Fusion inversion method and system for surface wave multi-mode dispersion curve, medium and equipment

Abstract

The invention belongs to the field of surface wave exploration, and provides a surface wave multi-mode dispersion curve fusion inversion method and system, a medium and equipment. The surface wave multi-mode frequency dispersion curve fusion inversion method comprises the steps of generating a surface wave frequency dispersion energy diagram and picking up observation frequency dispersion points from the surface wave frequency dispersion energy diagram; selecting a plurality of observation frequency dispersion points with identifiable modals in the picked observation frequency dispersion points as prior information; constructing a difference optimization objective function in a manner of automatically fitting observation frequency scatter points and theoretical frequency scatter points based on a principle of proximity; the prior information and the difference optimization objective function form a fusion inversion objective function through a weighting mode; and according to the fusion inversion objective function, performing inversion until an iteration termination condition is satisfied, and obtaining an inversion result.

Description

Technical Field

[0001] The present invention belongs to the field of surface wave exploration, and in particular relates to a surface wave multi-modal dispersion curve fusion inversion method, system, medium and equipment. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As a shallow surface seismic exploration method, surface wave exploration has been widely used in engineering geological exploration, non-destructive testing and other fields because of its advantages such as non-intrusion, low shallow surface attenuation, strong site adaptability and low construction cost. The two types of surface waves commonly used in surface wave exploration include Rayleigh Wave and Love Wave. Both waves have dispersion characteristics, that is, when the wave propagates in the medium, the phase velocity changes with the change of frequency. During the propagation process, the surface wave carries various parameter information of the propagation medium and is highly sensitive to the shear wave velocity of the medium. According to the dispersion characteristics of the surface wave, the original surface wave data is transformed into the frequency phase velocity domain, and the dispersion energy diagram can be obtained, and the dispersion curve of the surface wave can be extracted from it. The shape of the surface wave dispersion curve mainly depends on the shear wave velocity of the medium, so the shear wave velocity model of the medium can be inverted through the dispersion curve.

[0004] After extracting a high-precision dispersion curve from the dispersion spectrum, the ultimate goal is to establish a general relationship between the dispersion curve and the actual formation parameters, that is, dispersion curve inversion. Dispersion curve inversion continuously updates the optimization model to make the theoretical dispersion curve of the current iterative model approach the observed dispersion curve. The dispersion curve inversion process can be specifically described as follows: ① Given the initial model; ② Calculate the theoretical dispersion curve according to the dispersion curve forward modeling method; ③ Measure the fitting error based on the inversion objective function; ④ Update the model based on the inversion optimization algorithm.

[0005] At present, in practical exploration for engineering, most dispersion curve inversion still only uses the fundamental mode dispersion curve. However, when there are weak interlayers, hard interlayers and lateral inhomogeneous changes in the ground, higher-order modes are often easier to obtain than fundamental modes. At this time, if only the fundamental mode inversion is adopted, the effect will be less than ideal. Many studies have pointed out that considering that higher-order modes are more developed in some geological conditions, higher-order modes are more sensitive to changes in medium parameters than fundamental modes, and the addition of higher-order modes can effectively reduce the multi-solution of inversion and enhance the accuracy and stability of inversion, dispersion curve inversion should be carried out with fundamental and higher-order modes as inversion targets at the same time. Gabriels et al. first realized the joint inversion of multi-mode dispersion curves and obtained good inversion results. Beaty et al. realized the joint inversion of fundamental and higher-order modes by simulated annealing method, proving that the multi-order mode inversion effect is better than the single fundamental mode inversion. Limited by computational efficiency, this method has not been promoted in actual engineering. Xia et al. proved that multi-mode combination can effectively reduce the multi-solution of inversion through the inversion examples of fundamental and first-order higher-order modes. Feng et al. realized the joint inversion of multi-mode dispersion curves through genetic algorithm, effectively restored the velocity information of weak interlayers, and pointed out that when the number of model layers is greater than 5, the inversion is prone to fall into local extreme values. Luo Yinhe et al. tried multi-mode joint inversion with a six-layer model as an example, and also obtained a relatively reliable inversion effect, proving the important role of high-mode information in improving the inversion effect. Most of the above studies use lower-order high-order modes for inversion. At this time, it is relatively easy to distinguish the mode order. However, in actual engineering applications, it is extremely difficult to accurately distinguish the mode order. Once the judgment is wrong, it is very easy to cause the inversion to converge in the wrong direction. Researchers have also carried out a lot of research on the problem of mode discrimination, but there is still no effective method for modal order identification in actual engineering. To this end, Maraschini proposed a determinant objective function for dispersion curve inversion based on Ernst's idea of ​​minimizing the inversion of the stiffness matrix determinant, which successfully avoided the difficulty of modal order discrimination and significantly shortened the inversion time. However, when the dispersion point does not fit the theoretical dispersion curve, it may still cause the calculated determinant value to approach zero. The calculated determinant value may have a slow energy change and approach zero in a wide area close to the position of the real dispersion curve. Therefore, it has the defects of being easy to fall into local minima and having high requirements for the initial model, which limits its engineering application to a certain extent. Zhang Dazhou et al. classified dispersion curves into fundamental dispersion curves and non-fundamental dispersion curves based on the determinant objective function, and used directional fitting for fundamental dispersion curves and the generalized objective function method of determinant objective function for non-fundamental dispersion curves to reduce the problem of high requirements for the initial model in inversion.

