A method and system for optimizing key acquisition parameters

Abstract

The application provides a method and system for optimizing key acquisition parameters, and belongs to the field of seismic exploration acquisition. The method for optimizing key acquisition parameters comprises the following steps: establishing a near-surface layer velocity model according to existing data interpretation results; obtaining numerical simulation data by forward modeling according to existing data analysis results; then, obtaining an inversion model by inversion using the numerical simulation data and different key acquisition parameter degradation schemes; and finally, obtaining optimized key acquisition parameters using the inversion model. The application can provide a reference for the selection of key parameters in the acquisition design process. In a region with complex near-surface structure, the application can obtain accurate key acquisition parameters, so that better acquisition data can be obtained, the quality of the seismic original data is improved, and the exploration effect of a complex exploration area is improved.

Description

Technical Field

[0001] This invention belongs to the field of seismic exploration and acquisition, specifically relating to a method and system for optimizing key acquisition parameters. Background Technology

[0002] The root cause of exploration problems in complex areas such as piedmont zones is the low signal-to-noise ratio (SNR) of raw single-shot data caused by undulating surfaces and complex near-surface conditions, leading to a series of problems in subsequent seismic processing. From the initial acquisition design stage, it is necessary to study observation systems oriented towards shallow velocity modeling. Meanwhile, deep velocity models are also established using reflection-based tomography. Reflection tomography also requires high SNR in the imaging gathers, but the low SNR of piedmont data leads to inaccurate deep modeling. Improving the SNR of imaging gathers requires higher coverage times, better excitation and reception, and accurate static correction. In reality, due to the inability to accurately identify reflection signals in piedmont data, it is difficult to solve the static correction problem using residual static correction and other techniques. However, failing to properly address static correction will further cause problems in subsequent denoising and time-domain velocity modeling.

[0003] To improve the quality of imaging, seismic acquisition personnel have done a lot of work on various aspects such as excitation, reception, and coverage times, but with little effect, and have had to rethink the seismic imaging process.

[0004] Seismic imaging requires two essential conditions: first, complete wavefield sampling, meaning the acquired wavefield must meet the requirements for migration repositioning, must not produce spurious migration frequencies, and must have a certain number of effective coverage times and azimuth angles; second, imaging requires an accurate velocity model.

[0005] The velocity model includes shallow velocity models and deep velocity models. Because the signal-to-noise ratio of shallow seismic data is relatively low, shallow velocity models are generally established through tomographic inversion. Therefore, it is necessary to study observation systems that are geared towards shallow velocity modeling.

[0006] Meanwhile, the velocity model for the deep region is established using reflection-based tomography. Reflection tomography also requires a high signal-to-noise ratio (SNR) in the imaging gathers, but the SNR of data from the foreland zone is low, leading to inaccurate deep modeling. Improving the SNR of the imaging gathers requires higher coverage times, better excitation and reception, and accurate static correction.

[0007] Solving static correction requires establishing an accurate shallow layer model, so the problem returns to the shallow layer velocity model. Therefore, it is essential to study observation systems for fine-grained shallow layer modeling.

[0008] Complex exploration areas such as the piedmont zone have large surface undulations, and the velocity and thickness of the low-velocity zone vary drastically in space. The refractive interface is unstable and affected by strong interference noise, making it difficult to accurately pick up the first arrival of a single shot. Therefore, the complex near-surface structure and the difficulty in identifying the first arrival make the static correction problem in these areas particularly prominent, which has become a bottleneck restricting the accurate imaging of underground structures. Summary of the Invention

[0009] The purpose of this invention is to solve the problems existing in the prior art and provide a method and system for optimizing key acquisition parameters, which can improve the accuracy of key acquisition parameters in acquisition design and obtain better acquisition data.

[0010] This invention is achieved through the following technical solution:

[0011] In a first aspect, the present invention provides a method for optimizing key acquisition parameters. The method establishes a near-surface velocity model based on the interpretation results of existing data; performs forward modeling based on the analysis results of existing data to obtain numerical simulation data; then uses the numerical simulation data and different key acquisition parameter degradation schemes to perform inversion to obtain an inversion model; and finally uses the inversion model to obtain optimized key acquisition parameters.

