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

Abstract

An illumination analysis method based on a point spread function, comprising: obtaining an underground structure image on the basis of existing seismic observation data of an underground target area; extracting a point spread function in the underground structure image; 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 an imaging quality index of the underground structure image. The extracted characteristic parameters of the point spread function comprise spectrum, amplitude, morphological characteristics and the like. Further provided is an observation system optimization method, comprising: determining an observation system optimization solution on the basis of a quantitative relationship between characteristics parameters of a point spread function and an imaging quality index of an underground structure image. The methods improve the imaging effect of the underground structure image, and reduce the exploration risk and the data acquisition costs.

Description

Lighting analysis method and observation system optimization method based on point spread function Technical Field

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

[0002] Seismic exploration techniques generate seismic waves, receive their reflections, and invert and image these signals to understand subsurface structures. The entire process involves three key steps: data acquisition, processing, and interpretation. The data acquisition phase requires field work, which is often time-consuming and expensive. Flaws in the initial observation design can hinder accurate imaging of the subsurface structure, making subsequent data acquisition extremely difficult. Therefore, thorough analysis of the target area prior to data acquisition is crucial.

[0003] With technological advancements and increasing energy demands, the exploration of deep and ultra-deep oil and gas resources is gaining increasing attention. Geological evidence indicates that beneath one reservoir layer there may be another. For deep resources, the focus is primarily on exploration areas where data has already been collected. Because the target layers for early data collection are typically shallow, their data are limited for deep exploration, necessitating secondary exploration of specific areas. Given existing geological knowledge, how to efficiently and economically collect secondary data has become a pressing issue. Currently, illumination analysis is primarily performed through ray or wave equation simulations. These methods can quantitatively analyze coverage times and energy distribution, but they lack the accuracy of illumination angles and are limited in guiding the optimization of observation systems.

[0004] Summary of the Invention

[0005] In order 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.

[0006] In one aspect, the present invention provides a lighting analysis method based on a point spread function, comprising:

[0007] Obtain an image of the underground structure based on existing seismic observation data of the underground target area;

[0008] Extracting point spread functions from images of subsurface structures;

[0009] Parameterize the point spread function and extract characteristic parameters of the point spread function;

[0010] A quantitative relationship between the characteristic parameters of the point spread function and the imaging quality indicators of underground structure images is established.

[0011] In an embodiment of the present invention, obtaining an image of the underground structure based on existing seismic observation data of the underground target area includes:

[0012] Preprocess the collected surface seismic data and downhole vertical seismic profile data;

[0013] Extract source wavelets from actual field data;

[0014] Establish a velocity model of the underground medium based on the pre-processed seismic data and source wavelet;

[0015] Migration imaging is performed based on the velocity model of the underground medium to obtain an image of the underground structure.

[0016] In an embodiment of the present invention, extracting a point spread function from an image of an underground structure includes:

[0017] Select representative scattering points in the image of the underground structure as reflectivity models;

[0018] The retrieval method is used to generate scattering point seismic records;

[0019] The scattering point seismic records obtained by de-migration are migrated to obtain the point spread function.

[0020] In an embodiment of the present invention, parameterizing the point spread function and extracting characteristic parameters of the point spread function include: transforming the point spread function into the time domain and obtaining the spectrum of the point spread function in the time domain; quantitatively measuring the morphological characteristics of the point spread function of the target area; and quantitatively measuring the amplitude characteristics of the point spread function of the target area.

[0021] 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:

[0022] Where X is the spectrum of a multi-channel finite length sequence, k is the wave number, 0≤k≤N-1, M is the number of channels, N is the number of sequence samples, m is the channel index, n is the sequence sample index, i is the imaginary unit, e is a natural constant, π is the circumference of a circle, and x is the number of channels. m (n) is the discrete sample point of the input sequence.

[0023] In an embodiment of the present invention, the quantitative measurement of the amplitude characteristics of the point spread function of the target area includes:

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

[0025] Where E is the energy of the point spread function, M is the number of channels, N is the number of sequence samples, i is the channel index, j is the sequence sample index, and x(i,j) is the discrete sample of the point spread function.

