Method and device for predicting and suppressing high-density three-dimensional surface waves

Abstract

The invention provides a high-density three-dimensional surface wave prediction suppression method and device, and solves the problem that the denoising effect is not accurate enough due to the fact that a traditional surface wave suppression method does not consider the relationship and characteristics between three-dimensional gather data. The method for predicting and suppressing the high-density three-dimensional surface wave comprises the following steps: acquiring three-dimensional shot gather data, and obtaining a geophone stake number arrangement array based on the three-dimensional shot gather data; setting grid parameters, and obtaining an original gather; based on the geophone stake number arrangement array and the grid parameters, channels meeting adjacent conditions in the grid are synthesized into a new channel, and a frequency dispersion spectrum of the new channel is obtained; reconstructing surface wave noise based on the frequency dispersion spectrum of the new channel to obtain a reconstructed channel array; and obtaining a de-noised trace gather based on the original trace gather and the reconstructed trace array.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration, and in particular to a method and device for predicting and suppressing high-density three-dimensional surface waves. Background Art

[0002] Surface waves are common noises in three-dimensional seismic exploration, including fundamental surface waves, higher-order surface waves, etc. Traditional surface wave suppression methods include wavelet transform method, Wiener filtering method, FKK method, etc., which have characteristics such as residual surface wave removal, damage to low frequencies, and poor amplitude preservation, and generally only consider two-dimensional trace gather situations without considering the relationships and characteristics among three-dimensional trace gather data, so the denoising effect needs to be improved. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method and device for predicting and suppressing high-density three-dimensional surface waves, which solve the problem that traditional surface wave suppression methods do not consider the relationships and characteristics among three-dimensional trace gather data, resulting in inaccurate denoising effects.

[0004] In a first aspect, a method for predicting and suppressing high-density three-dimensional surface waves provided by an embodiment of the present invention includes:

[0005] Obtain three-dimensional shot gather data, and obtain a geophone stake number arrangement array based on the three-dimensional shot gather data;

[0006] Set grid parameters, and obtain an original trace gather;

[0007] Based on the geophone stake number arrangement array and the grid parameters, synthesize new traces from the traces within the grid that meet the adjacent conditions, and obtain the dispersion spectrum of the new traces;

[0008] Reconstruct surface wave noise based on the dispersion spectrum of the new traces to obtain a reconstructed trace array;

[0009] Obtain a denoised trace gather based on the original trace gather and the reconstructed trace array.

[0010] In one implementation, the three-dimensional shot gather data uses single-shot gather data as the smallest input unit.

[0011] In one implementation, the single-shot gather data includes multiple groups of geophone stake number arrangements, and the boundary of each group of geophone stake number arrangements corresponds to the trough position in the seismogram; the obtaining of the geophone stake number arrangement array based on the three-dimensional shot gather data includes: obtaining the geophone stake number arrangement array based on the actual offset and the geophone stake number arrangement.

[0012] In one implementation, the synthesizing of new traces from the traces within the grid that meet the adjacent conditions based on the geophone stake number arrangement array and the arrangement calculation grid parameters includes:

[0013] Obtain the grid trace range adjacent to the original trace based on the detection point station number arrangement array and the grid parameters;

[0014] Read the trace values in the grid cyclically and obtain the distance between the grid trace and the initial trace;

[0015] Based on the distance between the grid trace and the initial trace, combine the adjacent distance threshold and the proportion value of the grid trace to obtain the traces meeting the adjacent conditions and synthesize a new trace.

[0016] In one implementation manner, the grid trace range includes: the row number of the initial trace, the column number of the initial trace, the starting row number of the grid, the ending row number of the grid, the starting column number of the grid, and the ending column number of the grid.

[0017] In one implementation manner, the reconstructing the surface wave noise based on the dispersion spectrum of the new trace includes: picking up the surface wave dispersion spectrum values based on the dispersion spectrum of the new trace, and reconstructing the surface wave noise based on the surface wave dispersion spectrum values;

[0018] In one implementation manner, the obtaining the denoised trace gather based on the original trace gather and the reconstructed trace array includes: subtracting the reconstructed trace array from the original trace gather to obtain the denoised trace gather.