[0012] A further improvement of the present invention is that:

[0013] The key acquisition parameters include: track spacing, shot distance, receiver line spacing, and shot line spacing.

[0014] A further improvement of the present invention is that:

[0015] The method includes:

[0016] Step 1: Establish a near-surface velocity model based on the interpretation results of existing data;

[0017] Step 2: Design the observation system based on the analysis results of existing data;

[0018] Step 3: Obtain numerical simulation data using near-surface velocity models and observation systems;

[0019] Step 4: Obtain the inversion models corresponding to different key acquisition parameter degradation schemes;

[0020] Step 5: Use the inversion model to determine the key acquisition parameters for optimization.

[0021] A further improvement of the present invention is that:

[0022] The operations in step 3 include:

[0023] Using the near-surface velocity model established in step 1, forward modeling was performed using the observation system designed in step 2 to obtain numerical simulation data;

[0024] The forward modeling simulation employs the high-precision spectral element method.

[0025] A further improvement of the present invention is that:

[0026] The operations in step 4 include:

[0027] (41) Design a degradation scheme for key acquisition parameters;

[0028] (42) Obtain the inversion model corresponding to each key acquisition parameter degradation scheme.

[0029] A further improvement of the present invention is that:

[0030] The key acquisition parameter degradation scheme designed in step (41) includes:

[0031] Track spacing degradation scheme: Track spacing adopts multiple values, while shot distance, receiver line distance, and shot line distance remain unchanged. Track spacing is the key acquisition parameter in the track spacing degradation scheme.

[0032] Shot distance degradation scheme: The shot distance adopts multiple values, while the track distance, receiver line distance, and shot line distance remain unchanged. The shot distance is the key acquisition parameter in the shot distance degradation scheme.

[0033] Receiver spacing degradation scheme: The receiver spacing adopts multiple values, while the track spacing, shot point spacing, and shot line spacing remain unchanged. The receiver spacing is the key acquisition parameter in the receiver spacing degradation scheme.

[0034] Shot line distance degradation scheme: The shot line distance adopts multiple values, while the track distance, shot point distance, and receiver line distance remain unchanged. The shot line distance is the key acquisition parameter in the shot line distance degradation scheme.

[0035] A further improvement of the present invention is that:

[0036] The operation of step (42) includes:

[0037] The following processing is performed on each key acquisition parameter degradation scheme:

[0038] Based on the key acquisition parameters in the key acquisition parameter degradation scheme, the first arrival of the numerical simulation data is picked and the first arrival file is extracted; then, the first arrival wave tomography inversion is used to obtain the inversion model corresponding to the key acquisition parameter degradation scheme.

[0039] A further improvement of the present invention is that:

[0040] The first arrival wave tomography inversion is performed using the Tomodel inversion system.

[0041] A further improvement of the present invention is that:

[0042] The operations in step 5 include:

[0043] (51) The optimized track spacing is obtained by using the inversion model corresponding to multiple track spacing degradation schemes;

[0044] (52) The optimized shot distance is obtained by using the inversion model corresponding to multiple shot distance degradation schemes;

[0045] (53) The optimized receiver spacing is obtained by using the inversion model corresponding to multiple receiver spacing degradation schemes;

[0046] (54) The optimized shot distance is obtained by using the inversion model corresponding to multiple shot distance degradation schemes;

[0047] The optimized track spacing, optimized shot point spacing, optimized receiver line spacing, and optimized shot line spacing are the key acquisition parameters for optimization.

[0048] A further improvement of the present invention is that:

[0049] The operations in steps (51) to (54) all include:

[0050] The inversion model corresponding to each key acquisition parameter degradation scheme is compared with the established near-surface velocity model to find the inversion model with the required accuracy.

[0051] The depth and velocity values ​​of the inversion models that meet the accuracy requirements are extracted and compared with the micrologging interpretation results at the same location. The inversion model whose trend is most similar to that of the near-surface velocity model is found. The key acquisition parameters in the key acquisition parameter degradation scheme corresponding to this inversion model are the optimized key acquisition parameters.

