Four-component earth surface consistency deconvolution method and device, electronic equipment and medium

Abstract

The invention discloses a four-component earth surface consistency deconvolution method and device, electronic equipment and a medium. The method comprises the following steps: performing consistency spectrum analysis according to seismic data; performing four-component earth surface consistency spectrum decomposition; calculating a four-component earth surface consistency deconvolution operator; and applying the deconvolution operator to the seismic data to obtain processed data. According to the invention, the longitudinal resolution of the seismic data can be improved, and the amplitude, frequency and phase consistency of the seismic data can be maintained.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and more specifically, to a four-component surface consistency deconvolution method, device, electronic equipment and medium. Background Art

[0002] With the development of oil and gas exploration technology and the deepening of exploration and development, the precise imaging of complex, micro-structures and deep structures has attracted more and more attention. Due to the complex seismic geological conditions in most exploration areas, the signal-to-noise ratio of seismic data is low, the resolution is not high, and the imaging of small faults, small-amplitude structures and complex structures is unclear, especially the lithologic reservoirs, which are generally small in volume. At this time, conventional seismic exploration methods can no longer meet this requirement, and high-precision exploration must be carried out. High-precision processing has also developed accordingly with the implementation of high-precision acquisition. The overall goal of high-precision data processing technology is to pursue high signal-to-noise ratio, high resolution, and high fidelity, that is, "three highs" processing. Therefore, various processing methods to improve signal-to-noise ratio, resolution, and fidelity should be thoroughly studied to lay a good foundation for the next step of accurate interpretation. These technologies play a very important role in finding concealed oil and gas reservoirs such as complex fault blocks or thin interbeds, lithologic traps, etc. At the same time, they also reduce exploration risks to a minimum and improve exploration benefits. High resolution is the basic requirement for high-precision data processing, and deconvolution technology is one of the main means to improve the resolution of seismic data.

[0003] Deconvolution is the process of improving the temporal resolution of seismic data by compressing basic seismic wavelets. Deconvolution methods are based on the fact that the propagation process of seismic waves is a linear system that conforms to the convolution model, that is, seismic data is composed of the convolution of the source wavelet and the formation reflection coefficient sequence, plus some random noise. The reflection coefficient sequence itself has sufficient resolution, and as long as the influence of the wavelet is removed, the purpose of improving the vertical resolution of the data can be achieved.

[0004] The commonly used single-channel pulse deconvolution and single-channel predictive deconvolution are to autocorrelate or cross-correlate the data of each channel to obtain the deconvolution factor of the channel, and then use this deconvolution factor to convolve with the data of the channel to form the convolution result. The single-channel deconvolution method has two assumptions: one is that the reflection coefficient sequence is a white noise random sequence; the other is that the input wavelet is the minimum phase. However, the actual input seismic trace does not completely meet these two conditions. Since the conditions of each excitation point of the onshore earthquake (such as source coupling, explosive quantity, surface lithology, etc.) and the conditions of the receiving point (surface coupling, low-velocity zone changes) are different, the characteristics of adjacent seismic traces are different, and the calculated deconvolution factor becomes unstable. On the other hand, the influence of random noise aggravates this instability.

[0005] There is still a need to develop a four-component surface consistent deconvolution method.

[0006] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0007] The present invention proposes a four-component surface consistency deconvolution method, device, electronic equipment and medium, which can improve the vertical resolution of seismic data while maintaining the consistency of the amplitude, frequency and phase of the seismic data.

[0008] In a first aspect, an embodiment of the present disclosure provides a four-component surface consistency deconvolution method, comprising:

[0009] Conduct consistent spectrum analysis based on seismic data;

[0010] Perform four-component surface consistency spectrum decomposition;

[0011] Calculate the four-component surface consistent deconvolution operator;

[0012] The deconvolution operator is applied to the seismic data to obtain processed data.

[0013] As a specific implementation of the embodiment of the present disclosure, performing consistency spectrum analysis based on seismic data and performing four-component surface consistency spectrum decomposition includes:

[0014] Establish a convolution model of earthquake records;

[0015] Based on the surface consistency assumption, seismic wavelets are calculated;

[0016] Performing Fourier transform on the seismic wavelet to decompose it into an amplitude spectrum and a phase spectrum;

[0017] The logarithm of the amplitude spectrum is taken to obtain a logarithmic amplitude spectrum equation, and then a logarithmic amplitude spectrum equation group is obtained.

