A marine controlled source electromagnetic inversion method based on structural and physical property double constraints

Abstract

The application relates to a marine controlled source electromagnetic inversion method based on structural and physical property double constraints. The method of the application carries out marine controlled source electromagnetic inversion based on structural and physical property double constraints, introduces the structural constraint of high resolution of an earthquake into a marine controlled source electromagnetic inversion process through a cross gradient function, simultaneously introduces the prior physical property information constraint into the marine controlled source electromagnetic inversion process based on a fuzzy C mean clustering algorithm, realizes the marine controlled source electromagnetic inversion based on structural and physical property double constraints, effectively overcomes the problem of insufficient constraint information of a single method, improves the imaging precision of a marine controlled source electromagnetic underground structure and the accuracy of an inversion resistivity physical property value, and provides technical support for marine oil and gas and mineral resource exploration, submarine structure detection and marine engineering environment and the like.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of geophysics, and relates to a marine controlled source electromagnetic inversion method based on double constraints of structure and physical property. BACKGROUND

[0002] Seismic methods can generally obtain high-precision structures, but fluid properties cannot be identified due to low seismic velocity contrast between different fluids.

[0003] Marine controlled source electromagnetic method (CSEM) is sensitive to high resistivity oil and gas reservoirs, and can directly evaluate reservoir oil and water properties, and has gradually become an important method for offshore oil and gas exploration. Marine CSEM inversion, as an important basis for interpretation, has rapidly developed. Marine CSEM method uses low-frequency electromagnetic field, and has low resolution. In addition, marine CSEM inversion has the problem of non-uniqueness.

[0004] In view of the advantages and disadvantages of seismic method and marine CSEM method, joint inversion of seismic and marine CSEM data is an effective method to improve inversion resolution and reduce non-uniqueness.

[0005] Some studies rely on petrophysical relationship constraints to improve inversion resolution, mainly using empirical or petrophysical relationships to link resistivity and velocity in well data. Considering various relationships and uncertainties between different physical parameters, it is difficult to establish accurate physical relationships in joint inversion. Joint inversion based on structural constraints has rapidly developed, and provides a feasible method for using structural similarity between different physical property distributions in the survey area to make the underground structure consistent. A famous structural constraint is to use a cross-gradient function to improve structural similarity. Joint inversion based on cross-gradient does not need to explicitly specify the explicit relationship between physical parameters in advance, which shows higher flexibility and applicability. However, the key of structural constraint is the structural similarity between different model parameters. If there is no structural similarity between different geological bodies, the constraint will not work. Generally, the resolution of seismic inversion is higher than that of marine controlled source electromagnetic method (CSEM) inversion, and joint inversion using structural constraint can only improve the resolution of marine CSEM inversion. However, due to simultaneous inversion of seismic and marine CSEM data, the calculation time and memory requirement of joint inversion will increase, so it seems more efficient to perform marine CSEM inversion alone under seismic structural constraint.

[0006] The constraint of prior information is usually an effective method to reduce non-uniqueness. At present, marine controlled source electromagnetic (CSEM) inversion based on known information constraint has been widely used. However, the current research is mainly based on one of the above constraint inversion methods, either structural information constraint or rock physical property information constraint. The single structure or rock physical constraint cannot provide enough information for the inversion to generate a reliable subsurface model. SUMMARY

[0007] In view of the problems in the prior art, the present application provides a marine controlled source electromagnetic inversion method based on double constraints of structure and physical property, comprising the following steps:

[0008] Step 1: Collect or gather marine controlled source electromagnetic data and seismic shot gather data at the same position of the work area;

[0009] Step 2: Obtain the subsurface velocity structure by using the seismic shot gather data to carry out full waveform inversion

[0010] Step 3: Grid division is carried out on the target area of marine controlled source electromagnetic inversion, and an initial resistivity model is given according to the grid, the initial model is uniform resistivity distribution, and the background value of seabed resistivity is set to 1 ohm·m.

