Earthquake and electromagnetic joint inversion analysis method

Abstract

The invention discloses an earthquake and electromagnetic joint inversion analysis method, belongs to the technical field of geological exploration, and can solve the problem that accurate judgment on geothermal resources is affected due to the fact that an existing single exploration method is difficult to obtain fine structural information and attribute information of a thermal reservoir. The method comprises the following steps: S1, constructing a multi-attribute geological model of a target area according to geological survey data of the target area, and respectively performing seismic exploration and electromagnetic exploration on the target area according to exploration working parameters of the target area to obtain seismic wave observation data and apparent resistivity observation data; s2, according to the multi-attribute geologic model and the exploration working parameters, seismic wave forward modeling and electromagnetic wave forward modeling are carried out on the target area, and seismic wave forward modeling data and apparent resistivity forward modeling data are obtained; and S3, determining a target geologic model of the target area according to the seismic wave observation data, the seismic wave forward modeling data, the apparent resistivity observation data and the apparent resistivity forward modeling data. The method is used for geothermal resource exploration.

Description

Technical Field

[0001] The invention relates to a seismic and electromagnetic joint inversion analysis method, belonging to the technical field of geological exploration. Background Art

[0002] In the field of geothermal resource exploration, the commonly used exploration technologies currently include seismic exploration technology and electromagnetic exploration technology. Seismic exploration is mainly based on the propagation characteristics of seismic waves and can provide accurate underground stratigraphic structure information, including faults, folds, bedrock morphology, etc. However, the focus of seismic exploration is on the identification of stratigraphic boundaries and structures, and it is difficult to directly reflect underground fluid content, porosity, and water-rich information. For geothermal resources, water-rich conditions are directly related to the thermal conductivity of the thermal reservoir. Failure to obtain this information will lead to an incomplete assessment of the thermal reservoir. Electromagnetic exploration is good at identifying the distribution of water-rich layers and underground conductors, and can effectively locate aquifers or areas rich in groundwater. However, the electromagnetic method has a low resolution for stratigraphic structure and fine structure, and it is difficult to provide detailed information about fault structures and stratigraphic boundaries. In this case, it is difficult to accurately identify the geometric boundaries and depths of the thermal reservoir and to clearly characterize the structural characteristics of the thermal reservoir by relying solely on electromagnetic exploration.

[0003] The reservoir distribution of geothermal resources depends on the comprehensive information of stratigraphic structure, fault structure and water-rich conditions. However, a single exploration technology cannot provide a geological model that contains all the information, resulting in an insufficient description of the precise distribution of the reservoir. The limitations on detection depth and spatial resolution make it difficult to accurately characterize the geometric morphology of underground thermal reservoirs, especially in complex geological environments, such as areas with dense fault structures and frequent stratigraphic changes. Existing technologies make it difficult to obtain detailed thermal reservoir structural information, which in turn affects the spatial positioning and morphological description of thermal reservoirs. Secondly, the characteristics of geothermal reservoirs depend not only on the geometric structure, but also on the porosity, water content and other properties of the reservoir, and these attribute information are usually not available through a single seismic or electromagnetic detection. The lack of these key information leads to an incomplete assessment of the reservoir and also affects the accurate judgment of the development potential of geothermal resources. Summary of the invention

[0004] The present invention provides a joint seismic and electromagnetic inversion analysis method, which can solve the problem that the existing single exploration method is difficult to obtain detailed thermal reservoir structure information and attribute information, resulting in an incomplete assessment of the reservoir and affecting the accurate judgment of the development potential of geothermal resources.

[0005] The present invention provides a seismic and electromagnetic joint inversion analysis method, the method comprising:

[0006] S1. Construct a multi-attribute geological model of the target area based on the geological survey data of the target area, and conduct seismic exploration and electromagnetic exploration on the target area according to the exploration working parameters of the target area to obtain seismic wave observation data and apparent resistivity observation data;

[0007] S2. According to the multi-attribute geological model and the exploration working parameters, respectively, seismic wave forward modeling and electromagnetic wave forward modeling are performed on the target area to obtain seismic wave forward modeling data and apparent resistivity forward modeling data;

[0008] S3. Determine a target geological model of the target area according to the seismic wave observation data, the seismic wave forward modeling data, the apparent resistivity observation data and the apparent resistivity forward modeling data.

