A method for evaluating pre-stack migration de-noising capability of a seismic observation system

Abstract

This invention relates to the field of seismic exploration technology, and particularly to a method for evaluating the pre-stack migration noise suppression capability of a seismic observation system. The method includes the following steps: First, the target point location is set according to geological target parameters, and the observation system to be evaluated is deployed; second, pre-stack time migration response data of the observation system to be evaluated is obtained under two conditions: with and without added noise; next, the pre-stack time migration response data is normalized; finally, a noise analysis area is divided according to the profile diagram, and the root mean square amplitude of noise within the noise analysis area is statistically analyzed; the smaller the root mean square amplitude of noise, the better the noise suppression capability. This invention considers the pre-stack time migration response under both with and without added noise, and can evaluate the observation system's ability to suppress both migration noise and external noise, providing a more comprehensive evaluation of the observation system's noise suppression capability.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology, and in particular to a method for evaluating the pre-stack migration noise reduction capability of a seismic observation system. Background Technology

[0002] When designing a seismic observation system, two main issues should be addressed: first, facilitating seismic data processing, i.e., achieving a certain resolution and amplitude fidelity; and second, suppressing various related noises, i.e., improving the noise suppression capability of the observation system. The noise suppression capability of an observation system refers to its ability to suppress or reduce noise interference when receiving and processing seismic signals. Generally, shot density (or coverage number) is the main factor determining the signal-to-noise ratio (SNR) of seismic imaging. Furthermore, with the same excitation and reception methods, shot density is also a major factor determining the cost of seismic acquisition. Even with the same shot density, different observation systems exhibit varying noise suppression capabilities. Some systems may result in a lower SNR in the final seismic profile, thus affecting the seismic data's ability to characterize geological bodies. This is because: firstly, the non-uniformity of spatial sampling by the observation system leads to migration noise; secondly, the observation systems differ in their ability to suppress noise present in the seismic data during data processing.

[0003] Noise suppression capability is one of the important indicators for evaluating the performance of observation systems. Existing methods for evaluating the noise suppression capability of observation systems are often based on CMP superposition theory. In the 2015 paper "Noise Suppression Research of Observation Systems in Complex Piedmont Zones of Southern China" by Peng Daiping et al., through research on the superposition response of observation systems, it was concluded that reducing the receiver line spacing and shot line spacing can effectively reduce noise leakage, thereby achieving the purpose of noise suppression using the observation system. In the 2009 paper "Estimation and Application of Noise Suppression Capability of 3D Seismic Acquisition Observation Systems" by Xia Jianjun et al., targeting near-surface scattering interference, starting from establishing a noise time-distance equation, the ability of 3D seismic observation systems to suppress scattering interference during CMP superposition was analyzed by calculating the residual time difference of noise. Chinese invention patent CN101551463A, published on October 7, 2009, discloses a method for estimating noise suppression in 3D observation systems. This method designs various 3D seismic exploration observation system schemes, calculates CMP attribute information, calculates the residual time difference between the effective wave and noise, and calculates the superposition amplitude characteristics of the noise based on the residual time difference. In their 2011 paper, "Analysis of Noise Suppression Effect of Observation Systems Using Numerical Simulation Methods," Chen Gang et al. used a horizontally layered medium as the geological model for numerical simulation. They simulated three observation systems, adding random interference waves of the same amplitude and linear interference waves of the same amplitude and velocity to generate seismic data. Then, using the same processing flow and similar processing parameters, they processed the seismic data generated by the three different observation systems to obtain stacked data volumes. For the stacked data volumes generated by the three observation systems, they compared the differences by selecting the same time slice and profile locations, and optimized the observation system through comparative analysis. The aforementioned evaluation of the noise suppression capability of observation systems is often based on CMP stacking theory.