[0019] In a second aspect, an apparatus for high-density three-dimensional surface wave prediction and suppression provided by an embodiment of the present invention includes:

[0020] A data acquisition unit, configured to acquire three-dimensional shot gather data and obtain a detection point station number arrangement array based on the three-dimensional shot gather data;

[0021] A parameter setting unit, configured to set grid parameters and acquire the original trace gather;

[0022] A data processing unit, configured to synthesize new traces that meet the adjacent conditions in the grid based on the detection point station number arrangement array and the grid parameters, and obtain the dispersion spectrum of the new traces; reconstruct the surface wave noise based on the dispersion spectrum of the new traces to obtain a reconstructed trace array;

[0023] A denoising unit, configured to obtain a denoised trace gather based on the original trace gather and the reconstructed trace array.

[0024] In a third aspect, an electronic device provided by an embodiment of the present invention includes a memory and a processor, where the memory is used to store one or more computer instructions, and when the one or more computer instructions are executed by the processor, the method for high-density three-dimensional surface wave prediction and suppression described above is implemented.

[0025] Fourthly, a computer-readable storage medium provided by an embodiment of the present invention stores a computer program, which, when executed by one or more processors, is used to implement the method for high-density three-dimensional surface wave prediction and suppression described above.

[0026] The method and device for high-density three-dimensional surface wave prediction and suppression provided by the embodiments of the present invention combine relevant grid data from three-dimensional shot gathers for dispersion spectrum calculation, extract a high-precision surface wave dispersion curve spectrum, reconstruct surface wave noise, and then eliminate the noise from the original trace gather data to achieve the purpose of accurate noise removal, improve the quality of seismic data, and effectively suppress surface wave noise. Description of the Drawings

[0027] Figure 1 The figure shows a schematic flow chart of a method for high-density three-dimensional surface wave prediction and suppression provided by an embodiment of the present invention.

[0028] Figure 2 The figure shows a schematic flow chart of a method for high-density three-dimensional surface wave prediction and suppression provided by an embodiment of the present invention.

[0029] Figure 3 The figure shows a schematic diagram of original trace gather data provided by an embodiment of the present invention.

[0030] Figure 4 The figure shows a schematic diagram of the result of surface wave noise removal from three-dimensional shot gathers provided by an embodiment of the present invention.

[0031] Figure 5 The figure shows a schematic structural diagram of a high-density three-dimensional surface wave prediction and suppression device provided by an embodiment of the present invention. Detailed Embodiments

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the 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.

[0033] To solve the above problems, improve the quality of seismic data, and effectively suppress surface wave noise, the present invention provides a method for surface wave noise removal applicable to different types, extracts a high-precision surface wave dispersion curve spectrum from three-dimensional shot gathers, reconstructs surface wave noise, and then eliminates the noise from the original trace gather data to achieve the purpose of accurate noise removal. The detailed embodiments are as described in the following embodiments.

[0034] Example 1:

[0035] This embodiment provides a method for predicting and suppressing high-density three-dimensional surface waves, as Figure 1 shown. The method for predicting and suppressing high-density three-dimensional surface waves includes:

[0036] Step 01: Obtain three-dimensional shot gather data, and obtain a geophone stake number arrangement array based on the three-dimensional shot gather data.

[0037] This method is applicable to high-density common shot gathers, that is, a gather formed by all traces received by different geophones when excited by the same shot point. The seismic data must be regularized and sorted according to the shot number, with a consistent shot number interval, such as a shot number interval of 1 or 2, and the number of geophones per shot is the same. To improve the calculation efficiency, the seismic data is divided into individual shot gathers for batch processing. The keywords on which the trace headers depend are the coordinates of the shot and geophone. Each trace in the single-shot gather is stored in the array Trace. Optionally, the three-dimensional shot gather data takes a single shot as the minimum input unit.

[0038] In the single-shot record of the three-dimensional shot gather data, there are multiple groups of geophone stake number arrangements. The boundary of each group of the geophone stake number arrangements corresponds to the trough position in the seismogram.

[0039] Specifically, obtaining the geophone stake number arrangement array based on the three-dimensional shot gather data includes: obtaining the geophone stake number arrangement array based on the actual offset and the geophone stake number arrangement. According to the size of the actual offset, the array of each arrangement is calculated.