[0026] The energy distribution measure that defines the point spread function is:

[0027] Where V is the energy distribution measure of the point spread function, M is the number of channels, N is the number of sequence sample points, i is the channel index, j is the sequence sample point index, i0, j0 are the coordinates of the center point of the point spread function, and x(i, j) is the discrete sample point of the point spread function. A smaller V value indicates that the energy is concentrated in the central area of ​​the point spread function, and a larger V value indicates that the energy of the point spread function is more dispersed.

[0028] In an embodiment of the present invention, 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 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; and determining the relationship between the amplitude characteristics of the point spread function and the focus of the imaging image.

[0029] Another aspect of the present invention provides an observation system optimization method, comprising:

[0030] Obtain an image of the underground structure based on existing seismic observation data of the underground target area;

[0031] Extracting point spread functions from images of subsurface structures;

[0032] Parameterize the point spread function and extract characteristic parameters of the point spread function;

[0033] Establish a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of underground structure images;

[0034] Based on the quantitative relationship between the characteristic parameters of the point spread function and the imaging quality indicators of the underground structure image, the optimization scheme of the observation system is determined.

[0035] In an embodiment of the present invention, determining an optimization scheme for 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:

[0036] Determine the variation patterns of characteristic parameters of point spread function under different observation system conditions;

[0037] According to the variation law of the characteristic parameters of the point spread function, the main factors limiting the imaging quality of the observation system are determined;

[0038] According to the main factors that limit the imaging quality of the observation system, the observation system parameters that need to be optimized are determined, and a relationship model between the observation system parameters and the characteristic parameters of the point spread function is established;

[0039] Using the relationship model between observation system parameters and point spread function characteristic parameters, the point spread function characteristics and imaging quality of different observation systems are predicted;

[0040] The optimization scheme of the observation system is determined based on the point spread function characteristics of different observation systems and the prediction results of imaging quality.

[0041] In an embodiment of the present invention, the observation system parameters include one or more of the following: source spacing, receiver spacing; shot line spacing, receiver line spacing; maximum offset, aspect ratio; and coverage times.

[0042] In an embodiment of the present invention, the optimization scheme of the observation system is determined based on the point spread function characteristics and imaging quality prediction results of different observation systems, including: iterating the relationship model between the observation system parameters and the point spread function characteristic parameters until the imaging quality of the underground structure image obtained based on the seismic observation data of the observation system meets the relevant requirements.

[0043] The present invention also provides an observation system, which is optimized using the above-mentioned observation system optimization method.

[0044] The present invention also provides a computer device comprising: a memory, a processor, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor to implement the above-mentioned point spread function-based lighting analysis method or the above-mentioned observation system optimization method.

[0045] This paper uses the point spread function method to conduct a detailed analysis of the illumination of the underground target area. For deep and ultra-deep exploration, the effective observation range and depth of the exploration blocks where data has been collected are analyzed, including the effective angle of illumination and energy coverage. The optimization effect of the observation system is quantitatively analyzed using numerical simulation methods to improve imaging effects, provide more precise guidance for the optimization of the observation system, and reduce exploration risks and data collection costs.

[0046] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0048] 1 is a flow chart of a point spread function-based lighting analysis method provided by an embodiment of the present invention;

[0049] FIG2 is a flow chart of an observation system optimization method provided by an embodiment of the present invention;

[0050] FIG3a is a PSF at position a (left figure) and a KK spectrum corresponding to position a (right figure) in an application example of the present invention;

[0051] FIG3 b is a PSF at position c (left) and a KK spectrum corresponding to position c (right) in an application example of the present invention;

[0052] Figure 4 is a partial image of the PSF corresponding area at positions a and c;

[0053] The left figure in Figure 5 shows the PSF before the observation system is optimized, and the right figure shows the PSF after the observation system is optimized;

