Method for improving matching of forward modeling data and observation data in full waveform inversion

Abstract

The invention belongs to the technical field of exploration data processing, and particularly discloses a method for improving matching performance of forward modeling data and observation data in full-waveform inversion, which is characterized in that the characteristics of the forward modeling data and the observation data are matched consistently through an establishment method of a change speed model, the influence of interference of high-amplitude direct waves in the forward modeling data is eliminated, and the matching performance of the forward modeling data and the observation data is improved. And the situation that the target function in the full-waveform inversion cannot correctly reflect the change of the speed is avoided, so that the speed model after the full-waveform inversion is closer to a real underground medium speed model. The method is suitable for improving the data matching performance in full waveform inversion.

Description

Technical Field

[0001] The invention belongs to the technical field of exploration data processing, and relates to a method for improving data matching in full waveform inversion, and specifically to a method for improving the matching between forward modeling data and observation data in full waveform inversion. Background Art

[0002] In recent years, with the continuous development of exploration technology, people have higher and higher requirements for imaging accuracy. However, due to the lack of shallow reflection information in seismic data and the low signal-to-noise ratio of underground reflection waves, it is difficult to establish a high-precision velocity model. At present, full waveform inversion (FWI) technology, one of the cutting-edge technologies in the field of velocity model establishment, compares the simulated wave field with the actual seismic data, and then iterates to match them to obtain underground medium parameters, thereby obtaining velocity updates and establishing a high-precision velocity model. Full waveform inversion technology has initially achieved production capabilities in marine data processing, but its application in land data has not yet made a breakthrough.

[0003] The core goal of full waveform inversion technology is to minimize the difference between forward modeling data and observational data. When applied to actual land data, forward modeling data often has strong amplitude direct waves, while direct waves in observational data decay quickly, resulting in poor matching of early wave characteristics between the two, thus bringing non-velocity factors into the full waveform inversion process, affecting the accuracy of full waveform inversion, and may even lead to large velocity errors.

[0004] At present, in the full waveform inversion of onshore seismic data, in order to solve the problem of feature matching differences between forward data and observation data, the traditional processing method uses the excision method or Q absorption attenuation compensation method to avoid the feature difference between the two sets of data and minimize the impact on the accuracy of the velocity model obtained by full waveform inversion.

[0005] Among them, the excision method is to remove the direct wave with inconsistent characteristics between the forward data and the observed data, and keep the part with consistent characteristics in the two data. The difficulty lies in that the direct wave is part of the early arrival wave of the earthquake, which is easy to identify at far offsets, but difficult to identify at near offsets, and overlaps with the return wave information, which cannot be effectively distinguished, which easily leads to the difficulty in determining the excision parameters. At the same time, improper excision also brings different consequences. If too much excision is made, more early arrival wave information cannot be fully utilized, and if too little excision is made, the direct wave and the return wave information are likely to overlap, resulting in incorrect inversion results.

[0006] The Q absorption attenuation compensation method also has problems. For example, the direct wave is not completely suppressed. The direct wave in the forward modeling data after Q absorption attenuation compensation processing is only suppressed to a certain extent. The characteristic difference between it and the observed data is still obvious, and the correct inversion result cannot be obtained. In addition, the Q absorption attenuation compensation processing efficiency is low, and multiple rounds of iterations are required in the full waveform inversion, which increases a lot of computing costs and makes it difficult to apply it in industry. Summary of the invention

[0007] The purpose of the present invention is to provide a method for improving the matching of forward modeling data and observation data in full waveform inversion. By changing the method for establishing the initial velocity model, the characteristics of the forward modeling data and the observation data are made consistent, the influence of the direct wave in the forward modeling data is eliminated, and the objective function constructed in the full waveform inversion is avoided from correctly reflecting the change in velocity, so that the velocity model after full waveform inversion is closer to the real underground medium velocity model, so as to achieve the purpose of applying full waveform inversion to onshore seismic data.

