Illumination analysis method based on point spread function and observation system optimization method

Abstract

The invention relates to the technical field of seismic exploration, and provides an illumination analysis method based on a point spread function and an observation system optimization method. The illumination analysis method comprises the following steps: obtaining an image of an underground structure according to existing seismological observation data of an underground target area; extracting a point spread function in the image of the underground structure; parameterizing the point spread function, and extracting characteristic parameters of the point spread function; and establishing a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image. According to the method, the illumination of the underground target area is analyzed based on the point spread function, the imaging effect of the underground structure image is improved, more accurate guidance is provided for optimization of an observation system, and the exploration risk and the data acquisition cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration, and specifically relates to an illumination analysis method based on a point spread function, an observation system optimization method, an observation system, and a computer device. Background Art

[0002] Seismic exploration technology excites seismic waves, receives their reflections, and performs inversion and imaging on these signals to understand the subsurface structure. The whole process includes three major links: data acquisition, processing, and interpretation. The data acquisition stage requires field construction, which usually takes a lot of time and money. If there are defects in the initial observation design, accurate imaging of the subsurface structure will be challenged, and it will be extremely difficult to conduct data acquisition again. Therefore, it is crucial to conduct demonstration and analysis of the target area before collecting data.

[0003] With technological progress and increasing energy demand, the exploration of deep and ultra-deep oil and gas resources has received more and more attention. Geological demonstration points out that there may be another oil and gas reservoir under a reservoir. For deep resources, the exploration areas with existing data collection will be concerned first. Since the target layers of early data collection are usually relatively shallow, the data is limited in deep exploration. Therefore, it is necessary to conduct secondary exploration in specific areas. Considering the existing geological knowledge, how to conduct secondary data collection economically and efficiently has become an urgent problem to be solved. Currently, illumination analysis is mainly carried out through ray or wave equation simulation. These methods can quantitatively analyze the coverage times and energy distribution, but there are deficiencies in the accuracy of illumination angles and limitations in guiding the optimization of the observation system. Summary of the Invention

[0004] To solve the defects of the prior art, the present invention provides an illumination analysis method and an observation system optimization method based on a point spread function.

[0005] On the one hand, the present invention provides an illumination analysis method based on a point spread function, including:

[0006] Obtaining an image of the subsurface structure according to the existing seismic observation data of the underground target area;

[0007] Extracting the point spread function from the image of the subsurface structure;

[0008] Parametrizing the point spread function and extracting the characteristic parameters of the point spread function;

[0009] Establishing a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the subsurface structure image.

[0010] In an embodiment of the present invention, the obtaining an image of the subsurface structure according to the existing seismic observation data of the underground target area includes:

[0011] Preprocess the collected surface seismic data and downhole vertical seismic profile data;

[0012] Extract the source wavelet of the field actual data;

[0013] Establish a velocity model of the underground medium based on the preprocessed seismic data and the source wavelet;

[0014] Perform migration imaging according to the velocity model of the underground medium to obtain an image of the underground structure.

[0015] In the embodiment of the present invention, extracting the point spread function in the image of the underground structure includes:

[0016] Select representative scattering points in the image of the underground structure as the reflectivity model;

[0017] Generate the seismic record of the scattering points by using the reverse migration method;

[0018] Migrate the seismic record of the scattering points obtained by reverse migration to obtain the point spread function.

[0019] In the embodiment of the present invention, parameterizing the point spread function and extracting the characteristic parameters of the point spread function include: transforming the point spread function to the time domain, and obtaining the frequency spectrum of the point spread function in the time domain; quantitatively measuring the morphological characteristics of the point spread function in the target area; quantitatively measuring the amplitude characteristics of the point spread function in the target area.

[0020] In the embodiment of the present invention, the frequency spectrum of the multi-channel finite length sequence of the point spread function is expressed as:

[0021]

[0022] where k is the wave number, 0 ≤ k ≤ N - 1, N is the number of sequence samples, M is the number of channels, and x m (n) is the discrete sample of the input sequence.

[0023] In the embodiment of the present invention, quantitatively measuring the amplitude characteristics of the point spread function in the target area includes:

[0024] Based on the two-dimensional M*N point spread function, define the energy of the point spread function as:

[0025]

[0026] where x(i,j) is the discrete sample of the point spread function;

[0027] Define the energy distribution measure of the point spread function as:

[0028]

[0029] Among them, x(i,j) are the discrete samples of the point spread function, i0 and j0 are the coordinates of the center point of the point spread function. A smaller V value indicates that the energy is concentrated in the central region of the point spread function, and a larger V value indicates that the energy of the point spread function is more dispersed.

