Marine controllable source electromagnetic inversion method based on double constraints of structure and physical property

Abstract

The invention relates to an ocean controllable source electromagnetic inversion method based on structure and physical property double constraints. According to the method, ocean controllable source electromagnetic inversion is carried out based on structural and physical property double constraints, seismic high-resolution structural constraints are introduced into an ocean controllable source electromagnetic inversion process through a cross gradient function, and meanwhile, prior physical property information constraints are added into the ocean controllable source electromagnetic inversion process based on a fuzzy C-means clustering algorithm. The ocean controllable source electromagnetic inversion based on double constraints of structure and physical property is realized, the problem of insufficient constraint information of a single method is effectively overcome, and the imaging precision of the ocean controllable source electromagnetic underground structure and the accuracy of the inversion resistivity physical property value are improved. Technical support can be provided for the fields of ocean oil gas and mineral resource exploration, seabed structure detection, ocean engineering environment and the like.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical technology and relates to an ocean controlled source electromagnetic inversion method based on dual constraints of structure and physical properties. Background Art

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

[0003] Marine controlled-source electromagnetic (CSEM) is sensitive to high-resistivity oil and gas reservoirs and can directly evaluate reservoir oil and water properties. It has gradually become an important method for offshore oil and gas exploration. Marine CSEM inversion has rapidly developed as a key basis for interpretation. Marine controlled-source electromagnetic (CSEM) uses low-frequency electromagnetic fields, resulting in low resolution. Furthermore, marine CSEM inversion suffers from non-uniqueness issues.

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

[0005] Some studies rely on rock physics constraints to improve inversion resolution, primarily leveraging empirical or rock physics relationships to link resistivity and velocity in well data. Given the various relationships and uncertainties between different physical parameters, establishing accurate physical relationships in joint inversion is challenging. Joint inversion based on structural constraints has rapidly developed, providing a viable approach for leveraging structural similarities between the distributions of different physical properties within the survey area to reconcile subsurface structures. A well-known structural constraint is the use of a cross-gradient function to enhance structural similarity. Cross-gradient-based joint inversion eliminates the need for explicit specification of relationships between physical parameters, demonstrating greater flexibility and applicability. However, the key to structural constraints lies in structural similarity between different model parameters. If structural similarity does not exist between different geological bodies, the constraints will be ineffective. Generally, the resolution of seismic inversion is higher than that of marine controlled-source electromagnetic (CSEM) inversion. Joint inversion using structural constraints can only improve the resolution of marine CSEM inversion. However, since both seismic and marine CSEM data are inverted simultaneously, the computational time and memory requirements of the joint inversion increase. Therefore, performing marine CSEM inversion solely under seismic structural constraints appears to be more efficient.

[0006] Leveraging constraints based on prior information is often an effective method for reducing non-uniqueness. Marine controlled-source electromagnetic (CSEM) inversion based on known information constraints has been widely applied. However, current research primarily relies on one of these constrained inversion methods: either structural constraints or rock physical constraints. Structural or rock physical constraints alone do not provide sufficient information for inversion to generate reliable subsurface models. Summary of the Invention

[0007] In response to the problems existing in the prior art, the present invention proposes a marine controlled source electromagnetic inversion method based on dual constraints of structure and physical properties, comprising the following steps:

[0008] Step 1: Acquire or collect marine controlled source electromagnetic data and seismic shot gather data at the same location in the work area;

[0009] Step 2: Use seismic shot gather data to perform full waveform inversion to obtain the underground velocity structure

[0010] Step 3: Grid the target area of the marine controlled source electromagnetic inversion and give an initial resistivity model based on the grid. The initial model is a uniform resistivity distribution, which is set to 1 ohm·m based on the background resistivity value of the seabed.

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

[0012] Step 5: Using the objective function of marine controlled source electromagnetic inversion constructed by geophysical regularized inversion theory, dual constraints of seismic structure and known physical property information are introduced to complete the marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties.

[0013] Based on the above scheme, in step 2, the objective function of full waveform inversion of seismic shot gather data is:

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

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

[0016] Based on the above scheme, the model perturbation amount that makes the seismic full waveform inversion objective function extremely small is:

[0017]

[0018] in, is the Jacobian matrix, J T Represents transposition; the model parameter update formula is:

[0019] Δv=α k ▽φ k (3)

[0020] Where α k is the step size factor, and k is the number of iterations. The geophysical gradient inversion algorithm is used for iterative calculation, and finally the underground high-precision velocity structure v can be obtained through inversion.