[0008] To achieve the above object, the present invention provides a method for improving the matching of forward modeling data and observation data in full waveform inversion, the method comprising the following steps: The surface elevation of the seismic exploration observation data is used for small-scale smoothing, and the common center point gather and common shot point data are respectively corrected to the small smooth surface of the surface elevation to obtain the corrected observation shot gather data; The initial velocity model is established using the common center point gathers, prestack time migration RMS velocity and first arrival information. Using the common center gathers and initial velocity model, Gaussian beam migration prestack depth migration processing is performed to obtain common reflection angle deviation gathers; Using the initial velocity model and the common reflection angle deviation trace gather, the optimized velocity model is obtained through multi-azimuth travel time tomographic inversion iterative processing, the negative gradient velocity is filled, and the initial full waveform inversion velocity model is established; The forward data is obtained through wavelet extraction and forward modeling, and frequency iteration is performed using full waveform inversion. According to the convergence of the objective function and the accuracy of the full waveform inversion velocity model obtained by iteration, the frequency iteration is repeated until the full waveform inversion updated velocity model is obtained.

[0009] As a limitation of the present invention, the method comprises the following steps performed in sequence: S1. Small-scale smoothing of surface elevation and correction of gathers The elevation information of the shot point and the receiver point is processed with small-scale smoothing to obtain a small smooth surface of the surface elevation; Correct the common center point gather to the small smooth surface of the surface elevation to obtain the corrected common center point gather A1, obtain the pre-stack time migration root mean square velocity field, pre-stack time migration results and interpreted geological horizons; Correct the common shot point data to the small smooth surface of the surface elevation to obtain the corrected observed shot gather data B1; S2. Initial velocity model establishment Using the common center point gather A1 and the RMS velocity of prestack time migration, the initial depth domain velocity model is obtained through the constrained velocity inversion technology, and the velocity model C1 is established using the logging information and geological constraints of the work area. Obtain the first arrival information, establish the surface velocity model through tomographic inversion, embed the surface velocity model into the velocity model C1, and obtain the initial velocity model C2 embedded in the surface; S3. Speed ​​model optimization The common center gather A1 and the initial velocity model C2 are used to perform Gaussian beam migration prestack depth migration processing to obtain the common reflection angle deviation gather D1; Using the initial velocity model C2 and the common reflection angle deviation track gather, the optimized velocity model C3 is obtained through multi-azimuth travel time tomography inversion iterative processing; S4. Initial full waveform inversion velocity model establishment The velocity model C3 is filled with negative gradient velocity using the smooth surface as the interface, and a filling velocity body with decreasing velocity value is established as the initial full waveform inversion velocity model C4; S5. Wavelet extraction Determine the initial inversion frequency E1, use the corrected observed shot gather data B1 and the initial velocity model C4, select the near offset distance for wavelet extraction, and obtain the inversion wavelet F1; The initial inversion frequency E1 starts from the low frequency of the seismic observation data, preferably 3 Hz or 2 Hz; S6. Perform forward modeling Using the initial full waveform inversion velocity model C4 and the inversion wavelet F1, forward modeling is performed to obtain forward modeling data; S7. Perform full waveform inversion The full waveform inversion processing is performed using the initial full waveform inversion velocity model C4, the inversion wavelet F1 and the corrected observed shot gather data B1. Whether to perform the next frequency iteration is determined based on whether the objective function converges in the full waveform inversion: If the objective function does not converge, continue the iteration of this frequency to obtain the full waveform inversion update speed model of this frequency; If the objective function converges, the updated full waveform inversion velocity model C5 of the current frequency is obtained, and it is determined whether the accuracy of the full waveform inversion velocity model C5 needs to be iterated to a higher frequency: If iteration to a higher frequency is required, an inversion frequency E2 with a frequency higher than the current frequency is selected to replace the current frequency, and the full waveform inversion velocity model C5 is used to replace the initial full waveform inversion velocity model C4. Steps S5-S7 are repeated until a final full waveform inversion updated velocity model that does not require iteration is obtained.

[0010] The higher frequency is 1 Hz-2 Hz higher than the present frequency.

[0011] When the first iteration is performed, this frequency refers to the initial inversion frequency E1.

