A method for determining deconvolution parameters and a method for processing seismic data.

Abstract

This invention provides a method for determining deconvolution parameters and a method for processing seismic data, belonging to the field of seismic exploration of oil and gas. The method for determining deconvolution parameters includes the following steps: selecting N experimental parameter values ​​for the target exploration area and performing a first inverse selection process, a second inverse selection process, and a third inverse selection process respectively; based on the inverse selection results of the first, second, and third inverse selection processes, inversely selecting out N-1 experimental parameter values ​​to determine the deconvolution parameters, where N is an integer and N≥2. By integrating multiple qualitative and quantitative evaluation results and using an inverse selection method, the method achieves the determination of deconvolution parameters suitable for the target exploration area, and the results are unique, objective, and accurate, avoiding interference from subjective human factors, and obtaining deconvolution parameters that balance the vertical resolution and signal-to-noise ratio of seismic data.

Description

Technical Field

[0001] This invention belongs to the field of seismic exploration of oil and gas, and mainly relates to a method for determining deconvolution parameters and a method for processing seismic data. Background Technology

[0002] When processing high-fidelity, broadband seismic data, pre-stack deconvolution is required to eliminate differences in reflection waveforms caused by factors such as near-surface and subsurface non-perfectly elastic media, improve wavelet consistency, and obtain a relatively high-fidelity reflection coefficient sequence. However, determining suitable deconvolution parameters in practical applications is challenging, mainly because it is difficult to maintain a high signal-to-noise ratio while improving the vertical resolution of seismic data.

[0003] The determination of deconvolution parameters in existing technologies mainly includes the following steps: First, N experimental parameter values ​​are selected for deconvolution processing, and the deconvolution-processed single-shot records are output. Then, autocorrelation and spectral analysis are performed on the deconvolution-processed single-shot records. The results are evaluated based on factors such as the bandwidth broadening and wavelet sidelobe compression of the deconvolution-processed single-shot records to obtain the deconvolution parameters corresponding to the best-performing single-shot record. This method of determining deconvolution parameters mainly relies on the experience of seismic data processors, which is greatly influenced by subjective factors and is not objective or accurate enough. Summary of the Invention

[0004] The purpose of this invention is to provide a method for determining deconvolution parameters, which can objectively and accurately obtain deconvolution parameters that take into account both the vertical resolution and signal-to-noise ratio of seismic data.

[0005] Another objective of this invention is to provide a method for processing seismic data that can balance the vertical resolution and signal-to-noise ratio of the seismic data.

[0006] The method for determining the deconvolution parameters of this invention employs the following technical solution:

[0007] A method for determining deconvolution parameters includes the following steps: selecting N experimental parameter values ​​for the deconvolution parameters of the target exploration area and performing a first inverse selection process, a second inverse selection process, and a third inverse selection process respectively; based on the inverse selection results of the first inverse selection process, the second inverse selection process, and the third inverse selection process, inversely selecting out N-1 experimental parameter values ​​to determine the deconvolution parameters, where N is an integer and N≥2;

[0008] The first reverse selection process includes the following steps: performing quality analysis, autocorrelation analysis, and spectrum analysis on the single-shot records of each test parameter value output, and reverse selecting one or more of the following test parameter values ​​with the worst performance based on the analysis results: (a) signal-to-noise ratio; (b) noise; (c) energy; (d) spectral spread; (e) spectral distortion;

[0009] The second reverse selection process includes the following steps: superimposing the single-shot records of each test parameter value output to obtain a superimposed profile; performing quality analysis, autocorrelation analysis, and spectral analysis on the superimposed profile; and selecting one or more test parameter values ​​with the worst performance based on the analysis results: (a) signal-to-noise ratio; (b) noise; (c) energy; (d) spectral spread amplitude; and (e) spectral distortion.

[0010] The third inverse selection process includes the following steps: determining the lower limit and upper limit of resolution improvement for the original seismic data; performing statistical analysis on the signal-to-noise ratio and dominant frequency of the single-shot records output for each test parameter value; inversely selecting test parameter values ​​whose dominant frequency is outside the interval [lower limit of improvement, upper limit of improvement]; and inversely selecting one or more test parameter values ​​with the lowest dominant frequency and one or more test parameter values ​​with the lowest signal-to-noise ratio.