[0021] Based on the above solution, step 5 includes the following steps:

[0022] 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;

[0023] Step 5.2: In the marine controlled-source resistivity inversion process constrained by seismic velocity structure, the known resistivity physical property information obtained from the well logging data is added to the marine controlled-source electromagnetic inversion process through the fuzzy C-means clustering algorithm.

[0024] Based on the above scheme, in step 5.1, the cross gradient function is expressed as:

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

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

[0027] On the basis of the above scheme, when structural constraints are introduced, the objective function of ocean CSEM inversion can be expressed as:

[0028]

[0029] Where φ(ρ) is the objective function of ocean controlled source electromagnetic inversion, 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 subsurface resistivity model, ρ0 is the initial resistivity model, F(ρ) is the electromagnetic forward response, and λ is the constraint factor that controls the structural constraint weight in the inversion.

[0030] Based on the above scheme, under the constraints of the cross-gradient structure, the derivative of t with respect to the resistivity model ρ is: Using geophysical regularized inversion theory, the resistivity update equation in marine controlled source electromagnetic inversion can be expressed as:

[0031]

[0032] Where J is the sensitivity matrix.

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

[0034]

[0035] Among them, M is the number of data to be clustered; C is the number of cluster centers; ρ 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 the logging data; and η is the weight factor.

[0036] Based on the above scheme, after each iteration of marine controlled source electromagnetic inversion, the fuzzy C-means clustering property constraint expression is minimized and iteratively calculated to obtain the optimal cluster center w and membership function value μ. Then, the updated resistivity ρ model of the (j+1)th iteration is calculated as follows:

[0037]

[0038] Beneficial effects of the present invention:

[0039] The method of the present invention carries out marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties, introduces structural constraints of seismic high resolution into the marine controlled source electromagnetic inversion process through a cross gradient function, and simultaneously adds prior physical property information constraints to the marine controlled source electromagnetic inversion process based on a fuzzy C-means clustering algorithm, thereby realizing marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties, effectively overcoming the problem of insufficient constraint information of a single method, and improving the imaging accuracy of marine controlled source electromagnetic underground structures and the accuracy of inverted resistivity physical property values. The present invention can provide technical support for fields such as marine oil and gas and mineral resource exploration, seabed structure detection, and marine engineering environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a graph of amplitude data of a measurement line acquired by controlled source electromagnetic field in Example 2;

[0041] Figure 2 The seismic shot gather data collected in Example 2 along the same survey line as the controlled source electromagnetic data;

[0042] Figure 3This is the lithology and resistivity characteristic diagram of Well B1 in Example 2;

[0043] Figure 4 This is the underground velocity structure diagram obtained by full waveform inversion in Example 2;

[0044] Figure 5 Schematic diagram of inversion grid division in Example 2;

[0045] Figure 6 1 is a comparison chart of the inversion results under different conditions in Example 2; among them: (a) unconstrained inversion result; (b) inversion result considering structural constraints; (c) inversion result with joint constraints. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all of the embodiments. Generally, 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 invention provides a marine controlled source electromagnetic inversion method based on dual constraints of structure and physical properties. In marine controlled source electromagnetic inversion, both structural and prior rock physical property constraints are simultaneously applied to reduce non-uniqueness and improve inversion reliability. The specific steps are as follows:

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

[0050] Step 2: Use the seismic shot gather data to perform full waveform inversion to obtain the subsurface velocity structure, providing structural constraints for marine controlled-source electromagnetic inversion. Full waveform inversion utilizes all seismic wave information, resulting in high inversion accuracy and the ability to obtain a highly accurate velocity structure.

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

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

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

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

[0055]

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

[0057] Δv=α k ▽φ k (3)

[0058] Where α k is the step size factor, and k is the number of iterations. The geophysical gradient inversion algorithm is used for iterative calculation, and finally the underground high-precision velocity structure v can be obtained through inversion.

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

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

[0061] Step 5: Using the objective function of marine controlled source electromagnetic inversion constructed by geophysical regularized inversion theory, dual constraints of seismic structure and known physical property information are introduced to complete the marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties.

[0062] The step 5 comprises the following steps:

[0063] 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.