[0004] Currently, pre-stack migration has replaced CMP stacking as the primary imaging method for seismic data processing. With the widespread application of pre-stack migration, it is necessary to evaluate the noise suppression capability of observation systems from the perspective of pre-stack migration effectiveness. This is crucial for the design and optimization of observation systems. Pre-stack migration is a process of seismic reflection energy realignment and superposition, and this processing step itself has a strong noise suppression capability. Furthermore, due to insufficient and non-uniform spatial sampling by the observation system, the migration imaging algorithm itself will also generate residual energy in non-imaging areas, known as "migration noise." Migration noise can be reduced through continuous optimization of the observation system. Therefore, a good observation system should have a strong ability to suppress noise in seismic data while exhibiting weak "migration noise." In 2013, Li Weibao's paper, "Analysis of the Influence of Observation Systems on Migration Amplitude and Migration Noise," evaluated observation systems from the perspective of pre-stack migration noise analysis. Forward simulation was conducted on a horizontal layer model, and then integral pre-stack migration was performed on the simulated pre-stack data. Based on the migration data volume analysis, migration noise was evaluated, and the migration effectiveness of different observation systems was assessed. However, this method employs forward modeling of layered media and pre-stack migration processing to conduct migration noise analysis, making the analysis process quite complex. Furthermore, current methods for evaluating the noise suppression capabilities of observation systems only assess the ability to suppress migration noise or a single type of noise within the data, resulting in an insufficiently comprehensive evaluation. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the pre-stack migration noise suppression capability of a seismic observation system, which solves the problem that existing technologies cannot simultaneously evaluate the ability of an observation system to suppress migration noise and external noise.

[0006] To achieve the above objectives, the technical solution adopted by the pre-stack migration noise reduction capability evaluation method for seismic observation systems provided by this invention is as follows:

[0007] A method for evaluating the pre-stack migration noise reduction capability of a seismic observation system includes the following steps:

[0008] (1) Set the target point location based on the geological target parameters and deploy the observation system to be evaluated;

[0009] (2) Obtain the pre-stack time migration response data of the observation system to be evaluated in two cases: with and without added noise.

[0010] (3) Normalize the pre-stack time offset response data;

[0011] (4) Divide the noise analysis area on the pre-stack time migration response data profile and count the root mean square amplitude of noise within the noise analysis area.

[0012] (5) Obtain the root mean square amplitude of noise for different observation systems according to steps (1)-(4). The smaller the root mean square amplitude of noise, the better the noise suppression capability.

[0013] This invention is an improved version that considers both scenarios with and without added noise when calculating the pre-stack time migration response. By statistically analyzing the normalized pre-stack time migration response data within the noise analysis region on the pre-stack time migration response data profile, it can evaluate the observation system's ability to suppress both migration noise and external noise in the data, providing a more comprehensive evaluation of the observation system's pre-stack migration noise suppression capability. Furthermore, this invention utilizes a pre-stack time migration response analysis method to provide a self-improving, simple, and feasible method for evaluating the pre-stack migration noise suppression capability of observation systems, which requires no external processing. Using this method, by comparing the pre-stack migration noise suppression capabilities of different observation systems, can provide a valid basis for evaluating and selecting the best observation system.

[0014] In order to ensure that the integrity rate of the diffraction integral energy of the target point reaches more than 85%, preferably, in step (1), the observation system to be evaluated is set up with the target point projection on the ground as the center and the maximum angle of the shot illumination is 30° to 60°.

[0015] In order to ensure that the integrity rate of the diffraction integral energy of the target point reaches more than 95% and control the cost, it is further preferred that, in step (1), the observation system to be evaluated is set up with the target point projection on the ground as the center and the maximum angle of the shot illumination is 45°.

[0016] In order to reduce the boundary effects of migration response analysis, preferably, in step (1), the shot points and receiver points of the observation system to be evaluated are set with the target point projected on the ground as the center.

[0017] In order to more accurately simulate real earthquake scenarios and improve the accuracy of the evaluation of the pre-stack migration noise suppression capability of the earthquake observation system, preferably, in step (2), the noise added is random noise and linear noise, wherein the linear noise includes linear noise for simulating surface waves and linear noise for simulating shallow refracted waves.

[0018] In order to simply and effectively analyze the normalized pre-stack time migration response data within the noise analysis area, preferably, in step (4), the pre-stack time migration response data profile has an "x"-shaped energy distribution area, and the noise analysis area includes the left and right sides of the "x"-shaped energy distribution area.

