A method for matching P-S and P-P wave data based on wavelet compression

Abstract

The present application relates to oil and gas geophysical exploration technology field, more particularly, it relates to a kind of P-S wave data matching method based on wavelet compression, it includes the following steps: S1: PS wave seismic data is converted to compress to PP time domain;Extract seismic PP wavelet, PS wavelet, PP wavelet spectrum and PS wavelet spectrum;S2: introduce frequency variable scale conversion factor, utilize Fourier transform method to carry out wavelet compression to PS wavelet;S3: obtain filter according to PS wavelet and new PS wavelet;S4: utilize filter to carry out filter processing to PS wave seismic data after time domain conversion;S5: carry out waveform matching.The present application is based on the pre-matching in time domain, introduce frequency variable scale conversion factor, utilize the Fourier scale conversion method of frequency variable to carry out compression to PS wavelet.Then filter is designed using regularization method to carry out filter processing to original PS wave seismic record, can improve the similarity of PP, PS wave both seismic record waveform, improve the precision of final matching.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geophysical exploration, and more particularly to a method for matching longitudinal and shear wave data based on wavelet compression. Background Art

[0002] With the development of multi-wave and multi-component seismic technology, multi-wave exploration has become an effective means of reservoir prediction. Compared with single P-wave data, the use of multi-wave data for joint inversion and interpretation can effectively improve the accuracy of reservoir prediction. For example, when the target area is a gas-bearing reservoir, the P-wave will be significantly attenuated by the fluid, resulting in a fuzzy "gas cloud" area on the profile, making structural interpretation difficult. The propagation of S-waves, on the other hand, is mainly related to the stratigraphic framework and is almost unaffected by fluids. Therefore, combining S-wave interpretation with P-waves can help reduce the multi-solution nature of reservoir prediction. However, in multi-wave exploration, the generation and propagation mechanisms of P-waves and S-waves differ, resulting in significant differences in the propagation time and waveform of the acquired P- and S-wave data. Therefore, accurately matching P-waves with S-waves is the key to joint inversion and interpretation, and the quality of the matching results will affect the effectiveness of subsequent joint inversion and interpretation.

[0003] To combine information from PP waves (longitudinal waves) and PS waves (converted shear waves), the conventional method is to convert and compress the PS wave data into the PP wave time domain using the P-wave to S-wave velocity ratio, γ0. Time-shift correction is then performed based on waveform characteristics to match the PS and PP wave data. When an accurate P-wave to S-wave velocity ratio is obtained, the PS wave reflection time can be corrected to the PP wave reflection time, achieving matching. However, in most cases, the P-wave to S-wave velocity ratio is inaccurate. Therefore, nonlinear matching methods, such as dynamic time warping (DTW), are often used after time-domain matching to improve matching accuracy by applying local stretching and compression based on waveform characteristics.

[0004] However, in actual exploration, the source used is elastic waves. When propagating in the underground medium, they will be affected by the absorption and attenuation of the stratum, resulting in attenuation of amplitude and frequency band. And because PP waves and PS waves propagate differently, the degree to which they are affected by stratum absorption and attenuation is also different. Shear waves are a type of shear wave, and there are more external factors from the stratum that can cause energy attenuation, which makes their amplitude, bandwidth, and phase distortion much greater than that of longitudinal waves. Generally speaking, in actual seismic data, the bandwidth of converted shear waves is about half that of longitudinal waves, and the main frequency is also lower than that of longitudinal waves. This difference in the spectrum results in low waveform similarity between the two, longer converted shear wavelets, and lower resolution. Matching the seismic phase axis characteristics on this basis will result in more severe stretching distortion of the converted shear waves, and the final matching results are inaccurate. Summary of the Invention

[0005] In response to the problem of inaccurate P-wave and S-wave matching results in the above-mentioned prior art, the present invention provides a P-wave and S-wave data matching method based on wavelet compression, which can improve the similarity of the seismic recording waveforms of PP and PS waves and improve the accuracy of the final matching.

[0006] In order to solve the above technical problems, the technical solution provided by the present invention is:

[0007] A method for matching longitudinal and shear wave data based on wavelet compression comprises the following steps:

[0008] S1: Convert and compress the PS wave seismic data into the PP time domain to obtain the PS wave seismic data after time domain conversion, and realize time domain prematching; then extract the seismic PP wavelet, PS wavelet, PP wavelet spectrum and PS wavelet spectrum based on the PP wave seismic data and the PS wave seismic data after time domain conversion.