[0040] Due to high-density acquisition, reducing the bin size and increasing the data acquisition density in the spatial domain and time domain, multiple waveform undulations appear on the gather seismogram. Each waveform is a geophone stake number arrangement. Each input shot is divided into a two-dimensional geophone array. The boundary of each waveform is the turning point where the offset changes from large to small, that is, the trough of the waveform. The calculation process is as follows:

[0041] Calculate the actual offset corresponding to each trace according to the coordinates of the shot and geophone of each trace.

[0042] Define a two-dimensional array RevArr[xnum][ynum] of geophone arrangement points for each shot, where xnum is the set number of geophone arrangements, and ynum is the number of traces in each arrangement.

[0043] Loop to calculate the inflection points where the offset in the gather changes from large to small.

[0044] The offset array defined by the original input gather is offset[n], the trace array is Trace[n], (n is the number of traces in the gather), i ∈ [0, n]; the number of rows of the geophone array is represented by rowarr[i], the number of columns is represented by colarr[i], the geophone array is RevArr[RowNum][ColNum], and the offset array corresponding to the geophone array is

[0045] RevOffset[RowNum][ColNum], RowNum ∈ [0, xnum], ColNumrow ∈ [0, ynum].

[0046] When i = 0, it is used as the starting point of the first geophone array, starting from the 1st one, and calculating in a loop until the (n - 1)th one. The calculation description is:

[0047]

[0048]

[0049] Step 02: Set the grid parameters and obtain the original gather.

[0050] In the shot gather, the surface wave presents a broom shape. To improve the denoising effect, a window where the surface wave is located can be cut out in the shot gather, and the data within the window is used as the data to be processed, represented by MuteTrace[n]. The cut-off vertices and bottom points of each trace are represented by trMute[n][2]. The data outside the window is filled with 0 and not processed during the calculation.

[0051] Since the dispersion spectra of adjacent traces are similar, according to the data characteristics, the interval of trace scanning is set to winScanlen, and the traces within the interval are used as a group of data for processing. In high-density 3D processing, if the bin size is small enough and the offset of the calculated trace satisfies a given threshold range, the trace data adjacent to the geophone array can be involved in the calculation.

[0052] If the trace number of the current trace is k, represented by Trace[k], the offset threshold is offsetRange, the grid size of the geophone array is dx, dy, and the selected grid data block is DataDisper[idx][idy], and its corresponding offset satisfies

[0053] (RevOffset[idx][idy] - offset[k]) < offsetRange, idx ∈

[0054] [0, dx], idyx ∈ [0, dy].

[0055] Calculate the dispersion spectrum of the dispersion analysis data block.

[0056] Step 03: Synthesize new traces from the traces within the grid that meet the proximity condition based on the detection point station number arrangement array and the grid parameters, and obtain the dispersion spectrum of the new traces.

[0057] Further, the synthesizing of new traces from the traces within the grid that meet the proximity condition based on the detection point station number arrangement array and the arrangement calculation grid parameters includes:

[0058] Step 031: Obtain the grid trace range adjacent to the original trace based on the detection point station number arrangement array and the grid parameters.

[0059] Specifically, the grid trace range includes: the initial trace row number, the initial trace column number, the grid start row number, the grid end row number, the grid start column number, and the grid end column number.

[0060] Among them, the row number of trace k is row_k = row_flag[k], and the column number is col_k = col_flag[k];

[0061] The grid start row number start_row = row_k - X / 2, and the grid end row number end_row = row_k + X / 2;

[0062] The grid start column number start_col = col_k - Y / 2, and the grid end column number end_col = col_k + Y / 2.

[0063] Step 032: Loop through and read the trace values within the grid, and obtain the distance between the grid trace and the initial trace.

[0064] Step 033: Based on the distance between the grid trace and the initial trace, combine the proximity distance threshold and the proportion value of the grid trace to obtain the traces that meet the proximity condition, and synthesize new traces.

[0065] Loop through and read the trace values within the grid, judge the distance between the grid trace and trace k, and set the proximity distance threshold offsetFactor and the proportion value of the grid trace wFactor.

[0066] Judge the proximity distance:

[0067] DataDisper[idx][idy] = wFactor * (1 / (RevOffset[idx][idy] - offset[k])) * Trace[k + idx].