[0019] In order to obtain normalized pre-stack time migration response data, preferably, in step (3), the normalization process includes the following steps: dividing the value of each sample point of the pre-stack time migration response data by the maximum value of the absolute value of the pre-stack time migration response data to obtain normalized pre-stack time migration response data.

[0020] In order to obtain the pre-stack time migration response data of the observation system to be evaluated simply and effectively, and to control the cost, preferably, in step (2), the pre-stack time migration response data of the observation system to be evaluated is the pre-stack time migration response data in the Inline and Crossline directions.

[0021] To obtain an evaluation of the pre-stack migration noise suppression capability of the seismic observation system, preferably, in step (4), the method for calculating the root mean square amplitude of the noise is as shown in Equation 1:

[0022]

[0023] In the formula, A rms Let N be the root mean square amplitude of the noise, and N be the number of samples in the noise analysis area. i This represents the value of the i-th sample point in the noise analysis area. Attached Figure Description

[0024] Figure 1 This is a flowchart of the pre-stack migration noise reduction capability evaluation method for the seismic observation system according to Embodiment 4 of the present invention;

[0025] Figure 2 This is a map showing the locations of shot points and receiver points in the pre-stack migration noise reduction capability evaluation method of the seismic observation system in Embodiment 4 of the present invention.

[0026] Figure 3 This refers to the pre-stack migration noise reduction capability evaluation method of the seismic observation system in Embodiment 4 of the present invention, which includes the forward modeling single-shot records of observation system 1 with / without added noise.

[0027] Figure 4 This is a cross-sectional view of the pre-stack time migration response data in the Inline and Crossline directions of observation system 1 without added noise in the pre-stack migration noise reduction capability evaluation method of the seismic observation system in Embodiment 4 of the present invention.

[0028] Figure 5 This is a cross-sectional view of the pre-stack migration noise reduction capability evaluation method of the seismic observation system in Embodiment 4 of the present invention, showing the pre-stack time migration response data of observation system 1 with added noise in the Inline and Crossline directions.

[0029] Figure 6 This is a schematic diagram showing the division of noise analysis regions on the pre-stack migration noise reduction capability profile of observation system 1 in the inline and crossline directions without added noise in the pre-stack migration response data of the seismic observation system in Embodiment 4 of the present invention.

[0030] Figure 7This is a set of root mean square amplitude distribution diagrams of pre-stack migration response noise for a set of observation systems to be evaluated under two conditions: with and without added noise, according to Embodiment 4 of the present invention. Detailed Implementation

[0031] The features and performance of the present invention will be described in further detail below.

[0032] As cited in the background section, existing technologies cannot simultaneously evaluate the ability of an observation system to suppress migration noise and external noise in the data. Therefore, this invention proposes a method for evaluating the pre-stack migration noise suppression capability of a seismic observation system, comprising the following steps:

[0033] (1) Set the target point location according to the geological target parameters, and set up the observation system to be evaluated according to the requirement of full imaging of the target point;

[0034] (2) The pre-stack time migration response data of the observation system to be evaluated with / without added noise were obtained by forward modeling and pre-stack migration using Kirchhoff integral method;

[0035] (3) Normalize the pre-stack time offset response data;

[0036] (4) Divide the noise analysis area on the pre-stack time migration response data profile and statistically analyze the normalized pre-stack time migration response data within the noise analysis area to obtain the root mean square amplitude of the noise.

[0037] (5) Obtain the root mean square amplitude of noise for different observation systems according to steps (1)-(4). The smaller the root mean square amplitude of noise, the better the noise suppression capability.