[0009] S2: A frequency-dependent rescaling factor a is introduced as a compression or stretching factor. Fourier transforms are used to perform wavelet compression on the PS wavelet, aligning the PS wavelet spectrum with the PP wavelet spectrum to obtain a new PS wavelet spectrum. The frequency-dependent rescaling factor varies with frequency. In practice, the wavelet spectra of the PP and PS waves do not exhibit a single stretch or compression relationship, but rather exhibit a nonlinear relationship across frequency components. This means that a fixed rescaling factor cannot fully match the two spectra. Therefore, based on conventional Fourier scaling theory, a frequency-dependent rescaling factor a is proposed. This factor, a, is no longer fixed but varies with frequency. This allows for local stretching and compression of the spectra based on the spectral characteristics of the two waves, improving matching. Under normal circumstances, PP waves have a wide bandwidth, high wavelet resolution, and a standard waveform, embodying richer information. Therefore, it is only necessary to rescale the PS wave to bring its spectrum closer to that of the longitudinal wave, compressing the wavelet to improve resolution and achieve a more accurate matching effect.

[0010] S3: Calculate a filter based on the PS wavelet and the new PS wavelet. After wavelet compression, the similarity between the PS and PP wave seismic recording waveforms is effectively improved. To stabilize this process, a filter is introduced.

[0011] S4: Using the filter to filter the PS wave seismic data after the time domain conversion to obtain new PS wave seismic data.

[0012] S5: Perform waveform matching based on the PP wave seismic data and the new PS wave seismic data.

[0013] Preferably, in step S1, the PS wave seismic data is first converted and compressed into the PP time domain according to the initial aspect ratio velocity ratio γ0 to achieve time domain prematching; then the PP wavelet, the PS wavelet, the PP wavelet spectrum and the PS wavelet spectrum are extracted from the PP wave seismic data and the prematched PS wave seismic data respectively using a spectral simulation wavelet estimation method.

[0014] Preferably, in step S2, the Fourier transform spectrum is expressed as:

[0015] W(ω)=|W(ω)|e iφ(ω) ;

[0016] Where |W(ω)| is the modulus of the complex number, which is the amplitude spectrum; φ(ω) is the argument of the complex number, which is the phase spectrum; i is the imaginary unit;

[0017] For the seismic wavelet w(t), the relationship between the seismic wavelet and the frequency-varying scale conversion factor is:

[0018]

[0019] Where a is the frequency-varying scaling factor.

[0020] Preferably, in step S2, the frequency-variant scaling factor is expressed as:

[0021]

[0022] Where, ω r is the reference frequency; n>1, m>2, and the values ​​of the two are determined according to the characteristics of the longitudinal wave frequency band. It can be found that when it is in the low frequency band, a<1, and the spectrum is stretched to the low frequency; when it is in the intermediate frequency band - when the frequency of the converted shear wave and longitudinal wave spectrum overlaps, 1≤a<2, and the spectrum is stretched in a small range; when it is in the high frequency band, a=m, and the spectrum is stretched to the high frequency. Of course, various other situations will arise in actual situations. Assuming that in the low frequency band, the spectra of the two have matched, and there is no need to add the low-frequency component of the converted shear wave, a should be equal to 1 at this time. Therefore, when processing actual data, it is necessary to select an appropriate reference frequency ω based on the specific situation. r , parameters m and n. In summary, the frequency-varying Fourier scaling transform can better match the converted shear wave spectrum and the longitudinal wave spectrum in the frequency domain, compress the converted shear wavelet, and make the two waveforms consistent in the time domain, which can improve the matching effect.

[0023] Preferably, in step S2, ω r The value of is half of the main frequency of the longitudinal wave.

[0024] Preferably, in step S3, a new PS wavelet spectrum is calculated according to the new PS wavelet, and then the filter is obtained according to the relationship between the new PS wavelet spectrum and the wavelet spectrum.