[0009] Optionally, the S3 specifically includes:

[0010] S31, determining a seismic wave forward model according to the seismic wave observation data and the seismic wave forward modeling data, and determining an electromagnetic wave forward model according to the apparent resistivity observation data and the apparent resistivity forward modeling data;

[0011] S32. Determine a target geological model of the target area according to the seismic wave forward model and the electromagnetic wave forward model.

[0012] Optionally, determining the seismic wave forward model according to the seismic wave observation data and the seismic wave forward modeling data in S31 specifically includes:

[0013] Acquire the absolute value of the difference between the seismic wave observation data and the seismic wave forward modeling data as a seismic wave error value;

[0014] When the seismic wave error value is less than or equal to a first error threshold, the multi-attribute geological model is used as the seismic wave forward model; when the seismic wave error value is greater than the first error threshold, the seismic wave forward model is determined based on the seismic wave error value, the seismic wave forward modeling data and the multi-attribute geological model.

[0015] Optionally, determining the electromagnetic wave forward model according to the apparent resistivity observation data and the apparent resistivity forward modeling data in S31 specifically includes:

[0016] Obtaining the absolute value of the difference between the apparent resistivity observation data and the apparent resistivity forward modeling data as an apparent resistivity error value;

[0017] When the apparent resistivity error value is less than or equal to a second error threshold, the multi-attribute geological model is used as an electromagnetic wave forward model; when the apparent resistivity error value is greater than the second error threshold, the electromagnetic wave forward model is determined based on the apparent resistivity error value, the apparent resistivity forward modeling data and the multi-attribute geological model.

[0018] Optionally, the S32 specifically includes:

[0019] When the seismic wave error value is less than or equal to a first error threshold, and the apparent resistivity error value is less than or equal to a second error threshold, taking the average model of the seismic wave forward model and the electromagnetic wave forward model as the target geological model of the target area;

[0020] When the seismic wave error value is greater than the first error threshold, and / or the apparent resistivity error value is greater than the second error threshold, the multi-attribute geological model is updated according to the seismic wave forward model, the electromagnetic wave forward model, the seismic wave forward model data, the apparent resistivity forward model data, the seismic wave error value and the apparent resistivity error value, and S2 and S3 are re-executed.

[0021] Optionally, determining a seismic wave forward model according to the seismic wave error value, the seismic wave forward modeling data and the multi-attribute geological model specifically includes:

[0022] Obtaining the sum of the ratio of the seismic wave error value to the seismic wave forward modeling data and 1, and performing dimensionality thinning processing on the multi-attribute geological model;

[0023] The product of the processed multi-attribute geological model and the sum value is obtained, and spatial discrete smooth interpolation processing is performed on the product to obtain a seismic wave forward model.

[0024] Optionally, determining an electromagnetic wave forward model according to the apparent resistivity error value, the apparent resistivity forward modeling data and the multi-attribute geological model specifically includes:

[0025] Obtaining the sum of the ratio of the apparent resistivity error value to the apparent resistivity forward modeling data and 1, and performing dimensionality thinning processing on the multi-attribute geological model;

[0026] The product of the processed multi-attribute geological model and the sum value is obtained, and spatial discrete smooth interpolation processing is performed on the product to obtain an electromagnetic wave forward model.

[0027] Optionally, updating the multi-attribute geological model according to the seismic wave forward model, the electromagnetic wave forward model, the seismic wave forward modeling data, the apparent resistivity forward modeling data, the seismic wave error value and the apparent resistivity error value specifically includes:

[0028] Determine a seismic wave weight coefficient and an apparent resistivity weight coefficient according to the seismic wave forward modeling data, the apparent resistivity forward modeling data, the seismic wave error value and the apparent resistivity error value;

[0029] The sum of the product of the seismic wave forward model and the seismic wave weight coefficient and the product of the electromagnetic wave forward model and the apparent resistivity weight coefficient is obtained as an updated multi-attribute geological model.

[0030] Optionally, determining a seismic wave weight coefficient and an apparent resistivity weight coefficient according to the seismic wave forward modeling data, the apparent resistivity forward modeling data, the seismic wave error value, and the apparent resistivity error value specifically includes:

[0031] Acquire the product of the seismic wave forward modeling data and the apparent resistivity forward modeling data as a first product, acquire the sum of a second product of the apparent resistivity forward modeling data and the seismic wave error value and a third product of the seismic wave forward modeling data and the apparent resistivity error value, and acquire the difference between twice the first product and the sum as a first difference;

[0032] The ratio of the difference between the first product and the second product to the first difference is obtained as the seismic wave weight coefficient, and the ratio of the difference between the first product and the third product to the first difference is obtained as the apparent resistivity weight coefficient.