[0038] The technical concept of this invention is as follows: First, target points are set using geological target parameters. Then, the observation system to be evaluated is deployed according to the requirements for full imaging of the target points to assess the pre-stack migration noise reduction capability of the observation system. Next, Kirchhoff integral forward modeling and pre-stack migration are used to obtain pre-stack time migration response data of the observation system under two conditions: with added noise and without added noise. The pre-stack time migration response data of the observation system to be evaluated is normalized and applied to the acquisition of the noise root mean square amplitude. A profile of the pre-stack time migration response data is used to delineate the noise analysis area. Finally, the normalized pre-stack time migration response data within the noise analysis area is statistically analyzed to obtain the noise root mean square amplitude, thereby evaluating the pre-stack migration noise reduction capability of the seismic observation system. Through the above steps, the noise root mean square amplitude of different observation systems is obtained; the smaller the noise root mean square amplitude, the better the noise reduction capability.

[0039] This invention considers pre-stack time migration responses with and without added noise, enabling the evaluation of an observation system's ability to suppress both migration noise and external noise in the data. This provides a more comprehensive assessment of the observation system's pre-stack migration noise suppression capability. Furthermore, this invention utilizes a pre-stack time migration response analysis method to provide a self-contained, simple, and feasible method for evaluating the pre-stack migration noise suppression capability of observation systems, requiring no external processing. By comparing the pre-stack migration noise suppression capabilities of different observation systems, this method can provide a valid basis for evaluating and selecting the best observation system.

[0040] Specific embodiment 1 of the pre-stack migration noise reduction capability evaluation method of the seismic observation system of the present invention:

[0041] In this embodiment, firstly, the target point locations are set according to the geological target parameters, and the observation system to be evaluated is deployed according to the requirement of sufficient imaging of the target points. Secondly, under both cases with and without added noise, the pre-stack time migration response data and its profile of the observation system to be evaluated under the conditions of added / without added noise are obtained by forward modeling using Kirchhoff integral method and pre-stack migration. Next, the pre-stack time migration response data is normalized, that is, the maximum value of the absolute value of the pre-stack time migration response data is obtained, and the value of each homogeneous point of the pre-stack time migration response data is divided by the maximum value to obtain the normalized pre-stack time migration response data. Finally, a noise analysis area is divided on the profile of the pre-stack time migration response data of the observation system to be evaluated, and the root mean square amplitude of the noise is obtained by statistically analyzing the normalized pre-stack time migration response data within the noise analysis area.

[0042] The method for determining the root mean square amplitude of the noise is shown in Equation 1:

[0043]

[0044] In the formula, A rms Let N be the root mean square amplitude of the noise, and N be the number of samples in the noise analysis area. i This represents the value of the i-th sample point in the noise analysis area.

[0045] In this embodiment, the observation system to be evaluated is set up with the target point projected on the ground as the center and the maximum illumination angle of the shot point is 45°, so as to ensure that the integrity rate of the diffraction integral energy of the target point is more than 95%; the shot point and receiver point of the observation system to be evaluated are distributed with the target point projected on the ground as the center, which helps to reduce the boundary effect of migration response analysis.

[0046] In other embodiments, the observation system to be evaluated can be set up with the target point projected on the ground as the center and the maximum angle of the shot illumination can be 30° or 60° to ensure that the integrity rate of the diffraction integral energy of the target point reaches more than 85%.

[0047] Example 2 of the pre-stack migration noise reduction capability evaluation method for the seismic observation system of the present invention:

[0048] Based on the above-described technical concept of the present invention, or based on the specific embodiments of the present invention described above, another embodiment is provided below.

[0049] In this embodiment, considering the scenario of deep, weak reflection, when noise is added, the added noise consists of random noise and linear noise. The linear noise includes linear noise simulating surface waves and linear noise simulating shallow refracted waves. One set consists of linear noise simulating surface waves with velocities of 200–470 m / s, and the other set consists of linear noise simulating shallow refracted waves with velocities of 500–2000 m / s. The dominant frequency of both sets of linear noise is 10 Hz. By simulating more realistic seismic scenarios, the accuracy of the pre-stack migration noise suppression capability evaluation of the seismic observation system is improved.

[0050] Specific embodiment 3 of the pre-stack migration noise reduction capability evaluation method of the seismic observation system of the present invention:

[0051] Based on the above-described technical concept of the present invention, or based on the specific embodiments of the present invention described above, another embodiment is provided below.