[0025] Order s pp (t) is the longitudinal wave seismic record, s ps (t) is the seismic record after converting the shear wave to the longitudinal wave time domain, and t is the longitudinal wave time. At this time, it is assumed that the two time domains are basically corresponding. According to the convolution model, the seismic record can be expressed in the time domain as the convolution of the seismic wavelet w(t) and the reflection coefficient r(t), and in the frequency domain as the product of the seismic wavelet spectrum W(ω) and the reflection coefficient spectrum R(ω). Then, assuming that the reflection coefficients of the PP wave and the PS wave are consistent, their seismic records in the frequency domain can be expressed as:

[0026]

[0027] Where S pp (ω) is the spectrum of PP wave earthquake record; S ps (ω) is the PS wave seismic record spectrum; W pp (ω) is the PP wavelet spectrum; W ps (ω) is the PS wavelet spectrum.

[0028] Preferably, in step S4, the reflection coefficients of the PP wave and the PS wave are set to be consistent, and the new PS wave seismic record spectrum is expressed as:

[0029]

[0030] Where, It is the new PS wave seismic record spectrum; is the new PS wavelet spectrum; R(ω) is the reflection coefficient spectrum; the new PS wavelet spectrum is obtained according to the above formula. The new time domain PS wave seismic record can be obtained by inverse Fourier transform.

[0031] Preferably, in step S4, the new PS wave seismic record spectrum is expressed as:

[0032] Where, is the new PS wavelet spectrum; W ps (ω) is the PS wavelet spectrum; S ps (ω) is the PS wave seismic record spectrum; H(ω) is the filter; the filter is obtained according to the above formula. The original seismic record can be directly processed by the filter to obtain a new seismic record. The key to this process is to obtain the filter.

[0033] From the above formula we can get

[0034] Preferably, in step S4, the filter is obtained by using a regularization method.

[0035] If the original wavelet spectrum W of the PS wave seismic record is known ps (ω) and the compressed wavelet spectrum If , preferably, in step S4, the regularization solution vector expression of the filter is:

[0036]

[0037] Where H is the filter vector; W ps is the original PS wavelet spectrum vector, is its complex conjugate representation; is the new PS wavelet spectrum vector; λ is the regularization parameter; and I is the identity matrix. Adding λI avoids the problem of being unable to invert a singular matrix.

[0038] The present invention has the beneficial effects of introducing a frequency-dependent scaling factor based on time-domain pre-matching. This method utilizes a frequency-dependent Fourier scaling method to broaden the PS wavelet frequency band, aligning it with the PP wavelet frequency band, thereby compressing the PS wavelet. After wavelet compression, the new PS wavelet waveform is substantially identical to the PP wavelet waveform. A filter designed using a regularization method is then used to filter the original PS wave seismic recording, improving the similarity between the PP and PS wave seismic recording waveforms and enhancing the accuracy of the final matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of multiple wave reflections after longitudinal wave incidence;

[0040] Figure 2 It is a flow chart of a method for matching longitudinal and shear wave data based on wavelet compression;

[0041] Figure 3 It is a schematic diagram of the multi-layer uniform medium model in the depth domain;

[0042] Figure 4 It is a schematic diagram of the synthetic seismic record section of PP wave and PS wave;

[0043] Figure 5 This is a single-channel comparison diagram of PP wave and PS wave synthetic seismic records. The solid line in the figure represents PP wave, and the dotted line represents PS wave.

[0044] Figure 6 This is a schematic diagram of the PP wave and PS wave seismic record profile after time domain pre-matching;

[0045] Figure 7is a single channel comparison chart of PP wave and PS wave seismic record after time domain pre-matching; the solid line represents PP wave and the dotted line represents PS wave;

[0046] Figure 8 is a PP wave and PS wave and their amplitude spectrum chart after time domain pre-matching; the solid line represents PP wave and the dotted line represents PS wave;

[0047] Figure 9 is a schematic view of the change of frequency variable scale transform factor with frequency;

[0048] Figure 10 is a schematic view of PP wave, new PS wave and their amplitude spectrum after wavelet compression;

[0049] Figure 11 is a schematic view of final PP wave and PS wave seismic record section;

[0050] Figure 12 is a single channel comparison chart of PP wave and new PS wave seismic record after wavelet compression; the solid line represents PP wave and the dotted line represents PS wave. DETAILED DESCRIPTION

[0051] The technical scheme of the present application will be further described in detail below with specific examples and in conjunction with the drawings:

[0052] Example 1

[0053] Figure 1 The phenomenon of generating converted transverse wave when longitudinal wave incident meets elastic interface in seismic exploration is shown. R1 and R2 represent elastic interface of stratum; P wave represents incident longitudinal wave, PP wave represents reflected longitudinal wave and PS wave represents reflected converted transverse wave; V p1 , V s1 , V p2 and V s2 represent longitudinal and transverse wave velocities of upper and lower two media respectively; α1, β1, α2 and β2 represent PP wave and PS wave reflection angles when the wave propagates in the first layer and the second layer respectively; s pp1 , s ps1 , s pp2 and s ps2 represent PP wave and PS wave seismic record after reflection through one layer of medium and reflection through two layers of medium respectively. Due to different propagation path and velocity, the time of receiving PP wave and PS wave stratum reflection response is different in theory, there is time shift, generally PS wave is later than PP wave. In addition, due to different attenuation effects of stratum on longitudinal wave and transverse wave, the wavelet waveforms are different, the amplitude energy of received PP wave and PS wave is also different.

[0054] This embodiment is the first embodiment of a method for matching longitudinal and transverse wave data based on wavelet compression, which can improve the similarity of the seismic recording waveforms of PP and PS waves and improve the accuracy of the final matching. Figure 2 As shown, the method includes the following steps:

[0055] S1: Convert and compress the PS wave seismic data into the PP time domain to obtain the PS wave seismic data after time domain conversion, and realize time domain prematching; then extract the seismic PP wavelet, PS wavelet, PP wavelet spectrum and PS wavelet spectrum based on the PP wave seismic data and the PS wave seismic data after time domain conversion.

[0056] S2: A frequency-dependent rescaling factor a is introduced as a compression or stretching factor. Fourier transforms are used to perform wavelet compression on the PS wavelet, aligning the PS wavelet spectrum with the PP wavelet spectrum to obtain a new PS wavelet spectrum. The frequency-dependent rescaling factor varies with frequency. In practice, the wavelet spectra of the PP and PS waves do not exhibit a single stretch or compression relationship, but rather exhibit a nonlinear relationship across frequency components. This means that a fixed rescaling factor cannot fully match the two spectra. Therefore, based on conventional Fourier scaling theory, a frequency-dependent rescaling factor a is proposed. This factor, a, is no longer fixed but varies with frequency. This allows for local stretching and compression of the spectra based on the spectral characteristics of the two waves, improving matching. Under normal circumstances, PP waves have a wide bandwidth, high wavelet resolution, and a standard waveform, embodying richer information. Therefore, it is sufficient to rescale the PS wave to bring its spectrum closer to that of the longitudinal wave, compressing the wavelet to improve resolution and achieve a more accurate matching result.

[0057] S3: Calculate the filter based on the PS wavelet and the new PS wavelet. After wavelet compression, the similarity between the PS and PP seismic waveforms is significantly improved. To stabilize this process, a filter is introduced.

[0058] S4: Using a filter to filter the PS wave seismic data after time domain conversion to obtain new PS wave seismic data.

[0059] S5: Based on the PP wave seismic data and the new PS wave seismic data, a time-domain nonlinear matching method such as dynamic time warping (DTW) is used to perform waveform matching based on the formation response characteristics to obtain the final multi-wave matching results.

[0060] Furthermore, in step S1, the PS wave seismic data is first converted and compressed into the PP time domain according to the initial aspect ratio velocity ratio γ0 to achieve time domain prematching; then the PP wavelet, PS wavelet, PP wavelet spectrum and PS wavelet spectrum are extracted from the PP wave seismic data and the prematched PS wave seismic data respectively using the wavelet estimation method of spectral simulation.

[0061] Furthermore, in step S4, a filter is obtained by using a regularization method.

[0062] The effect of this embodiment is verified by designing a multi-layer model to verify the feasibility of the method. The model parameters are shown in Table 1. The model has five layers of reflection interfaces, with a depth of about 1200m. The velocity changes with depth as shown in Figure 3 As shown, the P-wave and S-wave velocity ratio is set to 2, and the overall structure presents an anticline structure.