[0006] In summary, one of the key issues facing dispersion curve inversion in surface wave exploration is the identification of dispersion curve modes in dispersion energy diagrams. The inventors found that, at present, fundamental modes in dispersion energy diagrams are considered easy to identify, while higher-order modes are not easy to identify. In order to solve the problem of mode identification, the main method at this stage is to use a determinant objective function, but this method has poor stability, and a new inversion method is needed for fast and stable dispersion curve inversion. Summary of the invention

[0007] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a surface wave multi-modal dispersion curve fusion inversion method, system, medium and equipment, which can avoid identifying high-order dispersion curves that are difficult to identify and obtain more accurate dispersion curve inversion results.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] A first aspect of the present invention provides a method for fusion inversion of multi-modal dispersion curves of surface waves.

[0010] A surface wave multi-mode dispersion curve fusion inversion method, comprising:

[0011] Generate a surface wave dispersion energy map and pick up observed dispersion points from it;

[0012] Select several observed frequency dispersion points with modal identifiability from the picked observed frequency dispersion points as prior information; construct the difference optimization objective function by automatically fitting the observed frequency dispersion points and the theoretical frequency dispersion points based on the proximity principle;

[0013] The prior information and the difference optimization objective function are combined into a fusion inversion objective function in a weighted manner;

[0014] The inversion is performed according to the fusion inversion objective function until the iteration termination condition is met and the inversion result is obtained.

[0015] As an implementation method, the difference optimization objective function R(m) is:

[0016]

[0017] Where l is a constant; is the phase velocity of the jth observed frequency dispersion point; For The phase velocity vector composed of theoretical dispersion points at the same frequency; p is the pth branch of the Rayleigh wave dispersion curve; is the minimum distance between the picked frequency dispersion point and the corresponding theoretical frequency dispersion point, and N is the number of observed frequency dispersion points.

[0018] As an implementation method, the calculation formula of the prior information P(m) is:

[0019]

[0020] Among them, M is the number of prior information; is the fundamental phase velocity of the ith prior information dispersion point, For The fundamental phase velocity of the theoretical dispersion point at the same frequency.

[0021] As an implementation method, the fusion inversion objective function O(m) is:

[0022]

[0023] Among them, w1 and w2 are weight coefficients.

[0024] As an implementation method, the inversion process is iterated step by step. In each iteration, the theoretical dispersion curve of the layered model is calculated by the dispersion curve forward modeling method, and the function value of the layered model is calculated according to the fused inversion objective function.

[0025] As an implementation method, when the inversion reaches the set iteration termination condition, that is, the objective function value is less than the set minimum fitting error or reaches the maximum number of iterations, the iteration process is terminated and the layered model with the smallest objective function value is output as the final result of the inversion.

[0026] A second aspect of the present invention provides a surface wave multi-modal dispersion curve fusion inversion system.

[0027] A surface wave multi-mode dispersion curve fusion inversion system, comprising:

[0028] An observation frequency dispersion point picking module is used to generate a surface wave dispersion energy map and pick up observation frequency dispersion points therefrom;

[0029] Prior information determination and difference optimization module, which is used to select several observed frequency dispersion points that are modally identifiable from the picked observed frequency dispersion points as prior information; based on the principle of proximity, the difference optimization objective function is constructed by automatically fitting the observed frequency dispersion points and the theoretical frequency dispersion points;

[0030] A fusion inversion objective function construction module is used to construct a fusion inversion objective function by weighting the prior information and the difference optimization objective function;

[0031] The inversion result acquisition module is used to perform inversion according to the fusion inversion objective function until the iteration termination condition is met to obtain the inversion result.