[0068] Step 04: Reconstruct the surface wave noise based on the dispersion spectrum of the new traces to obtain the reconstructed trace array.

[0069] Among them, the reconstruction of surface wave noise based on the dispersion spectrum of the new trace includes: picking up the surface wave dispersion spectrum value based on the dispersion spectrum of the new trace, and reconstructing the surface wave noise based on the surface wave dispersion spectrum value.

[0070] After normalizing the data of the grid data block DataDisper, according to the velocity and frequency ranges set by the parameters, perform the conversion from the time domain to the frequency domain, calculate the dispersion spectrum, find the maximum frequency along the velocity direction, and automatically pick up the surface wave dispersion curve. According to the picked dispersion curve, predict and reconstruct the surface wave noise.

[0071] Map the surface wave noise calculated for each grid to the calculated time window trace interval, and combine it into the surface wave noise of the seismic data. S(g) is the surface wave noise reconstructed for grid g, wmax is the number of time windows, and Sf(g) is the reconstructed noise of the entire seismic data.

[0072]

[0073] Step 05: Obtain the denoised trace gather based on the original trace gather and the reconstructed trace array.

[0074] Among them, subtract the noise from the original trace gather to obtain the denoised trace gather. A method for high-density three-dimensional surface wave prediction and suppression provided in this embodiment combines relevant grid data from a three-dimensional shot gather for dispersion spectrum calculation, extracts a high-precision surface wave dispersion curve spectrum, reconstructs the surface wave noise, and then eliminates the noise from the original trace gather data, achieving the purpose of accurate denoising, improving the quality of seismic data, and effectively suppressing surface wave noise.

[0075] Example 2:

[0076] This embodiment provides another method for high-density three-dimensional surface wave prediction and suppression, as Figures 2 - 4 shown. This high-density three-dimensional surface wave prediction and suppression method includes:

[0077] Select an actual three-dimensional shot gather seismic data with the number of sampling points being ns, and there are multiple geophone stake number arrangements for each shot, and perform surface wave prediction and suppression calculations. Specifically, as described in the following embodiments.

[0078] The first step is to input the shot gather data.

[0079] Taking the single-shot data as the smallest unit, obtain the total number of traces for a single shot as ntr, and store it in the two-dimensional array Trace[ntr][ns], as Figure 2 shown.

[0080] The second step is to obtain the geophone stake number arrangement array from the single-shot gather.

[0081] From the single-shot data, obtain the offset array offset[ntr].

[0082] Calculate the number of rows row and columns col in a single shot gather. Define the row number array corresponding to each trace as row_flag[ntr][sn], the column number array as col_flag[ntr], the new three-dimensional array of the gather as TraceNew[row][col][s], and the offset array as offset[row][col][1]. The program to implement the above process is as follows:

[0083] for(i = 1; i < ntr; i++)

[0084] {

[0085] if((offset[i] > offset[i - 1]) && (offset[i] >= offset[i + 1]))

[0086] {

[0087] row++;

[0088] col = 0;

[0089] row_flag[i] = row;

[0090] col_flag[i] = col;

[0091] }

[0092] else

[0093] {

[0094] col++;

[0095] row_flag[i] = row;

[0096] col_flag[i] = col;

[0097] memcpy(TraceNew[row][col], Trace[i], sizeof(float) * ns);

[0098] }

[0099] memcpy(TraceNew[row][col], Trace[i], sizeof(float) * ns);

[0100] offset[row][col][1] = offset[i];

[0101] }

[0102] Step 3: Calculate the three-dimensional dispersion spectrum.

[0103] The number of rows for calculating the grid size is 3, and the number of columns is 5.

[0104] Calculate the grid trace range adjacent to trace k, and synthesize the traces that meet the adjacent conditions within the grid into a new trace. The program to implement the above process is as follows:

[0105] for(i = 0; i < ntr; i++)

[0106] { ...

[0108] int row_i = row_flag[i];

[0109] int col_i = col_flag[i];

[0110] int start_row = row_i - 3 / 2;

[0111] int end_row = row_i + 3 / 2;

[0112] int start_col = col_i - 5 / 2;

[0113] int end_col = col_i + 5 / 2; ...

