Method for evaluating pre-stack migration noise suppression capability of seismological observation system

Abstract

The invention relates to the technical field of seismic exploration, in particular to a pre-stack migration noise suppression capability evaluation method of a seismological observation system. The pre-stack offset noise suppression capability evaluation method for the seismological observation system comprises the following steps: firstly, setting a target point position according to geological target parameters and arranging an observation system to be evaluated; secondly, acquiring pre-stack time migration response data of the to-be-evaluated observation system under two conditions of noise addition and no noise addition; then, carrying out normalization processing on the pre-stack time migration response data; finally, dividing a noise analysis area according to the profile map, and counting the noise root mean square amplitude in the noise analysis area; the smaller the noise root-mean-square amplitude is, the better the noise suppression capability is. According to the method, the pre-stack time migration response under the condition that noise is added or not is considered at the same time, the capability of the observation system in suppressing migration noise and exogenous noise can be evaluated, and the noise suppression capability of the observation system is evaluated more comprehensively.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of seismic exploration technology, and particularly relates to a pre-stack migration noise suppression capability evaluation method of a seismic observation system. BACKGROUND

[0002] When designing a seismic observation system, two problems should be solved: one is to facilitate seismic data processing, that is, to achieve a certain resolution and amplitude fidelity; the other is to suppress various related noises, that is, to improve the noise suppression capability of the observation system. The noise suppression capability of the observation system refers to the ability of the observation system to suppress or reduce noise interference when receiving and processing seismic signals. Generally, shot density (or fold) is the main factor determining the signal-to-noise ratio of seismic imaging, and at the same time, the shot density is also the main factor determining the cost of seismic acquisition when the excitation and receiving modes are the same. Under the condition of having the same shot density, different observation systems have certain differences in suppressing noise, and some observation systems may result in a low signal-to-noise ratio of the final seismic profile, thereby affecting the ability of the seismic data to depict geological bodies, because: on the one hand, the non-uniformity of the spatial sampling of the observation system will lead to migration noise; on the other hand, the observation system also has differences in the suppression capability of the noise existing in the seismic data in the data processing.

[0003] The noise suppression ability is one of the important indicators to evaluate the performance of the observation system. The existing methods to evaluate the noise suppression ability of the observation system are often based on the CMP stacking theory. In the paper "Research on noise suppression of observation system in complex southern mountain front zone" by Peng D P et al. in 2015, through the study on the stacking response of the observation system, it is concluded that reducing the receiver line distance and the shot line distance can effectively reduce the noise leakage, so as to achieve the purpose of noise suppression by using the observation system. In the paper "Estimation and application of noise suppression ability of 3D seismic acquisition observation system" by Xia J J et al. in 2009, aiming at the near-surface scattering interference, starting from the establishment of the noise time-distance equation, the noise residual time difference is calculated, and the ability of the 3D seismic observation system to suppress the scattering interference in the CMP stacking is analyzed. The Chinese invention patent with the publication date of October 7, 2009 and the publication number of CN101551463A discloses a method for estimating the noise suppression of a 3D observation system, which calculates the CMP attribute information by designing various 3D seismic exploration observation system schemes, calculates the residual time difference between the effective wave and the noise, and calculates the stacking amplitude characteristics of the noise according to the residual time difference. In the paper "Analysis of the noise suppression effect of observation system by using numerical simulation method" by Chen G et al. in 2011, a horizontal layered medium is used as the geological model for numerical simulation, three observation systems are simulated, the same amplitude random interference wave and the same amplitude linear interference wave with the same velocity are added, the seismic data is generated, then the same processing flow and similar processing parameters are used to process the seismic data generated by the three different observation systems to obtain the stacking data body, for the stacking data bodies generated by the three observation systems, the same time slice position and profile position are selected to compare the differences of the three, and the observation system is optimized by comparison and analysis. The above evaluation of the noise suppression ability of the observation system is often based on the CMP stacking theory.