[0052] In this embodiment, the pre-stack time migration response data profile of the observation system to be evaluated has an "x"-shaped energy distribution area, and the noise analysis area includes the left and right sides of the "x"-shaped energy distribution area. When dividing the noise analysis area without added noise, the noise analysis area is the left and right sides of the "x"-shaped energy distribution area on the pre-stack time migration response data profile of the observation system to be evaluated; when dividing the noise analysis area with added noise, the noise analysis area is also the left and right sides of the "x"-shaped energy distribution area on the pre-stack time migration response data profile of the observation system to be evaluated. This allows for simple and effective statistical analysis of the normalized pre-stack time migration response data within the noise analysis area.

[0053] Specific embodiment 4 of the pre-stack migration noise reduction capability evaluation method of the seismic observation system of the present invention:

[0054] Based on the above-described technical concept of the present invention, or based on the specific embodiments of the present invention described above, another embodiment is provided below.

[0055] The pre-stack time migration response data of the observation system to be evaluated can be three-dimensional pre-stack time migration response data in the time direction, inline, and crossline directions. However, performing three-dimensional pre-stack time migration response analysis is costly and inefficient. Therefore, in this embodiment, the pre-stack time migration response data of the observation system to be evaluated is preferably two-dimensional pre-stack time migration response data in the inline and crossline directions. This allows for simple and effective pre-stack time migration response analysis at low cost, thus obtaining the pre-stack time migration response data of the observation system to be evaluated.

[0056] In this embodiment, the pre-stack migration noise reduction capability evaluation method of the seismic observation system of the present invention is used to evaluate the noise reduction capability of a group of observation systems, such as... Figure 1 As shown, firstly, the basic geological and geophysical parameters of the work area and a set of observation systems were obtained. The basic geological and geophysical parameters included: geological target depth of 3000m, overlying stratum velocity of 2800m / s, wavelet dominant frequency of 30Hz, time sampling interval of 4ms, and recording length of 5s. A set of observation system schemes to be evaluated is shown in Table 1.

[0057] Table 1. A set of observation system schemes to be evaluated

[0058] serial number Observation system name Detector point / line spacing (m) Shot point / line distance (m) 1 20L4S120T 50 / 200 50 / 200 2 20L8S120T 50 / 200 25 / 400 3 10L16S240T 25 / 400 25 / 400 4 10S8S240T 25 / 400 50 / 200

[0059] Table 1 lists a group of observation systems to be evaluated that are arranged with similar length and width and have the same channel density of 240,000 channels / km. 2 The four 3D seismic observation system schemes differ primarily in their shot distance, receiver distance, shot line distance, receiver line distance, number of receiver lines, and number of shot lines. These differences lead to different layouts of the observation systems, as follows: Observation System 1 has relatively large receiver and shot distances of 50m, while receiver and shot line distances are relatively small of 200m; Observation System 2 has the same receiver and receiver line distances as Observation System 1, but reduces the shot distance to 25m and increases the shot line distance to 400m; Observation System 3, compared to Observation System 1, reduces both receiver and shot distances to 25m, while increasing both receiver and shot line distances to 400m; Observation System 4 has the same shot and shot line distances as Observation System 1, but reduces the receiver distance to 25m and increases the receiver line distance to 400m. All four observation system schemes are orthogonal bundled observation systems with central firing and single-line rolling. Other parameters of the observation system can be further obtained from the name of the observation system. For example, the name of observation system 1, "20L4S120T", indicates that there are 24 receiving lines, 120 channels per line, and 4 firings per template.

[0060] Observation system 1 was selected to evaluate the pre-stack migration noise reduction capability. The detector point distance and shot point distance of observation system 1 were both 50m, and the detector line distance and shot line distance were both 200m.