[0018] As a specific implementation of the embodiment of the present disclosure, the seismic record convolution model is:

[0019] x(t)=w(t)*y(t)+n(t)

[0020] Among them, x(t) is the seismic record, w(t) is the seismic wavelet, y(t) is the formation reflection coefficient, and n(t) is the random noise.

[0021] As a specific implementation of the embodiment of the present disclosure, the seismic wavelet is:

[0022] W ij (t) = S i (t)*Rj (t)*C k (t)*O l (t)

[0023] Among them, W ij (t) is the seismic wavelet, i is the shot point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, S i (t) is the wavelet component with the influence of the shot point, R j (t) is the wavelet component with the influence of the detection point, C k (t) is the wavelet component related to the common center point, O l (t) is the wavelet component related to the offset.

[0024] As a specific implementation of the embodiment of the present disclosure, the amplitude spectrum is:

[0025]

[0026] Among them, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0027] As a specific implementation of the embodiment of the present disclosure, calculating the four-component surface consistency deconvolution operator includes:

[0028] Solving the logarithmic amplitude spectrum equations by the conjugate gradient method to calculate the deconvolution factor;

[0029] exponentiating and squaring the deconvolution factor and then performing an inverse Fourier transform to obtain its autocorrelation function;

[0030] A prediction filter equation is constructed according to the autocorrelation function, and the prediction filter equation is solved to obtain a prediction filter operator, thereby obtaining the deconvolution operator.

[0031] As a specific implementation of the embodiment of the present disclosure, the logarithmic amplitude spectrum equation group is:

[0032]

[0033]

[0034]

[0035]

[0036] Where i is the firing point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0037] In a second aspect, the present disclosure also provides a four-component surface consistency deconvolution device, including:

[0038] Analysis module, which performs consistency spectrum analysis based on seismic data;

[0039] Decomposition module, which performs four-component surface consistency spectrum decomposition;

[0040] A calculation module, which calculates the four-component surface consistent deconvolution operator;

[0041] An application module applies the deconvolution operator to the seismic data to obtain processed data.

[0042] As a specific implementation of the embodiment of the present disclosure, performing consistency spectrum analysis based on seismic data and performing four-component surface consistency spectrum decomposition includes:

[0043] Establish a convolution model of earthquake records;

[0044] Based on the surface consistency assumption, seismic wavelets are calculated;

[0045] Performing Fourier transform on the seismic wavelet to decompose it into an amplitude spectrum and a phase spectrum;

[0046] The logarithm of the amplitude spectrum is taken to obtain a logarithmic amplitude spectrum equation, and then a logarithmic amplitude spectrum equation group is obtained.

[0047] As a specific implementation of the embodiment of the present disclosure, the seismic record convolution model is:

[0048] x(t)=w(t)*y(t)+n(t)

[0049] Among them, x(t) is the seismic record, w(t) is the seismic wavelet, y(t) is the formation reflection coefficient, and n(t) is the random noise.

[0050] As a specific implementation of the embodiment of the present disclosure, the seismic wavelet is:

[0051] W ij (t) = S i (t)*R j (t)*Ck (t)*O l (t)

[0052] Among them, W ij (t) is the seismic wavelet, i is the shot point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, S i (t) is the wavelet component with the influence of the shot point, R j (t) is the wavelet component with the influence of the detection point, C k (t) is the wavelet component related to the common center point, O l (t) is the wavelet component related to the offset.

[0053] As a specific implementation of the embodiment of the present disclosure, the amplitude spectrum is:

[0054]

[0055] Among them, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0056] As a specific implementation of the embodiment of the present disclosure, calculating the four-component surface consistency deconvolution operator includes:

[0057] Solving the logarithmic amplitude spectrum equations by the conjugate gradient method to calculate the deconvolution factor;

[0058] exponentiating and squaring the deconvolution factor and then performing an inverse Fourier transform to obtain its autocorrelation function;

[0059] A prediction filter equation is constructed according to the autocorrelation function, and the prediction filter equation is solved to obtain a prediction filter operator, thereby obtaining the deconvolution operator.