[0033] Optionally, the step of constructing a multi-attribute geological model of the target area according to geological survey data of the target area in S1 specifically includes:

[0034] Dividing the target area into a plurality of geological units according to geological survey data of the target area;

[0035] Initialize the seismic wave data and apparent resistivity data corresponding to each geological unit to obtain a multi-attribute geological model of the target area.

[0036] The beneficial effects that the present invention can produce include:

[0037] The seismic and electromagnetic joint inversion analysis method provided by the present invention combines the seismic and electromagnetic detection technologies and the two detection results data for joint inversion to determine the final geological model. This joint inversion technology can integrate the complementary information of seismic waves and electromagnetic waves to achieve all-round detection of geothermal reservoirs, thereby more accurately characterizing the distribution of thermal storage resources and improving the accuracy of geothermal reserve calculations.

[0038] The seismic and electromagnetic joint inversion analysis method provided by the present invention can achieve better results in identifying stratum structure, fault structure and water-rich areas through joint exploration of seismic and electromagnetic methods. Seismic exploration is suitable for detecting stratum structure and fault structure, while electromagnetic exploration is suitable for detecting water-rich conditions. Through joint use and inversion, the ability to identify complex underground structures can be improved, and the geological characteristics of geothermal systems can be accurately depicted.

[0039] The seismic and electromagnetic joint inversion analysis method provided by the present invention calculates the errors of seismic wave data and apparent resistivity data respectively during the model inversion process, updates the model according to the size of the errors, and ensures the fit between the model forward modeling data and the observed data. This error control mechanism allows the seismic wave forward model and the electromagnetic wave forward model to be optimized separately, and keeps the model unchanged under the condition that the threshold is met, thereby reducing unnecessary calculations and improving the inversion efficiency and model convergence speed.

[0040] The seismic and electromagnetic joint inversion analysis method provided by the present invention, due to the different dimensions of seismic wave data and apparent resistivity data, introduces generalized matrix multiplication and spatial discrete smooth interpolation technology when updating the inversion model, so as to reasonably merge data of different dimensions, ensure that the updated model matrix can meet the dimensional requirements of the original model, thereby realizing accurate model updating.

[0041] The seismic and electromagnetic joint inversion analysis method provided by the present invention applies a specific model update formula during the updating process of the multi-attribute geological model when the errors of seismic wave data and apparent resistivity data meet or do not meet certain conditions, so that the updated multi-attribute geological model can balance the data contributions of the two detection methods, and finally generate a more realistic geological model. This update mechanism avoids the influence of the deviation of a single method on the final result and improves the comprehensive reliability of the model. In short, the present invention optimizes the accuracy and efficiency in geothermal resource exploration through an innovative solution of multi-technical combination and multi-method collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of the seismic and electromagnetic joint inversion analysis method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention is described in detail below in conjunction with embodiments, but the present invention is not limited to these embodiments.

[0044] The embodiment of the present invention provides a seismic and electromagnetic joint inversion analysis method, which can be applied to the exploration of geothermal resources or water resources. Figure 1 As shown, the method includes:

[0045] S1. Based on the geological survey data of the target area, a multi-attribute geological model of the target area is constructed, and seismic exploration and electromagnetic exploration are carried out on the target area according to the exploration work parameters of the target area to obtain seismic wave observation data and apparent resistivity observation data.

[0046] Among them, based on the geological survey data of the target area, a multi-attribute geological model of the target area is constructed, including:

[0047] (1) Based on the geological survey data of the target area, the target area is divided into multiple geological units. Specifically including:

[0048] a. Data collection: Collect geological survey data such as geological structure, stratigraphic distribution, lithology, fault zones, groundwater information, etc. in the target area to ensure that the data covers the area and depth range for model construction.

[0049] b. Divide geological units: Divide the target area into several geological units or strata based on geological survey data. Each geological unit represents a specific lithology or sedimentary environment (such as sandstone, mudstone, basalt, etc.), and the main geological layers can be divided by elevation.

[0050] (2) Based on engineering geological experience, the seismic wave data and apparent resistivity data corresponding to each geological unit are initialized to obtain a multi-attribute geological model of the target area.