[0052] A second aspect of the present invention provides a system for optimizing key acquisition parameters, the system comprising:

[0053] Model building unit: used to build a near-surface velocity model based on the interpretation of existing data;

[0054] Observation system design unit: used to design observation systems based on the analysis results of existing data;

[0055] Numerical simulation unit: Connected to the model building unit and the observation system design unit respectively, it is used to obtain numerical simulation data using the near-surface velocity model and the observation system;

[0056] Inversion Unit: Connected to the numerical simulation unit, it is used to obtain the inversion model corresponding to different degradation schemes of key acquisition parameters;

[0057] Optimization Unit: Connected to the inversion unit, it is used to determine the key acquisition parameters for optimization using the inversion model.

[0058] A third aspect of the present invention provides a computer-readable storage medium storing at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the method for optimizing key acquisition parameters described above.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] This invention can provide a reference for the selection of key parameters in the acquisition design process. In areas with complex near-surface structures, this invention can be used to obtain accurate key acquisition parameters, thereby obtaining better acquisition data, improving the quality of raw seismic data, and enhancing the exploration effect in complex exploration areas. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0062] Figure 2 To establish a near-surface velocity model for a certain mountain foreland zone;

[0063] Figure 3 This is a single-shot record obtained from numerical simulation based on a velocity model;

[0064] Figure 4-1 Comparison diagram of velocity models inverted with a 5m track gauge;

[0065] Figure 4-2 Comparison diagram of velocity models inverted with a 10m track gauge;

[0066] Figure 4-3 Comparison diagram of velocity models inverted with a 20m track gauge;

[0067] Figure 4-4 Comparison diagram of velocity models inverted with a 40m track gauge;

[0068] Figure 5 This involves comparing the depth-velocity values ​​of inversion models with different trace spacings extracted from the same location with the micrologging interpretation results. Detailed Implementation

[0069] The present invention will now be described in further detail with reference to the accompanying drawings:

[0070] This invention provides a method for optimizing key acquisition parameters during the seismic exploration acquisition design process, which is beneficial to improving the quality of raw seismic data. This invention conducts a detailed near-surface modeling study using numerical simulation, establishing near-surface models for typical complex exploration areas such as piedmont zones. Through degradation analysis of numerical simulations, it studies the key acquisition factors affecting detailed near-surface modeling, providing a basis for observation system design.

[0071] This invention addresses the problem of significant near-surface influence during data acquisition design by proposing a method for optimizing key acquisition parameters, achieved through the following technical measures:

[0072] A near-surface velocity model is established based on the interpretation of existing data; forward modeling is performed based on the analysis results of existing data to obtain numerical simulation data; then, inversion is performed using the numerical simulation data and different degradation schemes for key acquisition parameters to obtain an inversion model; finally, the optimized key acquisition parameters are obtained using the inversion model. The key acquisition parameters include: track spacing, shot distance, receiver line spacing, and shot line spacing.

[0073] The embodiments of the method of the present invention are as follows:

[0074] Example 1

[0075] like Figure 1 As shown, a method for optimizing key acquisition parameters includes the following steps:

[0076] Step 1: Establish a near-surface velocity model based on the interpretation results of existing data;

[0077] Based on the interpretation results of existing data in the exploration area, including stratigraphic interpretation results of depth migration data, surface structure survey results, and comprehensive analysis of micrologging data, a relatively accurate near-surface velocity model is established. This step is implemented using existing technology and will not be elaborated further here.

[0078] Step 2: Design the observation system based on the analysis results of existing data:

[0079] Based on the analysis results of existing data in the exploration area (including the analysis of raw data and the advantages and disadvantages of the acquisition methods in the exploration area), as well as the advantages and disadvantages of the acquisition methods in adjacent exploration areas, a high-attribute observation system is designed to facilitate subsequent degradation analysis.