[0011] Step 4: Based on the grid division and the initial resistivity model, the marine controlled source electromagnetic forward response data is obtained based on the staggered finite difference algorithm.

[0012] Step 5: The marine controlled source electromagnetic inversion target function constructed by using the geophysical regularization inversion theory is used to introduce double constraints of seismic structure and known physical property information, and the marine controlled source electromagnetic inversion based on double constraints of structure and physical property is completed.

[0013] On the basis of the above scheme, in step 2, the target function of the seismic shot gather data full waveform inversion is:

[0014] φ(v)=||W d [d obs -F(v)]|| 2 (1)

[0015] Wherein, φ(v) is the seismic full waveform inversion target function, W d is the data weighting matrix, F(v) is the seismic forward response; d obs is the seismic shot gather data in step 1; v is the subsurface velocity structure; ||·|| 2 represents the 2-norm.

[0016] On the basis of the above scheme, the model perturbation quantity when the seismic full waveform inversion target function is very small is:

[0017]

[0018] where, is the Jacobian matrix, J T denotes the transpose; the model parameter updating formula is:

[0019] Delta v = alpha k ▽phi k (3)

[0020] where alpha k is a step factor, and k is an iteration number. Iteration calculation is performed by using the geophysical gradient type inversion algorithm, and finally the high-precision velocity structure v of the underground can be obtained through inversion.

[0021] On the basis of the above scheme, step 5 comprises the following steps:

[0022] Step 5.1 introduces the high-precision velocity structure v obtained by full waveform inversion in step 2 into the marine controlled source electromagnetic inversion objective function through a cross-gradient function;

[0023] Step 5.2 adds the known resistivity physical information obtained from the logging data into the marine controlled source electromagnetic inversion process through the fuzzy C-means clustering algorithm in the process of marine controlled source resistivity inversion constrained by the seismic velocity structure.

[0024] On the basis of the above scheme, in step 5.1, the cross-gradient function is represented as:

[0025] t(x,y,z) = ▽p(x,y,z) x ▽v(x,y,z), (4)

[0026] where p and v represent resistivity and velocity structure respectively, and ▽ represents the gradient operation.

[0027] On the basis of the above scheme, when the structural constraint is introduced, the objective function of marine CSEM inversion can be represented as:

[0028]

[0029] where phi(p) is the marine controlled source electromagnetic inversion objective function, W d is a data weighting matrix, W m is a model weighting matrix, d is the marine controlled source electromagnetic data in step 1, p is the underground resistivity model, p0 is the initial resistivity model, F(p) is the electromagnetic forward response, and lambda is a constraint factor for controlling the structural constraint weight value in inversion.

[0030] On the basis of the above scheme, under the cross-gradient structural constraint, the derivative of t with respect to the resistivity model p is The resistivity update equation in marine controlled source electromagnetic inversion can be expressed as:

[0031]

[0032] wherein J is a sensitivity matrix.

[0033] On the basis of the above scheme, the fuzzy C-means clustering algorithm is used to automatically calculate the membership degree of each data sample relative to the cluster center by specifying the number of cluster centers; when the physical property constraint is introduced, the fuzzy C-means clustering physical property constraint expression is:

[0034]

[0035] wherein M is the number of data to be clustered; C is the number of cluster centers; p is the resistivity value; w is the cluster center value; mu is the membership degree; q is the fuzzy coefficient, t is the known resistivity physical property information obtained from the logging data; and eta is the weight factor.