[0051] Specifically, basic lithology parameters are assigned to each geological unit, including density, P-wave velocity, S-wave velocity, and apparent resistivity. These parameters can be used for numerical forward modeling of seismic exploration and wide-area electromagnetic detection.

[0052] In practical applications, after the geological units are divided, the following can also be included:

[0053] c. Drawing of geological interfaces and faults: Draw geological interfaces and faults on geological units according to the distribution of geological structures. You can choose to simplify the main faults and draw them into representative fracture surfaces. Geological interfaces can usually be represented by planes or simplified curved surfaces to reflect the contact relationship of the strata.

[0054] d. Build a grid model: Grids are created on the divided geological units to describe the spatial distribution of underground geological structures. The grid type can be regular or irregular, and the size of the grid is determined according to the complexity of the model and the calculation requirements.

[0055] Finally, basic lithology parameters (i.e., density, P-wave velocity, S-wave velocity, and apparent resistivity) are assigned to each grid unit to obtain the initial multi-attribute geological model M.

[0056] Among them, according to the exploration parameters of the target area, seismic exploration and electromagnetic exploration are carried out on the target area respectively, and seismic wave observation data and apparent resistivity observation data are obtained, specifically:

[0057] According to the exploration parameters, seismic exploration and electromagnetic exploration are carried out on site to obtain seismic wave observation data S obs And apparent resistivity observation data E obs .

[0058] The above exploration work parameters include: source location, detector location, electrode location, etc.

[0059] S2. According to the multi-attribute geological model and exploration parameters, seismic wave forward modeling and electromagnetic wave forward modeling are respectively performed on the target area to obtain seismic wave forward modeling data and apparent resistivity forward modeling data.

[0060] Specifically, according to the multi-attribute geological model and exploration parameters, seismic wave forward simulation and electromagnetic wave forward simulation are carried out to obtain seismic wave forward data S and fwd and apparent resistivity forward modeling data E fwd .

[0061] S3. Determine a target geological model of the target area based on the seismic wave observation data, the seismic wave forward modeling data, the apparent resistivity observation data and the apparent resistivity forward modeling data.

[0062] Specifically include:

[0063] S31. Determine a seismic wave forward model based on the seismic wave observation data and the seismic wave forward modeling data, and determine an electromagnetic wave forward model based on the apparent resistivity observation data and the apparent resistivity forward modeling data.

[0064] Among them, determining the seismic wave forward model according to the seismic wave observation data and the seismic wave forward modeling data specifically includes: obtaining the absolute value of the difference between the seismic wave observation data and the seismic wave forward modeling data as the seismic wave error value; when the seismic wave error value is less than or equal to the first error threshold, using the multi-attribute geological model as the seismic wave forward modeling model; when the seismic wave error value is greater than the first error threshold, determining the seismic wave forward model according to the seismic wave error value, the seismic wave forward modeling data and the multi-attribute geological model.

[0065] Specifically, the seismic wave forward model is determined according to the seismic wave error value, the seismic wave forward modeling data and the multi-attribute geological model, which specifically includes: obtaining the sum of the ratio of the seismic wave error value to the seismic wave forward modeling data and 1, and performing dimensionality thinning processing on the multi-attribute geological model; obtaining the product of the processed multi-attribute geological model and the sum value, and performing spatial discrete smooth interpolation processing on the product to obtain the seismic wave forward model.

[0066] In practical applications, the seismic wave forward modeling data S fwd and the seismic wave observation data S obtained from on-site seismic exploration obs The error calculation between is: S =S fwd -S obs ; Among them, δ S The absolute value of is the seismic wave error value.

[0067] If |δ S |≤R S , where R S is the first error threshold, the seismic wave forward model remains unchanged, that is: M S =M; if |δ S |>R S , then the seismic wave forward model is updated: Since the matrix Different from the dimension of M, we need to first transform the matrix M according to S fwd The dimensions of are thinned out and generalized matrix multiplication is performed, and then spatial discrete smooth interpolation is performed on the product result to make it have the same dimension as the original model matrix M.