[0054] The left image in FIG6 represents the image before the observation system is optimized in FIG5 , and the right image represents the image after the observation system is optimized in FIG5 ;

[0055] Figure 7 shows the point spread function obtained by migrating the demigrated data;

[0056] Figure 8 shows the point spread function obtained by migrating the demigrated data after encrypting the shot points once;

[0057] Figure 9 shows the point spread function obtained by migrating the demigrated data after encrypting the shot points twice;

[0058] FIG10 shows the focusing degree distribution of the point spread function in FIG7 ;

[0059] FIG11 shows the focusing degree distribution of the point spread function in FIG8 ;

[0060] FIG12 shows the focusing degree distribution of the point spread function in FIG9;

[0061] FIG13 shows an image corresponding to FIG7 after migration of the de-migrated seismic data of the observation system;

[0062] FIG14 shows an image corresponding to the migration of the seismic data from the observation system of FIG8;

[0063] FIG15 shows an image corresponding to the de-migrated seismic data of the observation system in FIG9 after migration. DETAILED DESCRIPTION

[0064] To make the technical solutions and advantages of the embodiments of the present invention more clearly understood, exemplary embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments described are only a portion of the embodiments of the present invention, and are not an exhaustive list of all embodiments. It should be noted that the embodiments of the present invention and the features thereof may be combined with each other unless they conflict.

[0065] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions 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 cannot be understood as limiting 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 number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0066] The embodiments of the present invention provide a technology for illumination analysis and observation system optimization based on a point spread function. The technology uses a point spread function method to conduct a detailed analysis of the illumination of underground target areas. For deep and ultra-deep exploration, the effective observation range and depth of exploration blocks where data has been collected are analyzed, including the effective angle and energy coverage of the illumination. Numerical simulation methods are then used to quantitatively analyze the optimization effect of the observation system, improving imaging effects. This provides more precise guidance for optimizing the observation system and reduces exploration risks and data collection costs.

[0067] FIG1 is a flow chart of a method for analyzing lighting based on a point spread function according to an embodiment of the present invention. As shown in FIG1 , the method for analyzing lighting based on a point spread function according to this embodiment includes the following steps:

[0068] S110, obtaining an image of the underground structure based on existing seismic observation data of the underground target area;

[0069] S120, extracting a point spread function from an image of the underground structure;

[0070] S130, parameterizing the point spread function and extracting characteristic parameters of the point spread function;

[0071] S140, establishing a quantitative relationship between characteristic parameters of the point spread function and imaging quality indicators of the underground structure image.

[0072] In the above-mentioned step S110, the following sub-steps are included: preprocessing the collected surface seismic data and downhole vertical seismic profile (VSP) data; extracting the source wavelet of the actual field data; establishing a velocity model of the underground medium based on the preprocessed seismic data and the source wavelet; performing offset imaging according to the velocity model of the underground medium to obtain an image of the underground structure.

[0073] In step S120, representative scattering points are selected from the image of the underground structure as reflectivity models. Migration techniques such as Kirchhoff, one-way wave, and reverse time migration are used to generate scattering point seismic records using a démigration method. The scattering point seismic records obtained by démigration are then migrated to obtain a point spread function (PSF).

[0074] In the above step S130, the point spread function is transformed into the time domain, the spectrum of the point spread function is obtained in the time domain, and the morphological characteristics and amplitude characteristics of the point spread function of the target area are quantitatively measured. Step S130 specifically includes the following sub-steps:

[0075] (1) Perform deep time conversion on the point spread function, transform it into the time domain, and obtain its spectrum in the time domain. The spectrum of the multi-channel finite length sequence is as follows:

[0076] Where X is the spectrum of a multi-channel finite length sequence, k is the wave number, 0≤k≤N-1, M is the number of channels, N is the number of sequence samples, m is the channel index, n is the sequence sample index, i is the imaginary unit, e is a natural constant, π is the circumference of a circle, and x is the number of channels. m (n) is the discrete sample point of the input sequence;

[0077] (2) Quantitatively measure the morphological characteristics of the point spread function of the target area, such as the main lobe width, side lobe intensity, and directionality in the KK spectrum;

[0078] (3) Based on the two-dimensional M*N point spread function PSF, its energy is defined as:

[0079] Where E is the energy of the point spread function, M is the number of channels, N is the number of sequence samples, i is the channel index, j is the sequence sample index, and x(i,j) is the discrete sample of the point spread function.