[0011] This invention, by integrating multiple qualitative and quantitative evaluation results and employing a reverse selection method, determines the deconvolution parameters suitable for the target exploration area. The results are unique, objective, and accurate, avoiding interference from subjective human factors. The obtained deconvolution parameters enable the vertical resolution and signal-to-noise ratio of the target layer to be harmoniously unified. Under the premise of relatively high fidelity and amplitude preservation, the vertical resolution of seismic data is effectively improved, while taking into account the signal-to-noise ratio and high accuracy of the seismic data, thereby meeting the needs of structural interpretation and reservoir prediction in the target exploration area.

[0012] Preferably, the method for determining the lower limit of resolution improvement of the original seismic data includes the following steps: combining well logging data in the exploration area, establishing a geological model, performing forward modeling using Ricker wavelets of different dominant frequencies, and determining the lowest dominant frequency that can identify the target layer as the lower limit of resolution improvement of the original seismic data.

[0013] Preferably, the different main frequencies of the Reck wavelets are a series of Reck wavelets with an initial main frequency of 25Hz and an interval of 5Hz between adjacent main frequencies, such as Reck wavelets with main frequencies of 25Hz, 30Hz, and 35Hz.

[0014] Preferably, the method for determining the upper limit of resolution improvement for raw seismic data includes the following steps: performing frequency-division scanning on the raw single-shot data to determine the highest initial scanning frequency at which effective reflection signals of the target layer can be observed, and determining the upper limit of resolution improvement for the raw seismic data based on the highest initial scanning frequency. The highest initial scanning frequency of the frequency-division scanning is an octave band of the upper limit of resolution improvement for the raw seismic data; that is, the upper limit of resolution improvement for the raw seismic data is half of the highest initial scanning frequency. Due to the constraints of the quality of the raw data, the resolution of seismic data cannot be improved indefinitely, but is determined from the time the raw data is acquired. This invention establishes an upper limit for improving the resolution of raw seismic data, which is beneficial to ensuring the rationality of the selection of deconvolution parameter values ​​and improving the fidelity and reliability of the deconvolution processing results.

[0015] Preferably, the initial scanning frequency of the frequency division scan is 60Hz, and the interval between adjacent initial scanning frequencies is 10Hz. For example, the frequency division scan is performed using scanning frequency ranges of 60-120Hz, 70-140Hz, and 80-160Hz, respectively.

[0016] The method for determining deconvolution parameters of the present invention is applicable to parameter optimization in various seismic data processing stages. Preferably, the deconvolution parameters are selected from one of prediction step size, operator length, and statistical time window.

[0017] Preferably, the deconvolution parameters are selected from the prediction step size or the statistical time window. The prediction step size is also the prediction distance. These two types of deconvolution parameters are more sensitive in the deconvolution processing of seismic data, and using these two types of deconvolution parameters is beneficial to improving the accuracy of the processing results.

[0018] Preferably, the N experimental parameter values ​​for the deconvolution parameters of the target exploration area are selected, and the 6 experimental parameter values ​​for the prediction step size are selected, namely: 2ms, 4ms, 8ms, 12ms, 16ms, and 20ms.

[0019] The seismic data processing method of the present invention employs the following technical solution:

[0020] A method for processing seismic data includes the following steps:

[0021] 1) Select N test parameter values ​​for the deconvolution parameters of the target exploration area and perform the first inverse selection process, the second inverse selection process, and the third inverse selection process respectively. Based on the inverse selection results of the first inverse selection process, the second inverse selection process, and the third inverse selection process, remove N-1 test parameter values ​​to determine the deconvolution parameters. N is an integer and N≥2.

[0022] The first reverse selection process includes the following steps: performing quality analysis, autocorrelation analysis, and spectrum analysis on the single-shot records of each test parameter value output, and reverse selecting one or more of the following test parameter values ​​with the worst performance based on the analysis results: (a) signal-to-noise ratio; (b) noise; (c) energy; (d) spectral spread; (e) spectral distortion;

[0023] The second reverse selection process includes the following steps: superimposing the single-shot records of each test parameter value output to obtain a superimposed profile; performing quality analysis, autocorrelation analysis, and spectral analysis on the superimposed profile; and selecting one or more test parameter values ​​with the worst performance based on the analysis results: (a) signal-to-noise ratio; (b) noise; (c) energy; (d) spectral spread amplitude; and (e) spectral distortion.

