An Adaptive Multi-Order Dispersion Surface Wave Suppression Method, Device and Equipment

Abstract

The present invention discloses an adaptive multi-order dispersion surface wave suppression method, device and equipment. The method includes: performing data processing on the acquired original seismic shot gather data and selecting it according to preset conditions to obtain dispersion spectrum analysis source data; analyzing the dispersion spectrum analysis source data to obtain high-precision dispersion spectra of each control point; performing multi-order picking on the high-precision dispersion spectra to obtain multi-order dispersion curves of each control point; processing each order of the multi-order dispersion curves to obtain local dispersion curves at each grid position in the work area for each order of the dispersion curves; selecting adjacent trace seismic data of each trace seismic data in the original seismic shot gather data, performing phase shift processing on each adjacent trace seismic data respectively to obtain the surface wave noise prediction result of each trace seismic data; and performing matching subtraction on the surface wave noise prediction result and the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after eliminating surface wave noise of each order.

Description

Technical Field

[0001] The present invention relates to the technical field of geophysical seismic exploration, and particularly to an adaptive multi-order dispersion surface wave suppression method, device and equipment. Background Art

[0002] Surface waves are a very common type of noise, which are characterized by low frequency, low velocity, strong energy, and have dispersion and multiple models. In order to highlight effective signals such as reflected waves and refracted waves, it is necessary to suppress and eliminate surface wave noise first. All along, how to effectively, accurately and quickly perform surface wave denoising has been a hot issue in seismic data processing. With the continuous deepening of oil and gas exploration, oil and gas exploration in complex structural areas has become the main target of geophysical exploration. Suppressing surface wave noise and improving the signal-to-noise ratio are important links in seismic exploration data processing in complex areas. Strengthening refined processing requirements such as denoising ability and amplitude preservation performance is particularly important in seismic data processing in complex structural areas. In addition, with the successful application of wide-azimuth high-density exploration acquisition and efficient mixed acquisition technologies, the current seismic exploration data has become a massive data body, making processing methods based on massive data and efficient mixed acquisition the mainstream, and the processing efficiency has an increasing impact on data processing.

[0003] The dispersion phenomenon of surface waves in layered media is closely related to the shear wave velocity of the medium, which is an inherent property of the shallow surface medium in the work area. The surface conditions of large-scale and contiguous work areas in the field are constantly changing, and the dispersion effect of surface waves also changes with the change of the spatial position in the work area. Only accurate dispersion relationships can accurately eliminate the dispersion effect of surface waves, and then predict the surface wave generation, affecting the final denoising effect. Moreover, the computational complexity of dispersion spectrum analysis accounts for the main part in such methods, and performing dispersion spectrum analysis on massive data one by one is extremely time-consuming, inefficient and meaningless. In addition, surface waves have multiple orders. Traditional methods for simply performing overall dispersion correction on surface wave data cannot fully utilize the multi-order dispersion relationship, nor can they completely and correctly eliminate the dispersion effects of each frequency component, reducing the denoising ability of such methods. Summary of the Invention

[0004] The present invention provides an adaptive multi-order dispersion surface wave suppression method, device and equipment, which solves the problems of low signal-to-noise ratio and resolution of seismic data, and how to better maintain the waveform characteristics of the original seismic shot gather data after denoising.

[0005] An adaptive multi-order dispersion surface wave suppression method includes:

[0006] Obtain the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain the source data for dispersion spectrum analysis;

[0007] Perform non-linear signal similarity analysis on the source data for dispersion spectrum analysis to obtain the high-precision dispersion spectra of each control point;

[0008] Perform hierarchical picking on the high-precision dispersion spectra to obtain the multi-level dispersion curves of each control point;

[0009] According to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each level of dispersion curve in the multi-level dispersion curves to obtain the local dispersion curves at each grid position in the work area for each level of dispersion curve;

[0010] For each trace of seismic data in the original seismic shot gather data, select the adjacent trace of seismic data for each trace of seismic data according to the parameters set by the user. Perform phase shift processing on the adjacent trace of seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace, and perform stacking on the phase-shifted adjacent trace of seismic data to obtain the surface wave noise prediction result for each trace of seismic data;

[0011] According to the surface wave noise prediction result of each trace of seismic data, perform matching subtraction with the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after eliminating surface wave noise of each level.