[0060] As a specific implementation of the embodiment of the present disclosure, the logarithmic amplitude spectrum equation group is:

[0061]

[0062]

[0063]

[0064]

[0065] Where i is the firing point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0066] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0067] A memory storing executable instructions;

[0068] A processor runs the executable instructions in the memory to implement the four-component surface consistent deconvolution method.

[0069] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium storing a computer program, which implements the four-component surface consistency deconvolution method when executed by a processor.

[0070] Its beneficial effects are:

[0071] The present invention is based on the idea that seismic wavelets can be decomposed into multiple components such as common shot points, common receiving points, common offset distances, etc., and eliminates the mixed effects of filters in the directions of shot points, detection points, common depth points and shot offset distances. The deconvolution factor obtained is relatively stable, and the convolution effect makes the amplitude, frequency and phase of the seismic record consistent, which can better compress the seismic wavelets and is more conducive to improving the superposition and offset effects.

[0072] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0074] Figure 1 A flowchart showing the steps of a four-component surface consistent deconvolution method according to an embodiment of the present invention.

[0075] Figure 2aand Figure 2b Schematic diagrams comparing the effects before and after processing according to an embodiment of the present invention are respectively shown.

[0076] Figure 3 A block diagram of a four-component surface consistent deconvolution device according to an embodiment of the present invention is shown.

[0077] Description of reference numerals:

[0078] 201, analysis module; 202, decomposition module; 203, calculation module; 204, application module. DETAILED DESCRIPTION

[0079] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0080] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0081] Example 1

[0082] Figure 1 A flowchart showing the steps of a four-component surface consistent deconvolution method according to an embodiment of the present invention.

[0083] like Figure 1 As shown, the four-component surface consistent deconvolution method includes: step 101, performing consistent spectrum analysis based on seismic data; step 102, performing four-component surface consistent spectrum decomposition; step 103, calculating the four-component surface consistent deconvolution operator; step 104, applying the deconvolution operator to the seismic data to obtain processed data.

[0084] In one example, performing consistency spectrum analysis based on seismic data and performing four-component surface consistency spectrum decomposition includes:

[0085] Establish a convolution model of earthquake records;

[0086] Based on the surface consistency assumption, seismic wavelets are calculated;

[0087] Perform Fourier transform on the seismic wavelet and decompose it into amplitude spectrum and phase spectrum;

[0088] The logarithm of the amplitude spectrum is taken to obtain the logarithmic amplitude spectrum equation, and then the logarithmic amplitude spectrum equation group is obtained.

[0089] In one example, the seismic record convolution model is:

[0090] x(t)=w(t)*y(t)+n(t)

[0091] Among them, x(t) is the seismic record, w(t) is the seismic wavelet, y(t) is the formation reflection coefficient, and n(t) is the random noise.

[0092] In one example, the seismic wavelet is:

[0093] W ij (t) = S i (t)*R j (t)*C k (t)*O l (t)

[0094] Among them, W ij (t) is the seismic wavelet, i is the shot point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, S i (t) is the wavelet component with the influence of the shot point, R j (t) is the wavelet component with the influence of the detection point, C k (t) is the wavelet component related to the common center point, O l (t) is the wavelet component related to the offset.

[0095] In one example, the amplitude spectrum is:

[0096]

[0097] Among them, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0098] In one example, computing a four-component surface consistent deconvolution operator includes:

[0099] The deconvolution factors are calculated by solving the log-amplitude spectrum equations using the conjugate gradient method.

[0100] The deconvolution factor is exponentiated, squared, and then inverse Fourier transform is performed to obtain its autocorrelation function;

[0101] The prediction filter equation is constructed according to the autocorrelation function, and the prediction filter operator is obtained by solving the prediction filter equation, and then the deconvolution operator is obtained.