[0036] On the basis of the above scheme, the fuzzy C-means clustering physical property constraint expression is used to perform minimum iteration calculation after each marine controlled source electromagnetic inversion iteration, so as to obtain the optimal cluster center w and membership function value mu; then, the updated resistivity p model of the (j+1)th iteration is calculated as follows:

[0037]

[0038] The beneficial effects of the present application are as follows:

[0039] The method of the present application carries out marine controlled source electromagnetic inversion based on dual constraints of structure and physical property, introduces the high-resolution structure constraint of the earthquake into the marine controlled source electromagnetic inversion process through the cross gradient function, simultaneously introduces the prior physical property information constraint into the marine controlled source electromagnetic inversion process based on the fuzzy C-means clustering algorithm, realizes the marine controlled source electromagnetic inversion based on dual constraints of structure and physical property, effectively overcomes the problem of insufficient constraint information of a single method, improves the imaging precision of the marine controlled source electromagnetic underground structure and the accuracy of the inverted resistivity physical property value, and provides technical support for the fields of marine oil and gas and mineral resource exploration, submarine structure detection and marine engineering environment, etc. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 It is a graph of amplitude data of a measuring line collected in Example 2;

[0041] Figure 2 It is a seismic shot gather data of the same measuring line as the controlled source electromagnetic in Example 2;

[0042] Figure 3Fig. 2 is a lithology and resistivity characteristic diagram of well B1 in Example 2;

[0043] Figure 4 Fig. 3 is a subsurface velocity structure diagram obtained by full waveform inversion in Example 2;

[0044] Figure 5 Fig. 4 is a schematic diagram of inversion grid partitioning in Example 2;

[0045] Figure 6 Fig. 5 is a comparison diagram of inversion results in different cases in Example 2; wherein: (a) is an unconstrained inversion result; (b) is an inversion result considering structural constraints; (c) is an inversion result containing joint constraints. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0047] Example 1

[0048] The present application provides a marine controlled source electromagnetic inversion method based on structural and physical double constraints. In the marine controlled source electromagnetic inversion, structural and prior rock physical property constraints are simultaneously carried out to reduce non-uniqueness and improve the reliability of the inversion. The specific steps are as follows:

[0049] Step 1: Collect or gather marine controlled source electromagnetic data and seismic shot gather data at the same position in the work area;

[0050] Step 2: Use the seismic shot gather data to carry out full waveform inversion to obtain the subsurface velocity structure, and provide structural constraint information for marine controlled source electromagnetic inversion. Full waveform inversion uses all the information of seismic waves, has high inversion accuracy, and can obtain high-precision velocity structure.

[0051] The objective function of seismic shot gather data full waveform inversion is usually constructed by the difference between the simulated record and the observed record:

[0052] φ(v)=||W d [d obs -F(v)]|| 2 (1)

[0053] Wherein φ(v) is the seismic full waveform inversion objective function, W d is the data weighting matrix, F(v) is the seismic forward response; d obs is the seismic shot gather data in step 1; v is the subsurface velocity structure; ||·||2 denotes 2-norm.

[0054] The model perturbation quantity that makes the objective function (1) minimum is:

[0055]

[0056] is the Jacobian matrix, J T denotes transpose. The model parameter update formula is:

[0057] Δv = α k ▽φ k (3)

[0058] wherein α k is a step factor, and k is the iteration number. Iterative calculation is performed by using the geophysical gradient inversion algorithm, and finally the high-precision velocity structure v of the underground can be obtained through inversion.

[0059] Step 3: Grid division is performed on the marine controlled source electromagnetic inversion target area, and the initial model of resistivity is given according to the grid. The initial model is a uniform resistivity distribution, and the background value of the seabed resistivity is set to 1 ohm·m.

[0060] Step 4: Based on the grid division and the initial resistivity model, the marine controlled source electromagnetic forward response data is obtained based on the staggered finite difference algorithm.

[0061] Step 5: The marine controlled source electromagnetic inversion target function constructed by using the geophysical regularization inversion theory is introduced into the seismic structure and the known physical property information double constraint, and the marine controlled source electromagnetic inversion based on the structure and physical property double constraint is completed.

[0062] The step 5 comprises the following steps:

[0063] Step 5.1: The high-precision velocity structure v obtained by the full waveform inversion in step 2 is introduced into the marine controlled source electromagnetic inversion target function through the cross gradient function.

[0064] The cross gradient function can be expressed as

[0065] t(x,y,z) =▽ρ(x,y,z) x▽v(x,y,z), (4)

[0066] wherein ρ and v respectively represent resistivity and velocity structure, and▽ represents gradient operation.