[0012] In an embodiment of the present invention, the performing non-linear signal similarity analysis on the source data for dispersion spectrum analysis specifically includes: calculating the similarity characterization of the non-linear signal using the following formula:

[0013]

[0014] where, is the regularized similarity characterization of the non-linear signal; is the background value; is the seismic data at two receiving points in a control point for a certain frequency and the data frequency at the phase velocity of the non-linear signal correlation; is the coefficient for adjusting the dispersion spectrum resolution; is the circular frequency, is the phase velocity;

[0015] Perform similarity analysis on the source data for dispersion spectrum analysis according to the calculated similarity characterization.

[0016] In an embodiment of the present invention, interpolating each dispersion curve in the multi-order dispersion curves to obtain local dispersion curves at each grid position in the work area specifically includes: Interpolating each dispersion curve in the multi-order dispersion curves according to the relative positions of the work area grid and the control points to obtain the dispersion curves of each order at each grid position within the entire work area range.

[0017] In an embodiment of the present invention, performing phase shift processing on the adjacent trace seismic data according to the local dispersion curves and the offset information of each adjacent trace specifically includes: Performing phase shift processing on the adjacent trace seismic data respectively according to the local dispersion curves and the offset information of each adjacent trace through the following formula:

[0018]

[0019] Wherein, is the reconstructed surface wave data, is the original data, is the maximum range of the prediction window for the surface wave of the prediction trace, h is the prediction window for the surface wave, i is the imaginary part, is the circular frequency, is the phase velocity.

[0020] In an embodiment of the present invention, matching and subtracting it from the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after eliminating the surface wave noise of each order specifically includes: Matching and subtracting it from the corresponding seismic data in the original seismic shot gather data according to the following formula:

[0021]

[0022] Wherein, is the original seismic shot gather data, is the number of calculations, is the adaptive filter, is the surface wave data, is the error energy.

[0023] In one embodiment of the present invention, after processing the original seismic shot gather data, selecting the dispersion spectrum analysis source data according to preset conditions specifically includes: preprocessing the acquired original seismic shot gather data to obtain the first seismic shot gather data; based on parameters given by the user, performing filtering and normalization processing on the first seismic shot gather data to obtain the second seismic shot gather data; determining control points at different positions in the work area, selecting surface wave data meeting the preset conditions at the control points according to the surface wave development at the control points, and selecting surface wave data meeting the preset conditions from the second seismic shot gather data, and taking the selected surface wave data meeting the preset conditions as the dispersion spectrum analysis source data corresponding to each control point.

[0024] In one embodiment of the present invention, the selecting surface wave data meeting the preset conditions at the control points according to the surface wave development at the control points specifically includes: selecting surface wave data within a certain distance in the geophone line direction, a certain distance in the connecting line direction, and a certain offset at the control points according to the surface wave development at the control points as the dispersion spectrum analysis source data of the control point.

[0025] An adaptive multi-order dispersion surface wave suppression device includes:

[0026] A surface wave data acquisition module, configured to acquire original seismic shot gather data, and after processing the original seismic shot gather data, select the dispersion spectrum analysis source data according to preset conditions;

[0027] A dispersion spectrum acquisition module, configured to perform non-linear signal similarity analysis on the dispersion spectrum analysis source data to obtain high-precision dispersion spectra of each control point;

[0028] A dispersion curve acquisition module, configured to perform hierarchical picking on the high-precision dispersion spectra to obtain multi-order dispersion curves of each control point; according to the relative positions and grid settings of all control points in the work area, perform interpolation processing on each order of dispersion curve in the multi-order dispersion curves to obtain local dispersion curves of each order of dispersion curve at each grid position in the work area;

[0029] A noise screening module, configured to, for each seismic data trace in the original seismic shot gather data, select adjacent seismic data traces of each seismic data trace according to parameters set by the user, perform phase shift processing on the adjacent seismic data traces respectively according to the local dispersion curves and the offset information of each adjacent trace, and perform stacking on the phase shift processed adjacent seismic data traces to obtain the surface wave noise prediction result of each seismic data trace;

[0030] A denoising module, configured to match and subtract the predicted surface wave noise of each seismic data from the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after removing surface wave noise of each order.

[0031] An electronic device, comprising:

[0032] At least one processor; and,

[0033] A memory communicatively connected to the at least one processor via a bus; wherein,

[0034] The memory stores instructions executable by the at least one processor, and when the instructions are executed, to implement:

[0035] Obtain the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain dispersion spectrum analysis source data;

[0036] Perform non-linear signal similarity analysis on the dispersion spectrum analysis source data to obtain high-precision dispersion spectra of each control point;

[0037] Perform hierarchical picking on the high-precision dispersion spectra to obtain multi-order dispersion curves of each control point;

[0038] According to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each order of dispersion curve in the multi-order dispersion curves to obtain local dispersion curves at each grid position in the work area for each order of dispersion curve;

[0039] For each seismic data in the original seismic shot gather data, select adjacent seismic data of each seismic data according to parameters set by the user, perform phase shift processing on the adjacent seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace, and perform stacking on the phase shift processed adjacent seismic data to obtain the predicted surface wave noise results of each seismic data;

[0040] According to the predicted surface wave noise results of each seismic data, match and subtract it from the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after removing surface wave noise of each order.