[0032] As an implementation method, in the inversion result acquisition module, iterations are performed step by step during the inversion process. In each iteration, the theoretical dispersion curve of the layered model is calculated by the dispersion curve forward modeling method, and the function value of the layered model is calculated based on the fused inversion objective function.

[0033] As an implementation method, in the inversion result acquisition module, when the inversion reaches the set iteration termination condition, that is, the objective function value is less than the set minimum fitting error or reaches the maximum number of iterations, the iteration process is terminated and the layered model with the smallest objective function value is output as the final result of the inversion.

[0034] A third aspect of the present invention provides a computer-readable storage medium.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned method for fusion inversion of multi-modal dispersion curves of surface waves.

[0036] A fourth aspect of the present invention provides an electronic device.

[0037] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the above-mentioned method for fusion inversion of multi-modal dispersion curves of surface waves are implemented.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) The present invention integrates high-order dispersion curves into dispersion curve inversion through a difference optimization algorithm, and avoids the difficulty of high-order mode identification;

[0040] (2) When calculating the fusion inversion objective function, only the theoretical dispersion points at the corresponding frequencies of each picked dispersion point are calculated, which reduces the amount of calculation;

[0041] (3) By controlling the distance calculation formula between the picked frequency dispersion point and the theoretical frequency dispersion point, the problem that the determinant value of the non-theoretical dispersion curve position obtained by the determinant objective function may also approach zero and the inversion is unstable and prone to fall into local extreme values ​​is solved.

[0042] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0044] Figure 1 is a flow chart of a method for fusion inversion of multi-modal dispersion curves of surface waves according to an embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of the principle of the fusion inversion objective function of an embodiment of the present invention;

[0046] Figure 3 It is a schematic diagram of the fusion inversion result of the multi-modal dispersion curve of the surface wave according to an embodiment of the present invention.

[0047] Among them, 1. prior information, 2. common observation dispersion points, 3. theoretical dispersion curve of the layered model during the inversion iteration process. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0050] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0051] Embodiment 1

[0052] like Figure 1 As shown, this embodiment provides a method for fusion inversion of multi-modal dispersion curves of surface waves, which includes:

[0053] Step 1: Generate a surface wave dispersion energy map and pick observation dispersion points from it.

[0054] After obtaining the original Rayleigh wave data, it is first processed and then converted into the frequency-phase velocity domain by the dispersion curve extraction algorithm to generate a dispersion energy diagram.

[0055] After obtaining the dispersion energy diagram, Rayleigh wave dispersion points are picked from the diagram along the dispersion energy axis.

[0056] Step 2: Select several observed frequency dispersion points with modal identifiability from the picked observed frequency dispersion points as prior information; construct the difference optimization objective function by automatically fitting the observed frequency dispersion points and the theoretical frequency dispersion points based on the proximity principle.

[0057] The energy in the dispersion diagram is modally identified based on known experience. If there is a dispersion energy axis that can identify the mode corresponding to the dispersion energy, it is identified as prior information.

[0058] The calculation formula of prior information is:

[0059]

[0060] Among them, M is the number of prior information; is the fundamental phase velocity of the ith prior information dispersion point, For The fundamental phase velocity of the theoretical dispersion point at the same frequency.

[0061] Among them, the difference optimization objective function is:

[0062]

[0063] Among them, l is a constant, usually, the value is 2; is the phase velocity of the i-th observed dispersion point; For The phase velocity vector composed of theoretical dispersion points at the same frequency; p is the pth branch of the Rayleigh wave dispersion curve; is the minimum distance between the picked frequency dispersion point and the corresponding theoretical frequency dispersion point, and N is the number of observed frequency dispersion points.

[0064] The difference optimization objective function realizes the automatic fitting of the observed frequency dispersion points and the theoretical frequency dispersion points through the principle of proximity; Figure 2 As shown in the figure, the dispersion point 1 is determined as the prior information, and the mode corresponding to the dispersion point is the fundamental mode. When calculating the objective function, the prior information dispersion point is only matched with the fundamental theoretical dispersion curve, so that the fundamental dispersion curve of the theoretical dispersion curve 3 of the inversion iteration model is gradually fixed at the picked fundamental dispersion point; while the mode of the common observation dispersion point is unknown, the observation dispersion point 2 will automatically calculate the difference with the theoretical dispersion curve 3 of the iteration model closest to it during the inversion iteration process to optimize the objective function value.