[0115] / / Row loop

[0116] for(ir = start_row; ir < end_row; ir++)

[0117] { ...

[0119] / / Column loop

[0120] For(ic = start_col; ic < end_col; ic++)

[0121] { ...

[0123] / / Determine the adjacent distance

[0124] dist =

[0125] sqrt((ir - row_k) * (ir - row_k) + (ic - col_k) * (ic - col_k));

[0126] If(offsetNew[row][col][1] - offset[k] > offsetFactor)

[0127] break;

[0128] trace_tmp[k] += TraceNew[row][col] * wFactor * (1 / dist);

[0129] }

[0130] }}

[0131] / / Calculate the dispersion spectrum of the new trace

[0132] Define the size of the dispersion spectrum array as 100 * 100

[0133] DispSpecArr

[100]

[100] =

[0134] getDispersionSpectrum(trace_tmp,...)

[0135] Step 4: Pick up the surface wave dispersion spectrum values.

[0136] Enclose the calculation of picking up the surface wave dispersion spectrum values in the function

[0137] getPickDispersionSpectrum to obtain the picked dispersion spectrum values. The program to implement the above process is:

[0138]

[0139] Step 5: Reconstruct the surface wave noise according to the dispersion spectrum values.

[0140] Refer to Figure 4 As shown, enclose the reconstruction of the dispersion spectrum values in the function getPredictiveNoise to obtain the reconstructed trace array.

[0141] TraceNoise[k] = getPredictiveNoise(PickDispSpecArr,...);

[0142] Step 6: Subtract the noise from the original trace gather to obtain the denoised trace gather in the TraceResult array. Refer to Figure 3 As shown. The program to implement the above process is:

[0143]

[0144] The method for predicting and suppressing high-density three-dimensional surface waves proposed in this embodiment combines relevant grid data from three-dimensional shot gathers for dispersion spectrum calculation, extracts a high-precision surface wave dispersion curve spectrum, reconstructs surface wave noise, and then eliminates the noise from the original trace gather to achieve the purpose of accurate denoising. The present invention can be applied to processing and interpretation calculation systems, promoting the improvement of processing effects and contributing to increasing reserves and production.

[0145] Example 3:

[0146] This embodiment provides a device 100 for predicting and suppressing surface waves in three-dimensional shot gathers. Referring to Figure 5 as shown, the device 100 for predicting and suppressing surface waves in three-dimensional shot gathers includes: a data acquisition unit 10, a parameter setting unit 20, a data processing unit 30, and a denoising unit 40.

[0147] Among them:

[0148] The data acquisition unit 10 is used to acquire three-dimensional shot gather data and obtain a geophone stake number arrangement array based on the three-dimensional shot gather data. The three-dimensional shot gather data takes a single-shot gather data as the minimum input unit. The single-shot gather data includes multiple groups of geophone stake number arrangements, and the dividing line of each group of geophone stake number arrangements corresponds to the trough position in the seismogram.

[0149] The parameter setting unit 20 is used to set grid parameters and acquire the original trace gather.

[0150] The data processing unit 30 is used to synthesize new traces from the traces within the grid that meet the proximity conditions based on the geophone stake number arrangement array and the grid parameters, and obtain the dispersion spectrum of the new traces; reconstruct the surface wave noise based on the dispersion spectrum of the new traces to obtain a reconstructed trace array;

[0151] The denoising unit 40 is used to obtain a denoised trace gather based on the original trace gather and the reconstructed trace array.

[0152] Further, the data processing unit 30 is also used to obtain the grid trace range adjacent to the original trace based on the geophone stake number arrangement array and the grid parameters; cyclically read the trace values within the grid and obtain the distance between the grid trace and the initial trace; based on the distance between the grid trace and the initial trace, combine the adjacent distance threshold and the proportion value of the grid trace to obtain the traces that meet the proximity conditions and synthesize new traces. The grid trace range includes: the initial trace line number, the initial trace column number, the starting row number of the grid, the ending row number of the grid, the starting column number of the grid, and the ending column number of the grid.

[0153] Further, the data processing unit 30 is also used to pick up the surface wave dispersion spectrum values based on the dispersion spectrum of the new traces and reconstruct the surface wave noise based on the surface wave dispersion spectrum values.

