A surface wave attenuation method based on two-dimensional fourier transform

Abstract

The application discloses a surface wave attenuation method based on two-dimensional Fourier transform, which comprises the following steps: step 1, preparing surface wave data; step 2, setting control points with a certain density in seismic records, and performing two-dimensional Fourier transform on the data of the control points to obtain high-precision surface wave dispersion spectrum; step 3, performing hierarchical picking on the surface wave dispersion spectrum to obtain multi-order dispersion curves of each control point; step 4, extrapolating the multi-order dispersion curves at the control points to the whole work area to obtain local dispersion curves of the whole work area; step 5, performing interpolation processing according to the dispersion curves of each local position, and then simulating surface wave prediction results of all seismic records; and step 6, matching and subtracting the surface wave prediction results from the original seismic records to obtain seismic records after surface waves are eliminated. The method can effectively predict and eliminate dispersive surface waves, can protect reflection signals to the greatest extent, and can also well solve the spatial aliasing phenomenon caused by spatial sparse sampling.

Description

Technical Field

[0001] This invention relates to a surface wave attenuation method based on two-dimensional Fourier transform, belonging to the field of geophysical data processing technology. Background Technology

[0002] Low-frequency information in seismic data plays a crucial role in petroleum seismic exploration. Furthermore, low-frequency amplitude is the most important seismic characteristic parameter for data interpretation. However, during seismic data acquisition, low-frequency seismic signals are often interfered with by various noises, resulting in extremely low signal-to-noise ratios and hindering their effective utilization. Surface waves are a major source of low-frequency interference in seismic data, capable of masking other effective reflections, particularly impacting near-offset seismic data. Therefore, surface wave filtering is a critical step in seismic data processing and interpretation, and the quality of the filtering directly affects the effectiveness of subsequent processing.

[0003] Many techniques have been developed for surface wave suppression, such as frequency domain filtering, FK filtering, adaptive subtraction, wavelet transform, and C-transform. These methods have achieved certain results, but they also have certain limitations.

[0004] Frequency domain filtering transforms seismic data into the frequency domain based on the difference in frequency characteristics between surface waves and effective waves, and performs low-pass, high-pass, or band-pass filtering. However, this method is prone to damaging the effective waves in the corresponding frequency band.

[0005] FK filtering is a classic surface wave suppression technique, widely used due to its simplicity and speed. However, FK filtering requires regular spatial sampling; when seismic data exhibits amplitude differences or sparse spatial sampling, it can easily cause waveform distortion such as "worming" of the phase axis. Furthermore, FK filtering processes multiple records as a whole, which, while attenuating surface waves, also damages some of the low-frequency effective information superimposed on the surface waves.

[0006] The basic idea of ​​adaptive subtraction filtering is to estimate surface wave interference from the original seismic data, adjust the amplitude and phase to make the estimated surface wave interference consistent with the surface wave characteristics in the actual seismic data, and then subtract the estimated surface wave interference from the original seismic data to obtain the seismic record after surface wave suppression. Adaptive subtraction filtering is a relatively high-fidelity surface wave suppression technique, and its effectiveness depends on the predicted surface wave model. Often, in order to maximize the protection of the effective signal, it suffers from insufficient surface wave suppression.

[0007] Wavelet transform and Curvelet transform belong to the realm of multi-scale theoretical analysis, and their strong directional characteristics provide a good suppression effect for surface waves. Based on the different distribution regions of the main energy of surface waves and effective waves in the wavelet domain, continuous wavelet transform is used to suppress surface waves. However, since the basis functions of wavelet transform are isotropic, it is prone to failure in representing high-dimensional anisotropic signals. Curvelet transform, with its strong directional characteristics, can overcome the shortcomings of wavelet transform in representing high-dimensional anisotropic signals. However, the effectiveness of Curvelet transform in suppressing surface waves is affected by the degree of overlap between surface waves and effective waves in the Curvelet domain. Summary of the Invention