[0061] Secondly, the target point location is set according to the geological target parameters. The target point depth is 3000m as given above, and the planar position is the theoretical coordinate. In this example, the x and y values ​​of the theoretical coordinate are both 5000m. This is to provide a reference position for the target point, and its assignment does not affect the layout and evaluation of the observation system. The observation system to be evaluated is set up with a maximum shot illumination angle of 45°. Figure 2 The image shows the locations of the shot points and receiver points laid out according to the observation system 1 to be evaluated, as follows: Figure 2 As shown, the shot points and receiver points of the observation system to be evaluated are distributed with the geological target's projection location on the ground as the center.

[0062] Next, in both cases with and without added noise, the Kirchhoff integral method was used for forward modeling and pre-stack migration to obtain the pre-stack time migration response data in the inline and crossline directions.

[0063] Referring to the Chinese invention patent application published on November 12, 2014, with publication number CN104142518A, the Kirchhoff integral method is used for forward modeling and pre-stack migration to obtain pre-stack time migration response data in the Inline and Crossline directions with and without added noise.

[0064] This embodiment considers a scenario with deep weak reflection. With added noise, random noise and linear noise are added to the forward modeling single-shot data at a ratio 10 times larger than the effective diffraction wave amplitude, i.e., the data signal-to-noise ratio is about 0.1. The linear noise includes linear noise of simulated surface waves and linear noise of simulated shallow refracted waves. There is one set of linear noise of simulated surface waves with a velocity of 200-470 m / s and one set of linear noise of simulated shallow refracted waves with a velocity of 500-2000 m / s. The dominant frequency of both sets of linear noise is 10 Hz.

[0065] Figure 3 The figure shows forward modeling single-shot records with and without added noise, obtained from observation system 1. The left image shows the forward modeling single-shot record without added noise, where the effective diffraction waveform is clear and the background is clean. The middle image shows the forward modeling single-shot record with random and linear noise added to the data at a ratio 10 times larger than the effective diffraction wave amplitude. In this case, the signal-to-noise ratio (SNR) is approximately 0.1, and the effective diffraction wave is not visible, only messy random and linear noise. The right image shows the forward modeling single-shot record with noise added proportionally to the effective diffraction wave amplitude, illustrating the presence of noise in the data. In this case, the SNR is approximately 1. As seen in the right image, when noise is added proportionally to the effective diffraction wave amplitude, the effective diffraction wave phase axis and the linear noise phase axis are faintly visible, while the background of the single-shot record is quite messy.

[0066] Appendix Figure 4The image shows a profile of the pre-stack time migration response data in the Inline and Crossline directions obtained by observation system 1 without added noise. The image of the geological target point in the middle of the profile shows a strong energy distribution. The profile has an "x"-shaped energy distribution area, and weaker energy is distributed in the areas on the left and right sides of the "x"-shaped energy distribution area. This is the "migration noise".

[0067] Appendix Figure 5 The image shows a profile of the pre-stack time migration response data in the Inline and Crossline directions obtained by observation system 1 with added noise. The image of the geological target point in the middle of the profile shows a strong energy distribution. The profile has an "x"-shaped energy distribution area. Due to the addition of strong noise (amplitude 10 times that of the effective diffraction wave amplitude) to the pre-migration data, strong noise is distributed on both sides and the top and bottom sides of the "x"-shaped energy distribution area. That is, strong noise is distributed in other areas except for the image of the geological target point and the "x"-shaped energy distribution area. However, the image of the geological target point is still clearly visible at this time.

[0068] Then, the maximum absolute value of the pre-stack time migration response data in the Inline and Crossline directions is calculated, and the value of each point of the pre-stack time migration response data is divided by this maximum value to obtain the normalized pre-stack time migration response data.

[0069] Next, noise analysis regions were delineated on the profiles of the pre-stack time migration response data in the inline and crossline directions. The delineation of these regions was primarily based on avoiding imaging of geological target points and "X"-shaped energy distribution areas. When delineating noise analysis regions without adding noise, as shown... Figure 6 As shown, the noise analysis region is the area on both sides of the "x"-shaped energy distribution region on the profile of the pre-stack time migration response data of the observation system to be evaluated. By statistically analyzing the normalized pre-stack time migration response data within this noise analysis region, the root mean square amplitude without added noise is obtained. The method for calculating the root mean square amplitude of noise is shown in Equation 1.