[0024] The third inverse selection process includes the following steps: determining the lower limit and upper limit of the resolution improvement of the original seismic data, performing statistical analysis on the signal-to-noise ratio and dominant frequency of the single-shot records output by each test parameter value, inversely selecting test parameter values ​​whose dominant frequency is outside the interval [lower limit of improvement, upper limit of improvement], and inversely selecting one or more test parameter values ​​with the lowest dominant frequency and one or more test parameter values ​​with the lowest signal-to-noise ratio respectively.

[0025] 2) Using the deconvolution parameters determined in step 1), perform deconvolution operation on the original seismic data of the target exploration area to obtain the deconvolutioned seismic data.

[0026] The seismic data processing method of the present invention can obtain seismic data that balances high resolution and signal-to-noise ratio, thereby meeting the needs of structural interpretation and reservoir prediction in the target exploration area.

[0027] Preferably, the method for determining the lower limit of resolution improvement of the original seismic data includes the following steps: combining well logging data in the exploration area, establishing a geological model, performing forward modeling using Ricker wavelets of different dominant frequencies, and determining the lowest dominant frequency that can identify the target layer as the lower limit of resolution improvement of the original seismic data.

[0028] Preferably, the method for determining the upper limit of the resolution improvement of the original seismic data includes the following steps: performing frequency division scanning on the original single-shot data, determining the highest initial scanning frequency at which the effective reflection signal of the target segment can be observed, and determining the upper limit of the resolution improvement of the original seismic data based on the highest initial scanning frequency.

[0029] Preferably, the deconvolution parameter is selected from one of the following: prediction step size, operator length, and statistical window. Attached Figure Description

[0030] Figure 1The results of single-shot record quality analysis and autocorrelation analysis are presented for the target exploration area before deconvolution processing and after deconvolution processing with different prediction step sizes in Example 1.

[0031] Figure 2 The results of single-shot record spectrum analysis of the target exploration area before and after deconvolution processing with different prediction step sizes are shown in Example 1.

[0032] Figure 3 The results of the stacked profile quality analysis and autocorrelation analysis are shown for the target exploration area before and after deconvolution processing with different prediction step sizes in Example 1.

[0033] Figure 4 The results of the spectral analysis of the superimposed profiles of the target exploration area before and after deconvolution processing with different prediction step sizes are shown in Example 1.

[0034] Figure 5 The forward modeling results of the target exploration area geological model in Example 1 were obtained using Ricker wavelet forward modeling with different dominant frequencies. Left: dominant frequency is 25Hz, right: dominant frequency is 30Hz.

[0035] Figure 6 The results of frequency division scanning of the original single shot in the target exploration area of ​​Example 1 using different scanning frequency ranges are shown. Left: scanning frequency range is 60-120Hz, middle: scanning frequency range is 70-140Hz, and right: scanning frequency range is 80-160Hz.

[0036] Figure 7 The main frequency statistics of single-shot records in the target exploration area before and after deconvolution processing with different prediction step sizes are shown in Example 1.

[0037] Figure 8 The signal-to-noise ratio statistics of single-shot records in the target area before and after deconvolution processing with different prediction step sizes in Example 1 are shown.

[0038] Figure 9 This refers to the newly processed migration results and synthetic seismic records of the target exploration area in Example 2;

[0039] Figure 10 This is a previous offset profile of the target exploration area in Example 2;

[0040] Figure 11 This is a newly processed offset profile of the target exploration area in Example 2;

[0041] Figure 12 This is a layer coherence slice of previous migration results for the target exploration area in Example 2;

[0042] Figure 13 This is a layer-by-layer coherence slice of the newly processed migration results of the target exploration area in Example 2. Detailed Implementation

[0043] The technical effects of the present invention will be further explained below with reference to specific embodiments.