[0064] The cross gradient function can be expressed as

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

[0066] where ρ and v represent the resistivity and velocity structures, respectively, and ▽ represents the gradient operation.

[0067] When structural constraints are introduced, the objective function of ocean CSEM inversion can be expressed as

[0068]

[0069] Where φ(ρ) is the objective function of ocean controlled source electromagnetic inversion, 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 subsurface resistivity model, ρ0 is the initial resistivity model, F(ρ) is the electromagnetic forward response, and λ is the constraint factor that controls the structural constraint weight in the inversion.

[0070] The derivative of t with respect to the resistivity model ρ is Using geophysical regularized inversion theory, the resistivity update equation in marine controlled source electromagnetic 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 controlled-source electromagnetic inversion objective function. Although the seismic and electromagnetic inversion parameters are different, the corresponding underground structures are consistent. The beneficial effect of this step is that the resistivity structure is continuously constrained to tend to a high-precision velocity structure during the inversion process, thereby improving the inversion accuracy of the marine controlled-source electromagnetic resistivity ρ.

[0073] Step 5.2: During the marine controlled-source resistivity inversion process constrained by seismic velocity structure, the known resistivity property information obtained from the well logging data is added to the marine controlled-source electromagnetic inversion process through the fuzzy C-means clustering algorithm to improve the accuracy of the inverted resistivity properties.

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

[0075]

[0076] Where M is the number of data to be clustered; C is the number of cluster centers; ρ is the resistivity value; w is the cluster center value; μ is the membership degree; q is the fuzzy coefficient; and t is the known resistivity property information obtained from the well logging data. η is the weighting factor. A non-zero value for η increases the influence of the known property information. A value of zero for η eliminates the influence of the known property information.

[0077] The FCM clustering algorithm automatically calculates the membership of each data sample relative to a cluster center by specifying the number of cluster centers. After each iteration of the marine controlled source electromagnetic inversion, 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 ρ model for the (j+1)th iteration can be calculated as follows:

[0078]

[0079] The present invention first obtains high-precision velocity structure through full-waveform seismic inversion, introduces high-resolution seismic structural constraints into the marine controlled source electromagnetic inversion process through the cross-gradient function, and simultaneously adds prior physical property information constraints into the marine controlled source electromagnetic inversion process based on the fuzzy C-means clustering algorithm, realizing marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties.

[0080] To prevent instability caused by dual constraints during the inversion process, structural constraints are applied only in the early stages. Physical property constraints based on FCM clustering are then introduced when the data RMS value reaches 50% of the initial value and the inversion becomes more stable. This approach ensures the stability and effectiveness of the dual-constrained inversion.

[0081] Example 2

[0082] Based on the method of Example 1, the present invention provides a specific marine controlled source electromagnetic inversion method based on dual constraints of structure and physical properties, the method comprising the following steps:

[0083] Step 1: Geophysical data preparation

[0084] The survey area is located on the northern edge of the Junggar Basin. Deep volcanic rocks result in poor seismic data quality. Therefore, controlled source electromagnetic (CSEM) exploration is used to detect the distribution of deep volcanic rocks. The transmitter is a 6-kilometer-long line source with a current of approximately 65A and a transmission frequency range of 0.025Hz to 104Hz. The survey line is approximately 50 kilometers long, with a nominal station spacing of 100 meters. The distance between the transmitting and receiving lines is approximately 7.5 kilometers. Figure 1 This is the amplitude data of a measuring line collected by controlled source electromagnetic (the amplitude curve of the electromagnetic field component Ex actually observed by controlled source electromagnetic). Figure 2 It is the seismic shot gather data collected on the same survey line as the controlled source electromagnetic survey.

[0085] There is a borehole B1 along the survey line. Figure 3 The lithology and resistivity characteristics of Well B1 are shown, reflecting the electrical properties of the formations in the survey area. The vertical layers can be divided into five electrical zones, with resistivity increasing gradually from shallow to deep.

[0086] Step 2: Use seismic shot gather data to perform full waveform inversion to obtain the underground velocity structure

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

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

[0089] Where φ(v) is the seismic full waveform inversion objective function, Wd is the data weighting matrix, F(v) is the earthquake forward response; d obs is the seismic shot data in step 1; v is the underground velocity structure; ||·|| 2 represents the 2-norm.

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

[0091]

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

[0093] Δv=α k ▽φ k (3)

[0094] Where α k is the step size factor, and k is the number of iterations.