[0067] When the structure constraint is introduced, the target function of the marine CSEM inversion can be expressed as

[0068]

[0069] where φ(ρ) is the objective function of marine CSEM inversion, W d is the data weighting matrix, W m is the model weighting matrix, d is the marine CSEM data in step 1, p is the subsurface resistivity model, p0 is the initial resistivity model, F(p) is the electromagnetic forward response, and λ is the constraint factor to control the weight of structural constraint in inversion.

[0070] The derivative of the resistivity model p with respect to t is Using the geophysical regularization inversion theory, the resistivity update equation in marine CSEM inversion can be expressed as

[0071]

[0072] where J is the sensitivity matrix. Under the action of the cross-gradient function t, the seismic velocity structure v is introduced into the marine CSEM inversion objective function. Although the seismic and electromagnetic inversion parameters are different, the corresponding subsurface structure is consistent. The beneficial effect of this step is to continuously constrain the resistivity structure to tend towards the high-precision velocity structure in the inversion process, thereby improving the marine CSEM resistivity p inversion accuracy.

[0073] Step 5.2 In the process of marine CSEM resistivity inversion constrained by seismic velocity structure, the known resistivity physical property information obtained from well logging data is added to the marine CSEM inversion process through the fuzzy C-means clustering algorithm, thereby improving the accuracy of the inversion resistivity physical property.

[0074] When the physical property constraint is introduced, the fuzzy C-means clustering physical property constraint expression is:

[0075]

[0076] where M is the number of data to be clustered; C is the number of cluster centers; p is the resistivity value; w is the cluster center value; μ is the membership degree; q is the fuzzy coefficient, t is the known resistivity physical property information obtained from well logging data. η is the weight factor. Taking a non-zero value of η can increase the influence of known physical property information. Taking a zero value of η will eliminate the influence of known physical property information.

[0077] Using the FCM clustering algorithm, the membership degree of each data sample with respect to the cluster center is automatically calculated by specifying the number of cluster centers. After each marine CSEM inversion iteration, the optimal cluster center w and membership function value μ are obtained by minimizing the iterative calculation according to equation (7). Then, the updated resistivity p model of the (j+1)th iteration can be calculated as follows:

[0078]

[0079] The application firstly obtains high-precision velocity structure through full waveform seismic inversion, introduces high-resolution structural constraints of the earthquake into the marine controlled source electromagnetic inversion process through the cross gradient function, simultaneously introduces prior physical property information constraints into the marine controlled source electromagnetic inversion process based on the fuzzy C-means clustering algorithm, and realizes marine controlled source electromagnetic inversion based on double constraints of structure and physical property.

[0080] In order to prevent the double constraints from causing instability in the inversion process, the structural constraint is only applied in the early stage. When the root mean square value of the data reaches 50% of the initial value, the inversion becomes more stable, and then the physical property constraint based on the FCM clustering is introduced. The method ensures the stability and effectiveness of the double constraint inversion.

[0081] Embodiment 2

[0082] Based on the method of embodiment 1, the application provides a specific marine controlled source electromagnetic inversion method based on double constraints of structure and physical property, and the method comprises the following steps:

[0083] Step 1: Geophysical data preparation

[0084] The survey area is located in the northern margin of the Junggar Basin, and deep volcanic rocks cause poor seismic data quality. Therefore, the controlled source electromagnetic (CSEM) exploration is used to detect the distribution of deep volcanic rocks. The transmitter is a line source with a length of 6 kilometers and an electric current of about 65a, and the transmission frequency range is 0.025Hz to 104Hz. The measurement line is about 50 kilometers long, and the nominal station spacing is 100 meters. The distance between the transmission line and the receiving line is about 7.5 kilometers. Figure 1 It is amplitude data of a survey line of the controlled source electromagnetic (a graph of amplitude curves of the electromagnetic field component Ex actually observed by the controlled source electromagnetic). Figure 2 It is seismic shot gather data collected on the same survey line as the controlled source electromagnetic.