[0012] As a limitation of the present invention, the smoothing diameter of the small-scale smooth surface treatment is not greater than 500m; After the small-scale smooth surface processing, the number of rapid mutation points of near-surface elevation is zero.

[0013] As a further limitation of the present invention, in step S4, the filling negative gradient velocity is a filling thickness of not less than 1000m and a gradient value of not less than 1000m / s. The "negative" in the negative gradient means starting from the sea level as 0, with positive values ​​for downward and negative values ​​for upward, so the filling negative gradient velocity is upward filling.

[0014] As a further limitation of the present invention, in step S5, the near offset distance is no greater than 500 m.

[0015] As a further limitation of the present invention, in the full waveform inversion, the frequency iteration should be performed from low to high, and each frequency should be subjected to wavelet extraction through step S5.

[0016] Due to the adoption of the above scheme, the present invention has the following beneficial effects compared with the prior art: The present invention modifies the velocity model by filling the gradient velocity model, thereby eliminating the difference in direct wave characteristics between the forward modeling data and the observation data, so that the early arrival wave characteristics of the forward simulation data and the actual observation data are matched consistently without generating false images, thereby solving the problem of matching the characteristics of the forward simulation data and the actual observation data in the onshore full waveform inversion; thereby avoiding the influence of the direct wave in the forward simulation data being brought into the full waveform inversion and affecting the accuracy of the full waveform inversion.

[0017] The present invention is also applicable to onshore full waveform inversion in exploration data processing, and is used to improve the matching of forward modeling data and observation data in full waveform inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Figure 1 is a flow chart of full waveform inversion in an embodiment of the present invention; Figure 2 This is a C4 model diagram of the initial full waveform inversion velocity model in an embodiment of the present invention; Figure 3 This is a diagram of the final full waveform inversion update velocity model obtained in the example of the present invention; DETAILED DESCRIPTION

[0020] The present invention is further described below in conjunction with embodiments, but those skilled in the art should understand that the present invention is not limited to the following embodiments, and any improvements and equivalent changes made based on the specific embodiments of the present invention are within the scope of protection of the claims of the present invention.

[0021] Embodiment Method for improving the matching of forward modeling data and observation data in full waveform inversion This embodiment is a method for improving the matching of forward modeling data and observation data in full waveform inversion. This method is used to perform onshore full waveform inversion in the eastern Bohai Bay Basin.

[0022] Among them, the eastern Bohai Bay Basin has superior reservoir formation conditions and is a key zone for risk exploration of deep natural gas with great exploration potential. This zone has developed multiple large-scale geological bodies such as Paleozoic buried hills, Shahejie Formation conglomerate fans and volcanic intrusive rocks, and is a key target for risk exploration. However, the current data processing method obtains poor imaging effects of seismic data, the external morphology of the buried hills and the internal structure are poorly implemented, the low-frequency information loss of seismic data inside the buried hills is large, and the imaging effects of the buried hill top morphology and the interior are relatively poor, which makes it difficult to meet the needs of fine characterization of the buried hill interior and fine structural interpretation and geological body characterization.

[0023] Data processing is performed by improving the matching of forward modeling data and observation data in full waveform inversion. The process is as follows: Figure 1 , the specific method is as follows: S1. Small-scale smoothing of surface elevation and correction of gathers The elevation information of the shot points and the receiver points is smoothed on a small scale with a smoothing diameter of 500 m until the number of rapid mutation points in the near-surface elevation is zero.

