082] W a =W m W b Z

[0083] Where W m is the focusing constraint matrix; W b is the prior weighting matrix; Z is the depth weighting matrix;

[0084] Focus constraint matrix W m for:

[0085]

[0086] Focus constraint matrix W m is the focusing constraint diagonal matrix considering the grid volume in order to obtain a focused inversion result.

[0087] is the focusing weight factor associated with the jth grid volume The focusing weight vector composed of

[0088] The depth weighting matrix Z is as follows:

[0089]

[0090] Where z is the discrete grid center depth matrix, and z0 is a small positive value imposed to avoid singularities on the surface. The depth weighting matrix Z can be used to improve the depth resolution of the inversion results.

[0091] The high-precision inversion method and system for gravity data considering the influence of polar ice cover described in the specific embodiment of the present invention is preferably proposed and introduced to strengthen the protection of the initial density distribution model of the ice layer part during the inversion iteration process and highlight the anomalies caused by the rock layer part model. b , the prior weight matrix is ​​obtained by the following formula:

[0092] W b =diag(ζ)

[0093] Where ζ is the model weight coefficient ζ corresponding to the jth grid j By giving a larger weight to the ice layer model, the physical property information of the ice layer model is strictly controlled near the initial density distribution model, and a smaller weight is given to the rock layer model in order to obtain the real physical property model of the rock layer part. Usually, the weighting coefficient of the ice layer part model is 冰 =1, rock formation model weighting coefficient ζ 岩 =0.01.

[0094] In the high-precision inversion method and system for gravity data considering the influence of polar ice cover described in the specific embodiment of the present invention, preferably, the ice layer-rock layer physical property constraint item is:

[0095] B 新 =B 冰 +B 岩

[0096] Where B 冰 is the ice layer physical property constraint; B 岩 is the rock formation physical property constraint;

[0097] Ice layer properties range:

[0098] Rock physical property range:

[0099] Ice layer physical property constraints:

[0100] Where λ is the barrier parameter; k is the number of grid cells in the ice layer segmentation model; Z i is the depth weighting coefficient; m 冰 To model the ice layer;

[0101] Rock physical property constraints:

[0102] Where p is the number of grid cells in the rock formation segmentation model; m 岩 It is a rock formation segmentation model.

[0103] The high-precision inversion method and system for gravity data considering the influence of polar ice cover described in the specific embodiment of the present invention is preferably determined by the following formula

[0104] Regularization parameter:

[0105]

[0106] Where D is a random matrix with 1 and -1 accounting for 50% each; GCV(μ) is the calculated value of the GCV formula corresponding to the input regularization parameter μ. Specifically, the present invention determines the regularization parameter by the GCV method. The equation is solved using the conjugate gradient method. The regularization parameter μ corresponding to the input of the minimum GCV(μ) value is taken as the optimal regularization parameter.

[0107] Preferably, the inversion result can be obtained by iteratively minimizing the high-precision physical property inversion objective function using the Newton method. During the iteration process, the regularization parameter μ remains unchanged, and the barrier parameter λ decreases with the iteration. When the number of iterations reaches the preset maximum number of iterations or the objective function changes by less than 1%, the iteration is stopped and the inversion result is output.

[0108] The embodiment of the present invention also provides a high-precision inversion system for gravity data taking into account the influence of polar ice cover, such as Figure 3 As shown, the system comprises:

[0109] A data acquisition unit 301 is used to acquire geological data of a research area and construct a three-dimensional ice-rock thickness matrix based on the geological data;

[0110] The model acquisition unit 302 is used to divide the underground space of the study area into discrete grids, and preliminarily calibrate the physical properties of the discrete grids according to the three-dimensional ice-rock thickness matrix and the ice layer density to obtain an initial density distribution model;

[0111] A function construction unit 303 is used to construct a high-precision physical property inversion objective function based on the discrete grid and the initial density distribution model;

[0112] The function solving unit 304 is used to solve the high-precision physical property inversion objective function and output the inversion result.

[0113] A computer-readable storage medium in an embodiment of the present invention stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the method described in any one of the embodiments.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] The foregoing description of specific exemplary embodiments of the present invention is for the purpose of illustration and demonstration. These descriptions are not intended to limit the present invention to the precise form disclosed, and it is clear that many changes and variations can be made based on the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical application, so that those skilled in the art can realize and utilize various different exemplary embodiments of the present invention and various different selections and changes. The scope of the present invention is intended to be limited by the claims and their equivalents.