[0102] In one example, the system of equations for the logarithmic amplitude spectrum is:

[0103]

[0104]

[0105]

[0106]

[0107] Where i is the firing point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of the detection point, A Ck is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0108] Specifically, the main function of surface consistent deconvolution is wavelet shaping and improving vertical resolution. The seismic record convolution model is expressed as:

[0109] x(t)=w(t)*y(t)+n(t)

[0110] Among them, x(t) is the seismic record, w(t) is the seismic wavelet, y(t) is the formation reflection coefficient, and n(t) is the random noise.

[0111] According to the surface consistency assumption, for the seismic wavelet W recorded at shot point i and receiving point j, ij (t) can be expressed as:

[0112] W ij (t) = S i (t)*R j (t)*C k (t)*O l (t)

[0113] Where k = (i + j) / 2, l = (ij) / 2, S i (t) is the wavelet component with the influence of the shot point, R j (t) is the wavelet component with the influence of the detection point, C k (t) is the wavelet component related to the common center point, O l (t) is the wavelet component related to the offset.

[0114] The Fourier transform of the above equation is:

[0115] W ij (w) = S i (w)*R j (w)*C k(w)*O l (w)

[0116] The above formula is decomposed into the amplitude spectrum A ij and phase spectrum Two parts:

[0117]

[0118]

[0119] in, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset, and the same is true for the phase spectrum.

[0120] Assuming the wavelet is of minimum phase, we only need to consider the amplitude spectrum of the wavelet and take the logarithm of the amplitude spectrum:

[0121]

[0122] To achieve the minimum, the module mainly uses the conjugate gradient method to solve the linear equations and find the deconvolution factor.

[0123] When the shot point and the receiver point change, the logarithmic amplitude spectrum equation will become a series of equations. The number of equations is generally greater than the number of unknowns, but the number of independent equations is generally less than the number of unknowns, so the equation group has no definite solution.

[0124] In order to solve the equations, the least square method is used, that is, the error energy between the input spectrum and the decomposition spectrum is required to be minimized. Then the iterative calculation formula of the four components can be obtained, and then the Gauss-Seidel method is used to calculate the shot point, detection point, common center point and common offset distance components of the wavelet logarithmic amplitude spectrum.

[0125] Decompose each component, and the total error energy between the sum of the decomposed components and the original logarithmic amplitude spectrum is:

[0126]

[0127] The error is required to be minimized, thus obtaining the following four Gauss-Seidel iterative equations:

[0128]

[0129]

[0130]

[0131]

[0132] For the four components of the wavelet logarithmic amplitude spectrum, add together the components of a recorded wavelet to obtain the logarithmic amplitude spectrum of the seismic trace.

[0133] If all four components are added together, the result is the autocorrelation function of the entire seismic wavelet w(t). The deconvolution operator obtained with it can compress the wavelet and improve the resolution. If the prediction distance parameter is set to a sampling interval, the result is a pulse deconvolution in the sense of surface consistency.

[0134] However, excessively improving the resolution before stacking may increase the impact of the error of dynamic and static correction on stacking. Therefore:

[0135] a. If four components are used to calculate the deconvolution operator, the prediction distance parameter should be appropriately increased;

[0136] b. If the purpose is only to eliminate the influence of surface inconsistency on the wavelet, then it is only necessary to use the common shot point and common detection point components to calculate the deconvolution operator. In this case, the prediction distance parameter can be smaller, even using one sampling interval.

[0137] The deconvolution factor is exponentiated, squared, and then inverse Fourier transform is performed to obtain its autocorrelation function. With the autocorrelation function, the prediction filter equation can be constructed, and the prediction filter operator is solved to obtain the deconvolution operator. The deconvolution operator is then convolved with the seismic record to obtain the processed data.

[0138] Example 2

[0139] The present invention also provides a four-component surface consistency deconvolution device, comprising:

[0140] Analysis module, which performs consistency spectrum analysis based on seismic data;

[0141] Decomposition module, which performs four-component surface consistency spectrum decomposition;

[0142] A calculation module, which calculates the four-component surface consistent deconvolution operator;

[0143] The application module applies the deconvolution operator to the seismic data to obtain the processed data.