[0095] The geophysical gradient inversion algorithm is used for iterative calculation, and finally the underground high-precision velocity structure v can be obtained through inversion.

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

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

[0098] Figure 5 This is a schematic diagram of the inversion grid. The grid is uneven, with densely packed cells in the center and sparse cells on the edges. Vertically, the grid is small in the shallow layers and gradually increases in size with depth. The thick black line represents the inversion core area. The initial resistivity of all grids is 1 ohm·m.

[0099] Step 4: Based on the grid generation and the initial resistivity model, the staggered finite difference algorithm is used (this method is an existing method, for details, please refer to: 2.5-dimensional forward modeling of marine controlled source electromagnetic method in frequency domain based on improved receiving point interpolation algorithm, Li Gang et al., 2017) to obtain the marine controlled source electromagnetic forward response data.

[0100] Step 5: Based on the forward response data obtained in step 4, the geophysical regularized inversion theory is used to construct the marine controlled source electromagnetic inversion objective function, introduce the dual constraints of seismic structure and known physical property information, and complete the marine controlled source electromagnetic inversion based on the dual constraints of structure and physical properties.

[0101] The step 5 comprises the following steps:

[0102] 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.

[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 the resistivity and velocity structures, respectively, and ▽ represents the gradient operation.

[0106] When structural constraints are introduced, the objective function of ocean CSEM inversion can be expressed as

[0107]

[0108] Where φ(ρ) is the objective function of ocean controlled source electromagnetic inversion, 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 subsurface resistivity model, ρ0 is the initial resistivity model, F(ρ) is the electromagnetic forward response, and λ is the constraint factor that controls the structural constraint weight in the inversion.

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

[0110]

[0111] 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 controlled-source electromagnetic inversion objective function. Although the seismic and electromagnetic inversion parameters are different, the corresponding underground structures are consistent. The beneficial effect of this step is that the resistivity structure is continuously constrained to tend to a high-precision velocity structure during the inversion process, thereby improving the inversion accuracy of the marine controlled-source electromagnetic resistivity ρ.

[0112] Step 5.2: During the marine controlled-source resistivity inversion process constrained by seismic velocity structure, the known resistivity property information obtained from the well logging data is added to the marine controlled-source electromagnetic inversion process through the fuzzy C-means clustering algorithm to improve the accuracy of the inverted resistivity properties.

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

[0114]

[0115] Where M is the number of data to be clustered; C is the number of cluster centers; ρ is the resistivity value; w is the cluster center value; μ is the membership degree; q is the fuzzy coefficient; and t is the known resistivity property information obtained from the well logging data. η is the weighting factor. A non-zero value for η increases the influence of the known property information. A value of zero for η eliminates the influence of the known property information.

[0116] The FCM clustering algorithm automatically calculates the membership of each data sample relative to a cluster center by specifying the number of cluster centers. After each iteration of the marine controlled source electromagnetic inversion, 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 ρ model for the (j+1)th iteration can be calculated as follows:

[0117]

[0118] Figure 6 This is the inversion result of actual data. 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 seismic velocity structure constraint. It is worth noting that the known velocity structure extends to a depth of 6000m, while the resistivity inversion reaches 10,000m. Considering the differences between constrained and unconstrained areas, this patent can flexibly apply structural constraints to different areas. In the constrained area, the cross-gradient value is calculated and incorporated into the objective function for constrained inversion. In the unconstrained area below 6000m, the cross-gradient value is set to 0, effectively performing a separate electromagnetic data inversion. Under the seismic velocity structure constraint, the vertical resolution of the shallow resistivity inversion results is significantly improved, and it is more consistent with the seismic velocity structure. In the absence of structural constraints below 6000m, the inversion results are similar to the unconstrained electromagnetic inversion results. A joint constrained inversion based on structural and known physical property information constraints is then performed. The physical property constraints based on FCM clustering require the provision of cluster centers and reference values. Accurate and reasonable parameters are determined using well logging and known rock physical 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 Ω·m, respectively. Figure 6 (c) shows the inversion result using both structural and physical constraints. The drill hole locations and lithologic formations are annotated on the inversion section. The inversion results clearly show that the inverted resistivity is consistent with the lithologic variations observed during drilling. This demonstrates the effectiveness of the dual structural and physical constraint method proposed in this patent. It improves the accuracy of the electromagnetic inversion results, creates clearer boundaries, and makes the inversion results more conducive to geological interpretation.