[0030] In the embodiments of the present invention, establishing the quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image includes: determining the relationship between the spectrum of the point spread function and the imaging resolution; determining the relationship between the morphological characteristics of the point spread function and the imaging phase; determining the relationship between the amplitude characteristics of the point spread function and the focusing of the imaging image.

[0031] On the other hand, the present invention provides an observation system optimization method, including:

[0032] Obtaining an image of the underground structure based on the existing seismic observation data of the underground target area;

[0033] Extracting the point spread function from the image of the underground structure;

[0034] Parametrizing the point spread function and extracting the characteristic parameters of the point spread function;

[0035] Establishing the quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image;

[0036] Based on the quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image, determining the optimization scheme of the observation system.

[0037] In the embodiments of the present invention, the determining the optimization scheme of the observation system based on the quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image includes:

[0038] Determining the variation law of the characteristic parameters of the point spread function under different observation system conditions;

[0039] Judging the main factors restricting the imaging quality of the observation system according to the variation law of the characteristic parameters of the point spread function;

[0040] Determining the observation system parameters to be optimized according to the main factors restricting the imaging quality of the observation system, and establishing a relationship model between the observation system parameters and the characteristic parameters of the point spread function;

[0041] Using the relationship model between the observation system parameters and the characteristic parameters of the point spread function to predict the characteristic and imaging quality of the point spread function of different observation systems;

[0042] Determining the optimization scheme of the observation system according to the prediction results of the characteristic and imaging quality of the point spread function of different observation systems.

[0043] In the embodiments of the present invention, the observation system parameters include one or more of the following: source spacing, geophone spacing; shot line distance, geophone line distance; maximum offset, aspect ratio; coverage.

[0044] In the embodiments of the present invention, determining the optimization scheme of the observation system according to the point spread function characteristics of different observation systems and the prediction results of imaging quality includes: iterating the relationship model between the observation system parameters and the point spread function characteristic parameters until the imaging quality of the subsurface structure image obtained from the seismic observation data of the observation system meets the relevant requirements.

[0045] The present invention also provides an observation system, which is optimized by using the above-mentioned observation system optimization method.

[0046] The present invention also provides a computer device, including: a memory, a processor, and a computer program, where the computer program is stored in the memory and is configured to be executed by the processor to implement the above-mentioned illumination analysis method based on the point spread function or the above-mentioned observation system optimization method.

[0047] The present invention uses the point spread function method to conduct a detailed analysis of the illumination of the subsurface target area. For deep and ultra-deep exploration, it analyzes the effective observation range and depth of the exploration blocks where data collection has been carried out, including the effective angle and energy coverage of illumination, etc., and quantitatively analyzes the optimization effect of the observation system by means of numerical simulation, improves the imaging effect, provides more accurate guidance for the optimization of the observation system, and reduces exploration risks and data collection costs.

[0048] Other features and advantages of the technical solution of the present invention will be described in detail in the following specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0050] Figure 1 is a flowchart of the illumination analysis method based on the point spread function provided by the embodiments of the present invention;

[0051] Figure 2 is a flowchart of the observation system optimization method provided by the embodiments of the present invention;

[0052] Figure 3a is the PSF at position a (left figure) and the corresponding KK spectrum at position a (right figure) in the application example of the present invention;

[0053] Figure 3bIt is the PSF at position c in the application example of the present invention (left figure) and the KK spectrum corresponding to position c (right figure);

[0054] Figure 4 They are partial images of the corresponding regions of the PSFs at positions a and c;

[0055] Figure 5 In the left figure, it represents the PSF before the optimization of the observation system, and in the right figure, it represents the PSF after the optimization of the observation system;

[0056] Figure 6 In the left figure, it represents the corresponding Figure 5 image before the optimization of the observation system in Figure 5 and in the right figure, it represents the corresponding

[0057] Figure 7 It represents the point spread function obtained by offsetting the reverse-offset data;

[0058] Figure 8 It represents the point spread function obtained by offsetting the reverse-offset data after encrypting the shot points once;

[0059] Figure 9 It represents the point spread function obtained by offsetting the reverse-offset data after encrypting the shot points twice;

[0060] Figure 10 It represents Figure 7 the distribution of the focusing degree of the point spread function in

[0061] Figure 11 It represents Figure 8 the distribution of the focusing degree of the point spread function in