[0070]

[0071] In the formula, A rms Let N be the root mean square amplitude of the noise, and N be the number of samples in the noise analysis area. i This represents the value of the i-th sample point in the noise analysis area.

[0072] When dividing the noise analysis region with added noise, the noise analysis region is also the left and right sides of the "x"-shaped energy distribution area on the profile of the pre-stack time migration response data of the observation system to be evaluated, thus obtaining the root mean square amplitude of the observation system 1 with added noise.

[0073] Then, the above steps were applied to the other observation systems to be evaluated in Table 1 to obtain a statistical table of all observation systems to be evaluated and their noise root mean square amplitude, as shown in Table 2:

[0074] Table 2. Statistics of a set of observation systems to be evaluated and their root mean square noise amplitude.

[0075]

[0076] The root mean square (RMS) amplitude of noise without added noise reflects the observation system's ability to suppress migration noise, while the RMS amplitude with added noise reflects the system's ability to suppress external noise through pre-stack migration. When evaluating pre-stack migration noise suppression capabilities, observation systems with lower RMS amplitudes in both noise-added and noise-free scenarios are preferred. Secondly, since the RMS amplitude with added noise is larger than that without added noise, it is generally used as the second evaluation metric (the difference in RMS amplitude between different observation systems is still at least an order of magnitude higher than that without added noise). The smaller this value, the better the suppression capability of external noise. Finally, when the difference in RMS amplitude with added noise between different observation systems is no more than an order of magnitude higher than that without added noise, the noise-free scenario is used as the third evaluation metric. This allows for the identification of observation systems with higher suppression capabilities for both migration noise and external noise.

[0077] As shown in Table 2, there are significant differences in the ability of different observation system schemes to suppress migration noise. The biggest difference is between observation system 1 and observation system 4, where the migration noise amplitude differs by more than double. There are also significant differences in the ability of different observation system schemes to suppress external noise added to the data. The biggest difference is between observation system 1 and observation system 3.

[0078] Finally, based on the noise root mean square amplitude statistics table in Table 2, a noise root mean square amplitude distribution map was plotted to show the variation of the observed systems under evaluation. The observed systems were then selected from weakest to strongest based on the noise root mean square amplitude. Observation system 1 showed the best noise suppression capability, while observation systems 2, 3, and 4 had comparable suppression capabilities for external noise. Furthermore, the difference in noise root mean square amplitude under the added noise condition was on the same order of magnitude as under the unadded noise condition, and the difference was relatively small (indicating minimal difference in suppression capability for external noise). Therefore, the noise root mean square amplitude under the unadded noise condition better reflects the differences in suppression capability among the different observed systems. Based on the above evaluation principles, the noise suppression capability of the four evaluated systems, ranked from best to worst, is: Observation system 1, Observation system 3, Observation system 2, and Observation system 4.

[0079] Appendix Figure 7 The figure shows the root mean square amplitude distribution of pre-stack migration response noise for a set of observation systems to be evaluated in this embodiment, with and without added noise. As can be seen from the figure, observation system 1, due to its relatively uniform sampling method, has a better effect in suppressing migration noise and external noise added to the data. Observation system 1 is preferred as the observation system scheme for the target area. The analysis of this embodiment also shows that an observation system with a relatively uniform distribution of shot points and receiver points is beneficial to suppressing migration noise and external noise in the data, which is beneficial to improving the signal-to-noise ratio of seismic results and improving the seismic exploration effect. It also has great reference value for seismic exploration in other regions.