[0085] There is a drilling well B1 along the measurement line. Figure 3 The lithology and resistivity characteristics of the B1 well are shown, reflecting the electrical properties of the strata in the survey area. The vertical layer can be divided into five electrical zones, and the resistivity gradually increases from shallow to deep.

[0086] Step 2: Obtain the underground velocity structure by using the seismic shot gather data to carry out full waveform inversion

[0087] The objective function of the seismic shot gather data full waveform inversion is usually constructed by the difference between the simulated record and the observed record:

[0088] φ(v)=||W d [d obs -F(v)]|| 2 (1)

[0089] Wherein φ(v) is the seismic full waveform inversion objective function, Wd F(v) is the seismic forward response; d obs is the seismic shot gather data in step 1; v is the subsurface velocity structure; ||·||2 2 denotes the 2-norm.

[0090] The model perturbation quantity that makes the objective function (1) minimum is:

[0091]

[0092] is the Jacobian matrix, J T denotes the transpose. The model parameter update formula is:

[0093] Δv = α k ▽φ k (3)

[0094] wherein α k is the step factor, and k is the iteration number.

[0095] Iterative calculation is performed by using the geophysical gradient inversion algorithm, and finally the high-precision subsurface velocity structure v can be obtained through inversion.

[0096] The high-precision velocity structure is obtained by full waveform inversion of the seismic shot gather data. Figure 4 is the velocity structure obtained by inversion, the velocity gradually increases with depth, and the layered distribution is obvious.

[0097] Step 3: Grid division is performed on the marine controlled source electromagnetic inversion target area, and the initial resistivity model is given according to the grid. The initial model is a uniform resistivity distribution, and the seabed resistivity background value is set to 1 ohm·m.

[0098] Figure 5 is a schematic diagram of inversion grid division. The grid is a non-uniform grid, which is dense in the middle and sparse on both sides in the horizontal direction, and small in the shallow layer and gradually larger with depth in the vertical direction. The black thick line is the core area of inversion, and the initial resistivity of all grids is 1 ohm·m.

[0099] Step 4: Based on the grid division and the initial resistivity model, the marine controlled source electromagnetic forward response data is obtained based on the staggered finite difference algorithm (this method is an existing method, which can be referred to in detail in: Frequency domain marine controlled source electromagnetic method 2.5D forward based on improved receiving point interpolation algorithm, Li Gang et al., 2017).

[0100] Step 5: Based on the forward response data obtained in step 4, the marine controlled source electromagnetic inversion objective function is constructed by using the geophysical regularization inversion theory, the seismic structure and known physical property information are introduced for double constraints, and the marine controlled source electromagnetic inversion based on structure and physical property double constraints is completed.

[0101] The step 5 comprises the following steps:

[0102] Step 5.1: The high-precision velocity structure v obtained by full waveform inversion in step 2 is introduced into the marine controlled source electromagnetic inversion objective function through a cross-gradient function.

[0103] The cross-gradient function can be expressed as

[0104] t(x,y,z) = ∇ρ(x,y,z) × ∇v(x,y,z), (4)

[0105] where ρ and v represent resistivity and velocity structure respectively, and ∇ represents the gradient operation.

[0106] When the structural constraint is introduced, the objective function of marine CSEM inversion can be expressed as

[0107]

[0108] where φ(ρ) is the marine controlled source electromagnetic inversion objective function, W d is the data weighting matrix, W m is the model weighting matrix, d is the marine controlled source electromagnetic data in step 1, ρ is the underground resistivity model, ρ0 is the initial resistivity model, F(ρ) is the electromagnetic forward response, and λ is the constraint factor controlling the weight of the structural constraint in the inversion.