[0062] Figure 12 It represents Figure 9 the distribution of the focusing degree of the point spread function in

[0063] Figure 13 It represents the corresponding Figure 7 image after offsetting the reverse-offset seismic data of the observation system;

[0064] Figure 14 It represents the corresponding Figure 8 image after offsetting the reverse-offset seismic data of the observation system;

[0065] Figure 15 It represents the corresponding Figure 9 image after offsetting the reverse-offset seismic data of the observation system. Specific implementation manner

[0066] In order to make the technical solutions and advantages in the embodiments of the present invention clearer and more understandable, the following further details the exemplary embodiments of the present invention with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than an exhaustive list of all embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0067] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0068] The embodiment of the present invention provides a technology for lighting analysis and observation system optimization based on the point spread function. The point spread function method is used to conduct a detailed analysis of the lighting in the underground target area. For deep and ultra-deep exploration, the effective observation range and depth of the exploration blocks where data collection has been carried out are analyzed, including the effective angle and energy coverage of lighting, etc., and the numerical simulation method is used to quantitatively analyze the optimization effect of the observation system, improve the imaging effect, provide more accurate guidance for the optimization of the observation system, and reduce the exploration risk and data collection cost.

[0069] Figure 1 is the flowchart of the lighting analysis method based on the point spread function provided by the embodiment of the present invention. As Figure 1 shown, the lighting analysis method based on the point spread function provided in this embodiment includes the following steps:

[0070] S110, obtain an image of the underground structure based on the existing seismic observation data of the underground target area;

[0071] S120, extract the point spread function from the image of the underground structure;

[0072] S130, parameterize the point spread function and extract the characteristic parameters of the point spread function;

[0073] S140. Establish a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the subsurface structure image.

[0074] In the above step S110, the following sub-steps are included: preprocess the collected surface seismic data and downhole vertical seismic profile (VSP) data; extract the source wavelet of the actual field data; establish a velocity model of the subsurface medium based on the preprocessed seismic data and the source wavelet; perform migration imaging according to the velocity model of the subsurface medium to obtain an image of the subsurface structure.

[0075] In the above step S120, select representative scattering points in the image of the subsurface structure as the reflectivity model, and use migration techniques such as Kirchhoff, one-way wave, and reverse time migration to generate scattering point seismic records by the inverse migration method. Migrate the scattering point seismic records obtained by the inverse migration to obtain the point spread function (abbreviated as PSF).

[0076] In the above step S130, transform the point spread function to the time domain, obtain the frequency spectrum of the point spread function in the time domain, and quantitatively measure the morphological characteristics and amplitude characteristics of the point spread function in the target area. Step S130 specifically includes the following sub-steps:

[0077] (1) Perform a depth-time conversion on the point spread function to transform it to the time domain, and obtain its frequency spectrum in the time domain. The frequency spectrum of a multi-channel finite-length sequence is as follows:

[0078]

[0079] where k is the wave number, 0 ≤ k ≤ N - 1, N is the number of sequence samples, M is the number of channels, and x m (n) is the discrete sample of the input sequence;

[0080] (2) Quantitatively measure the morphological characteristics of the point spread function in the target area, such as: main lobe width, sidelobe intensity, directionality in the KK spectrum, etc.;

[0081] (3) Based on the two-dimensional M * N point spread function PSF, define its energy as:

[0082]

[0083] where x(i,j) is the discrete sample of the point spread function;

[0084] On the premise of assuming a certain energy, define the energy distribution measure of the PSF as:

[0085]

[0086] Among them, x(i,j) are the discrete samples of the point spread function, i0,j0 are the coordinates of the center point of the point spread function. A smaller V value indicates that the energy is concentrated in the central region of the PSF, and a larger V value indicates that the energy of the PSF is more dispersed.

[0087] In the above step S140, a quantitative relationship between the PSF characteristic parameters and the imaging quality indicators is established, including imaging quality indicators such as resolution and phase distortion. Specifically, it includes:

[0088] Determine the relationship between the spectrum of the point spread function and the imaging resolution according to the sub-step (1) of step S130: the higher the PSF frequency band, the higher the imaging resolution;

[0089] Determine the relationship between the morphological characteristics of the point spread function and the imaging phase according to the sub-step (2) of step S130: the KK spectrum distribution of the PSF is uneven, and there is an illumination gap;

[0090] Determine the relationship between the amplitude characteristics of the point spread function and the focusing of the imaging image according to the sub-step (3) of step S130: the smaller the V value of the PSF, the more focused the image.