[0041] A non-volatile storage medium stores computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the following steps:

[0042] Obtain the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain dispersion spectrum analysis source data;

[0043] Perform non-linear signal similarity analysis on the dispersion spectrum analysis source data to obtain high-precision dispersion spectra of each control point;

[0044] Perform hierarchical picking on the high-precision dispersion spectra to obtain multi-order dispersion curves of each control point;

[0045] According to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each order of the dispersion curves in the multi-order dispersion curves to obtain local dispersion curves at each grid position in the work area for each order of the dispersion curves;

[0046] For each trace of seismic data in the original seismic shot gather data, select adjacent traces of seismic data for each trace of seismic data according to the parameters set by the user. Perform phase shift processing on the adjacent traces of seismic data respectively according to the local dispersion curves and the offset information of each adjacent trace, and perform stacking on the phase-shifted adjacent traces of seismic data to obtain the surface wave noise prediction result for each trace of seismic data;

[0047] According to the surface wave noise prediction result of each trace of seismic data, perform matching subtraction with the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after removing surface wave noise of each order.

[0048] The present invention provides an adaptive multi-order dispersion surface wave suppression method, device and equipment, which at least includes the following beneficial effects: By utilizing the dispersion characteristics of surface wave data, analyzing and statistically processing it within the entire work area and applying it to the data, the accuracy of the dispersion relationship is ensured and redundant calculations are reduced. Through the picking and application of multi-order dispersion curves and adaptive subtraction, the noise suppression ability and amplitude preservation of the dispersion-based surface wave denoising method are enhanced, and it is particularly suitable for ultra-large shot gather data with developed dispersion surface waves or 3D exploration projects with aliased acquisitions. Through the method of the present invention, the signal-to-noise ratio and resolution of seismic data are improved, the noise suppression ability and processing efficiency are improved, and the waveform characteristics of the original seismic data can be better maintained after denoising, which can provide favorable conditions for seismic imaging, attribute extraction, reservoir development, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0050] Figure 1 It is a schematic diagram of the steps of an adaptive multi-order dispersion surface wave suppression method provided by an embodiment of the present invention;

[0051] Figure 2 It is the high-precision dispersion spectrum of the control point and the multi-order dispersion curve picked up provided by an embodiment of the present invention;

[0052] Figure 3 The prediction result of the fundamental surface wave noise of the single-shot data provided by the embodiment of the present invention;

[0053] Figure 4 The prediction result of the first-order surface wave noise of the single-shot data provided by the embodiment of the present invention;

[0054] Figure 5 The prediction result of the second-order surface wave noise of the single-shot data provided by the embodiment of the present invention;

[0055] Figure 6 The original single-shot data provided by the embodiment of the present invention;

[0056] Figure 7 The surface wave denoising result provided by the embodiment of the present invention;

[0057] Figure 8 The schematic diagram of an adaptive multi-order dispersion surface wave suppression device provided by the embodiment of the present invention;

[0058] Figure 9 The schematic diagram of an electronic device provided by the embodiment of the present invention. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0060] It should be noted that those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present invention may be combined with other embodiments without conflict. Unless otherwise defined, the technical terms or scientific terms involved in the present invention shall have the ordinary meaning understood by those of ordinary skill in the technical field to which the present invention belongs. The terms "a", "one", "kind", "the" and the like involved in the present invention do not represent a limitation in quantity and may represent a single or plural number. The terms "include", "comprise", "have" and any deformation thereof involved in the present invention are intended to cover non-exclusive inclusion; the terms "first", "second", "third" and the like involved in the present invention are only used to distinguish similar objects and do not represent a specific order for the objects.

[0061] According to the spatial distribution of surface waves and the topographic features of the work area, the present invention determines several dispersion spectrum analysis control points, selects corresponding source data for analysis, processes the obtained multi-order dispersion curves in the entire work area to obtain the dispersion curves of each order at all positions in the work area, then reconstructs the surface wave model, and finally eliminates it from the original data step by step using the method of adaptive subtraction. The present invention utilizes the dispersion characteristics of surface wave data, adopts the method of control points to obtain the dispersion curve data of the entire work area, and uses the step-by-step processing method and the method of adaptive subtraction to improve the denoising ability and amplitude preservation performance of the method. The following is a specific description.