[0044] Example 1

[0045] The method for determining deconvolution parameters in this embodiment was applied to 200 km of mountainous data from the southern Ordos Basin. 2 Seismic data processing: The goal of this seismic data processing was to improve the imaging accuracy of the Lower Paleozoic weathering crust and faults. This included the following steps:

[0046] (1) Prediction step sizes of 2ms (Gap2, abbreviated as G2), 4ms (Gap4, abbreviated as G4), 8ms (Gap8, abbreviated as G8), 12ms (Gap12, abbreviated as G12), 16ms (Gap16, abbreviated as G16), and 20ms (Gap20, abbreviated as G20) were selected as experimental parameter values ​​for the deconvolution parameters of the target exploration area (N=6). The experimental parameter values ​​for the prediction step sizes of the target exploration area were subjected to a first inverse selection process, a second inverse selection process, and a third inverse selection process. Based on the inverse selection results of the first inverse selection process, the second inverse selection process, and the third inverse selection process, a total of 5 experimental parameter values ​​were inversely selected to determine the deconvolution parameters.

[0047] (2) The first inverse selection process in this embodiment includes the following steps: performing quality analysis, autocorrelation analysis, and spectral analysis on single-shot records before and after deconvolution processing with different prediction step sizes, wherein the results of quality analysis and autocorrelation analysis are as follows: Figure 1 As shown, the spectral analysis results are as follows: Figure 2 As shown; from Figure 1 The results show that the experimental parameter values ​​for different prediction step sizes are very similar, making it difficult to select the optimal parameters. It can only be observed that G20 has the lowest signal-to-noise ratio in the shallow to mid-depth layers (see the arrow indicating the location); Figure 2 It can be seen that G2 and G4 have the highest high-frequency noise and energy, while G20 has the smallest low-frequency extension. The qualitative analysis results are then reverse-selected to exclude the experimental parameter values ​​of G2, G4 and G20.

[0048] (3) The second inverse selection process in this embodiment includes the following steps: superimposing the single-shot records of the predicted step length test parameter values ​​to obtain a superimposed profile; performing quality analysis, autocorrelation analysis, and spectral analysis on the superimposed profile, wherein the quality and autocorrelation analysis of the superimposed profile are as follows: Figure 3 As shown, the results of the superimposed profile spectral analysis are as follows: Figure 4 As shown, from Figure 3 The results show that G2 has slightly more high-frequency noise, G20 has slightly poorer consistency in autocorrelation analysis, and the results for other parameter values ​​are very similar, making optimal selection difficult. Figure 4The results showed that G2 had heavy high-frequency noise and weak low-frequency energy, while G16 and G20 had slight local distortion in their high-frequency spectra. The analysis results were reverse-selected to exclude the three experimental parameter values ​​of G2, G16 and G20.

[0049] (4) The third reverse selection process in this embodiment includes the following steps:

[0050] 4.1 Determining the lower limit of resolution improvement for original seismic data: A geological model was established based on well logging data from the exploration area. Forward modeling was performed using Ricker wavelets with dominant frequencies of 25Hz and 30Hz. The results are as follows: Figure 5 As shown; from Figure 5 As indicated by the arrow, the target layer cannot be distinguished at a dominant frequency of 25Hz. Only when the dominant frequency reaches 30Hz can the target layer be distinguished, meeting the requirements for reservoir prediction. Therefore, a dominant frequency of 30Hz is the minimum frequency required to identify the target layer, which is also the lower limit for improving the resolution of the original seismic data.

[0051] 4.2 Determining the upper limit of resolution improvement for raw seismic data: The raw single-shot data was divided into frequency ranges of 60–120 Hz, 70–140 Hz, and 80–160 Hz for frequency division scanning. The scanning results are as follows: Figure 6 As shown; by Figure 6 As can be seen (see the arrow), a frequency division scan with an initial scan frequency of 60Hz can reveal a clear and effective reflection signal in the target layer, a frequency division scan with 70Hz can reveal a faint effective reflection signal in the target layer, and a frequency division scan above 70Hz cannot reveal any effective reflection signal in the target layer. Therefore, this initial scan frequency is the highest initial scan frequency at which an effective reflection signal in the target layer can be observed. In other words, the high-frequency end of the data in this exploration area is 70Hz, which is the octave band of the upper limit of the main frequency. Determining that the upper limit for improving the resolution of the target exploration area data is 35Hz is reasonable, as it can ensure the fidelity of the processing results and the reliability of the output results.

[0052] 4.3 The signal-to-noise ratio and dominant frequency of single-shot records before and after deconvolution processing with different prediction step sizes were statistically analyzed. The dominant frequency statistics are shown in [reference missing]. Figure 7 For signal-to-noise ratio statistics, please refer to [link / reference]. Figure 8 .pass Figure 7 Remove Gap12 from the remaining test parameter values ​​whose main frequency is not in the range [30Hz, 35Hz], and then select the two test parameter values ​​with the lowest main frequency, Gap16 and Gap20. Figure 8 The two experimental parameter values ​​with the lowest signal-to-noise ratios, Gap16 and Gap20, were selected by reverse selection.