[0091] The above method conducts a detailed analysis of the illumination of the underground target area based on the point spread function, including the effective angle and energy coverage of the illumination, etc., improving the imaging effect of the underground structure image.

[0092] The embodiment of the present invention also provides an observation system optimization method. This method is based on the above illumination analysis method of the point spread function. An image of the underground structure is obtained according to the existing seismic observation data of the underground target area. The point spread function is extracted from the image of the underground structure, the point spread function is parameterized, the characteristic parameters of the point spread function are extracted, a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality indicators of the underground structure image is established, and an optimization scheme for the observation system is determined based on this quantitative relationship.

[0093] Figure 2 It is a flowchart of the observation system optimization method provided by the embodiment of the present invention. As Figure 2 shown, the observation system optimization method provided by this embodiment includes the following steps:

[0094] S210, obtain an image of the underground structure according to the existing seismic observation data of the underground target area;

[0095] S220, extract the point spread function from the image of the underground structure;

[0096] S230, parameterize the point spread function and extract the characteristic parameters of the point spread function;

[0097] S240, establish a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality indicators of the underground structure image;

[0098] S250. Determine the variation law of the characteristic parameters of the point spread function under different observation system conditions;

[0099] S260. Judge the main factors restricting the imaging quality of the observation system according to the variation law of the characteristic parameters of the point spread function;

[0100] S270. Determine the observation system parameters to be optimized according to the main factors restricting the imaging quality of the observation system, and establish a relationship model between the observation system parameters and the characteristic parameters of the point spread function;

[0101] S280. Use the relationship model between the observation system parameters and the characteristic parameters of the point spread function to predict the characteristics of the point spread function and the imaging quality of different observation systems;

[0102] S290. Determine the optimization scheme of the observation system according to the prediction results of the characteristics of the point spread function and the imaging quality of different observation systems.

[0103] In the above step S210, the following sub-steps are included: preprocess the collected surface seismic data and downhole vertical seismic profile (VSP) data; extract the source wavelet of the field actual data; establish a velocity model of the underground medium based on the preprocessed seismic data and the source wavelet; perform migration imaging according to the velocity model of the underground medium to obtain an image of the underground structure.

[0104] In the above step S220, select representative scattering points in the image of the underground structure as the reflectivity model, and use migration techniques such as Kirchhoff, one-way wave, and reverse time migration to generate scattering point seismic records by the reverse migration method, and migrate the scattering point seismic records obtained by the reverse migration to obtain the point spread function PSF.

[0105] In the above step S230, transform the point spread function to the time domain, obtain the spectrum of the point spread function in the time domain, and quantitatively measure the morphological characteristics and amplitude characteristics of the point spread function in the target area. Specifically, the following sub-steps are included:

[0106] (1) Perform a depth-time conversion on the point spread function, transform it to the time domain, and obtain its spectrum in the time domain. The spectrum of a multi-channel finite-length sequence is as follows:

[0107]

[0108] where k is the wave number, 0 ≤ k ≤ N - 1, N is the number of sequence samples, M is the number of channels, and x m (n) is the discrete sample of the input sequence;

[0109] (2) Quantitatively measure the morphological characteristics of the point spread function in the target area, such as: main lobe width, sidelobe intensity, directivity in the KK spectrum, etc.;

[0110] (3) Based on the two-dimensional M*N point spread function PSF, its energy is defined as:

[0111]

[0112] where x(i,j) are the discrete sample points of the point spread function;

[0113] On the premise of assuming a certain energy, the energy distribution measure of the PSF is defined as:

[0114]

[0115] where x(i,j) are the discrete sample points of the point spread function, i0,j0 are the coordinates of the center point of the point spread function, a smaller V value indicates that the energy is concentrated in the central region of the PSF, and a larger V value indicates that the energy of the PSF is more dispersed.

[0116] In the above step S240, a quantitative relationship between the PSF characteristic parameters and the imaging quality indicators is established, including imaging quality indicators such as resolution and phase distortion. Specifically, it includes:

[0117] According to the sub-step (1) of step S230, determine the relationship between the spectrum of the point spread function and the imaging resolution: the higher the PSF frequency band, the higher the imaging resolution;

[0118] According to the sub-step (2) of step S230, determine the relationship between the morphological characteristics of the point spread function and the imaging phase: the KK spectrum distribution of the PSF is uneven and there is an illumination gap;

[0119] According to the sub-step (3) of step S230, determine the relationship between the amplitude characteristics of the point spread function and the focusing of the imaging image: the smaller the V value of the PSF, the more focused the image.