[0004] Currently, pre-stack migration has replaced CMP stacking and become the main imaging method of seismic data processing. With the popularization and application of pre-stack migration, it is necessary to evaluate the noise suppression ability of the pre-stack migration of the observation system from the perspective of the effect of pre-stack migration, which plays an important role and significance in the design and optimization of the observation system. Pre-stack migration is a process of seismic reflection energy homing and stacking, which has strong noise suppression ability. In addition, due to insufficient and uneven spatial sampling of the observation system, the migration imaging algorithm itself will also generate residual energy in the non-imaging area, which is called "migration noise". The migration noise can be reduced by continuous optimization of the observation system. Therefore, a good observation system should have strong noise suppression ability in seismic data and weak "migration noise". In 2013, Li Weibo's paper "Analysis of the influence of the observation system on the migration amplitude and migration noise" evaluated the observation system from the perspective of pre-stack migration noise analysis. Forward simulation was carried out for the horizontal layer model, then the pre-stack data of the simulation was integrated for pre-stack migration, and the migration noise was analyzed based on the migration data volume, and the migration effect of different observation systems was evaluated. However, the method uses layered medium model forward simulation and pre-stack migration processing to analyze migration noise, and the analysis process is relatively complex. At the same time, the current noise suppression ability evaluation method of the observation system is only for the evaluation of migration noise or single noise suppression ability of the data, and the evaluation is not comprehensive. SUMMARY

[0005] The purpose of the present application is to provide a pre-stack migration noise suppression ability evaluation method of a seismic observation system, which solves the problem that the prior art cannot evaluate the ability of the observation system to suppress migration noise and external noise at the same time.

[0006] To achieve the above purpose, the pre-stack migration noise suppression ability evaluation method of the seismic observation system provided by the present application adopts the technical scheme of:

[0007] A pre-stack migration noise suppression ability evaluation method of a seismic observation system, comprising the following steps:

[0008] (1) setting the target point position according to the geological target parameters, and laying out the observation system to be evaluated;

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

[0010] (3) normalizing the pre-stack time migration response data;

[0011] (4) dividing the noise analysis area on the pre-stack time migration response data profile, and calculating the root mean square amplitude of the noise in the noise analysis area;

[0012] (5) obtain the noise root mean square amplitude of different observation systems according to steps (1)-(4), and the smaller the noise root mean square amplitude is, the better the noise suppression ability is.

[0013] The present application is an improved application, and the present application considers two cases of adding noise and not adding noise to obtain pre-stack time migration response. Through statistics of the normalized pre-stack time migration response data in the noise analysis area on the pre-stack time migration response data profile, the ability of the observation system to suppress migration noise and external noise in the data can be evaluated, and the evaluation of the noise suppression ability of the observation system is more comprehensive. Meanwhile, the present application provides a self-improving, simple and feasible method for evaluating the noise suppression ability of the observation system by using the pre-stack time migration response analysis method, which can be realized without external processing flow. By comparing the noise suppression ability of different observation systems, the method can provide an effective basis for the evaluation and optimization of the observation system.

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

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

[0016] In order to facilitate the reduction of the boundary effect of the migration response analysis, preferably, in step (1), the shot points and the geophones of the to-be-evaluated observation system are distributed and arranged with the target point projection position on the ground as the center.

[0017] In order to more accurately simulate the real seismic scene and improve the accuracy of the pre-stack migration noise suppression ability evaluation of the seismic observation system, preferably, in step (2), the added noise is random noise and linear noise, and the linear noise includes linear noise simulating surface wave and linear noise simulating shallow refraction wave.

[0018] In order to simply and effectively statistically analyze the normalized pre-stack time migration response data in 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 areas of the "x" shaped energy distribution area.

[0019] In order to obtain the normalized pre-stack time migration response data, preferably, in step (3), the normalization processing 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.

[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] Number Observation system name Shotpoint / line spacing (m) Shotpoint / line spacing (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 target point location based on 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 within 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.

2. 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 (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°.

3. The method for evaluating the pre-stack migration noise reduction capability of a seismic observation system according to claim 2, characterized in that, 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 for evaluating the pre-stack migration noise reduction capability of a seismic observation system according to claim 1, characterized in that, 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 for evaluating the pre-stack migration noise reduction capability of a seismic observation system according to claim 1, characterized in that, 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 for evaluating the pre-stack migration noise reduction capability of a seismic observation system according to claim 1 or 4, characterized in that, 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 for evaluating the pre-stack migration noise reduction capability of a seismic observation system according to claim 7, characterized in that, In step (4), the method for obtaining the root mean square amplitude of the noise is shown in Equation 1: In the formula, A rms The root mean square amplitude of the noise is N, where N is the number of samples in the noise analysis region, and a i This represents the value of the i-th sample point in the noise analysis area.

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