[0068] The above-mentioned determination of the electromagnetic wave forward model based on the apparent resistivity observation data and the apparent resistivity forward modeling data specifically includes: obtaining the absolute value of the difference between the apparent resistivity observation data and the apparent resistivity forward modeling data as the apparent resistivity error value; when the apparent resistivity error value is less than or equal to the second error threshold, using the multi-attribute geological model as the electromagnetic wave forward model; when the apparent resistivity error value is greater than the second error threshold, determining the electromagnetic wave forward model based on the apparent resistivity error value, the apparent resistivity forward modeling data and the multi-attribute geological model.

[0069] Specifically, the electromagnetic wave forward model is determined according to the apparent resistivity error value, the apparent resistivity forward modeling data and the multi-attribute geological model, which specifically includes: obtaining the sum of the ratio of the apparent resistivity error value to the apparent resistivity forward modeling data and 1, and performing dimensionality thinning processing on the multi-attribute geological model; obtaining the product of the processed multi-attribute geological model and the sum value, and performing spatial discrete smooth interpolation processing on the product to obtain the electromagnetic wave forward model.

[0070] In practical applications, the apparent resistivity forward modeling data E obtained by electromagnetic wave forward modeling is first fwd and the apparent resistivity observation data E obtained from on-site electromagnetic exploration obs The error calculation between is: E =E fwd -E obs ; Among them, δ E The absolute value of is the apparent resistivity error.

[0071] If |δ E |≤R E , where R E is the second error threshold, the electromagnetic wave forward model remains unchanged, that is: M E =M; if |δ E |>R E , then the electromagnetic wave forward model is updated: Since the matrix Different from the dimension of M, we need to first transform the matrix M according to E fwd The dimensions of are thinned out and generalized matrix multiplication is performed, and then spatial discrete smooth interpolation is performed on the product result to make it have the same dimension as the original model matrix M.

[0072] S32. Determine a target geological model of the target area based on the seismic wave forward model and the electromagnetic wave forward model.

[0073] Specifically, when the seismic wave error value is less than or equal to a first error threshold, and the apparent resistivity error value is less than or equal to a second error threshold, the average model of the seismic wave forward model and the electromagnetic wave forward model is used as the target geological model of the target area.

[0074] That is, if |δ S |≤R S And |δ E |≤R E , using formula M opt =M S / 2+M E / 2 Output the final target geological model M opt .

[0075] When the seismic wave error value is greater than the first error threshold, and / or the apparent resistivity error value is greater than the second error threshold, the multi-attribute geological model is updated according to the seismic wave forward model, the electromagnetic wave forward model, the seismic wave forward model data, the apparent resistivity forward model data, the seismic wave error value and the apparent resistivity error value, and S2 and S3 are re-executed.

[0076] Among them, the multi-attribute geological model is updated according to the seismic wave forward model, the electromagnetic wave forward model, the seismic wave forward model data, the apparent resistivity forward model data, the seismic wave error value and the apparent resistivity error value, specifically including:

[0077] (1) Determine the seismic wave weight coefficient and the apparent resistivity weight coefficient based on the seismic wave forward modeling data, the apparent resistivity forward modeling data, the seismic wave error value and the apparent resistivity error value.

[0078] Specifically, the method includes: firstly obtaining the product of the seismic wave forward modeling data and the apparent resistivity forward modeling data as the first product, obtaining the sum of the second product of the apparent resistivity forward modeling data and the seismic wave error value and the third product of the seismic wave forward modeling data and the apparent resistivity error value, and obtaining the difference between twice the first product and the sum as the first difference; then obtaining the ratio of the difference between the first product and the second product to the first difference as the seismic wave weight coefficient, and obtaining the ratio of the difference between the first product and the third product to the first difference as the apparent resistivity weight coefficient.

[0079] In the embodiment of the present invention, the seismic wave weight coefficient W S The calculation is done using the following formula:

[0080]

[0081] The apparent resistivity weight coefficient W E The calculation is done using the following formula:

[0082]

[0083] (2) Obtain the sum of the product of the seismic wave forward model and the seismic wave weight coefficient and the product of the electromagnetic wave forward model and the apparent resistivity weight coefficient as the updated multi-attribute geological model.

[0084] In the embodiment of the present invention, if |δ S |≤R S , |δ E |>R E , or |δ S |>R S , |δ E |>R E , or |δ S |>R S , |δ E |≤R E , using the formula M=M S W S +M E W E The multi-attribute geological model is updated, and execution of S2 is returned.