[0024] Get a small smooth surface of surface elevation; Correct the common center point gather to the small smooth surface of the surface elevation to obtain the corrected common center point gather A1, obtain the pre-stack time migration root mean square velocity field, pre-stack time migration results and interpreted geological horizons; Correct the common shot point data to the small smooth surface of the surface elevation to obtain the corrected observed shot gather data B1; S2. Establishment of initial velocity model for prestack depth migration Using the common center point gather A1 and the RMS velocity of prestack time migration, the initial depth domain velocity model is obtained through the constrained velocity inversion technology, and the velocity model C1 is established using the logging information and geological constraints of the work area. Obtain the first arrival information and establish the surface velocity model through tomographic inversion. The surface velocity model is embedded into the velocity model C1 to obtain the initial velocity model C2 embedded in the surface, which is the initial velocity model of prestack depth migration. S3. Prestack depth migration velocity model optimization The common center gather A1 and the initial velocity model C2 are used to perform Gaussian beam migration prestack depth migration processing to obtain the common reflection angle deviation gather D1; Using the initial velocity model C2 and the common reflection angle deviation trace gather, the optimized velocity model C3 is obtained through multi-azimuth travel time tomographic inversion iterative processing, which is the pre-stack depth migration velocity model. S4. Establishment of initial velocity model for full waveform inversion The velocity model C3 is divided into two parts by the smooth surface of the ground. The negative gradient velocity is filled 1000m above the interface, where the gradient value is 1000m / s. The velocity body C4 with decreasing velocity value is established as the initial full waveform inversion velocity model C4, as shown in Figure 2 As shown; The "negative" in the negative gradient means that the sea level is 0, the downward direction is positive, and the upward direction is negative. Therefore, the filling negative gradient speed is filling upward; S5. Wavelet extraction The low frequency 3Hz of the seismic observation data is determined as the initial inversion frequency E1, the observed shot data B1 and the initial velocity model C4 are corrected, the near offset distance is selected as 500m, and the wavelet is extracted to obtain the inversion wavelet F1; S6. Perform forward modeling Using the initial full waveform inversion velocity model C4 and the inversion wavelet F1, forward modeling is performed to obtain forward modeling data; S7. Perform full waveform inversion The full waveform inversion processing is performed using the initial full waveform inversion velocity model C4, the inversion wavelet F1 and the corrected observed shot gather data B1. Whether to perform the next frequency iteration is determined based on whether the objective function converges in the full waveform inversion: If the objective function does not converge, continue the iteration at this frequency, i.e., 3 Hz, to obtain the full waveform inversion update speed model at this frequency; If the objective function converges, the updated full waveform inversion velocity model C5 of the current frequency is obtained, and it is determined whether the accuracy of the full waveform inversion velocity model C5 needs to be iterated to a higher frequency: If iteration to a higher frequency is required, an inversion frequency E2 with a frequency higher than the current frequency by 1 Hz is selected, that is, the current frequency is replaced by a 4 Hz frequency, and the initial full waveform inversion velocity model C4 is replaced by the full waveform inversion velocity model C5. Steps S5-S7 are repeated until the final full waveform inversion updated velocity model that does not require iteration is obtained, such as Figure 3 shown.

[0025] The work area of ​​this embodiment is the eastern Bohai Bay Basin. The initial full waveform inversion velocity model C4 obtained through steps S2-S4 is as follows: Figure 2 As shown, Figure 3 It is obtained after steps S4-S7. By comparison, it can be seen that Figure 3 The final full waveform inversion updated velocity model shown is rich in shallow details and the resolution of the velocity model is significantly improved.

[0026] In addition, in another embodiment, the smoothing diameter of the small-scale smooth surface processing is selected to be no greater than 500m, in step S5, the near offset distance is no greater than 500m, in step S7, the higher frequency is 2Hz higher than the current frequency, and the other steps are basically the same as the above embodiment. Each embodiment achieves a resolution higher than Figure 2 The final full waveform inversion updates the velocity model.

[0027] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of protection of the claims of the present invention.

Claims

1. A method for improving the matching of forward modeling data and observation data in full waveform inversion, characterized in that: The method comprises the following steps: The surface elevation of the seismic exploration observation data is used for small-scale smoothing, and the common center point gather and common shot point data are respectively corrected to the small smooth surface of the surface elevation to obtain the corrected observation shot gather data; The initial velocity model is established using the common center point gathers, prestack time migration RMS velocity and first arrival information. Using the common center gathers and initial velocity model, Gaussian beam migration prestack depth migration processing is performed to obtain common reflection angle deviation gathers; Using the initial velocity model and the common reflection angle deviation trace gather, the optimized velocity model is obtained through multi-azimuth travel time tomographic inversion iterative processing, the negative gradient velocity is filled, and the initial full waveform inversion velocity model is established; The forward data is obtained through wavelet extraction and forward modeling, and frequency iteration is performed using full waveform inversion. According to the convergence of the objective function and the accuracy of the full waveform inversion velocity model obtained by iteration, the frequency iteration is repeated until the full waveform inversion updated velocity model is obtained.