Claims

1. A high-precision inversion method for gravity data considering the influence of polar ice cover, characterized in that: The method comprises: Acquire geological data of a study area, and construct a three-dimensional ice-rock thickness matrix based on the geological data; Dividing the underground space of the study area into discrete grids, and preliminarily calibrating the physical properties of the discrete grids according to the three-dimensional ice-rock thickness matrix and the ice layer density to obtain an initial density distribution model; Constructing a high-precision physical property inversion objective function based on the discrete grid and the initial density distribution model; Solve the high-precision physical property inversion objective function and output the inversion result.

2. The high-precision inversion method for gravity data considering the influence of polar ice cover according to claim 1 is characterized in that: Constructing a three-dimensional ice-rock thickness matrix based on the geoscience data includes: Obtaining the areal ice thickness distribution of the study area based on the geological data; the geological data at least includes: well logging data, seismic data, geological data and ice radar data; A three-dimensional ice-rock thickness matrix is ​​constructed based on the areal ice layer thickness distribution.

3. The high-precision inversion method for gravity data considering the influence of polar ice cover according to claim 1 is characterized in that: The method further comprises: using irregular polyhedral grids to divide the underground space of the research area into discrete grids.

4. The high-precision inversion method for gravity data considering the influence of polar ice cover according to claim 1 is characterized in that: The high-precision physical property inversion objective function is: f=f d +mf m +B 新 In the formula, φ d is the data fitting matrix; φ m is the regularization term; μ is the regularization parameter; B 新 is the physical property constraint of ice layer-rock layer.

5. The high-precision inversion method for gravity data considering the influence of polar ice cover according to claim 4 is characterized in that: The data fitting matrix is ​​obtained by the following formula: Where G∈R a×b is the kernel matrix, a is the number of observation data, b is the number of discrete grids; m∈R b is the model parameter to be determined, that is, the density value of each discrete grid; d obs ∈R a is the observed data vector; W d is the data weighting matrix, which is in the following form: W d =diag(1 / σ) The i-th element of σ is the noise standard deviation σ of the i-th data. i ; The regularization term is obtained by: Where m apr is the initial density distribution model of underground space in the study area; W a is the model weight matrix, which is as follows: IN a =In m IN b WITH Where W m is the focusing constraint matrix; W b is the prior weighting matrix; Z is the depth weighting matrix; Focus constraint matrix W m for: IN m =diag(θ) θ is the focusing weight factor θ associated with the jth grid volume j The focusing weight vector composed of The depth weighting matrix Z is as follows: Where z is the discrete grid center depth matrix, and z0 is a positive value.

6. The high-precision inversion method for gravity data considering the influence of polar ice cover according to claim 5 is characterized in that: The priori weight matrix is ​​obtained by the following formula: W b =diag(ζ) Where ζ is the model weight coefficient ζ corresponding to the jth grid j Vector composed of.

7. The high-precision inversion method for gravity data considering the influence of polar ice cover according to claim 6 is characterized in that: The ice layer-rock layer physical property constraint term is: B 新 =B 冰 +B 岩 Where B 冰 is the ice layer physical property constraint; B 岩 is the rock formation physical property constraint; Ice layer physical properties range: m 冰min ≤m 冰 ≤m 冰max Rock physical property range: m 岩min ≤m 岩 ≤m 岩max Ice layer physical property constraints: Where λ is the barrier parameter; k is the number of grid cells in the ice layer segmentation model; Z i is the depth weighting coefficient; m 冰 To model the ice layer; Rock physical property constraints: Where p is the number of grid cells in the rock formation segmentation model; m 岩 It is a rock formation segmentation model.

8. The high-precision inversion method for gravity data considering the influence of polar ice cover according to claim 7 is characterized in that: The regularization parameter is determined by the following formula: Where D is a random matrix with 1 and -1 accounting for 50% each; GCV(μ) is the calculated value of the GCV formula corresponding to the input regularization parameter μ.

9. A high-precision inversion system for gravity data taking into account the influence of polar ice cover, characterized in that: The system comprises: A data acquisition unit, used to acquire geological data of a research area and construct a three-dimensional ice-rock thickness matrix based on the geological data; A model acquisition unit, used for dividing the underground space of the study area into discrete grids, and preliminarily calibrating the physical properties of the discrete grids according to the three-dimensional ice-rock thickness matrix and the ice layer density to obtain an initial density distribution model; A function construction unit, used for constructing a high-precision physical property inversion objective function based on the discrete grid and the initial density distribution model; The function solving unit is used to solve the high-precision physical property inversion objective function and output the inversion result.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of claims 1 to 8.

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