[0080] Through the above description of specific embodiments of the pre-stack migration noise reduction capability evaluation method for seismic observation systems of the present invention, it can be seen that the method provided by the present invention first uses a geological target as the target point and deploys the observation system to be evaluated according to the requirement of full imaging of the target point to evaluate the pre-stack migration noise reduction capability of the observation system. Then, using Kirchhoff integral forward modeling and pre-stack migration, the pre-stack time migration response data of the observation system to be evaluated under two conditions, with and without added noise, are obtained respectively. The pre-stack time migration response data of the observation system to be evaluated is normalized and applied to the root mean square amplitude of noise. The pre-stack time migration response data profile is used to divide the noise analysis area. Finally, the normalized pre-stack time migration response data within the noise analysis area is statistically analyzed to obtain the root mean square amplitude of noise, thereby evaluating the pre-stack migration noise reduction capability of the seismic observation system. Through the above steps, the root mean square amplitude of noise of different observation systems is obtained. The smaller the root mean square amplitude of noise, the better the noise reduction capability. This invention considers pre-stack time migration responses with and without added noise, enabling the evaluation of an observation system's ability to suppress both migration noise and external noise in the data. This provides a more comprehensive assessment of the observation system's pre-stack migration noise suppression capability. Furthermore, this invention utilizes a pre-stack time migration response analysis method to provide a self-contained, simple, and feasible method for evaluating the pre-stack migration noise suppression capability of observation systems, requiring no external processing. By comparing the pre-stack migration noise suppression capabilities of different observation systems, this method can provide a valid basis for evaluating and selecting the best observation system.

[0081] The above is a detailed description of the embodiments, but it is not intended to limit the technical solutions of the present invention. Those skilled in the art should understand that any modifications, partial substitutions, and variations can be made to the above embodiments within the scope of the present invention, and all such modifications and variations should be covered within the scope of the present invention.

Claims

1. A method for evaluating the pre-stack migration noise reduction capability of a seismic observation system, characterized in that, Includes the following steps: (1) Set the location of the target point according to the geological target parameters and set up the observation system to be evaluated; (2) Obtain the pre-stack time migration response data of the observation system to be evaluated in two cases: with and without added noise; (3) Normalize the pre-stack time offset response data; (4) Divide the noise analysis area on the pre-stack time migration response data profile and count the root mean square amplitude of noise in the noise analysis area; (5) Obtain the root mean square amplitude of noise for different observation systems according to steps (1)-(4). The smaller the root mean square amplitude of noise, the better the noise suppression capability. The root mean square amplitude of noise without added noise reflects the observation system's ability to suppress migration noise, while the root mean square amplitude of noise with added noise reflects the observation system's ability to suppress external noise through pre-stack migration. When evaluating the pre-stack migration noise suppression capability, observation systems with low root mean square amplitudes in both noise-with and noise-free scenarios are selected. The root mean square amplitude of noise with added noise is used as the second evaluation index. When the difference in root mean square amplitude of noise with added noise between different observation systems is no more than one order of magnitude greater than that without added noise, the result without added noise is used as the third evaluation index.

2. The method of claim 1, wherein, In step (1), the observation system to be evaluated is set up with the target point projected on the ground as the center and the maximum angle of the shot illumination is 30° to 60°.

3. The method of claim 2, wherein the method further comprises: In step (1), the shot points and receiver points of the observation system to be evaluated are set up with the target point projected onto the ground as the center.

4. The method of claim 1, wherein, In step (2), the noise added is random noise and linear noise, and the linear noise includes linear noise of simulated surface waves and linear noise of simulated shallow refracted waves.

5. The method for evaluating the pre-stack migration noise reduction capability of a seismic observation system according to claim 1, characterized in that, In step (4), the pre-stack time migration response data profile has an "x"-shaped energy distribution area, and the noise analysis area includes the left and right sides of the "x"-shaped energy distribution area.

6. The method of claim 1, wherein, In step (3), the normalization process includes the following steps: dividing the value of each sample point of the pre-stack time migration response data by the maximum value of the absolute value of the pre-stack time migration response data to obtain the normalized pre-stack time migration response data.

7. The method of claim 1 or 4, wherein, In step (2), the pre-stack time migration response data of the observation system to be evaluated are the pre-stack time migration response data in the Inline and Crossline directions.

8. The method of claim 7, wherein the method further comprises: In step (4), the method for obtaining the root mean square amplitude of the noise is shown in Equation 1: Formula 1 In the formula, Here, denoted as , and N is the root mean square amplitude of the noise, where N is the number of samples in the noise analysis region. For the noise analysis area The values ​​of each sample point.

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