[0065] Step 3: The prior information and the difference optimization objective function are weighted to form a fusion inversion objective function.

[0066] The prior information can be set to an empty set in the objective function. In this case, the fusion inversion objective function is completely controlled by the difference optimization objective function. The fusion inversion objective function is as follows:

[0067]

[0068] Among them, w1 and w2 are weight coefficients.

[0069] During the inversion process, the weight setting is not fixed. At the beginning, the inversion direction is mainly determined by prior information. At this time, w1=1, w2=0. As the inversion proceeds, w1 gradually decreases and w2 gradually increases. Finally, the prior information and the difference optimization objective function provide the same contribution in the inversion.

[0070] Step 4: Perform inversion according to the fusion inversion objective function until the iteration termination condition is met to obtain the inversion result.

[0071] Calculate the observed frequency dispersion points in the nth inversion iteration The steps to find the nearest theoretical frequency dispersion point are:

[0072] (1) Given the search range of phase velocity and the search velocity interval dv, in general, when only the common mode of the theoretical dispersion curve is calculated, the minimum search value v is set min Vs is the minimum shear wave velocity of the current iteration model min 0.88 times, set the maximum search value v max Vs is the maximum shear wave velocity of the current iteration model max :

[0073] v min =0.88×Vs min , v max =Vs max

[0074] (2) According to the phase velocity search range and search interval, the root search method is used to calculate the observed frequency dispersion points. The corresponding frequency f j All theoretical frequency dispersion points of the current iteration model;

[0075] (3) Observe the frequency dispersion points Subtract all theoretical frequency dispersion points calculated in step (2) from the original value;

[0076] (4) Select all the theoretical frequency dispersion points obtained in step (3) that are consistent with the observed frequency dispersion points The theoretical frequency dispersion point with the smallest error is the desired theoretical frequency dispersion point.

[0077] The inversion process is iterated step by step. In each iteration, the theoretical dispersion curve of the layered model is calculated by the dispersion curve forward modeling method, and the function value of the layered model is calculated according to the fused inversion objective function.

[0078] When the inversion reaches the set iteration termination condition, that is, the objective function value is less than the set minimum fitting error or the maximum number of iterations is reached, the iteration process is terminated and the layered model with the smallest objective function value is output as the final result of the inversion.

[0079] Inversion iteration is performed to obtain the shear wave velocity profile results. The optimal method for inversion iteration can be a quasi-linear inversion method or a heuristic inversion method. The inversion process is iterated step by step. In each iteration, the theoretical dispersion curve of the layered model is calculated by the dispersion curve forward modeling method, and the function value of the layered model is calculated according to the fused inversion objective function. When the inversion reaches the set iteration termination condition, that is, the objective function value is less than the set minimum fitting error or the maximum number of iterations is reached, the iteration process is terminated and the layered model with the smallest objective function value is output as the final result of the inversion, such as Figure 3 shown.

[0080] It should be noted here that the surface wave multi-modal dispersion curve fusion inversion method can be applied to the inversion of various surface wave dispersion data including Rayleigh waves and Love waves for which theoretical dispersion curves exist.

[0081] Embodiment 2

[0082] This embodiment provides a surface wave multi-mode dispersion curve fusion inversion system, which specifically includes the following modules:

[0083] An observation frequency dispersion point picking module is used to generate a surface wave dispersion energy map and pick up observation frequency dispersion points therefrom;

[0084] Prior information determination and difference optimization module, which is used to select several observed frequency dispersion points that are modally identifiable from the picked observed frequency dispersion points as prior information; based on the principle of proximity, the difference optimization objective function is constructed by automatically fitting the observed frequency dispersion points and the theoretical frequency dispersion points;

[0085] A fusion inversion objective function construction module is used to construct a fusion inversion objective function by weighting the prior information and the difference optimization objective function;

[0086] The inversion result acquisition module is used to perform inversion according to the fusion inversion objective function until the iteration termination condition is met to obtain the inversion result.

[0087] Among them, in the inversion result acquisition module, the inversion process is iterated step by step. In each iteration, the theoretical dispersion curve of the layered model is calculated by the dispersion curve forward modeling method, and the function value of the layered model is calculated according to the fused inversion objective function.