[0062] Figure 1 FIG. is a schematic diagram of the steps of an adaptive multi-order dispersion surface wave suppression method provided by an embodiment of the present invention, which may include the following steps:

[0063] S110: Obtain the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain the source data for dispersion spectrum analysis.

[0064] In an embodiment of the present invention, performing data processing on the original seismic shot gather data and selecting it according to preset conditions to obtain the source data for dispersion spectrum analysis specifically includes: preprocessing the obtained original seismic shot gather data to obtain the first seismic shot gather data; based on the parameters given by the user, performing filtering and normalization processing on the first seismic shot gather data to obtain the second seismic shot gather data; determining control points at different positions in the work area, selecting surface wave data that meets the preset conditions at the control points according to the development of surface waves at the control points, and selecting surface wave data that meets the preset conditions from the second seismic shot gather data, and taking the selected surface wave data that meets the preset conditions as the source data for dispersion spectrum analysis corresponding to each control point.

[0065] Specifically, the preprocessing of the collected original seismic shot gather data includes processing such as decoding, setting the observation system, static correction, deconvolution, and energy compensation. Based on the preprocessed seismic shot gather data and according to the parameters given by the user, arranging according to the shot line number, shot point number, geophone line number, and geophone point number to complete the filtering and normalization processing of the seismic shot gather data.

[0066] According to the development of surface waves at each control point, select the surface wave data within a certain distance in the geophone line direction, a certain distance in the connecting line direction, and a certain offset within the data after normalization processing at each control point, and select data from the results of the normalization processing according to the spatial wave field range of the development of surface waves as the source data for dispersion spectrum analysis of the control point, which can concentrate the main energy of the surface waves and avoid the interference of effective signals.

[0067] Select dispersion spectrum analysis control points within the work area and select corresponding surface wave data around the control points for dispersion spectrum analysis. In the case of an extremely large work area or massive data, the time-consuming dispersion spectrum analysis greatly affects the efficiency of surface wave denoising, and the accuracy of using an accurate dispersion relationship directly affects the accuracy of surface wave denoising. Selecting a certain number of control points and surface wave data according to the topographic characteristics of the work area and the distribution of surface waves in the seismic data can not only ensure a comprehensive statistics of the surface wave dispersion characteristics but also avoid the redundancy of dispersion spectrum calculation.

[0068] S120: Conduct a non-linear signal similarity analysis on the source data of the dispersion spectrum analysis to obtain a high-precision dispersion spectrum for each control point.

[0069] In an embodiment of the present invention, conducting a non-linear signal similarity analysis on the source data of the dispersion spectrum analysis specifically includes: calculating the similarity characterization of the non-linear signal using the following formula:

[0070]

[0071] where is the regularized similarity characterization of the non-linear signal; is the background value; is the seismic data at two receiving points in a control point for a certain frequency and the phase velocity of the data frequency at is non-linearly correlated; is the coefficient for adjusting the dispersion spectrum resolution; is the circular frequency, is the phase velocity;

[0072] Conduct a similarity analysis on the source data of the dispersion spectrum analysis according to the calculated similarity characterization.

[0073] Specifically, conduct a non-linear signal similarity analysis on the selected data in each control point to obtain a high-precision dispersion spectrum for each control point. Accurately obtaining the dispersion characteristics of the data and picking up an accurate dispersion spectrum are crucial for correcting the surface wave dispersion effect and predicting surface waves.

[0074] The dispersion spectrum obtained by the traditional method has less energy focus in the low-frequency part than in the high-frequency part, and it is difficult to obtain a reliable time shift amount in the low-frequency band where surface waves exist, thus seriously affecting the prediction of surface waves. However, the dispersion spectrum obtained by the non-linear signal similarity analysis method used in the present invention has balanced energy focus in both high and low frequencies and higher resolution. In order to obtain a high-precision dispersion spectrum of surface wave data, the present invention uses a non-linear signal similarity analysis method, and the formula used in this method is as follows:

[0075]

[0076] where is the normalized characterization of the similarity of non-linear signals, is the background value, is the coefficient for adjusting the dispersion spectrum resolution, is the seismic data at two receiving points at different positions in a certain control point for a certain frequency and the data frequency at the phase velocity of the non-linear signal correlation,

[0077] ,

[0078] is the circular frequency, T is the signal length, t is the time, and x is the signal coordinate, is the seismic data, is the phase velocity.