[0144] In one example, performing consistency spectrum analysis based on seismic data and performing four-component surface consistency spectrum decomposition includes:

[0145] Establish a convolution model of earthquake records;

[0146] Based on the surface consistency assumption, seismic wavelets are calculated;

[0147] Perform Fourier transform on the seismic wavelet and decompose it into amplitude spectrum and phase spectrum;

[0148] The logarithm of the amplitude spectrum is taken to obtain the logarithmic amplitude spectrum equation, and then the logarithmic amplitude spectrum equation group is obtained.

[0149] In one example, the seismic record convolution model is:

[0150] x(t)=w(t)*y(t)+n(t)

[0151] Among them, x(t) is the seismic record, w(t) is the seismic wavelet, y(t) is the formation reflection coefficient, and n(t) is the random noise.

[0152] In one example, the seismic wavelet is:

[0153] W ij (t) = S i (t)*R j (t)*C k (t)*O l (t)

[0154] Among them, W ij (t) is the seismic wavelet, i is the shot point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, S i (t) is the wavelet component with the influence of the shot point, R j (t) is the wavelet component with the influence of the detection point, C k (t) is the wavelet component related to the common center point, O l (t) is the wavelet component related to the offset.

[0155] In one example, the amplitude spectrum is:

[0156]

[0157] Among them, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0158] In one example, computing a four-component surface consistent deconvolution operator includes:

[0159] The deconvolution factors are calculated by solving the log-amplitude spectrum equations using the conjugate gradient method.

[0160] The deconvolution factor is exponentiated, squared, and then inverse Fourier transform is performed to obtain its autocorrelation function;

[0161] The prediction filter equation is constructed according to the autocorrelation function, and the prediction filter operator is obtained by solving the prediction filter equation, and then the deconvolution operator is obtained.

[0162] In one example, the system of equations for the logarithmic amplitude spectrum is:

[0163]

[0164]

[0165]

[0166]

[0167] Where i is the firing point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0168] Specifically, the main function of surface consistent deconvolution is wavelet shaping and improving vertical resolution. The seismic record convolution model is expressed as:

[0169] x(t)=w(t)*y(t)+n(t)

[0170] Among them, x(t) is the seismic record, w(t) is the seismic wavelet, y(t) is the formation reflection coefficient, and n(t) is the random noise.

[0171] According to the surface consistency assumption, for the seismic wavelet W recorded at shot point i and receiving point j, ij (t) can be expressed as:

[0172] W ij (t) = S i (t)*R j (t)*C k (t)*O l (t)

[0173] Where k = (i + j) / 2, l = (ij) / 2, S i (t) is the wavelet component with the influence of the shot point, R j (t) is the wavelet component with the influence of the detection point, C k (t) is the wavelet component related to the common center point, O l(t) is the wavelet component related to the offset.

[0174] The Fourier transform of the above equation is:

[0175] W ij (w) = S i (w)*R j (w)*C k (w)*O l (w)

[0176] The above formula is decomposed into the amplitude spectrum A ij and phase spectrum Two parts:

[0177]

[0178]

[0179] in, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset, and the same is true for the phase spectrum.

[0180] Assuming the wavelet is of minimum phase, we only need to consider the amplitude spectrum of the wavelet and take the logarithm of the amplitude spectrum:

[0181]

[0182] To achieve the minimum, the module mainly uses the conjugate gradient method to solve the linear equations and find the deconvolution factor.

[0183] When the shot point and the receiver point change, the logarithmic amplitude spectrum equation will become a series of equations. The number of equations is generally greater than the number of unknowns, but the number of independent equations is generally less than the number of unknowns, so the equation group has no definite solution.

[0184] In order to solve the equations, the least square method is used, that is, the error energy between the input spectrum and the decomposition spectrum is required to be minimized. Then the iterative calculation formula of the four components can be obtained, and then the Gauss-Seidel method is used to calculate the shot point, detection point, common center point and common offset distance components of the wavelet logarithmic amplitude spectrum.

[0185] Decompose each component, and the total error energy between the sum of the decomposed components and the original logarithmic amplitude spectrum is:

[0186]

[0187] The error is required to be minimized, thus obtaining the following four Gauss-Seidel iterative equations:

[0188]

[0189]

[0190]

[0191]

[0192] For the four components of the wavelet logarithmic amplitude spectrum, add together the components of a recorded wavelet to obtain the logarithmic amplitude spectrum of the seismic trace.