[0120] In the above step S270, the observation system parameters to be optimized include one or more of the following: source spacing, geophone spacing; shot line distance, geophone line distance; maximum offset, aspect ratio; number of coverage. These parameters can be used to determine the effective excitation and reception ranges.

[0121] In the above step S290, the relationship model between the observation system parameters and the PSF characteristic parameters is iterated until the imaging quality of the underground structure image obtained from the seismic observation data of the observation system meets the relevant requirements. For example, repeat the analysis of steps S220 - S270 to predict the improvement of imaging quality; iterate steps S220 - S290 for optimization until a satisfactory imaging effect is obtained.

[0122] The above method uses the point spread function to quantitatively analyze the illumination of the underground target area, including the uniformity of illumination, the defects of the observed illumination angle, etc., to improve the imaging effect. Through numerical simulation, the optimization effect of the observation system is iteratively demonstrated, providing more accurate guidance for the optimization of the observation system, reducing exploration risks and data acquisition costs. The present invention has better adaptability to the observation design of field data, and is low in cost and high in efficiency, suitable for secondary exploration of deep and ultra-deep layers.

[0123] The following introduces the process steps of the present invention for specific application scenarios:

[0124] 1) Design an observation system for the public model sigsbee2a, forward model seismic data, and perform necessary data preprocessing on the generated seismic data;

[0125] 2) Obtain the seismic imaging of the model by prestack depth migration; in a specific application example, the PSF at position a is as shown in the left figure of Figure 3a and the corresponding KK spectrum at position a is as shown in the right figure of Figure 3a ; the local images of the corresponding regions of the PSFs at positions a and c are as shown in Figure 4 ; the PSF before the optimization of the observation system is as shown in the left figure of Figure 5 and the PSF after the optimization of the observation system is as shown in the right figure of Figure 5 ; the image before the optimization of the observation system corresponding to Figure 5 is as shown in the left figure of Figure 6 and the image after the optimization of the observation system corresponding to Figure 5 is as shown in the right figure of Figure 6 ;

[0126] 3) Design a scatter point model according to the model, and use the same observation system to generate data of the scatter point model through reverse migration;

[0127] 4) Migrate the data in step 3 to obtain the point spread function, as shown in Figure 7 ; the point spread function obtained by migrating the reverse migrated data after encrypting the shot points once is as shown in Figure 8 and the point spread function obtained by migrating the reverse migrated data after encrypting the shot points twice is as shown in Figure 9 ;

[0128] 5) Conduct quantitative analysis on the point spread function, select specific point spread functions in the target area, and calculate parameters such as morphological characteristics, amplitude characteristics, direction characteristics, and energy distribution;

[0129] 6) According to the PSF direction characteristics and the poor focusing degree, add shot points to the left side of the model and increase the distribution of geophones, as shown in Figure 10 , Figure 11 , Figure 12As shown, the purplish red in the figure represents that the larger the value, the worse the focusing degree;

[0130] 7) According to the optimized observation system, forward model the seismic data again, repeat steps 1 to 4, and quantitatively evaluate the results again;

[0131] 8) According to the evaluation results, if the imaging requirements are still not met, further optimize the observation system, repeat steps 1 to 7 until the image meets the requirements, or the point spread function in the target area no longer improves with the optimization of the observation system, as Figure 13 、 Figure 14 、 Figure 15 shown;

[0132] 9) If the point spread function does not improve after optimization, it means that due to geological structure factors, the surface seismic observation system cannot accurately image the target area, and other observation methods such as VSP need to be introduced.

[0133] The embodiment of the present invention also provides an observation system, which is optimized by using the above-mentioned observation system optimization method.

[0134] The embodiment of the present invention also provides a computer device, including: a memory, a processor, and a computer program, where the computer program is stored in the memory and is configured to be executed by the processor to implement the above-mentioned illumination analysis method based on the point spread function, or the above-mentioned observation system optimization method.

[0135] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0136] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1One or more processes and / or blocks Figure 1 Apparatus for the functions specified in one or more blocks

[0137] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction apparatus that implements the functions in the process Figure 1 One or more processes and / or blocks Figure 1 Specified in one or more blocks

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the process Figure 1 One or more processes and / or blocks Figure 1 Specified in one or more blocks

[0139] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations

Claims

1. A lighting analysis method based on a point spread function, characterized in that, Comprising: Obtaining an image of the underground structure based on existing seismic observation data in the underground target area; Extracting a point spread function from the image of the underground structure; Parametrizing the point spread function and extracting characteristic parameters of the point spread function; Establishing a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image.