[0154] Further, the denoising unit 40 also obtains the denoised trace gather by subtracting the reconstructed trace array from the original trace gather.

[0155] The apparatus for high-density three-dimensional surface wave prediction and suppression provided in this embodiment combines relevant grid data from a three-dimensional shot gather for dispersion spectrum calculation, extracts a high-precision surface wave dispersion curve spectrum, reconstructs surface wave noise, and then eliminates the noise from the original trace gather data, achieving the purpose of accurate denoising, improving the quality of seismic data, and effectively suppressing surface wave noise.

[0156] Example 4:

[0157] This embodiment provides an electronic device, which can be a mobile phone, a computer, a tablet computer, etc. The electronic device includes a memory and a processor. A computer program is stored on the memory, and when the computer program is executed by the processor, it implements the method for high-density three-dimensional surface wave prediction and suppression as described in Embodiment 1. It can be understood that the electronic device may further include an input / output (I / O) interface and a communication component.

[0158] Among them, the processor is used to execute all or part of the steps in the method for high-density three-dimensional surface wave prediction and suppression in Embodiment 1.

[0159] The method for high-density three-dimensional surface wave prediction and suppression provided in this embodiment combines relevant grid data from a three-dimensional shot gather for dispersion spectrum calculation, extracts a high-precision surface wave dispersion curve spectrum, reconstructs surface wave noise, and then eliminates the noise from the original trace gather data, achieving the purpose of accurate denoising, improving the quality of seismic data, and effectively suppressing surface wave noise.

[0160] Example 5:

[0161] This embodiment also provides a computer-readable storage medium. In each embodiment of the present invention, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0162] Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0163] The aforementioned storage medium includes: flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, server, APP application mall, and various other media that can store program verification codes. A computer program is stored thereon, and when the computer program is executed by a processor, the following method steps can be implemented:

[0164] Step 01: Obtain three-dimensional shot gather data, and obtain a geophone stake number arrangement array based on the three-dimensional shot gather data.

[0165] This method is applicable to high-density common shot gathers. A high-density common shot gather is a gather formed by all the traces received by different geophones when excited by the same shot point. The seismic data must be regularized, sorted according to the shot number, with a consistent shot number interval, such as a shot number interval of 1 or 2, and the number of geophones per shot is the same. To improve the calculation efficiency, the seismic data is divided into individual shot gathers for batch processing. The keywords on which the trace header depends include the coordinates of the shot and geophone. Each trace in the single-shot gather is stored in an array Trace. Optionally, the three-dimensional shot gather data takes a single shot as the minimum input unit.

[0166] In the single-shot record of the three-dimensional shot gather data, there are multiple groups of geophone stake number arrangements. The dividing line of each group of the geophone stake number arrangements corresponds to the trough position in the seismic map.

[0167] Specifically, obtaining the geophone stake number arrangement array based on the three-dimensional shot gather data includes: obtaining the geophone stake number arrangement array based on the actual offset and the geophone stake number arrangement. According to the size of the actual offset, the array of each arrangement is calculated.

[0168] Due to high-density acquisition, reducing the bin size and increasing the data acquisition density in the spatial domain and time domain, multiple waveform undulations appear on the gather seismic map. Each waveform is a geophone stake number arrangement. Each input shot is divided into a two-dimensional geophone array. The boundary of each waveform is the turning point where the offset changes from large to small, that is, the trough of the waveform. The calculation process is as follows:

[0169] Calculate the actual offset corresponding to each trace according to the coordinates of the shot and geophone of each trace.

[0170] Define a two-dimensional geophone arrangement point array RevArr[xnum][ynum] for each shot, where xnum is the set number of geophone arrangements, and ynum is the number of traces in each arrangement.

[0171] Loop to calculate the inflection points where the offset in the gather decreases from large to small.

[0172] The offset array defined by the original input gather is offset[n], the trace array is Trace[n], (n is the number of traces in the gather), i ∈ [0, n]; the number of rows of the geophone array is represented by rowarr[i], the number of columns is represented by colarr[i], the geophone array is RevArr[RowNum][ColNum], and the offset array corresponding to the geophone array is

[0173] RevOffset[RowNum][ColNum], RowNum ∈ [0, xnum], ColNumrow ∈ [0, ynum]. When i = 0, it is used as the starting point of the first geophone array, starting from the 1st one, and calculating in a loop until the (n - 1)th one. The calculation description is as follows:

[0174]

[0175]

[0176] Step 02: Set the grid parameters and obtain the original gather.