[0063] Table 1 Parameter information of the depth domain multi-layer model

[0064]

[0065] The model is used to synthesize seismic record profiles. The self-excited and self-reflected two-way travel time of the PP wave and PS wave stratum interface is calculated according to the P-wave and S-wave velocities and stratum thickness. In order to simulate the different absorption and attenuation effects of the underground medium on P-wave and S-wave, 40Hz and 15Hz Ricker wavelets are used to synthesize seismic records, respectively. The final PP wave and PS wave synthetic seismic record profiles are as follows: Figure 4 To intuitively analyze the different response characteristics of PP and PS waves to the same stratum, the 5th, 10th and 15th synthetic seismic records are compared, as shown in Figure 2. Figure 5 As shown in the figure, since the purpose is only to verify the effect of wavelet compression on PP and PS waveform matching, amplitude is not considered. Therefore, the amplitudes of both waves are normalized to match the maximum amplitude. The solid black line represents the PP wave seismic record, and the dashed black line represents the PS wave seismic record. A comparison of the profile and single traces shows that for reflections from the same formation, the PS wave has a slower velocity and a longer two-way travel time, and this time difference increases with depth.

[0066] Generally, seismic inversion interpretation is performed in the PP time domain. According to the P-wave and S-wave velocity ratio, the PS wave synthetic seismic record is converted and compressed into the PP time domain to obtain the PP and PS wave seismic profiles after time domain pre-matching, such as Figure 6 As shown. At this time, it can be seen from the profile that the reflection time and depth of the same stratum are basically corresponding, and as time is compressed, the vertical resolution of the PS wave seismic profile changes and the phase axis becomes narrower. Still taking the 5th, 10th and 15th seismic records for comparison, as shown Figure 7 As shown in Figure 2, it can be found that although the response times of PP and PS waves to the same formation have matched, there are still differences in the waveforms, and this difference will be more obvious in actual data.

[0067] After pre-matching, the PP and PS wavelets are extracted using the spectrum simulation method. The results are as follows: Figure 7As shown, the black solid line is the PP wavelet and its amplitude spectrum, and the black dotted line is the PS wavelet and its amplitude spectrum. It can be found that the main frequency of the PP wavelet is still 40Hz, while the main frequency of the PS wavelet is about 24Hz, which means that after time domain compression, the PS wavelet becomes narrower and the main frequency is improved to a certain extent. Since the PP and PS wavelets used in the implementation of the synthetic data in this patent are both Ricker wavelets, after the time domain pre-matching, the wavelet morphologies of the two are already close. At this time, the PS waveform can be directly transformed to be consistent with the PP wave through some time domain nonlinear matching methods according to the waveform characteristics to complete the final matching. However, in actual seismic records, there are obvious differences in the waveforms of the two. The PS wave is more affected by absorption attenuation, the wavelet tail is longer, and obvious distortion will occur. If they are directly matched according to the waveform characteristics, the error between the two will become larger and larger. Therefore, when faced with complex situations, the method proposed in this patent stretches and compresses the PS wavelet spectrum in the frequency domain, supplements its low-frequency or high-frequency components to make it consistent with the PP wavelet spectrum. The PS wavelet will be compressed in the time domain, the wavelet morphology will also change, and the similarity between the PP and PS waveforms will be improved, which is a more reasonable choice.

[0068] In view of the inconsistency between PS wavelet and PP wavelet, the frequency-variable Fourier scaling transform method is used to compress the PS wavelet. Figure 8 By comparing the amplitude spectra of , we can find that the PS wavelet spectrum needs to be compressed at low frequencies and stretched at high frequencies. By calculating the amplitude spectra of the two, we can obtain the scale transformation factor that varies with frequency. The results are as follows: Figure 9 As shown. Figure 9 The frequency-varying scaling factor shown in the figure processes the PS wavelet, and the result is compared with the PP wavelet. Figure 10 As shown in the figure, the solid black line represents the PP wavelet and its amplitude spectrum, while the dashed black line represents the new PS wavelet and its amplitude spectrum after Fourier scaling. By comparison, we can see that after Fourier scaling wavelet compression, the new PS wavelet and PP wavelet waveforms completely overlap, and the amplitude spectrum bandwidth is consistent, demonstrating the feasibility of this method and its ability to effectively achieve consistency between the PS and PP wavelets.

[0069] After obtaining the new PS wavelet after Fourier scale transform wavelet compression, the filter is solved by using the regularization method, and the PS wave seismic record is filtered to obtain the seismic record profile after the overall wavelet compression. The result is compared with the PP wave seismic record. Figure 11 As shown in the figure, it can be found that the PS wave phase axis becomes narrower, the resolution is improved, and the profile characteristics are basically consistent with those of the PP wave. Figure 12 This is a comparison of single-channel seismic records before and after processing, where the black solid line is the PP wave seismic record and the black dotted line is the new PS wave seismic record. Figure 12It can be found that after wavelet compression, the PS wave seismic record and the PP wave seismic record have a very high matching degree and the waveforms correspond accurately, which proves the correctness of the proposed method and has good application prospects.