[0053] (5) Based on the qualitative analysis and quantitative evaluation results of the test parameter values ​​in steps (1) to (4) above, a comprehensive optimization table of test parameter values ​​is formed, see Table 1 below (in Table 1, × indicates that the result meets the conditions and is rejected, and √ indicates that the result does not meet the conditions and is retained).

[0054] Table 1. Optimal Parameter Values ​​for Example 1

[0055]

[0056]

[0057] Table 1 provides a very intuitive and objective way to select the optimal deconvolution parameters for the target region. Through comprehensive optimization, the deconvolution prediction step size that does not meet the inverse selection criteria in both qualitative and quantitative analysis results is Gap8. This result is unique and relatively objective, avoiding interference from subjective human factors.

[0058] Example 2

[0059] The seismic data processing method in this embodiment is for the target exploration area of ​​200km in Embodiment 1. 2 Seismic data processing includes the following steps:

[0060] (1) Select a series of prediction step length test parameter values ​​for the deconvolution parameters of the target exploration area and perform the first inverse selection process, the second inverse selection process and the third inverse selection process respectively according to the method of Example 1. Based on the inverse selection results of the first inverse selection process, the second inverse selection process and the third inverse selection process, determine the deconvolution parameter as Gap8 (prediction step length is 8ms).

[0061] (2) Use the deconvolution parameters determined in step (1) to perform deconvolution operation on the original seismic data of the target exploration area to obtain the deconvolutioned seismic data.

[0062] Experimental Example 1

[0063] The seismic data after deconvolution in Example 2 were processed to obtain newly processed migration results and synthetic seismic records, as shown below. Figure 9 As shown, from Figure 9 It can be concluded that using the deconvolution parameters determined in Example 1 for deconvolution processing results in a moderate target layer resolution, a high signal-to-noise ratio, and a high well-seismic agreement.

[0064] Experiment Example 2

[0065] The seismic data after deconvolution in Example 2 were migrated to obtain a new processed migration profile, as shown in the figure. Figure 11 As shown. The target exploration area is compared with previous offset profiles (see...). Figure 10The new processed offset profile after finely optimizing the deconvolution parameters was compared with that of the previous one. Figure 10 and Figure 11 The comparison shows that after performing deconvolution calculations using the deconvolution parameters determined in Example 1, the geological phenomena are richer, the small faults are clearer, the weathering crust is more finely depicted, and the resolution is moderate, which can meet the needs of structural interpretation and reservoir prediction in this exploration area.

[0066] Experimental Example 3

[0067] Coherence property analysis was performed on the new processed offset results after deconvolution in Example 2, and the results are as follows: Figure 13 As shown. Previous migration results of the target exploration area are sliced ​​along the stratigraphic coherence (see...). Figure 12 Comparing the newly processed migration results with the refined deconvolution parameters (using the same parameters) along the stratigraphic coherence slices, it can be seen that the data after refined deconvolution parameter optimization has a higher signal-to-noise ratio and higher fault identification. Furthermore, with improved longitudinal and lateral resolution, small fractures and gullies near the weathering crust are more clearly depicted. Figure 13 More coherent attribute anomalies (fractures and gullies) can be seen in the middle.