[0177] In the shot gather, the surface wave presents a broom shape. To improve the denoising effect, a window where the surface wave is located can be cut out in the shot gather, and the data within the window is used as the data to be processed, represented by MuteTrace[n]. The cut-off vertices and bottom points of each trace are represented by trMute[n][2]. The data outside the window is filled with 0 and not processed during the calculation.

[0178] Since the dispersion spectra of adjacent traces are similar, according to the data characteristics, the interval of trace scanning is set to winScanlen, and the traces within the interval are used as a group of data for processing. In high-density 3D processing, if the bin size is small enough and the offset of the calculated trace satisfies a given threshold range, the trace data of adjacent geophone arrays can be involved in the calculation.

[0179] If the trace number of the current trace is k, represented by Trace[k], the offset threshold is offsetRange, the grid size of the geophone array is dx, dy, and the selected grid data block is DataDisper[idx][idy], and its corresponding offset satisfies

[0180] (RevOffset[idx][idy] - offset[k]) < offsetRange, idx ∈

[0181] [0, dx], idyx ∈ [0, dy].

[0182] Obtain the dispersion spectrum of the dispersion analysis data block.

[0183] Step 03: Synthesize new traces from the traces within the grid that meet the proximity condition based on the geophone stake number arrangement array and the grid parameters, and obtain the dispersion spectrum of the new traces.

[0184] Further, the synthesizing of new traces from the traces within the grid that meet the proximity condition based on the geophone stake number arrangement array and the arrangement calculation grid parameters includes:

[0185] Step 031: Obtain the range of grid traces adjacent to the original trace based on the geophone stake number arrangement array and the grid parameters.

[0186] Specifically, the range of grid traces includes: the initial trace row number, the initial trace column number, the starting row number of the grid, the ending row number of the grid, the starting column number of the grid, and the ending column number of the grid.

[0187] Among them, the row number of trace k is row_k = row_flag[k], and the column number is col_k = col_flag[k];

[0188] The starting row number of the grid start_row = row_k - X / 2, and the ending row number of the grid end_row = row_k + X / 2;

[0189] The starting column number of the grid start_col = col_k - Y / 2, and the ending column number of the grid end_col = col_k + Y / 2.

[0190] Step 032: Read the trace values within the grid in a loop and obtain the distance between the grid trace and the initial trace.

[0191] Step 033: Based on the distance between the grid trace and the initial trace, combine the adjacent distance threshold and the proportion value of the grid trace to obtain the traces that meet the proximity condition and synthesize new traces.

[0192] Read the trace values within the grid in a loop, judge the distance between the grid trace and trace k, and set the adjacent distance threshold offsetFactor and the proportion value of the grid trace wFactor.

[0193] Judge the adjacent distance:

[0194] DataDisper[idx][idy] = wFactor * (1 / (RevOffset[idx][idy] - offset[k])) * Trace[k + idx].

[0195] Step 04: Reconstruct the surface wave noise based on the dispersion spectrum of the new trace to obtain the reconstructed trace array.

[0196] Among them, the reconstruction of surface wave noise based on the dispersion spectrum of the new trace includes: picking up the surface wave dispersion spectrum values based on the dispersion spectrum of the new trace, and reconstructing the surface wave noise based on the surface wave dispersion spectrum values.

[0197] After normalizing the data of the grid data block DataDisper, perform the time-domain to frequency-domain conversion according to the velocity and frequency ranges set by the parameters, calculate the dispersion spectrum, find the maximum frequency along the velocity direction, and automatically pick up the surface wave dispersion curve. Predict and reconstruct the surface wave noise according to the picked dispersion curve.

[0198] Map the surface wave noise calculated for each grid to the calculated time window trace interval, and combine it into the surface wave noise of the seismic data. S(g) is the surface wave noise reconstructed for grid g, wmax is the number of time windows, and Sf(g) is the reconstructed noise of the entire seismic data.

[0199]

[0200] Step 05: Obtain the denoised trace gather based on the original trace gather and the reconstructed trace array.