[0079] For example, the non-linear signal similarity analysis of the seismic data selected within a certain control point in the work area obtains a high-precision dispersion spectrum as shown in Figure 2 , and the multi-order dispersion curves of the dispersion spectrum at this control point are obtained through multi-order picking.

[0080] S130: Perform multi-order picking on the high-precision dispersion spectrum to obtain the multi-order dispersion curves of each control point.

[0081] Specifically, the multi-order dispersion curves of each control point are obtained by picking the high-precision dispersion spectrum calculated for each control point.

[0082] S140: According to the relative positions and grid settings of all control points in the work area, perform interpolation processing on each order of the multi-order dispersion curves to obtain the local dispersion curves of each order of the dispersion curves at each grid position in the work area.

[0083] In an embodiment of the present invention, performing interpolation processing on each order of the multi-order dispersion curves to obtain the local dispersion curves of each order of the dispersion curves at each grid position in the work area specifically includes: Interpolating each order of the multi-order dispersion curves according to the work area grid and the relative positions of the control points to obtain the dispersion curves of each order at each grid position within the entire work area.

[0084] Specifically, for each order of the multi-order dispersion curves, interpolation processing is performed according to the relative positions and grid settings of all control points in the work area to obtain the local dispersion curves of each order at each grid position in the work area.

[0085] The dispersion spectra calculated for each control point are picked in stages to obtain multi-stage dispersion curves. For each stage of the multi-stage dispersion curves, and according to the grid of the work area and the relative positions of the control points, interpolation is performed on each stage of the dispersion curve to obtain the dispersion curves of each stage at the positions of each point within the entire work area. The method used in the present invention is weighted parabolic interpolation, that is, interpolation is performed between two known control points, and two different curves can be obtained. A interpolation curve is obtained through weighted fusion. If there is a known function , and it is necessary to insert a point and between two adjacent points , interpolation curves and can be made at the known points and , respectively:

[0086]

[0087] wherein, is the Lagrange interpolation basis function,

[0088]

[0089] are the known functions at the known points , and k is the number of known points.

[0090] Then the direct weighted interpolation curve can be expressed as:

[0091]

[0092] wherein, is the weight value of ,

[0093]

[0094] is the weight value of ,

[0095]

[0096] For example, Figure 3 , Figure 4 , Figure 5 show the fundamental, first-order, and second-order surface wave data predicted from a certain seismic data in the work area through multi-stage dispersion curves respectively.

[0097] S150: For each trace of seismic data in the original seismic shot gather data, select the adjacent trace of seismic data for each trace of seismic data according to the parameters set by the user. Perform phase shift processing on the adjacent trace seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace, and stack the adjacent trace seismic data after phase shift processing to obtain the surface wave noise prediction result of each trace of seismic data.

[0098] In an embodiment of the present invention, performing phase shift processing on the adjacent trace seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace specifically includes: performing phase shift processing on the adjacent trace seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace through the following formula:

[0099]

[0100] Wherein, is the reconstructed surface wave data, is the original data, is the maximum range of the prediction trace surface wave time window, h is the predicted surface wave time window, i is the imaginary part, is the circular frequency, is the phase velocity.

[0101] Specifically, for each shot of seismic data in the work area, select the seismic data of adjacent traces within a certain range of this trace of seismic data according to the user parameters. Use each order dispersion curve at the position where the data is located, and the offset information of each adjacent trace of seismic data to perform phase shift processing on these adjacent trace seismic data respectively, and stack the adjacent trace seismic data after phase shift processing to obtain the surface wave noise prediction result of this trace of seismic data, as the surface wave noise prediction result of the final record of this shot. Performing this step of work on each shot of seismic data in the seismic shot gather data can obtain the surface wave noise prediction results of each order dispersion curve of each shot of seismic data.

[0102] Correct the dispersion effect of the surface wave through phase shift. Using each order dispersion curve obtained, comprehensively use the offset information of adjacent trace seismic data to perform phase shift on the adjacent trace seismic data to correct the dispersion effect of the surface wave. The formula used in this process is:

[0103]

[0104] is the reconstructed surface wave data, is the original seismic data, is the maximum range of the prediction trace surface wave time window, i is the imaginary part.

[0105] In the adjacent-trace seismic data with dispersion characteristics removed, the surface wave signal exhibits a true linear characteristic, rather than the apparent linearity in the original seismic shot gather data. The hyperbolic characteristic of the effective signal in the shot gather becomes random noise. By stacking, the influence of the effective signal can be effectively eliminated, enhancing the surface wave signal, and thus obtaining the predicted surface wave noise.