[0193] If all four components are added together, the result is the autocorrelation function of the entire seismic wavelet w(t). The deconvolution operator obtained with it can compress the wavelet and improve the resolution. If the prediction distance parameter is set to a sampling interval, the result is a pulse deconvolution in the sense of surface consistency.

[0194] However, excessively improving the resolution before stacking may increase the impact of the error of dynamic and static correction on stacking. Therefore:

[0195] a. If four components are used to calculate the deconvolution operator, the prediction distance parameter should be appropriately increased;

[0196] b. If the purpose is only to eliminate the influence of surface inconsistency on the wavelet, then it is only necessary to use the common shot point and common detection point components to calculate the deconvolution operator. In this case, the prediction distance parameter can be smaller, even using one sampling interval.

[0197] The deconvolution factor is exponentiated, squared, and then inverse Fourier transform is performed to obtain its autocorrelation function. With the autocorrelation function, the prediction filter equation can be constructed, and the prediction filter operator is solved to obtain the deconvolution operator. The deconvolution operator is then convolved with the seismic record to obtain the processed data.

[0198] Example 1

[0199] Figure 2a and Figure 2b Schematic diagrams comparing the effects before and after processing according to an embodiment of the present invention are respectively shown.

[0200] In order to verify the effect of this method, prestack seismic data of a certain work area were selected. Figure 2a , Figure 2b As shown, the results show that the vertical resolution of seismic data is significantly improved by using this method.

[0201] This method is based on the idea that seismic wavelets can be decomposed into multiple components such as common shot points, common receiving points, common offsets, etc. It eliminates the mixed effects of filters in the directions of shot points, detection points, common depth points and shot offsets. The deconvolution factor obtained is relatively stable. The convolution effect makes the amplitude, frequency and phase of the seismic record consistent, which can better compress the seismic wavelets and is more conducive to improving the stacking and offset effects.

[0202] Example 4

[0203] Figure 3 A block diagram of a four-component surface consistent deconvolution device according to an embodiment of the present invention is shown.

[0204] like Figure 3 As shown, the four-component surface consistency deconvolution device comprises:

[0205] An analysis module 201 performs a consistency spectrum analysis based on seismic data;

[0206] Decomposition module 202, performing four-component surface consistency spectrum decomposition;

[0207] A calculation module 203 calculates a four-component surface consistency deconvolution operator;

[0208] The application module 204 applies the deconvolution operator to the seismic data to obtain processed data.

[0209] As a specific implementation of the embodiment of the present disclosure, performing consistency spectrum analysis based on seismic data and performing four-component surface consistency spectrum decomposition includes:

[0210] Establish a convolution model of earthquake records;

[0211] Based on the surface consistency assumption, seismic wavelets are calculated;

[0212] Perform Fourier transform on the seismic wavelet and decompose it into amplitude spectrum and phase spectrum;

[0213] The logarithm of the amplitude spectrum is taken to obtain the logarithmic amplitude spectrum equation, and then the logarithmic amplitude spectrum equation group is obtained.

[0214] As a specific implementation of the embodiment of the present disclosure, the seismic record convolution model is:

[0215] x(t)=w(t)*y(t)+n(t)

[0216] Among them, x(t) is the seismic record, w(t) is the seismic wavelet, y(t) is the formation reflection coefficient, and n(t) is the random noise.

[0217] As a specific implementation of the embodiment of the present disclosure, the seismic wavelet is:

[0218] W ij (t) = S i (t)*R j (t)*C k (t)*O l (t)

[0219] Among them, W ij (t) is the seismic wavelet, i is the shot point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, S i (t) is the wavelet component with the influence of the shot point, R j (t) is the wavelet component with the influence of the detection point, C k (t) is the wavelet component related to the common center point, O l (t) is the wavelet component related to the offset.