[0109] The derivative of t with respect to the resistivity model ρ is Using the geophysical regularization inversion theory, the resistivity update equation in marine controlled source electromagnetic inversion can be expressed as

[0110]

[0111] where J is the sensitivity matrix, and the seismic velocity structure v is introduced into the marine controlled source electromagnetic inversion objective function under the action of the cross-gradient function t. Although the seismic and electromagnetic inversion parameters are different, the corresponding underground structure is consistent. The beneficial effect of this step is to continuously constrain the resistivity structure to tend towards high-precision velocity structure during the inversion process, thereby improving the marine controlled source electromagnetic resistivity ρ inversion precision.

[0112] Step 5.2: In the process of marine controlled source resistivity inversion under the constraint of seismic velocity structure, the known resistivity physical property information obtained from logging data is added to the marine controlled source electromagnetic inversion process through fuzzy C-means clustering algorithm, thereby improving the accuracy of the inversion resistivity physical property.

[0113] When the physical property constraint is introduced, the fuzzy C-means clustering physical property constraint expression is:

[0114]

[0115] where M is the number of data to be clustered; C is the number of cluster centers; p is the resistivity value; w is the cluster center value; m is the membership; q is the fuzzy coefficient, t is the known resistivity property information obtained from the logging data. h is the weight factor. h takes a non-zero value to increase the impact of the known property information. h takes a zero value to eliminate the impact of the known property information.

[0116] The FCM clustering algorithm is used to automatically calculate the membership of each data sample with respect to the cluster center by specifying the number of cluster centers. After each marine controlled source electromagnetic inversion iteration, the optimal cluster center w and membership function value m are obtained by minimizing the iteration calculation according to equation (7). Then, the updated resistivity p model of the (j+1) iteration can be calculated as follows:

[0117]

[0118] Figure 6 The actual data inversion result. Figure 6 (a) is the unconstrained electromagnetic resistivity inversion result, which generally reflects the electrical characteristics of the formation. Figure 6 (b) is the electromagnetic inversion result considering the constraint of the seismic velocity structure. It is worth noting that the known velocity structure extends to a depth of 6000 m, while the resistivity inversion reaches 10000 m. Considering the difference between the constrained and unconstrained areas, the present patent can flexibly apply the structural constraint to different areas. In the constrained area, the cross-gradient value is calculated and included in the objective function for constrained inversion. In the unconstrained area below 6000 m, the cross-gradient value is set to 0, effectively performing a separate electromagnetic data inversion. Under the constraint of the seismic velocity structure, the vertical resolution of the shallow resistivity inversion result is significantly improved, and it is more consistent with the seismic velocity structure. Without structural constraint below 6000 m, the inversion result is similar to the unconstrained electromagnetic inversion result. Then, a joint constraint inversion based on structure and known property information constraint is performed. The FCM clustering-based property constraint requires the cluster center and reference value to be given. The accurate and reasonable parameters are determined using logging and known rock physics information. The cluster center is set to 7, and the cluster reference values are 15.8, 25.1, 31.6, 33.5, 34.5, 201.8, and 364 Wm, respectively. Figure 6 (c) is the inversion result with dual constraints of structure and property. The drilling locations and lithologic formations are marked on the inversion profile. The inversion result clearly shows that the inversion resistivity is consistent with the lithology observed in the drilling. This demonstrates the effectiveness of the dual constraint method based on structure and property proposed in the present patent. The accuracy of the electromagnetic inversion result is improved, the boundary is clearer, and the inversion result is more conducive to geological interpretation.

[0119] In the present application, whether introducing structural constraints or material property constraints, the entire inversion process is solved by using the conjugate gradient method (for details, please refer to: Two-dimensional forward and inversion research of magnetotelluric non-isotropic medium, Yang Moxin, 2016). Other methods and steps not specifically described in the embodiments of the present application can be obtained by using the ordinary technical knowledge and conventional technical means of those skilled in the art.

[0120] It should be noted that the features in the embodiments of the present application can be combined with each other without conflict.