[0106] S160: According to the predicted result of the surface wave noise of each trace of seismic data, match and subtract it from the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after removing surface wave noise of each order.

[0107] In an embodiment of the present invention, matching and subtracting it from the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after removing surface wave noise of each order specifically includes: matching and subtracting it from the corresponding seismic data in the original seismic shot gather data according to the following formula:

[0108]

[0109] where is the original seismic shot gather data, is the number of calculation times, is the adaptive filter, is the surface wave data, is the error energy.

[0110] Furthermore, for each order of surface wave data in the predicted result of the surface wave noise, match and subtract it from the original seismic shot gather data to obtain the optimal denoising result of this order of surface wave, and perform adaptive subtraction on each order of surface wave data in turn starting from the fundamental order, finally obtaining the result after removing surface wave noise of each order.

[0111] Specifically, each order of predicted surface wave data is attenuated through adaptive subtraction and eliminated from the original data. There is a certain mismatch in amplitude and phase between the reconstructed surface wave models of each order and the actual surface wave data. Adaptive subtraction through the adaptive filter can improve the consistency between the surface wave noise in the data and the model. The adaptive filter can be obtained by the least squares method.

[0112] Assume is the original seismic data, is the number of calculation times, is the adaptive filter, is the surface wave data, is the error energy.

[0113]

[0114] The process of solving the adaptive filter can be transformed into a problem of minimizing the error energy, that is, solving for the partial derivatives of the error energy with respect to each filter to be zero.

[0115] For example, Figure 6 Shown is the original seismic data of a certain shot in the work area. Figure 7 Shown is the final denoising result obtained after adaptive matching subtraction.

[0116] The above is an adaptive multi-order dispersion surface wave suppression method provided by an embodiment of the present invention. Based on the same inventive concept, an embodiment of the present invention also provides a corresponding adaptive multi-order dispersion surface wave suppression device, as Figure 8 shown.

[0117] A surface wave data acquisition module 802, configured to acquire original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain dispersion spectrum analysis source data; a dispersion spectrum acquisition module 804, configured to perform non-linear signal similarity analysis on the dispersion spectrum analysis source data to obtain high-precision dispersion spectra of each control point; a dispersion curve acquisition module 806, configured to perform hierarchical picking on the high-precision dispersion spectra to obtain multi-order dispersion curves of each control point; according to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each order of the dispersion curves in the multi-order dispersion curves to obtain local dispersion curves of each order of the dispersion curves at each grid position in the work area; a noise screening module 808, configured to, for each trace of seismic data in the original seismic shot gather data, select adjacent traces of seismic data for each trace of seismic data according to parameters set by the user, perform phase shift processing on the adjacent traces of seismic data respectively according to the local dispersion curves and the offset information of each adjacent trace, and perform superposition on the phase shift processed adjacent traces of seismic data to obtain the surface wave noise prediction result of each trace of seismic data; a denoising module 810, configured to, according to the surface wave noise prediction result of each trace of seismic data, perform matching subtraction on it and the corresponding seismic data in the original seismic shot gather data to obtain seismic shot gather data after eliminating surface wave noise of each order.

[0118] An embodiment of the present invention also provides a corresponding electronic device, as Figure 9 shown, including:

[0119] At least one processor 910, a communication interface 920, a memory 930, and a communication bus 940; wherein, the processor 910, the communication interface 920, and the memory 930 communicate with each other through the communication bus 940; the processor 910 can call logic instructions stored in the memory 930 to enable at least one processor 910 to execute:

[0120] Obtain the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain the source data for dispersion spectrum analysis; perform non-linear signal similarity analysis on the source data for dispersion spectrum analysis to obtain the high-precision dispersion spectra of each control point; perform step-by-step picking on the high-precision dispersion spectra to obtain the multi-order dispersion curves of each control point; according to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each order of the multi-order dispersion curves to obtain the local dispersion curves of each order of the dispersion curves at each grid position in the work area; for each seismic trace in the original seismic shot gather data, select the adjacent seismic traces of each seismic trace according to the parameters set by the user, perform phase shift processing on the adjacent seismic traces respectively according to the local dispersion curves and the offset information of each adjacent trace, and perform stacking on the phase-shifted adjacent seismic traces to obtain the surface wave noise prediction result of each seismic trace; according to the surface wave noise prediction result of each seismic trace, perform matching subtraction with the corresponding seismic trace in the original seismic shot gather data to obtain the seismic shot gather data after eliminating each order of surface wave noise.