[0220] As a specific implementation of the embodiment of the present disclosure, the amplitude spectrum is:

[0221]

[0222] Among them, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0223] As a specific implementation of the embodiment of the present disclosure, calculating the four-component surface consistency deconvolution operator includes:

[0224] The deconvolution factors are calculated by solving the log-amplitude spectrum equations using the conjugate gradient method.

[0225] The deconvolution factor is exponentiated, squared, and then inverse Fourier transform is performed to obtain its autocorrelation function;

[0226] The prediction filter equation is constructed according to the autocorrelation function, and the prediction filter operator is obtained by solving the prediction filter equation, and then the deconvolution operator is obtained.

[0227] As a specific implementation of the embodiment of the present disclosure, the logarithmic amplitude spectrum equation group is:

[0228]

[0229]

[0230]

[0231]

[0232] Where i is the firing point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

[0233] Example 5

[0234] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the above-mentioned four-component surface consistency deconvolution method.

[0235] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0236] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0237] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0238] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0239] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0240] Example 6

[0241] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, which implements the four-component surface consistency deconvolution method when executed by a processor.

[0242] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.

[0243] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0244] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0245] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A four-component surface consistency deconvolution method, characterized in that: include: Conduct consistent spectrum analysis based on seismic data; Perform four-component surface consistency spectrum decomposition; Calculate the four-component surface consistent deconvolution operator; The deconvolution operator is applied to the seismic data to obtain processed data.

2. The four-component surface consistency deconvolution method according to claim 1, wherein: According to the seismic data, the consistency spectrum analysis is carried out, and the four-component surface consistency spectrum decomposition includes: Establish a convolution model of earthquake records; Based on the surface consistency assumption, seismic wavelets are calculated; Performing Fourier transform on the seismic wavelet to decompose it into an amplitude spectrum and a phase spectrum; The logarithm of the amplitude spectrum is taken to obtain a logarithmic amplitude spectrum equation, and then a logarithmic amplitude spectrum equation group is obtained.

3. The four-component surface consistency deconvolution method according to claim 2, wherein: The seismic record convolution model is: x(t)=w(t)*y(t)+n(t) Among them, x(t) is the seismic record, w(t) is the seismic wavelet, y(t) is the formation reflection coefficient, and n(t) is the random noise.

4. The four-component surface consistency deconvolution method according to claim 2, wherein: The seismic wavelet is: W ij (t)=S i (t)*R j (t)*C k (t)*O l (t) Among them, W ij (t) is the seismic wavelet, i is the shot point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, S i (t) is the wavelet component with the influence of the shot point, R j (t) is the wavelet component with the influence of the detection point, C k (t) is the wavelet component related to the common center point, O l (t) is the wavelet component related to the offset.

5. The four-component surface consistency deconvolution method according to claim 2, wherein: The amplitude spectrum is: Among them, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

6. The four-component surface consistency deconvolution method according to claim 1, wherein: Calculation of the four-component surface consistent deconvolution operator includes: Solving the logarithmic amplitude spectrum equations by the conjugate gradient method to calculate the deconvolution factor; exponentiating and squaring the deconvolution factor and then performing an inverse Fourier transform to obtain its autocorrelation function; A prediction filter equation is constructed according to the autocorrelation function, and the prediction filter equation is solved to obtain a prediction filter operator, thereby obtaining the deconvolution operator.

7. The four-component surface consistency deconvolution method according to claim 6, wherein: The logarithmic amplitude spectrum equation group is: Where i is the firing point, j is the receiving point, k = (i + j) / 2, l = (ij) / 2, A ij is the amplitude spectrum, is the amplitude spectrum component with shot point influence, is the amplitude spectrum component with the influence of detection point, is the amplitude spectrum component related to the common center point, is the amplitude spectrum component related to the offset.

8. A four-component surface consistency deconvolution device, characterized in that: include: Analysis module, which performs consistency spectrum analysis based on seismic data; Decomposition module, which performs four-component surface consistency spectrum decomposition; A calculation module, which calculates the four-component surface consistent deconvolution operator; An application module applies the deconvolution operator to the seismic data to obtain processed data.

9. An electronic device, characterized in that: The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the four-component surface consistent deconvolution method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the four-component surface consistent deconvolution method described in any one of claims 1 to 7.

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