2. The illumination analysis method based on the point spread function according to claim 1, wherein The obtaining an image of the underground structure based on existing seismic observation data in the underground target area includes: Preprocessing the collected surface seismic data and downhole vertical seismic profile data; Extracting the source wavelet of the field actual data; Establishing a velocity model of the underground medium based on the preprocessed seismic data and the source wavelet; Performing migration imaging according to the velocity model of the underground medium to obtain an image of the underground structure.

3. The illumination analysis method based on the point spread function according to claim 1, wherein The extracting a point spread function from the image of the underground structure includes: Selecting representative scattering points in the image of the underground structure as the reflectivity model; Generating a scattering point seismic record using the reverse migration method; Performing migration on the scattering point seismic record obtained by reverse migration to obtain the point spread function.

4. The illumination analysis method based on the point spread function according to claim 1, wherein The parametrizing the point spread function and extracting characteristic parameters of the point spread function includes: Transforming the point spread function to the time domain and obtaining the frequency spectrum of the point spread function in the time domain; Quantitatively measuring the morphological characteristics of the point spread function in the target area; Quantitatively measuring the amplitude characteristics of the point spread function in the target area.

5. The illumination analysis method based on the point spread function according to claim 4, wherein The frequency spectrum of the multi-channel finite-length sequence of the point spread function is expressed as: where k is the wave number, 0 ≤ k ≤ N - 1, N is the number of sequence samples, M is the number of channels, and x m (n) is the discrete sample of the input sequence.

6. The illumination analysis method based on the point spread function according to claim 5, wherein The quantitatively measuring the amplitude characteristics of the point spread function in the target area includes: Based on the two-dimensional M*N point spread function, defining the energy of the point spread function as: where x(i,j) is the discrete sample point of the point spread function; Defining the energy distribution measure of the point spread function as: where x(i,j) is the discrete sample point of the point spread function, i0,j0 are the coordinates of the center point of the point spread function, and a smaller V value indicates that the energy is concentrated in the central region of the point spread function, while a larger V value indicates that the energy of the point spread function is more dispersed.

7. The illumination analysis method based on a point spread function according to claim 4, wherein The establishing a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image includes: Determining the relationship between the frequency spectrum of the point spread function and the imaging resolution; Determining the relationship between the morphological characteristics of the point spread function and the imaging phase; Determining the relationship between the amplitude characteristics of the point spread function and the focusing of the imaging image.

8. An observation system optimization method, characterized in that, Comprising: Obtaining an image of the underground structure based on existing seismic observation data in the underground target area; Extracting a point spread function from the image of the underground structure; Parametrizing the point spread function and extracting characteristic parameters of the point spread function; Establishing a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image; Based on the quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image, determining an optimization scheme for the observation system.

9. The observation system optimization method according to claim 8, wherein The based on the quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image, determining an optimization scheme for the observation system includes: Determining the variation law of the characteristic parameters of the point spread function under different observation system conditions; Judging the main factors restricting the imaging quality of the observation system according to the variation law of the characteristic parameters of the point spread function; Determine the observation system parameters to be optimized according to the main factors restricting the imaging quality of the observation system, and establish a relationship model between the observation system parameters and the characteristic parameters of the point spread function; Utilize the relationship model between the observation system parameters and the characteristic parameters of the point spread function to predict the point spread function characteristics and imaging quality of different observation systems; Determine the optimization scheme of the observation system according to the prediction results of the point spread function characteristics and imaging quality of different observation systems.

10. The optimization method for an observation system according to claim 9, wherein The observation system parameters include one or more of the following: Source spacing, geophone spacing; Shot line distance, geophone line distance; Maximum offset, aspect ratio; Coverage.

11. The optimization method of the observation system according to claim 9, wherein The determining of the optimization scheme of the observation system according to the prediction results of the point spread function characteristics and imaging quality of different observation systems includes: Iterate the relationship model between the observation system parameters and the characteristic parameters of the point spread function until the imaging quality of the subsurface structure image obtained from the seismic observation data of the observation system meets the relevant requirements.

12. An observation system, characterized in that, The observation system is optimized by using the observation system optimization method described in any one of claims 8-11.

13. A computer device, characterized in that, Including: A memory; A processor; And A computer program; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the illumination analysis method based on the point spread function described in any one of claims 1-7, or the observation system optimization method described in any one of claims 8-11.

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