[0080] For example, during the track spacing design process, based on the analysis of existing data, a track spacing of 20m may meet the requirements. However, to design parameters more accurately, observation system parameters with smaller track spacings (e.g., 10m or 5m) can be designed, which facilitates later degradation analysis and comparison. This step is implemented using existing technology and will not be elaborated further here.

[0081] Step 3: Obtain numerical simulation data using near-surface velocity models and observation systems;

[0082] Using the accurate near-surface velocity model established in step 1, forward modeling was performed using the high-attribute observation system designed in step 2 to obtain high-precision numerical simulation data. This step was implemented using existing technology and will not be described in detail here.

[0083] Optionally, a high-precision spectral element method can be used in forward modeling to improve the accuracy of numerical simulation. The spectral element method is a high-order finite element method that combines the finite element method and the spectral expansion method. The spectral element method divides the computational domain into quadrilateral (or hexahedral) elements. On each element, the solution is represented as a high-dimensional tensor product of the Lagrange interpolation function. The interpolation nodes are GLL nodes. The Galerkin method is used to solve the variational scheme of the elastic wave equation, resulting in discrete equations. By selecting orthogonal basis functions as weights and trial functions for the variational problem on the finite element, the mass matrix of the final global finite element equation is diagonalized, thus avoiding the inversion process of the ultra-large mass matrix in traditional finite element methods, greatly improving computational efficiency, and it is also available at high order. Due to its spectral properties, it satisfies low spatial sampling requirements; typically, only 4 to 5 nodes per unit wavelength are needed to obtain sufficient spatial accuracy. Therefore, the spectral element method is a high-order, high-precision, and low-dispersion numerical method.

[0084] The spectral element method naturally satisfies the free boundary condition, making it highly suitable for simulating elastic waves under undulating surface conditions. Belonging to the wave equation framework, the spectral element method better reflects the dynamic characteristics of seismic waves, providing higher accuracy in simulating seismic wave fields. It is a wave equation simulation algorithm adapted to undulating surfaces and complex structures.

[0085] Step 4: Obtain the inversion models corresponding to different key acquisition parameter degradation schemes;

[0086] (41) Design a degradation scheme for key acquisition parameters:

[0087] Degradation of the observation system includes: degradation of track spacing, degradation of shot distance, degradation of receiver line spacing, and degradation of shot line spacing.

[0088] The key parameter degradation scheme designed in this invention includes:

[0089] Track spacing degradation scheme: Track spacing adopts various values, such as 5m, 10m, 20m, and 40m, while the shot distance, receiver line distance, and shot line distance remain unchanged. n track spacings correspond to n track spacing degradation schemes, and track spacing is the key acquisition parameter in the track spacing degradation scheme.

[0090] Shot distance degradation scheme: The shot distance adopts multiple values, such as 10m, 20m, and 40m, while the track spacing, receiver line spacing, and shot line spacing remain unchanged. The n shot distances correspond to n shot distance degradation schemes, and the shot distance is the key acquisition parameter in the shot distance degradation scheme.

[0091] Receiver spacing degradation scheme: The receiver spacing adopts various values, such as 120m, 240m, and 480m, while the track spacing, shot point spacing, and shot line spacing remain unchanged. The n receiver spacings correspond to n receiver spacing degradation schemes, and the receiver spacing is the key acquisition parameter in the receiver spacing degradation scheme.

[0092] Shot line distance degradation scheme: The shot line distance adopts various values, such as 100m, 200m, 300m, and 400m, while the track distance, shot point distance, and receiver line distance remain unchanged. The n running line distances correspond to n shot line distance degradation schemes. The shot line distance is the key acquisition parameter in the shot line distance degradation scheme.

[0093] By designing the above degradation scheme, when comparing and analyzing a certain key acquisition parameter, the other key acquisition parameters remain unchanged. Only a single key acquisition parameter is compared, thus making the factor that changes singular.