[0085] The present invention combines seismic exploration technology with electromagnetic exploration technology, and uses the advantages of the two exploration methods to achieve all-round detection of geothermal reservoirs. Seismic exploration is used to accurately characterize the stratum structure and fault structure, while electromagnetic exploration is used to identify underground aquifers and water content. By combining these two methods, more complete underground geological information can be obtained.

[0086] The present invention integrates seismic data and electromagnetic data through joint inversion to establish a comprehensive model that includes stratum structure and water-rich information. Data joint inversion overcomes the limitations of a single physical detection method, and can take into account both stratum structure and conductive characteristics in the same model, accurately describing the distribution, boundaries and water-rich properties of thermal reservoirs.

[0087] The present invention uses data joint inversion technology to obtain more accurate geothermal reservoir information, including reservoir geometry, fracture characteristics, and water-rich conditions. And through the precise description of this information, the present invention significantly improves the accuracy of geothermal reserve assessment and provides more reliable basic data for geothermal resource development.

[0088] The beneficial effects of the present invention are mainly manifested in the following points:

[0089] (1) Comprehensiveness of information: Existing seismic exploration or electromagnetic exploration can usually only obtain single aspects of stratum structure or water-rich information, while the present invention uses two exploration methods in combination to achieve dual detection of stratum structure and water-rich conditions. This method makes up for the shortcomings of a single technology and makes the underground information obtained more comprehensive.

[0090] (2) Data fusion capability: The present invention uses joint inversion technology to organically integrate seismic data and electromagnetic data. Compared with the traditional method of processing seismic and electromagnetic data separately, the joint inversion technology of the present invention can combine multiple data in a unified model, thereby improving the accuracy of geothermal reservoir description. This technology overcomes the compatibility problem between different physical parameters and can more finely characterize the distribution and properties of thermal reservoirs in space.

[0091] (3) Improved detection accuracy: The geothermal reservoir model obtained by the joint inversion of the present invention not only contains the details of the stratigraphic structure, but also contains information on the distribution of water-rich layers, which greatly improves the detection accuracy. Compared with a single exploration method, the present invention can more accurately locate the boundary of the thermal reservoir, obtain clearer thermal reservoir morphology and fracture characteristics, and contribute to the precise development of geothermal resources.

[0092] (4) Improved accuracy of reserve calculation: In the calculation of geothermal reserves, the traditional technology lacks comprehensive information on the formation structure and water abundance, and the calculation results are usually not accurate enough. The present invention provides more accurate formation and water abundance data through a comprehensive description of geothermal reservoirs, significantly improving the accuracy of reserve calculation and providing a scientific basis for resource development.

[0093] In general, the present invention adopts an innovative method of combining seismic and electromagnetic exploration with data inversion, which achieves high-precision, comprehensive and multi-dimensional characterization of geothermal reservoirs, greatly improving the accuracy of geothermal resource assessment and reserve calculation.

[0094] The above are only a few embodiments of the present application and do not constitute any form of limitation to the present application. Although the present application is disclosed as above with preferred embodiments, it is not intended to limit the present application. Any technician familiar with the profession, without departing from the scope of the technical solution of the present application, using the technical content disclosed above to make slight changes or modifications are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A seismic and electromagnetic joint inversion analysis method, characterized in that: The method comprises: S1. Construct a multi-attribute geological model of the target area based on the geological survey data of the target area, and conduct seismic exploration and electromagnetic exploration on the target area according to the exploration working parameters of the target area to obtain seismic wave observation data and apparent resistivity observation data; S2. According to the multi-attribute geological model and the exploration working parameters, respectively, seismic wave forward modeling and electromagnetic wave forward modeling are performed on the target area to obtain seismic wave forward modeling data and apparent resistivity forward modeling data; S3. Determine a target geological model of the target area according to the seismic wave observation data, the seismic wave forward modeling data, the apparent resistivity observation data and the apparent resistivity forward modeling data.

2. The method according to claim 1, characterized in that The S3 specifically includes: S31, determining a seismic wave forward model according to the seismic wave observation data and the seismic wave forward modeling data, and determining an electromagnetic wave forward model according to the apparent resistivity observation data and the apparent resistivity forward modeling data; S32. Determine a target geological model of the target area according to the seismic wave forward model and the electromagnetic wave forward model.