2. The method for improving the matching of forward modeling data and observation data in full waveform inversion according to claim 1, characterized in that: The method comprises the following steps performed in sequence: S1. Small-scale smoothing of surface elevation and correction of gathers The elevation information of the shot point and the receiver point is processed with small-scale smoothing to obtain a small smooth surface of the surface elevation; Correct the common center point gather to the small smooth surface of the surface elevation to obtain the corrected common center point gather A1, obtain the pre-stack time migration root mean square velocity field, pre-stack time migration results and interpreted geological horizons; Correct the common shot point data to the small smooth surface of the surface elevation to obtain the corrected observed shot gather data B1; S2. Initial velocity model establishment Using the common center point gather A1 and the RMS velocity of prestack time migration, the initial depth domain velocity model is obtained through the constrained velocity inversion technology, and the velocity model C1 is established using the logging information and geological constraints of the work area. Obtain the first arrival information, establish the surface velocity model through tomographic inversion, embed the surface velocity model into the velocity model C1, and obtain the initial velocity model C2 embedded in the surface; S3. Speed ​​model optimization The common center gather A1 and the initial velocity model C2 are used to perform Gaussian beam migration prestack depth migration processing to obtain the common reflection angle deviation gather D1; Using the initial velocity model C2 and the common reflection angle deviation track gather, the optimized velocity model C3 is obtained through multi-azimuth travel time tomography inversion iterative processing; S4. Initial full waveform inversion velocity model establishment The velocity model C3 is filled with negative gradient velocity using the smooth surface as the interface, and a filling velocity body with decreasing velocity value is established as the initial full waveform inversion velocity model C4; S5. Wavelet extraction Determine the initial inversion frequency E1, use the corrected observed shot gather data B1 and the initial velocity model C4, select the near offset distance for wavelet extraction, and obtain the inversion wavelet F1; S6. Perform forward modeling Using the initial full waveform inversion velocity model C4 and the inversion wavelet F1, forward modeling is performed to obtain forward modeling data; S7. Perform full waveform inversion The full waveform inversion processing is performed using the initial full waveform inversion velocity model C4, the inversion wavelet F1 and the corrected observed shot gather data B1. Whether to perform the next frequency iteration is determined based on whether the objective function converges in the full waveform inversion: If the objective function does not converge, continue the iteration of this frequency to obtain the full waveform inversion update speed model of this frequency; If the objective function converges, the updated full waveform inversion velocity model C5 of the current frequency is obtained, and it is determined whether the accuracy of the full waveform inversion velocity model C5 needs to be iterated to a higher frequency: If iteration to a higher frequency is required, an inversion frequency E2 with a frequency higher than the current frequency is selected to replace the current frequency, and the full waveform inversion velocity model C5 is used to replace the initial full waveform inversion velocity model C4. Steps S5-S7 are repeated until a final full waveform inversion updated velocity model that does not require iteration is obtained.

3. The method for improving the matching of forward modeling data and observation data in full waveform inversion according to claim 2, characterized in that: The smooth diameter of the small-scale smooth surface treatment is not greater than 500m; After the small-scale smooth surface processing, the number of rapid mutation points of near-surface elevation is zero.

4. The method for improving the matching of forward modeling data and observation data in full waveform inversion according to claim 2, characterized in that: In step S4, the filling negative gradient speed is such that the filling thickness is not less than 1000m and the gradient value is not less than 1000m / s.

5. The method for improving the matching of forward modeling data and observation data in full waveform inversion according to claim 2, characterized in that: In step S5, the near offset distance is no greater than 500m.

6. The method for improving the matching of forward modeling data and observation data in full waveform inversion according to claim 2, characterized in that: In the full waveform inversion, the frequency iteration should be performed from low to high, and each frequency should be subjected to wavelet extraction through step S5.

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