[0121] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for electromagnetic inversion of a controllable source in the ocean based on dual constraints of structure and physical properties, characterized in that, Includes the following steps: Step 1: Collect or gather marine controlled-source electromagnetic data and seismic shot gather data at the same location in the work area; Step 2: Use seismic shot gather data to perform full waveform inversion to obtain the subsurface velocity structure; Step 3: Grid the target area for the controllable source electromagnetic inversion in the ocean and provide an initial resistivity model based on the grid. The initial model is a uniform resistivity distribution and is set to 1 ohm-meter based on the background value of seabed resistivity. Step 4: Based on the grid partitioning and initial resistivity model, obtain the electromagnetic forward modeling response data of the ocean controllable source using the staggered finite difference algorithm; Step 5: Using the objective function of marine controlled-source electromagnetic inversion constructed by geophysical regularization inversion theory, the dual constraints of seismic structure and known physical property information are introduced to complete the marine controlled-source electromagnetic inversion based on the dual constraints of structure and physical properties; Step 5 includes the following steps: Step 5.1 Obtain the high-precision velocity structure from the full waveform inversion in Step 2. The objective function for electromagnetic inversion of ocean-controlled sources is introduced by introducing a cross-gradient function. Step 5.2 In the process of seismic velocity-structure constrained marine controlled-source resistivity inversion, the known resistivity property information obtained from well logging data is added to the marine controlled-source electromagnetic inversion process using the fuzzy C-means clustering algorithm; In step 5.1, the cross gradient function is expressed as: (4) in and Representing the resistivity model and velocity structure, respectively. Indicates gradient operation; When structural constraints are introduced, the objective function for ocean CSEM inversion can be expressed as: (5) in The objective function for electromagnetic inversion of ocean-controlled sources is... Weighted matrix of data, The weighting matrix for the model, For the ocean-controlled source electromagnetic data in step 1, For resistivity model, For the initial resistivity model, For electromagnetic forward modeling response, The constraint factor is used to control the structural constraint weights in the inversion.

2. The marine controlled-source electromagnetic inversion method based on dual constraints of structure and physical properties as described in claim 1, characterized in that, In step 2, the objective function for the full waveform inversion of the seismic shot gather data is: （1） in, The objective function for seismic full waveform inversion is... Weighted matrix of data, Forward modeling of earthquake response; This refers to the seismic shot gather data from step 1; For speed structure; It represents the 2-norm.

3. The marine controlled-source electromagnetic inversion method based on dual constraints of structure and physical properties as described in claim 2, characterized in that, The model perturbation that minimizes the objective function of the full-wavelength seismic inversion is: (2) in, For Jacobian matrices, This indicates transpose; the model parameter update formula is: (3) In the formula Step size factor The iteration count is used; iterative calculations are performed using a geophysical gradient-based inversion algorithm, and the high-precision underground velocity structure can be obtained through inversion. .

4. The marine controlled-source electromagnetic inversion method based on dual constraints of structure and physical properties according to claim 1, characterized in that, Under the constraint of the cross gradient structure For resistivity model Find the derivative. Using geophysical regularization inversion theory, the resistivity update equation in ocean controlled-source electromagnetic inversion can be expressed as: (6) in This is the sensitivity matrix.

5. The marine controlled-source electromagnetic inversion method based on dual constraints of structure and physical properties according to claim 1, characterized in that, The fuzzy C-means clustering algorithm automatically calculates the membership degree of each data sample relative to a cluster center by specifying the number of cluster centers. When physical property constraints are introduced, the expression for the physical property constraints in fuzzy C-means clustering is: (7) Where M is the number of data points that need to be clustered; C is the number of cluster centers; For resistivity model; The value represents the cluster center. q represents the membership degree; q is the fuzzy coefficient; and p represents the known resistivity property information obtained from well logging data. This refers to the known resistivity property information obtained from well logging data; This is the weighting factor.

6. The marine controlled-source electromagnetic inversion method based on dual constraints of structure and physical properties according to claim 5, characterized in that, After each iteration of the ocean-controlled source electromagnetic inversion, the optimal cluster centers are obtained by minimizing the fuzzy C-means clustering property constraint expression. membership degree Then, calculate the updated resistivity for the (j+1)th iteration. The model is as follows: 。

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