[0008] The purpose of this invention is to provide a surface wave attenuation method based on two-dimensional Fourier transform. By utilizing two-dimensional Fourier transform extraction technology, surface wave prediction technology, and surface wave model matching reduction technology, and making full use of high-resolution dispersion spectrum analysis, this method can be applied in practice and produce good results. This method can effectively predict and eliminate dispersion surface waves, while protecting the reflected signal to the greatest extent, and can also effectively solve the spatial aliasing phenomenon caused by spatial sparse sampling.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is: a surface wave attenuation method based on two-dimensional Fourier transform, which includes the following steps:

[0010] Step 1: After processing the raw seismic records, surface wave data is obtained;

[0011] Step 2: Set control points of a certain density in the seismic records within the work area, and perform a two-dimensional Fourier transform on the data of the control points to obtain a high-precision surface wave dispersion spectrum.

[0012] Step 3: Perform order picking on the high-precision surface wave dispersion spectrum to obtain the multi-order dispersion curves of each control point;

[0013] Step 4: Extrapolate the multi-order dispersion curves at the control points to the entire work area to obtain the local dispersion curves of the entire work area;

[0014] Step 5: Perform interpolation based on the dispersion curves at each local location to simulate and obtain the surface wave prediction results for all earthquake records.

[0015] Step 6: Based on the surface wave prediction results, match and subtract them from the original seismic record to obtain the seismic record after surface waves have been eliminated.

[0016] Furthermore, in step 1, surface wave data within the 18Hz range are extracted for analysis.

[0017] Furthermore, in step 2, a control point is set at regular intervals along the survey line in the work area, and 11-19 channels are extracted at the control point to perform a two-dimensional Fourier transform to obtain a high-precision surface wave dispersion spectrum.

[0018] Furthermore, in step 2, the formula for the two-dimensional Fourier transform is expressed as:

[0019]

[0020] Where f(X,Y) is the spatial domain representation of the two-dimensional signal, and F(U,V) is the frequency domain representation of the signal; the variables U and V in the transform represent the horizontal and vertical components in the frequency domain, respectively, while X and Y represent the coordinates in the spatial domain; the exponent term... It is a complex exponential function that calculates the value of the corresponding point in the frequency domain based on the signal dimensions M and N and the current spatial domain coordinates (X,Y).

[0021] Furthermore, in step 3, the peak position of the dispersion curve is picked.

[0022] Furthermore, in step 4, the dispersion curve with effective pickup is extrapolated to each boundary position of the work area to ensure that the dispersion curve can be interpolated over the entire work area.

[0023] Furthermore, in step 5, based on the frequency band range of the dispersive surface wave and the acquisition parameters such as the total number of traces, trace spacing, minimum shot-receiver distance, and sampling interval of the actual seismic record, the model record of the dispersive surface wave can be obtained by using a two-dimensional inverse Fourier transform.

[0024] Furthermore, in step 6, an adaptive subtraction method based on the L2 norm is used to match and subtract the actual data from the surface wave model in order to improve the suppression effect of the dispersive surface wave.

[0025] The beneficial effects of this invention are as follows: Addressing the problem of suppressing dissipative surface waves, this invention employs a two-dimensional Fourier transform to establish a high-precision dispersion spectrum. Utilizing the relationship between the phase velocity and frequency of the dispersion curve, a dissipative surface wave model is simulated, achieving noise suppression of dissipative surface waves. Compared with traditional surface wave suppression methods, the surface wave simulation process is simpler to implement, more computationally efficient, and achieves better suppression results. Actual seismic data processing results verify the effectiveness of the proposed method, demonstrating its significant application value. Attached Figure Description

[0026] Figure 1 This is a flowchart of the present invention.

[0027] Figure 2 The data includes: a) surface wave data preparation; b) raw seismic records; c) extracted surface wave data; and d) surface wave spectrum.

[0028] Figure 3 This is a control point location map (red dots represent preset control points).

[0029] Figure 4 It is a dispersion spectrum.

[0030] Figure 5 This is a plot of the fundamental dispersion curve.

[0031] Figure 6 This is the extrapolated diagram of the dispersion curve.

[0032] Figure 7 It consists of the original seismic record before and after suppression of the basic surface wave, and the surface wave model; a) the original seismic record, b) the record after suppression, and c) the surface wave model.

[0033] Figure 8 It contains the original records and surface wave spectra of the application cases.