[0121] Based on the same idea, some embodiments of the present invention also provide a medium corresponding to the above method.

[0122] A storage medium provided by some embodiments of the present invention stores computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the following steps:

[0123] Obtain the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain the source data for dispersion spectrum analysis; perform non-linear signal similarity analysis on the source data for dispersion spectrum analysis to obtain the high-precision dispersion spectra of each control point; perform step-by-step picking on the high-precision dispersion spectra to obtain the multi-order dispersion curves of each control point; according to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each order of the multi-order dispersion curves to obtain the local dispersion curves of each order of the dispersion curves at each grid position in the work area; for each seismic trace in the original seismic shot gather data, select the adjacent seismic traces of each seismic trace according to the parameters set by the user, perform phase shift processing on the adjacent seismic traces respectively according to the local dispersion curves and the offset information of each adjacent trace, and perform stacking on the phase-shifted adjacent seismic traces to obtain the surface wave noise prediction result of each seismic trace; according to the surface wave noise prediction result of each seismic trace, perform matching subtraction with the corresponding seismic trace in the original seismic shot gather data to obtain the seismic shot gather data after eliminating each order of surface wave noise.

[0124] Each embodiment in the present invention is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the description of the method embodiments.

[0125] The devices and media provided in the embodiments of the present invention correspond one-to-one with the methods. Therefore, the devices and media also have beneficial technical effects similar to those of the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be elaborated here.

[0126] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or method comprising a series of elements not only includes those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or method. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, commodity or method comprising the element.

[0127] The above are only the embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with general descriptions and specific embodiments above, on the basis of the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention fall within the scope of the present invention claimed.

Claims

1. An adaptive multi-order dispersion surface wave suppression method, characterized in that, Including: Obtain the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain the source data for dispersion spectrum analysis; Perform non-linear signal similarity analysis on the source data for dispersion spectrum analysis to obtain the high-precision dispersion spectra of each control point; Perform hierarchical picking on the high-precision dispersion spectra to obtain the multi-order dispersion curves of each control point; According to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each order of the dispersion curves in the multi-order dispersion curves to obtain the local dispersion curves of each order of the dispersion curves at each grid position in the work area; For each trace of seismic data in the original seismic shot gather data, select the adjacent trace of seismic data for each trace of seismic data according to the parameters set by the user, perform phase shift processing on the adjacent trace of seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace, and perform stacking on the phase shift processed adjacent trace of seismic data to obtain the surface wave noise prediction result of each trace of seismic data; According to the surface wave noise prediction result of each trace of seismic data, perform matching subtraction with the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after eliminating surface wave noise of each order.

2. The method according to claim 1, characterized in that, The performing non-linear signal similarity analysis on the source data for dispersion spectrum analysis specifically includes: Use the following formula to calculate the similarity characterization of non-linear signals: ； Among them, is the regularized non-linear signal similarity characterization; is the background value; is the seismic data at two receiving points in a control point for a certain frequency and the data frequency at the phase velocity of the non-linear signal correlation; is the coefficient for adjusting the dispersion spectrum resolution; is the circular frequency, is the phase velocity; Perform similarity analysis on the source data for dispersion spectrum analysis according to the calculated similarity characterization.

3. The method according to claim 1, wherein The performing interpolation processing on each order of the dispersion curves in the multi-order dispersion curves to obtain the local dispersion curves of each order of the dispersion curves at each grid position in the work area specifically includes: Perform interpolation on each order of the dispersion curves in the multi-order dispersion curves according to the work area grid and the relative positions of the control points to obtain the dispersion curves of each order at each grid position within the entire work area.

4. The method according to claim 1, wherein The performing phase shift processing on the adjacent trace of seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace specifically includes: Perform phase shift processing on the adjacent trace of seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace through the following formula: ； Among them, is the reconstructed surface wave data, is the original data, is the maximum range of the predicted surface wave time window, h is the predicted surface wave time window, i is the imaginary part, is the circular frequency, is the phase velocity.

5. The method according to claim 1, characterized in that The performing matching subtraction with the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after eliminating surface wave noise of each order specifically includes: Perform matching subtraction with the corresponding seismic data in the original seismic shot gather data according to the following formula: ； Among them, is the original seismic shot gather data, is the number of calculations, is the adaptive filter, is the surface wave data, is the error energy.