[0080] Assuming that the energy is constant, the energy distribution measure of PSF is defined as:

[0081] Where V is the energy distribution measure of the point spread function, M is the number of channels, N is the number of sequence sample points, i is the channel index, j is the sequence sample point index, i0, j0 are the coordinates of the center point of the point spread function, and x(i, j) is the discrete sample point of the point spread function. A smaller V value indicates that the energy is concentrated in the central area of ​​the PSF, and a larger V value indicates that the energy of the PSF is more dispersed.

[0082] In the above step S140, a quantitative relationship between PSF characteristic parameters and imaging quality indicators is established, including imaging quality indicators such as resolution and phase distortion. Specifically, it includes:

[0083] According to sub-step (1) of step S130 , the relationship between the frequency spectrum of the point spread function and the imaging resolution is determined: the higher the PSF frequency band, the higher the imaging resolution;

[0084] According to sub-step (2) of step S130, the relationship between the morphological characteristics of the point spread function and the imaging phase is determined: the PSF is unevenly distributed in the KK spectrum and there is an illumination gap;

[0085] According to sub-step (3) of step S130 , the relationship between the amplitude characteristics of the point spread function and the focus of the image is determined: the smaller the V value of the PSF, the more focused the image.

[0086] 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, thereby improving the imaging effect of the underground structure image.

[0087] An embodiment of the present invention also provides an observation system optimization method, which is based on the above-mentioned point spread function illumination analysis method, obtains an image of the underground structure according to the existing seismic observation data of the underground target area, extracts the point spread function from the image of the underground structure, parameterizes the point spread function, extracts the characteristic parameters of the point spread function, establishes a quantitative relationship between the characteristic parameters of the point spread function and the imaging quality index of the underground structure image, and determines the optimization scheme of the observation system based on the quantitative relationship.

[0088] FIG2 is a flow chart of an observation system optimization method provided by an embodiment of the present invention. As shown in FIG2 , the observation system optimization method provided by this embodiment includes the following steps:

[0089] S210, obtaining an image of the underground structure based on existing seismic observation data of the underground target area;

[0090] S220, extracting a point spread function from an image of the underground structure;

[0091] S230, parameterizing the point spread function and extracting characteristic parameters of the point spread function;

[0092] S240, establishing a quantitative relationship between characteristic parameters of the point spread function and imaging quality indicators of the underground structure image;

[0093] S250, determining the variation pattern of characteristic parameters of the point spread function under different observation system conditions;

[0094] S260, judging the main factors limiting the imaging quality of the observation system based on the variation pattern of the characteristic parameters of the point spread function;

[0095] S270, determining observation system parameters that need to be optimized based on the main factors that limit imaging quality of the observation system, and establishing a relationship model between the observation system parameters and characteristic parameters of the point spread function;

[0096] S280, using the relationship model between observation system parameters and point spread function characteristic parameters, predicts the point spread function characteristics and imaging quality of different observation systems;

[0097] S290: Determine an optimization scheme for the observation system based on point spread function characteristics of different observation systems and prediction results of imaging quality.

[0098] In the above-mentioned step S210, the following sub-steps are included: preprocessing the collected surface seismic data and downhole vertical seismic profile (VSP) data; extracting the source wavelet of the actual field data; establishing a velocity model of the underground medium based on the preprocessed seismic data and the source wavelet; performing offset imaging according to the velocity model of the underground medium to obtain an image of the underground structure.

[0099] In the above step S220, representative scattering points are selected from the image of the underground structure as reflectivity models, and scattering point seismic records are generated by démigration using migration techniques such as Kirchhoff, one-way wave, and reverse time migration. The scattering point seismic records obtained by démigration are migrated to obtain a point spread function (PSF).