[0094] (42) Obtain the inversion model corresponding to the degradation scheme of each key acquisition parameter, as follows:

[0095] The following processing is performed on each key acquisition parameter degradation scheme:

[0096] Based on the key acquisition parameters in the key acquisition parameter degradation scheme, the first arrivals of the numerical simulation data obtained in step 3 are picked (the first arrivals are picked using existing methods, which will not be elaborated here). The first arrival files are extracted (for example, when comparing track spacing, first arrival files of different track spacings such as 10m and 20m can be extracted). Then, first arrival tomography inversion is used to obtain the inverted near-surface velocity model corresponding to the key acquisition parameter degradation scheme, that is, the inversion model corresponding to the key acquisition parameter degradation scheme.

[0097] Optionally, the first-arrival tomographic inversion can be performed using the Tomodel inversion system. It employs a fast-stepping wavefront tracking technique based on the wave equation, enabling small-grid modeling, resulting in high accuracy and computational efficiency. Furthermore, it uses a flattened mesh instead of a square one, improving the ability to characterize the interface and thus increasing depth resolution.

[0098] Step 4 yielded the inversion model corresponding to the degradation scheme of each key acquisition parameter.

[0099] Step 5: Use the inversion model to determine the key acquisition parameters for optimization:

[0100] (51) Obtain the optimized track spacing using the inversion model corresponding to multiple track spacing degradation schemes:

[0101] The inversion model corresponding to each track spacing degradation scheme is compared with the established near-surface velocity model to find the inversion model that meets the accuracy requirements. That is, we check which inversion model has a similarity to the established near-surface velocity model that meets the requirements (the similarity between the two models is compared using existing methods, which will not be elaborated here). This can provide a reference for designing accurate key acquisition parameters.

[0102] The depth and velocity values ​​of the inversion models that meet the accuracy requirements are extracted separately (using existing technology, which will not be elaborated here). These values ​​are then compared with the micrologging interpretation results at the same location. The inversion model whose variation trend is most similar to that of the near-surface velocity model is found (i.e., the variation of the values ​​in the inversion model is most similar to that in the theoretical model). The trace spacing in the trace spacing degradation scheme corresponding to this inversion model is the preferred trace spacing.

[0103] (52) The optimized shot distance is obtained by using the inversion model corresponding to multiple shot distance degradation schemes:

[0104] The inversion model corresponding to each shot distance degradation scheme is compared with the established near-surface velocity model to find the inversion model with the required accuracy.

[0105] The depth and velocity values ​​of the inversion models that meet the accuracy requirements are extracted and compared with the micrologging interpretation results at the same location. The inversion model whose variation trend is most similar to that of the near-surface velocity model is found. The shot distance in the shot distance degradation scheme corresponding to this inversion model is the preferred shot distance.

[0106] (53) Obtain the optimized receiver spacing using the inversion model corresponding to multiple receiver spacing degradation schemes:

[0107] The inversion model corresponding to each receiver spacing degradation scheme is compared with the established near-surface velocity model to find the inversion model with the required accuracy.

[0108] The depth and velocity values ​​of the inversion models that meet the accuracy requirements are extracted and compared with the micrologging interpretation results at the same location. The inversion model whose trend is most similar to that of the near-surface velocity model is found. The receiver spacing in the receiver spacing degradation scheme corresponding to this inversion model is the preferred receiver spacing.

[0109] (54) The optimized shot distance is obtained by using the inversion model corresponding to multiple shot distance degradation schemes:

[0110] The inversion model corresponding to each shot distance degradation scheme is compared with the established near-surface velocity model to find the inversion model with the required accuracy.

[0111] The depth and velocity values ​​of the inversion models that meet the accuracy requirements are extracted and compared with the micrologging interpretation results at the same location. The inversion model whose variation trend is most similar to that of the near-surface velocity model is found. The shot distance in the shot distance degradation scheme corresponding to this inversion model is the preferred shot distance.

[0112] The optimized track spacing, optimized shot distance, optimized receiver line spacing, and optimized shot line spacing obtained in step 5 are the key acquisition parameters for optimization.

[0113] To demonstrate the correctness and effectiveness of the method and to show that it has higher accuracy, an example is provided below.