[0034] Figure 9 These are application case studies of surface wave suppression before and after the basic level; a) original seismic record; b) record after suppression; c) surface wave model.

[0035] Figure 10 These are the cross-sections before and after the suppression of the surface wave in the application case; a is the cross-section before suppression, and b is the cross-section after suppression. Detailed Implementation

[0036] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 A surface wave attenuation method based on two-dimensional Fourier transform is proposed. First, surface wave data is obtained by processing the original seismic records. Second, control points of a certain density are set in the seismic records within the work area, and the data from these control points are subjected to two-dimensional Fourier transform to obtain a high-precision surface wave dispersion spectrum. Third, the high-precision dispersion spectrum is sorted by order to obtain multi-order dispersion curves for each control point. Fourth, the dispersion curves at the control points are extrapolated to the entire work area to obtain local dispersion curves for the entire work area. Fifth, interpolation is performed based on the dispersion curves at each local location to simulate the surface wave prediction results for all seismic records. Sixth, the surface wave prediction results are matched and subtracted from the original seismic records to obtain the seismic records after surface wave elimination.

[0038] (1) Surface wave data preparation

[0039] Analysis of the surface wave spectrum of the seismic records revealed that the surface wave bandwidth was 18 Hz, and surface wave data was extracted by cutting out (e.g.) Figure 2 This prevents the effective signal from affecting the surface wave analysis. Therefore, we can analyze the extracted surface wave data within the 18Hz range.

[0040] (2) Two-dimensional Fourier transform

[0041] On the survey lines in the work area, a control point is set at regular intervals (e.g., ...). Figure 3 A high-precision surface wave dispersion spectrum is obtained by extracting a certain number of channels at the control points and performing a two-dimensional Fourier transform. Testing and analysis show that the highest resolution dispersion spectrum is generally obtained when 11-19 channels are extracted.

[0042] The formula for the two-dimensional Fourier transform can be expressed as:

[0043]

[0044] Here, f(X,Y) is the spatial domain representation of the two-dimensional signal, and F(U,V) is the frequency domain representation of the signal. The variables U and V in the transform represent the horizontal and vertical components in the frequency domain, respectively, while X and Y represent the coordinates in the spatial domain. (Exponential term) It is a complex exponential function that calculates the value at the corresponding point in the frequency domain based on the signal dimensions M and N and the current spatial domain coordinates (X, Y). The two-dimensional Fourier transform transforms surface wave data in the space-time domain to obtain the surface wave dispersion spectrum in the frequency-wavenumber domain (e.g., ...). Figure 4 In the time-space domain, in-phase axes with different apparent velocities, or even in-phase axes that interfere with each other, can be separated in the frequency-wavenumber domain. This allows us to extract the information we need for individual use.

[0045] (3) Dispersion curve picking

[0046] In the dispersion spectrum of a surface wave, the dispersion curve is picked out, mainly focusing on the position of the wave crests, which represent the components of the surface wave. First, the fundamental order dispersion curve is picked out (e.g., ...). Figure 5 After suppressing the fundamental surface wave, the dispersion curves of higher orders will be clearer, which is beneficial for accurate picking.

[0047] (4) Extrapolation of dispersion curve

[0048] Due to signal-to-noise ratio and boundary issues, the dispersion curves of many control points have very low resolution, making accurate acquisition difficult. It is necessary to extrapolate the effectively acquired dispersion curves to various boundary positions within the work area to ensure that the dispersion curves can be interpolated across the entire work area (e.g., ...). Figure 6 ).

[0049] (5) Surface wave prediction

[0050] Based on the frequency band range of the dispersive surface wave and the acquisition parameters such as the total number of traces, trace spacing, minimum shot-receiver distance, and sampling interval of the actual seismic record, the model record of the dispersive surface wave can be obtained using a two-dimensional inverse Fourier transform. The formula for the two-dimensional inverse Fourier transform can be expressed as:

[0051]

[0052] (6) Surface wave attenuation

[0053] The final predicted surface wave model differs from the surface waves in the actual seismic data in terms of amplitude and phase. Using an adaptive subtraction method based on the L2 norm, matching and subtracting the actual data from the surface wave model can improve the suppression effect of dispersive surface waves (e.g., ...). Figure 7 ).