6. The method according to claim 1, characterized in that The performing data processing on the original seismic shot gather data and selecting it according to preset conditions to obtain the source data for dispersion spectrum analysis specifically includes: Perform preprocessing on the obtained original seismic shot gather data to obtain the first seismic shot gather data; Based on the parameters given by the user, perform filtering and normalization processing on the first seismic shot gather data to obtain the second seismic shot gather data; Determine control points at different positions in the work area, select surface wave data that meet the preset conditions at the control points according to the development of surface waves at the control points, and select surface wave data that meet the preset conditions from the second seismic shot gather data. The selected surface wave data that meet the preset conditions are used as the dispersion spectrum analysis source data corresponding to each control point.

7. The method according to claim 6, wherein The step of selecting surface wave data that meet the preset conditions at the control points according to the development of surface waves at the control points specifically includes: Select surface wave data within a certain distance in the geophone line direction, a certain distance in the connecting line direction, and a certain offset within the control point according to the development of surface waves at the control point, and use it as the dispersion spectrum analysis source data for the control point.

8. An adaptive multi-order dispersion surface wave suppression device, characterized in that, It includes: A surface wave data acquisition module, configured to acquire the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain the dispersion spectrum analysis source data; A dispersion spectrum acquisition module, configured to perform non-linear signal similarity analysis on the dispersion spectrum analysis source data to obtain high-precision dispersion spectra of each control point; A dispersion curve acquisition module, configured to perform step-by-step picking on the high-precision dispersion spectra to obtain multi-order dispersion curves of each control point; according to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each order of dispersion curves in the multi-order dispersion curves to obtain local dispersion curves at each grid position in the work area for each order of dispersion curves; A noise screening module, configured to, for each seismic data trace in the original seismic shot gather data, select adjacent seismic data traces of each seismic data trace according to parameters set by the user, perform phase shift processing on the adjacent seismic data traces respectively according to the local dispersion curves and the offset information of each adjacent trace, and perform stacking on the phase shift processed adjacent seismic data traces to obtain the surface wave noise prediction result of each seismic data trace; A denoising module, configured to, according to the surface wave noise prediction result of each seismic data trace, perform matching subtraction with the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after eliminating surface wave noise of each order.

9. An electronic device, characterized in that, It includes: At least one processor; And, A memory communicatively connected to the at least one processor via a bus; wherein, The memory stores instructions executable by the at least one processor, and when the instructions are executed, it is to implement: Acquire the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain the dispersion spectrum analysis source data; Perform non-linear signal similarity analysis on the dispersion spectrum analysis source data to obtain high-precision dispersion spectra of each control point; Perform step-by-step picking on the high-precision dispersion spectra to obtain multi-order dispersion curves of each control point; According to the relative positions of all control points in the work area and the grid settings, perform interpolation processing on each order of dispersion curves in the multi-order dispersion curves to obtain local dispersion curves at each grid position in the work area for each order of dispersion curves; For each trace of seismic data in the original seismic shot gather data, adjacent traces of seismic data for each trace are selected according to parameters set by the user. Phase shift processing is performed on the adjacent trace seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace, and the adjacent trace seismic data after phase shift processing are stacked to obtain the surface wave noise prediction result for each trace of seismic data; According to the surface wave noise prediction result of each trace of seismic data, it is matched and subtracted from the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after eliminating surface wave noise of each order.

10. A non-volatile storage medium stores computer-executable instructions, characterized in that, The computer-executable instructions are executed by a processor to implement the following steps: Obtain the original seismic shot gather data, perform data processing on the original seismic shot gather data, and select it according to preset conditions to obtain the source data for dispersion spectrum analysis; Perform non-linear signal similarity analysis on the source data for dispersion spectrum analysis to obtain high-precision dispersion spectra of each control point; Perform step-by-step picking on the high-precision dispersion spectra to obtain multi-order dispersion curves of each control point; According to the relative positions of all control points in the work area and the grid setting conditions, interpolation processing is performed on each order of dispersion curve in the multi-order dispersion curves to obtain local dispersion curves at each grid position in the work area for each order of dispersion curve; For each trace of seismic data in the original seismic shot gather data, adjacent traces of seismic data for each trace are selected according to parameters set by the user. Phase shift processing is performed on the adjacent trace seismic data respectively according to the local dispersion curve and the offset information of each adjacent trace, and the adjacent trace seismic data after phase shift processing are stacked to obtain the surface wave noise prediction result for each trace of seismic data; According to the surface wave noise prediction result of each trace of seismic data, it is matched and subtracted from the corresponding seismic data in the original seismic shot gather data to obtain the seismic shot gather data after eliminating surface wave noise of each order.

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