[0114]

Example 2

[0115] In step 101, the near-surface structure of a certain foreland zone is complex, with a large number of high-dipping strata. Based on a comprehensive study including the interpretation of existing data, surface lithology surveys, and micro-logging-constrained tomographic inversion models, a model was established as follows: Figure 2 The three-dimensional near-surface model shown includes a 40m thick low-velocity zone near the surface, with a P-wave velocity of 1200m / s, a S-wave velocity of 675m / s, and a density of 1727kg / m³. Four sets of high-dipping strata were also established.

[0116] ①The longitudinal wave velocity is 3200m / s, the transverse wave velocity is 1746m / s, and the density is 2230kg / m3;

[0117] ②The longitudinal wave velocity is 2300 m / s, the transverse wave velocity is 1330 m / s, and the density is 2124 kg / m3;

[0118] ③The longitudinal wave velocity is 1800 m / s, the transverse wave velocity is 1050 m / s, and the density is 1994 kg / m3;

[0119] ④ The longitudinal wave velocity is 3300 m / s, the transverse wave velocity is 1904 m / s, and the density is 2245 kg / m3.

[0120] In step 102, to meet the requirements of the observation system research based on three-dimensional numerical simulation, it is necessary to degrade the simulated acquisition observation system and analyze the key parameters of the observation system that affect the high-precision imaging of steep structures. The degradation of the observation system mainly includes the degradation of track spacing, shot distance, receiver line distance, and shot line distance, so the simulated acquisition observation system scheme should be a relatively advanced scheme. In this embodiment, an 8L60S observation system scheme is designed, with a track spacing of 5m, a shot distance of 10m, a receiver line distance of 120m, and a shot line distance of 100m, for a total workload of 1100 shots. Based on the understanding from the previous exploration of the target area, in order to improve the imaging of the fracture, the simulated acquisition observation direction is perpendicular to the fracture direction.

[0121] In step 103, the Ricker wavelet, with its zero-phase, short sidelobe duration, and fast convergence, has been widely used in numerical simulations. The dominant reflection frequency of the data from the piedmont zone of the target area is approximately 15Hz, so a Ricker wavelet with a dominant frequency of 15Hz was used for the numerical simulation. To better reflect the dynamic characteristics of seismic waves, the simulated seismic wavefield achieved higher accuracy. The finite element method, adapted to undulating surfaces and complex structures, was employed to meet the required accuracy for numerical simulations under these conditions. Key simulation parameters:

[0122] Grid: 10m x 10m x 7m;

[0123] Clock speed: 15Hz;

[0124] Surface: Absorbs from the surface;

[0125] Recording length: 4 seconds;

[0126] Sampling interval: 2ms;

[0127] Figure 3 This is one of the single-shot records in the numerical simulation. It has very similar characteristics to existing data and can meet the needs of the research.

[0128] In step 104, based on the analysis of existing data and the advantages and disadvantages of past acquisition parameters, this embodiment designs the following key acquisition parameter degradation schemes: ① Track spacing degradation analysis: 5m, 10m, 20m, 40m; ② Shot point distance degradation analysis: 10m, 20m, 40m; ③ Receiver line distance degradation analysis: 120m, 240m, 480m; ④ Shot line distance degradation analysis: 100m, 200m, 300m, 400m. First-arrival information of the model data is extracted according to different degradation schemes (i.e., first-arrival information is extracted from the model data using the above 14 degradation schemes, and then inversion is performed to obtain 14 inversion models). High-precision tomographic inversion is performed using Tomodel tomographic inversion software to obtain near-surface velocity models inverted with different key acquisition parameters. When comparing and analyzing a single key parameter, other parameters remain constant; only a single parameter is compared. For example, in track spacing degradation analysis, only the track spacing parameters are changed, while the shot distance, receiver line distance, and shot line distance remain the same. For instance, in degradation schemes with track spacings of 5, 10, 20, and 40m, the shot distance, receiver line distance, and shot line distance are all the same (e.g., all using a shot distance of 10m, a shot line distance of 100m, and a receiver line distance of 120m). In degradation schemes with shot distances of 10m, 20m, and 40m, the track spacing, receiver line distance, and shot line distance are all the same.