[0100] In the above step S230, the point spread function is transformed into the time domain, the spectrum of the point spread function is obtained in the time domain, and the morphological characteristics and amplitude characteristics of the point spread function of the target area are quantitatively measured. Specifically, the following sub-steps are included:

[0101] (1) Perform deep time conversion on the point spread function, transform it into the time domain, and obtain its spectrum in the time domain. The spectrum of the multi-channel finite length sequence is as follows:

[0102] Where X is the spectrum of a multi-channel finite length sequence, k is the wave number, 0≤k≤N-1, M is the number of channels, N is the number of sequence samples, m is the channel index, n is the sequence sample index, i is the imaginary unit, e is a natural constant, π is the circumference of a circle, and x is the number of channels. m (n) is the discrete sample point of the input sequence;

[0103] (2) Quantitatively measure the morphological characteristics of the point spread function of the target area, such as the main lobe width, side lobe intensity, and directionality in the KK spectrum;

[0104] (3) Based on the two-dimensional M*N point spread function PSF, its energy is defined as:

[0105] Where E is the energy of the point spread function, M is the number of channels, N is the number of sequence samples, i is the channel index, j is the sequence sample index, and x(i,j) is the discrete sample of the point spread function.

[0106] Assuming that the energy is constant, the energy distribution measure of PSF is defined as:

[0107] Where V is the energy distribution measure of the point spread function, M is the number of channels, N is the number of sequence sample points, i is the channel index, j is the sequence sample point index, i0, j0 are the coordinates of the center point of the point spread function, and x(i, j) is the discrete sample point of the point spread function. A smaller V value indicates that the energy is concentrated in the central area of ​​the PSF, and a larger V value indicates that the energy of the PSF is more dispersed.

[0108] In the above step S240, a quantitative relationship between PSF characteristic parameters and imaging quality indicators is established, including imaging quality indicators such as resolution and phase distortion. Specifically, it includes:

[0109] According to sub-step (1) of step S230 , the relationship between the frequency spectrum of the point spread function and the imaging resolution is determined: the higher the PSF frequency band, the higher the imaging resolution;

[0110] According to sub-step (2) of step S230, the relationship between the morphological characteristics of the point spread function and the imaging phase is determined: the PSF is unevenly distributed in the KK spectrum and there is an illumination gap;

[0111] According to sub-step (3) of step S230 , the relationship between the amplitude characteristic of the point spread function and the focus of the image is determined: the smaller the V value of the PSF, the more focused the image.

[0112] In step S270, the observation system parameters that need to be optimized include one or more of the following: source spacing, receiver spacing, shot line spacing, receiver line spacing, maximum offset, aspect ratio, and coverage times. These parameters can be used to determine the effective excitation and reception ranges.

[0113] In step S290, the relationship model between the observation system parameters and the characteristic parameters of the point spread function 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, the analysis of steps S220-S270 is repeated to predict the improvement of imaging quality; steps S220-S290 are iterated to perform optimization until satisfactory imaging results are achieved.

[0114] This method uses a point spread function to quantitatively analyze the illumination of the underground target area, including its uniformity and observation angle flaws, to improve imaging quality. Furthermore, numerical simulations are used to iteratively demonstrate the optimization effect of the observation system, providing more precise guidance for optimizing the observation system and reducing exploration risks and data acquisition costs. This method has improved adaptability to field data observation designs, is low-cost, and highly efficient, making it suitable for secondary exploration of deep and ultra-deep layers.