[0070] This embodiment has the beneficial effects of introducing a frequency-dependent scaling factor based on time-domain pre-matching. Using a frequency-dependent Fourier scaling method, the PS wavelet frequency band is broadened to align with the PP wavelet frequency band, compressing the PS wavelet. After wavelet compression, the new PS wavelet waveform is substantially identical to the PP wavelet waveform. A filter designed using a regularization method is then used to filter the original PS wave seismic record, improving the similarity between the PP and PS wave seismic record waveforms and ultimately enhancing the accuracy of the final matching.

[0071] Example 2

[0072] This embodiment further illustrates step S2 based on embodiment 1.

[0073] Furthermore, in step S2, the Fourier transform spectrum is expressed as:

[0074] W(ω)=|W(ω)|e iφ(ω) ;

[0075] Where |W(ω)| is the modulus of the complex number, which is the amplitude spectrum; φ(ω) is the argument of the complex number, which is the phase spectrum; i is the imaginary unit;

[0076] For the seismic wavelet w(t), the relationship between the seismic wavelet and the frequency-varying scale transformation factor is:

[0077]

[0078] Where a is the frequency-varying scaling factor.

[0079] Furthermore, in step S2, the frequency-varying scaling factor is expressed as:

[0080]

[0081] Where, ω r is the reference frequency; n>1, m>2, and the values ​​of the two are determined according to the characteristics of the longitudinal wave frequency band. It can be found that when it is in the low frequency band, a<1, and the spectrum is stretched to the low frequency; when it is in the intermediate frequency band - when the frequency of the converted shear wave and longitudinal wave spectrum overlaps, 1≤a<2, and the spectrum is stretched in a small range; when it is in the high frequency band, a=m, and the spectrum is stretched to the high frequency. Of course, various other situations will arise in actual situations. Assuming that in the low frequency band, the spectra of the two have matched, and there is no need to add the low-frequency component of the converted shear wave, a should be equal to 1 at this time. Therefore, when processing actual data, it is necessary to select an appropriate reference frequency ω based on the specific situation. r, parameters m and n. In summary, the frequency-varying Fourier scaling transform can better match the converted shear wave spectrum and the longitudinal wave spectrum in the frequency domain, compress the converted shear wavelet, and make the two waveforms consistent in the time domain, which can improve the matching effect.

[0082] Furthermore, in step S2, ω r The value of is half of the main frequency of the longitudinal wave.

[0083] Other features, working principles and beneficial effects of this embodiment are consistent with those of embodiment 1.

[0084] Example 3

[0085] This embodiment further illustrates steps S3 and S4 based on embodiment 2.

[0086] Order s pp (t) is the longitudinal wave seismic record, s ps (t) is the seismic record after converting the shear wave to the longitudinal wave time domain, and t is the longitudinal wave time. At this time, it is assumed that the two time domains are basically corresponding. According to the convolution model, the seismic record can be expressed in the time domain as the convolution of the seismic wavelet w(t) and the reflection coefficient r(t), and in the frequency domain as the product of the seismic wavelet spectrum W(ω) and the reflection coefficient spectrum R(ω). Then, assuming that the reflection coefficients of the PP wave and the PS wave are consistent, their seismic records in the frequency domain can be expressed as:

[0087]

[0088] Where S pp (ω) is the spectrum of PP wave earthquake record; S ps (ω) is the PS wave seismic record spectrum; W pp (ω) is the PP wavelet spectrum; W ps (ω) is the PS wavelet spectrum.

[0089] Furthermore, in step S4, the reflection coefficients of the PP wave and the PS wave are set to be consistent, and the new PS wave seismic record spectrum is expressed as:

[0090]

[0091] Where, It is the new PS wave seismic record spectrum; is the new PS wavelet spectrum; R(ω) is the reflection coefficient spectrum; the new PS wavelet spectrum is obtained according to the above formula. The new time domain PS wave seismic record can be obtained by inverse Fourier transform.