[0129] In step 105, by comparing the accuracy of the inversion models for different key acquisition parameters, a reference basis can be provided for designing accurate key acquisition parameters.

[0130] Figures 4-1 to 4-4 To illustrate the results of near-surface model inversion with different trace spacings, comparative analyses were conducted for trace spacings of 5m, 10m, 20m, and 40m. The near-surface models with different trace spacings show that the basic morphology is relatively consistent between 5m and 10m trace spacings. However, when the trace spacing increases to 20m, the velocity model morphology changes significantly, mainly manifested in larger velocity variations in the area between two high-dipping strata (e.g., when...). Figures 4-1 to 4-4 The inversion model in the box region and Figure 2 (Comparison of similarity between theoretical models shown) When the track spacing reaches 40m, it can no longer reflect the shape of the near-surface theoretical model, and the velocity model is significantly different from the actual model.

[0131] In step 106, in order to further compare the accuracy of the inversion, the depth-velocity values ​​of the inversion model at the same location as a certain microlog are extracted and compared with the microlog interpretation results to select accurate key acquisition parameters.

[0132] Figure 5 This is a comparison chart of the interpretation results and inversion velocities of micrologging point 18375. From top to bottom, it corresponds to the interpretation results of micrologging point 18375, and the inversion velocities at 40 meters, 20 meters, 10 meters, and 5 meters. It can be seen that the velocity variation trends and values ​​of the tomographic inversion at 5m and 10m trace spacings are very close, and they can reflect the velocity changes of the theoretical velocity model. When the trace spacing increases to 20m, the velocity inflection point response becomes sluggish and cannot correspond well with the theoretical velocity. When the trace spacing further increases to 40m, the velocity variation trend becomes a smooth curve, which differs significantly from the theoretical velocity at that point. This indicates that in complex surface areas with high dip angles, to obtain a detailed near-surface velocity model using tomographic inversion, the trace spacing should not exceed 10m.

[0133] Similarly, by comparing the inversion models with different acquisition parameters, the values ​​of other key acquisition parameters can be obtained. The conclusion of the observation system parameters in this example is: track spacing 10m, receiver line spacing 240m, shot point spacing 20m, and shot line spacing 200-300m.

[0134] The present invention also provides a system for optimizing key acquisition parameters, and an embodiment of the system is as follows:

[0135]

Example 3

[0136] The system includes:

[0137] Model building unit: used to build a near-surface velocity model based on the interpretation of existing data;

[0138] Observation system design unit: used to design observation systems based on the analysis results of existing data;

[0139] Numerical simulation unit: Connected to the model building unit and the observation system design unit respectively, it is used to obtain numerical simulation data using the near-surface velocity model and the observation system;

[0140] Inversion Unit: Connected to the numerical simulation unit, it is used to obtain the inversion model corresponding to different degradation schemes of key acquisition parameters;

[0141] Optimization Unit: Connected to the inversion unit, it is used to determine the key acquisition parameters for optimization using the inversion model.

[0142] The present invention also provides a computer-readable storage medium, embodiments of which are as follows:

[0143]

Example 4

[0144] The computer-readable storage medium stores at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the method for optimizing key acquisition parameters described above.

[0145] Thus, the method of this invention can optimize the acquisition design for acquisition areas with complex surface geological conditions, and propose an operable three-dimensional observation system scheme.

[0146] Finally, it should be noted that the above technical solution is only one embodiment of the present invention. For those skilled in the art, based on the application methods and principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the methods described in the above specific embodiments of the present invention. Therefore, the methods described above are only preferred and have no limiting significance.