[0088] When the inversion reaches the set iteration termination condition, that is, the objective function value is less than the set minimum fitting error or the maximum number of iterations is reached, the iteration process is terminated and the layered model with the smallest objective function value is output as the final result of the inversion.

[0089] It should be noted here that each module in this embodiment corresponds to each step in Example 1 one by one, and the specific implementation process is the same, which will not be repeated here.

[0090] Embodiment 3

[0091] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the method for fusion inversion of multi-modal dispersion curves of surface waves as described above are implemented.

[0092] Embodiment 4

[0093] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for fusion inversion of multi-modal dispersion curves of surface waves as described above are implemented.

[0094] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of the processes and / or blocks 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 produce 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.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for fusion inversion of multi-modal dispersion curves of surface waves, characterized in that: include: Generate a surface wave dispersion energy map and pick up observed dispersion points from it; Select several observed frequency dispersion points with modal identifiability from the picked observed frequency dispersion points as prior information; construct the difference optimization objective function by automatically fitting the observed frequency dispersion points and the theoretical frequency dispersion points based on the proximity principle; The prior information and the difference optimization objective function are combined into a fusion inversion objective function in a weighted manner; The inversion is performed according to the fusion inversion objective function until the iteration termination condition is met and the inversion result is obtained.

2. The surface wave multi-mode dispersion curve fusion inversion method according to claim 1, characterized in that: The difference optimization objective function R(m) is: Where l is a constant; is the phase velocity of the jth observed frequency dispersion point; For The phase velocity vector composed of theoretical dispersion points at the same frequency; p is the pth branch of the Rayleigh wave dispersion curve; is the minimum distance between the picked frequency dispersion point and the corresponding theoretical frequency dispersion point, and N is the number of observed frequency dispersion points.

3. The surface wave multi-mode dispersion curve fusion inversion method according to claim 2, characterized in that: The calculation formula of prior information P(m) is: Among them, M is the number of prior information; is the fundamental phase velocity of the ith prior information dispersion point, For The fundamental phase velocity of the theoretical dispersion point at the same frequency.

4. The surface wave multi-mode dispersion curve fusion inversion method according to claim 3, characterized in that: The fusion inversion objective function O(m) is: Among them, w1 and w2 are weight coefficients.

5. The surface wave multi-mode dispersion curve fusion inversion method according to claim 1, characterized in that: The inversion process is iterated step by step. In each iteration, the theoretical dispersion curve of the layered model is calculated by the dispersion curve forward modeling method, and the function value of the layered model is calculated according to the fused inversion objective function.

6. The surface wave multi-mode dispersion curve fusion inversion method according to claim 1, characterized in that: When the inversion reaches the set iteration termination condition, that is, the objective function value is less than the set minimum fitting error or the maximum number of iterations is reached, the iteration process is terminated and the layered model with the smallest objective function value is output as the final result of the inversion.

7. A surface wave multi-mode dispersion curve fusion inversion system, characterized in that: include: An observation frequency dispersion point picking module is used to generate a surface wave dispersion energy map and pick up observation frequency dispersion points therefrom; Prior information determination and difference optimization module, which is used to select several observed frequency dispersion points that are modally identifiable from the picked observed frequency dispersion points as prior information; based on the principle of proximity, the difference optimization objective function is constructed by automatically fitting the observed frequency dispersion points and the theoretical frequency dispersion points; A fusion inversion objective function construction module is used to construct a fusion inversion objective function by weighting the prior information and the difference optimization objective function; The inversion result acquisition module is used to perform inversion according to the fusion inversion objective function until the iteration termination condition is met to obtain the inversion result.

8. The frontal wave multi-modal dispersion curve fusion inversion system according to claim 7, characterized in that: In the inversion result acquisition module, the inversion process is iterated step by step. In each iteration, the theoretical dispersion curve of the layered model is calculated by the dispersion curve forward modeling method, and the function value of the layered model is calculated according to the fusion inversion objective function. Or in the inversion result acquisition module, when the inversion reaches the set iteration termination condition, that is, the objective function value is less than the set minimum fitting error or the maximum number of iterations is reached, the iteration process is terminated and the layered model with the smallest objective function value is output as the final result of the inversion.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the surface wave multi-mode dispersion curve fusion inversion method as described in any one of claims 1 to 6 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the surface wave multi-modal dispersion curve fusion inversion method according to any one of claims 1 to 6 are implemented.

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