[0054] This method demonstrates excellent application results in practical data processing and is highly applicable to the spatial aliasing problem caused by sparse sampling, which cannot be solved by traditional denoising methods. Figure 8 The data presented comes from a region in North China, where the surface is complex and the weathering layer is severe, resulting in particularly well-developed surface waves with high energy and low apparent velocity, severely affecting effective reflection in the middle and deep layers. Observation of the pseudo-frequency surface waves predicted using the method presented in this paper shows a significant improvement in the signal-to-noise ratio of the denoising results, a more thorough suppression of surface wave energy in the triangular region, and the recovery of reflected wave information. Figure 9 Traditional apparent velocity filtering methods are ineffective against this spurious surface wave. Therefore, the method presented in this paper can effectively suppress surface wave energy while keeping the frequency band of the effective signal as unchanged as possible. Figure 10 ).

[0055] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the scope of protection of the present invention in any way, and all technical solutions obtained by equivalent substitution or other means fall within the scope of protection of the present invention. Parts not covered in this invention are the same as or can be implemented using existing technology.

Claims

1. A surface wave attenuation method based on two-dimensional Fourier transform, characterized in that, Includes the following steps: Step 1: After processing the raw seismic records, surface wave data is obtained; Step 2: Set control points of a certain density in the seismic records within the work area, and perform a two-dimensional Fourier transform on the data of the control points to obtain a high-precision surface wave dispersion spectrum. Step 3: Perform order-wise picking on the high-precision surface wave dispersion spectrum to obtain the multi-order dispersion curves for each control point; Step 4: Extrapolate the multi-order dispersion curves at the control points to the entire work area to obtain the local dispersion curves of the entire work area; Step 5: Perform interpolation based on the dispersion curves at each local location to simulate and obtain the surface wave prediction results for all earthquake records. Step 6: Based on the surface wave prediction results, match and subtract them from the original seismic record to obtain the seismic record after surface waves have been eliminated.

2. The surface wave attenuation method based on two-dimensional Fourier transform according to claim 1, characterized in that, In step 1, surface wave data within the 18Hz range are extracted for analysis.

3. The surface wave attenuation method based on two-dimensional Fourier transform according to claim 1, characterized in that, In step 2, a control point is set at regular intervals along the survey line in the work area. 11-19 channels are extracted at the control point and subjected to two-dimensional Fourier transform to obtain a high-precision surface wave dispersion spectrum.

4. A surface wave attenuation method based on two-dimensional Fourier transform according to claim 1 or 3, characterized in that, In step 2, the formula for the two-dimensional Fourier transform is expressed as follows: Where f(X,Y) is the spatial domain representation of the two-dimensional signal, and F(U,V) is the frequency domain representation of the signal; the variables U and V in the transform represent the horizontal and vertical components in the frequency domain, respectively, while X and Y represent the coordinates in the spatial domain; the exponent term... It is a complex exponential function that calculates the value of the corresponding point in the frequency domain based on the signal dimensions M and N and the current spatial domain coordinates (X,Y).

5. The surface wave attenuation method based on two-dimensional Fourier transform according to claim 1, characterized in that, In step 3, the peak position of the dispersion curve is picked.

6. The surface wave attenuation method based on two-dimensional Fourier transform according to claim 1, characterized in that, In step 4, the dispersion curve with effective pickup is extrapolated to each boundary position of the work area to ensure that the dispersion curve can be interpolated over the entire work area.

7. The surface wave attenuation method based on two-dimensional Fourier transform according to claim 1, characterized in that, In step 5, based on the frequency band range of the dispersive surface wave and the acquisition parameters such as the total number of traces, trace spacing, minimum shot-receiver distance, and sampling interval of the actual seismic record, the model record of the dispersive surface wave can be obtained by using a two-dimensional inverse Fourier transform.

8. The surface wave attenuation method based on two-dimensional Fourier transform according to claim 1, characterized in that, In step 6, an adaptive subtraction method based on the L2 norm is used to match and subtract the actual data from the surface wave model in order to improve the suppression effect of dispersive surface waves.

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