[0119] In the present invention, regardless of whether structural or physical constraints are introduced, the entire inversion process is solved using the conjugate gradient method (for details, see: Two-Dimensional Forward and Inversion Research of Magnetotelluric Non-Isotropic Media, Yang Miaoxin, 2016). Other methods and steps not described in detail in the embodiments of the present invention can be solved using the common technical knowledge and conventional techniques of those skilled in the art.

[0120] It should be noted that, unless there is any conflict, the features in the embodiments of this application can be combined with each other.

[0121] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A marine controlled source electromagnetic inversion method based on dual constraints of structure and physical properties, characterized by: The steps include: Step 1: Acquire or collect 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 underground velocity structure Step 3: Grid the target area of the marine controlled source electromagnetic inversion and give an initial resistivity model based on the grid. The initial model is a uniform resistivity distribution, which is set to 1 ohm·m based on the background resistivity value of the seabed. Step 4: Based on the grid generation and the initial resistivity model, the staggered finite difference algorithm is used to obtain the marine controlled source electromagnetic forward response data. Step 5: Using the objective function of marine controlled source electromagnetic inversion constructed by geophysical regularized inversion theory, dual constraints of seismic structure and known physical property information are introduced to complete the marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties.

2. The method for marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties according to claim 1 is characterized in that: In step 2, the objective function of full waveform inversion of seismic shot gather data is: φ(v)=||W d [d obs -F(v)]|| 2 (1) Among them, φ(v) is the seismic full waveform inversion objective function, W d is the data weighting matrix, F(v) is the earthquake forward response; d obs is the seismic shot data in step 1; v is the underground velocity structure; ||·|| 2 represents the 2-norm.

3. The marine controlled source electromagnetic inversion method based on structural and physical property dual constraints according to claim 2 is characterized in that: The model perturbation amount that makes the seismic full waveform inversion objective function extremely small is: in, is the Jacobian matrix, J T represents transpose; the model parameter update formula is: Δv=a k ▽φ k (3) Where α k is the step size factor, and k is the number of iterations. The geophysical gradient inversion algorithm is used for iterative calculation, and finally the underground high-precision velocity structure v can be obtained through inversion.

4. The method for marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties according to claim 1 is characterized in that: Step 5 includes the following steps: 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; Step 5.2: In the marine controlled-source resistivity inversion process constrained by seismic velocity structure, the known resistivity physical property information obtained from the well logging data is added to the marine controlled-source electromagnetic inversion process through the fuzzy C-means clustering algorithm.

5. The method for marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties according to claim 1 is characterized in that: In step 5.1, the cross gradient function is expressed as: t(x,y,z)=▽ρ(x,y,z)×▽v(x,y,z), (4) where ρ and v represent the resistivity and velocity structures, respectively, and ▽ represents the gradient operation.

6. The method for marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties according to claim 5 is characterized in that: When structural constraints are introduced, the objective function of ocean CSEM inversion can be expressed as: Where φ(ρ) is the objective function of ocean controlled source electromagnetic inversion, 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 subsurface resistivity model, ρ0 is the initial resistivity model, F(ρ) is the electromagnetic forward response, and λ is the constraint factor that controls the structural constraint weight in the inversion.

7. The method for marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties according to claim 6 is characterized in that: Under the constraints of the cross-gradient structure, the derivative of t with respect to the resistivity model ρ is: Using geophysical regularized inversion theory, the resistivity update equation in marine controlled source electromagnetic inversion can be expressed as: Where J is the sensitivity matrix.

8. The method for marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties according to claim 6 is characterized in that: The fuzzy C-means clustering algorithm is used to automatically calculate the membership of each data sample relative to the cluster center by specifying the number of cluster centers. When the physical property constraint is introduced, the physical property constraint expression of the fuzzy C-means clustering is: Among them, M is the number of data to be clustered; C is the number of cluster centers; ρ 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 the logging data; and η is the weight factor.

9. The method for marine controlled source electromagnetic inversion based on dual constraints of structure and physical properties according to claim 8, characterized in that: After each iteration of the marine controlled source electromagnetic inversion, the fuzzy C-means clustering property constraint expression is minimized and iteratively calculated to obtain the optimal cluster center w and membership function value μ. Then, the updated resistivity ρ model for the (j+1)th iteration is calculated as follows:

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