Claims

1. A method of optimizing key acquisition parameters, characterized by: The application relates to a key acquisition parameter optimization method and system. The method comprises the following steps: Step 1: building a near-surface layer velocity model according to existing data interpretation results; Step 2: designing an observation system according to existing data analysis results; Step 3: obtaining numerical simulation data by using the near-surface layer velocity model and the observation system; Step 4: obtaining an inversion model corresponding to a different key acquisition parameter degradation scheme; Step 5: determining optimized key acquisition parameters by using the inversion model. The operation of step 4 comprises: Step 41: designing a key acquisition parameter degradation scheme; Step 42: obtaining an inversion model corresponding to each key acquisition parameter degradation scheme; The key acquisition parameter degradation scheme designed in step 41 comprises: A trace interval degradation scheme: the trace interval adopts multiple numerical values, and the shot point interval, the receiving line interval and the shot line interval remain unchanged; the trace interval is the key acquisition parameter in the trace interval degradation scheme; A shot point interval degradation scheme: the shot point interval adopts multiple numerical values, and the trace interval, the receiving line interval and the shot line interval remain unchanged; the shot point interval is the key acquisition parameter in the shot point interval degradation scheme; A receiving line interval degradation scheme: the receiving line interval adopts multiple numerical values, and the trace interval, the shot point interval and the shot line interval remain unchanged; the receiving line interval is the key acquisition parameter in the receiving line interval degradation scheme; A shot line interval degradation scheme: the shot line interval adopts multiple numerical values, and the trace interval, the shot point interval and the receiving line interval remain unchanged; the shot line interval is the key acquisition parameter in the shot line interval degradation scheme. The operation of step 42 comprises: The following processing is performed on each key acquisition parameter degradation scheme: According to the key acquisition parameter in the key acquisition parameter degradation scheme, the first arrival of the numerical simulation data is picked up, and a first arrival file is extracted; then first arrival wave tomographic inversion is adopted to obtain an inversion model corresponding to the key acquisition parameter degradation scheme. The operation of step 5 comprises: Step 51: obtaining optimized trace intervals by using the inversion models corresponding to multiple trace interval degradation schemes; Step 52: obtaining optimized shot point intervals by using the inversion models corresponding to multiple shot point interval degradation schemes; Step 53: obtaining optimized receiving line intervals by using the inversion models corresponding to multiple receiving line interval degradation schemes; Step 54: obtaining optimized shot line intervals by using the inversion models corresponding to multiple shot line interval degradation schemes; The optimized trace intervals, the optimized shot point intervals, the optimized receiving line intervals and the optimized shot line intervals are the optimized key acquisition parameters. The operations of steps 51 to 54 all comprise: Comparing the inversion model corresponding to each key acquisition parameter degradation scheme with the built near-surface layer velocity model to find an inversion model with required precision; 2. The method of optimizing key acquisition parameters according to claim 1, wherein: Extracting the depth and velocity value of the inversion model with required precision, and comparing the depth and velocity value with the micro-logging interpretation results at the same position to find an inversion model with the most similar change trend to the change trend of the near-surface layer velocity model; the key acquisition parameter in the key acquisition parameter degradation scheme corresponding to the inversion model is the optimized key acquisition parameter.

3. The method of optimizing key acquisition parameters according to claim 1, wherein: The key acquisition parameters comprise the trace interval, the shot point interval, the receiving line interval and the shot line interval. The operation of step 3 comprises:

4. The method of optimizing key acquisition parameters according to claim 1, wherein: The forward simulation adopts a high-precision spectral element method.

5. A system for optimizing key acquisition parameters based on the method of any of claims 1-4, characterized by: The first arrival wave tomographic inversion is realized by using a Tomodel inversion system. The system comprises: The model establishing unit is configured to establish a near-surface layer velocity model according to existing data interpretation results; The observation system designing unit is configured to design an observation system according to existing data analysis results; The numerical simulation unit is connected with the model establishing unit and the observation system designing unit respectively, and is configured to obtain numerical simulation data by using the near-surface layer velocity model and the observation system; The inversion unit is connected with the numerical simulation unit, and is configured to obtain an inversion model corresponding to different key acquisition parameter degradation schemes; The optimization unit is connected with the inversion unit, and is configured to determine optimized key acquisition parameters by using the inversion model.

6. A computer-readable storage medium, characterized in that: The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the method for optimizing key acquisition parameters according to any one of claims 1-4.

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