[0115] The following describes the process steps of the present invention for specific application scenarios:

[0116] 1) Design an observation system for the public model SIGSBEE2A, perform forward modeling of seismic data, and perform necessary data preprocessing on the generated seismic data;

[0117] 2) Seismic imaging of the model is obtained by prestack depth migration. In a specific application example, the PSF at position a is shown in the left figure of Figure 3a, and the corresponding KK spectrum at position a is shown in the right figure of Figure 3a; the PSF at position c is shown in the left figure of Figure 3b, and the corresponding KK spectrum at position c is shown in the right figure of Figure 3b; the local images of the areas corresponding to the PSFs at positions a and c are shown in Figure 4; the PSF before optimization of the observation system is shown in the left figure of Figure 5, and the PSF after optimization of the observation system is shown in the right figure of Figure 5; the image corresponding to the observation system before optimization in Figure 5 is shown in the left figure of Figure 6, and the image corresponding to the observation system after optimization in Figure 5 is shown in the right figure of Figure 6;

[0118] 3) Design a scattering point model based on the model and use the same observation system to generate data for the scattering point model through de-migration;

[0119] 4) The data from step 3 is migrated to obtain a point spread function, as shown in FIG7 ; the point spread function obtained by migrating the demigrated data after encrypting the shot points once is shown in FIG8 ; the point spread function obtained by migrating the demigrated data after encrypting the shot points twice is shown in FIG9 ;

[0120] 5) Conduct quantitative analysis of the point spread function, select a specific point spread function for the target area, and calculate parameters such as morphological characteristics, amplitude characteristics, directional characteristics, and energy distribution;

[0121] 6) Based on the PSF directional characteristics and poor focusing, additional shot points are added to the left side of the model and the distribution of detection points is increased, as shown in Figures 10, 11, and 12. In the figures, purple-red represents a larger value, indicating a worse focusing degree.

[0122] 7) Based on the optimized observation system, re-forward model the seismic data, repeat steps 1 to 4, and quantitatively evaluate the results again;

[0123] 8) Based on the evaluation results, if the imaging requirements are still not met, further optimize the observation system and repeat steps 1 to 7 until the image meets the requirements or the point spread function of the target area no longer improves with the optimization of the observation system, as shown in Figures 13, 14, and 15;

[0124] 9) If the point spread function does not improve after optimization, it means that the ground seismic observation system cannot accurately image the target area due to geological structural factors, and other observation methods such as VSP need to be introduced.

[0125] An embodiment of the present invention further provides an observation system, which is optimized using the above-mentioned observation system optimization method.

[0126] An embodiment of the present invention further provides a computer device, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the above-mentioned point spread function-based lighting analysis method or the above-mentioned observation system optimization method.

[0127] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0128] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0129] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0131] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A lighting analysis method based on the point spread function, characterized in that Comprising: Obtaining an image of the underground structure based on existing seismic observation data of 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 of 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 scattering point seismic records using the reverse migration method; Performing migration on the scattering point seismic records 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 spectral representation of the multi-channel finite-length sequence of the point spread function is as follows: where X is the spectrum of a multi-trace finite-length sequence, k is the wavenumber with 0 ≤ k ≤ N - 1, M is the number of traces, N is the number of sequence samples, m is the trace index, n is the sequence sample index, i is the imaginary unit, e is the natural constant, π is the pi, and x m (n) are the discrete samples of the input sequence.

6. The illumination analysis method based on the point spread function according to claim 5, characterized in that, 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, the energy of this point spread function is defined as: Where E is the energy of the point spread function, M is the number of channels, N is the number of sequence sample points, i is the channel index, j is the sequence sample point index, and x(i,j) is the discrete sample point of the point spread function; Define the energy distribution measure of the point spread function as follows: Where V is the measure of the energy distribution of the point spread function, M is the number of channels, N is the number of sequence sample points, i is the channel index, j is the sequence sample point index, i0,j0 are the coordinates of the center point of the point spread function, x(i,j) is the discrete sample 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 the 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 of 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 method for optimizing an observation system 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; Use 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 method for optimizing an observation system according to claim 9, wherein The determining 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 according to any one of claims 8-11.

13. A computer device, characterized in that, It includes: A memory storing a computer program; A processor for executing the computer program to implement the illumination analysis method based on the point spread function according to any one of claims 1-7, or the observation system optimization method according to any one of claims 8-11.

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