Claims

1. A method for determining deconvolution parameters, characterized in that: The process includes the following steps: N experimental parameter values ​​for the deconvolution parameters of the target exploration area are selected and subjected to a first inverse selection process, a second inverse selection process, and a third inverse selection process. Based on the inverse selection results of the first, second, and third inverse selection processes, N-1 experimental parameter values ​​are inversely selected to determine the deconvolution parameters, where N is an integer and N≥2. The deconvolution parameters are selected from one of the following: prediction step size, operator length, and statistical time window. The first reverse selection process includes the following steps: performing quality analysis, autocorrelation analysis, and spectrum analysis on the single-shot records of each test parameter value output, and reverse selecting one or more of the following test parameter values ​​with the worst performance based on the analysis results: (a) signal-to-noise ratio; (b) noise; (c) energy; (d) spectral spread; (e) spectral distortion; The second reverse selection process includes the following steps: superimposing the single-shot records of each test parameter value output to obtain a superimposed profile; performing quality analysis, autocorrelation analysis, and spectral analysis on the superimposed profile; and selecting one or more test parameter values ​​with the worst performance based on the analysis results: (a) signal-to-noise ratio; (b) noise; (c) energy; (d) spectral spread amplitude; and (e) spectral distortion. The third inverse selection process includes the following steps: determining the lower limit and upper limit of resolution improvement for the original seismic data; performing statistical analysis on the signal-to-noise ratio and dominant frequency of the single-shot records output for each test parameter value; inversely selecting test parameter values ​​whose dominant frequency is outside the interval [lower limit of improvement, upper limit of improvement]; and inversely selecting one or more test parameter values ​​with the lowest dominant frequency and one or more test parameter values ​​with the lowest signal-to-noise ratio.

2. The method for determining deconvolution parameters as described in claim 1, characterized in that: The method for determining the lower limit of resolution improvement for original seismic data includes the following steps: combining well logging data within the exploration area, establishing a geological model, performing forward modeling using Ricker wavelets of different dominant frequencies, and determining the lowest dominant frequency that can identify the target layer as the lower limit of resolution improvement for the original seismic data.

3. The method for determining deconvolution parameters as described in claim 1, characterized in that: The method for determining the upper limit of resolution improvement of raw seismic data includes the following steps: performing frequency division scanning on the raw single-shot data, determining the highest initial scanning frequency at which effective reflection signals of the target segment can be observed, and determining the upper limit of resolution improvement of the raw seismic data based on the highest initial scanning frequency.

4. A method for processing seismic data, characterized in that: Includes the following steps: 1) Select N test parameter values ​​for the deconvolution parameters of the target exploration area and perform the first inverse selection process, the second inverse selection process, and the third inverse selection process respectively. Based on the inverse selection results of the first inverse selection process, the second inverse selection process, and the third inverse selection process, remove N-1 test parameter values ​​to determine the deconvolution parameters. N is an integer and N≥2. The first reverse selection process includes the following steps: performing quality analysis, autocorrelation analysis, and spectrum analysis on the single-shot records of each test parameter value output, and reverse selecting one or more of the following test parameter values ​​with the worst performance based on the analysis results: (a) signal-to-noise ratio; (b) noise; (c) energy; (d) spectral spread; (e) spectral distortion; The second reverse selection process includes the following steps: superimposing the single-shot records of each test parameter value output to obtain a superimposed profile; performing quality analysis, autocorrelation analysis, and spectral analysis on the superimposed profile; and selecting one or more test parameter values ​​with the worst performance based on the analysis results: (a) signal-to-noise ratio; (b) noise; (c) energy; (d) spectral spread amplitude; and (e) spectral distortion. The third inverse selection process includes the following steps: determining the lower limit and upper limit of the resolution improvement of the original seismic data, performing statistical analysis on the signal-to-noise ratio and dominant frequency of the single-shot records output by each test parameter value, inversely selecting test parameter values ​​whose dominant frequency is outside the interval [lower limit of improvement, upper limit of improvement], and inversely selecting one or more test parameter values ​​with the lowest dominant frequency and one or more test parameter values ​​with the lowest signal-to-noise ratio respectively. 2) Using the deconvolution parameters determined in step 1), perform deconvolution operation on the original seismic data of the target exploration area to obtain the deconvolutioned seismic data.

5. The method for processing seismic data as described in claim 4, characterized in that: The method for determining the lower limit of resolution improvement for original seismic data includes the following steps: combining well logging data within the exploration area, establishing a geological model, performing forward modeling using Ricker wavelets of different dominant frequencies, and determining the lowest dominant frequency that can identify the target layer as the lower limit of resolution improvement for the original seismic data.

6. The method for processing seismic data as described in claim 4, characterized in that: The method for determining the upper limit of resolution improvement of raw seismic data includes the following steps: performing frequency division scanning on the raw single-shot data, determining the highest initial scanning frequency at which effective reflection signals of the target segment can be observed, and determining the upper limit of resolution improvement of the raw seismic data based on the highest initial scanning frequency.

7. The method for processing seismic data as described in claim 4, characterized in that: The deconvolution parameters are selected from one of the following: prediction step size, operator length, and statistical window.

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