[0092] Furthermore, in step S4, the new PS wave seismic record spectrum is expressed as:

[0093]

[0094] Where, is the new PS wavelet spectrum; W ps (ω) is the PS wavelet spectrum; S ps (ω) is the PS wave seismic record spectrum; H(ω) is the filter; the filter is obtained according to the above formula. The original seismic record can be directly processed by the filter to obtain a new seismic record. The key to this process is to obtain the filter. From the above formula, we can get

[0095] If the original wavelet spectrum W of the PS wave seismic record is known ps (ω) and the compressed wavelet spectrum Then, further, in step S4, the regularization solution vector expression of the filter is:

[0096]

[0097] Where H is the filter vector; W ps is the original PS wavelet spectrum vector, is its complex conjugate representation; is the new PS wavelet spectrum vector; λ is the regularization parameter; and I is the identity matrix. Adding λI avoids the problem of being unable to invert a singular matrix.

[0098] Other features, working principles and beneficial effects of this embodiment are consistent with those of Example 2.

[0099] In the specific contents of the above-mentioned specific implementation methods, the various technical features can be combined in any non-contradictory manner. In order to make the description concise, not all possible combinations of the above-mentioned technical features are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0100] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description, and it is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for matching longitudinal and transverse wave data based on wavelet compression, characterized in that: The following steps are involved: S1: converting and compressing the PS wave seismic data into the PP time domain to obtain the PS wave seismic data after time domain conversion; then extracting the seismic PP wavelet, PS wavelet, PP wavelet spectrum and PS wavelet spectrum based on the PP wave seismic data and the PS wave seismic data after time domain conversion; S2: Introducing a frequency-variant scaling factor as a compression or stretching scale, and using the Fourier transform method to perform wavelet compression on the PS wavelet, so that the PS wavelet spectrum is consistent with the PP wavelet spectrum, thereby obtaining a new PS wavelet; wherein the frequency-variant scaling factor changes with the frequency; S3: Obtain a filter based on the PS wavelet and the new PS wavelet; S4: Using the filter to filter the PS wave seismic data after the time domain conversion to obtain new PS wave seismic data; S5: performing waveform matching based on the PP wave seismic data and the new PS wave seismic data; In step S2, the Fourier transform spectrum is expressed as: ； Where, is the modulus of the complex number, which is the amplitude spectrum; is the argument of the complex number, which is the phase spectrum; is an imaginary unit; For seismic wavelets , the relationship between the seismic wavelet and the frequency-varying scale transformation factor is: ； Where, is the frequency-varying scaling factor; In step S2, the frequency-varying scaling factor is expressed as: ； Where, is the reference frequency; , , and the values ​​of both are determined according to the characteristics of the longitudinal wave frequency band; In the step S3, a new PS wavelet spectrum is calculated based on the new PS wavelet, and then the filter is obtained based on the relationship between the new PS wavelet spectrum and the PS wavelet spectrum; In step S4, the new PS wave seismic record spectrum is expressed in the frequency domain as: ； Where, is the new PS wavelet spectrum; is the PS wavelet spectrum; is the PS wave seismic record spectrum; is a filter; the filter is obtained according to the above formula.

2. The method for matching longitudinal and shear wave data based on wavelet compression according to claim 1, characterized in that: In step S1, firstly according to the initial aspect ratio speed ratio , converting and compressing the PS wave seismic data into the PP time domain; and then using a spectral simulation wavelet estimation method to extract the PP wavelet, the PS wavelet, the PP wavelet spectrum and the PS wavelet spectrum from the PP wave seismic data and the PS wave seismic data after the time domain conversion.

3. The method for matching longitudinal and shear wave data based on wavelet compression according to claim 1, characterized in that: In step S2, The value of is half of the main frequency of the longitudinal wave.

4. The method for matching longitudinal and shear wave data based on wavelet compression according to claim 1, characterized in that: In step S4, the reflection coefficients of the PP wave and the PS wave are set to be consistent, and the new PS wave seismic record spectrum is expressed as: ； Where, It is the new PS wave seismic record spectrum; is the new PS wavelet spectrum; is the reflection coefficient spectrum; the new PS wavelet spectrum is obtained according to the above formula.

5. The method for matching longitudinal and shear wave data based on wavelet compression according to claim 4, characterized in that: In the step S4, the filter is obtained by using a regularization method.

6. The method for matching longitudinal and shear wave data based on wavelet compression according to claim 5, characterized in that: In step S4, the regularization solution vector expression of the filter is: ; Where, is the filter vector; is the original PS wavelet spectrum vector, is its complex conjugate representation; is the new PS wavelet spectrum vector; is the regularization parameter; is the identity matrix.

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