3. The method according to claim 2, characterized in that Determining the seismic wave forward model according to the seismic wave observation data and the seismic wave forward modeling data in S31 specifically includes: Acquire the absolute value of the difference between the seismic wave observation data and the seismic wave forward modeling data as a seismic wave error value; When the seismic wave error value is less than or equal to a first error threshold, the multi-attribute geological model is used as the seismic wave forward model; when the seismic wave error value is greater than the first error threshold, the seismic wave forward model is determined based on the seismic wave error value, the seismic wave forward modeling data and the multi-attribute geological model.

4. The method according to claim 2, characterized in that: Determining the electromagnetic wave forward model according to the apparent resistivity observation data and the apparent resistivity forward modeling data in S31 specifically includes: Obtaining the absolute value of the difference between the apparent resistivity observation data and the apparent resistivity forward modeling data as an apparent resistivity error value; When the apparent resistivity error value is less than or equal to a second error threshold, the multi-attribute geological model is used as an electromagnetic wave forward model; when the apparent resistivity error value is greater than the second error threshold, the electromagnetic wave forward model is determined based on the apparent resistivity error value, the apparent resistivity forward modeling data and the multi-attribute geological model.

5. The method according to claim 2, characterized in that: The S32 specifically includes: When the seismic wave error value is less than or equal to a first error threshold, and the apparent resistivity error value is less than or equal to a second error threshold, taking the average model of the seismic wave forward model and the electromagnetic wave forward model as the target geological model of the target area; When the seismic wave error value is greater than the first error threshold, and / or the apparent resistivity error value is greater than the second error threshold, the multi-attribute geological model is updated according to the seismic wave forward model, the electromagnetic wave forward model, the seismic wave forward model data, the apparent resistivity forward model data, the seismic wave error value and the apparent resistivity error value, and S2 and S3 are re-executed.

6. The method according to claim 3, characterized in that Determining a seismic wave forward model according to the seismic wave error value, the seismic wave forward modeling data and the multi-attribute geological model specifically includes: Obtaining the sum of the ratio of the seismic wave error value to the seismic wave forward modeling data and 1, and performing dimensionality thinning processing on the multi-attribute geological model; The product of the processed multi-attribute geological model and the sum value is obtained, and the product is subjected to spatial discrete smooth interpolation processing to obtain a seismic wave forward model.

7. The method according to claim 4, characterized in that Determining an electromagnetic wave forward model according to the apparent resistivity error value, the apparent resistivity forward modeling data and the multi-attribute geological model specifically includes: Obtaining the sum of the ratio of the apparent resistivity error value to the apparent resistivity forward modeling data and 1, and performing dimensionality thinning processing on the multi-attribute geological model; The product of the processed multi-attribute geological model and the sum value is obtained, and spatial discrete smooth interpolation processing is performed on the product to obtain an electromagnetic wave forward model.

8. The method according to claim 5, characterized in that Updating the multi-attribute geological model according to the seismic wave forward model, the electromagnetic wave forward model, the seismic wave forward modeling data, the apparent resistivity forward modeling data, the seismic wave error value and the apparent resistivity error value specifically includes: Determine a seismic wave weight coefficient and an apparent resistivity weight coefficient according to the seismic wave forward modeling data, the apparent resistivity forward modeling data, the seismic wave error value and the apparent resistivity error value; The sum of the product of the seismic wave forward model and the seismic wave weight coefficient and the product of the electromagnetic wave forward model and the apparent resistivity weight coefficient is obtained as an updated multi-attribute geological model.

9. The method according to claim 8, characterized in that Determining a seismic wave weight coefficient and an apparent resistivity weight coefficient according to the seismic wave forward modeling data, the apparent resistivity forward modeling data, the seismic wave error value, and the apparent resistivity error value specifically includes: Acquire the product of the seismic wave forward modeling data and the apparent resistivity forward modeling data as a first product, acquire the sum of a second product of the apparent resistivity forward modeling data and the seismic wave error value and a third product of the seismic wave forward modeling data and the apparent resistivity error value, and acquire the difference between twice the first product and the sum as a first difference; The ratio of the difference between the first product and the second product to the first difference is obtained as the seismic wave weight coefficient, and the ratio of the difference between the first product and the third product to the first difference is obtained as the apparent resistivity weight coefficient.

10. The method according to claim 1, characterized in that The step S1 of constructing a multi-attribute geological model of the target area according to the geological survey data of the target area specifically includes: Dividing the target area into a plurality of geological units according to geological survey data of the target area; Initialize the seismic wave data and apparent resistivity data corresponding to each geological unit to obtain a multi-attribute geological model of the target area.

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