[0201] Among them, subtract the noise from the original trace gather to obtain the denoised trace gather. A method for high-density three-dimensional surface wave prediction and suppression provided in this embodiment combines relevant grid data from three-dimensional shot gathers for dispersion spectrum calculation, extracts a high-precision surface wave dispersion curve spectrum, reconstructs the surface wave noise, and then eliminates the noise from the original trace gather data to achieve the purpose of precise denoising, improving the quality of seismic data and effectively suppressing surface wave noise.

[0202] A method for high-density three-dimensional surface wave prediction and suppression provided in this embodiment combines relevant grid data from three-dimensional shot gathers for dispersion spectrum calculation, extracts a high-precision surface wave dispersion curve spectrum, reconstructs the surface wave noise, and then eliminates the noise from the original trace gather data to achieve the purpose of precise denoising, improving the quality of seismic data and effectively suppressing surface wave noise. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0203] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0204] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations. It cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. In addition, the above-disclosed specific details are only for the purposes of illustration and easy understanding, rather than limitations. The above details do not limit the present application to necessarily adopt the above specific details for implementation.

[0205] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present application are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any way.

[0206] It should also be noted that in the devices, equipment, and methods of the present application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0207] The above description of the disclosed aspects enables any person skilled in the art to make or use the present application. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but to the broadest scope consistent with the principles and novel features disclosed herein.

[0208] In the description of this application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise specifically defined. In the embodiments of this application, all directional indications (such as up, down, left, right, front, back, top, bottom...) are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0209] In addition, the mention of "embodiments" in this document means that the specific features, structures or characteristics described in connection with the embodiments may be included in at least one embodiment of this application. The appearance of this phrase at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0210] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0211] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting and suppressing high-density three-dimensional surface waves, characterized in that, including: Obtain three-dimensional shot gather data, and obtain a geophone stake number arrangement array based on the three-dimensional shot gather data; Set grid parameters and obtain the original gather; Based on the geophone stake number arrangement array and the grid parameters, synthesize new traces that meet the adjacent conditions within the grid, and obtain the dispersion spectrum of the new traces; Reconstruct surface wave noise based on the dispersion spectrum of the new traces to obtain a reconstructed trace array; Obtain the denoised gather based on the original gather and the reconstructed trace array.

2. The method for predicting and suppressing high-density three-dimensional surface waves according to claim 1, characterized in that, The three-dimensional shot gather data uses single-shot gather data as the minimum input unit.

3. The method for predicting and suppressing high-density three-dimensional surface waves according to claim 2, characterized in that, The single-shot gather data includes multiple groups of geophone stake number arrangements, and the boundary of each group of geophone stake number arrangements corresponds to the trough position in the seismogram; obtaining the geophone stake number arrangement array based on the three-dimensional shot gather data includes: obtaining the geophone stake number arrangement array based on the actual offset and the geophone stake number arrangement.

4. The method for predicting and suppressing high-density three-dimensional surface waves according to claim 1, characterized in that, The synthesizing new traces that meet the adjacent conditions within the grid based on the geophone stake number arrangement array and the arrangement calculation grid parameters includes: Obtain the grid trace range adjacent to the original trace based on the geophone stake number arrangement array and the grid parameters; Loop to read the trace values within the grid and obtain the distance between the grid trace and the initial trace; Based on the distance between the grid trace and the initial trace, combine the adjacent distance threshold and the proportion value of the grid trace to obtain the traces that meet the adjacent conditions and synthesize new traces.

5. The method for predicting and suppressing high-density three-dimensional surface waves according to claim 4, characterized in that, The grid trace range includes: the initial trace row number, the initial trace column number, the starting row number of the grid, the ending row number of the grid, the starting column number of the grid, and the ending column number of the grid.

6. The method for predicting and suppressing high-density three-dimensional surface waves according to claim 1, characterized in that, The reconstructing surface wave noise based on the dispersion spectrum of the new traces includes: picking up the surface wave dispersion spectrum values based on the dispersion spectrum of the new traces, and reconstructing surface wave noise based on the surface wave dispersion spectrum values.

7. The method for predicting and suppressing high-density three-dimensional surface waves according to claim 1, characterized in that, The obtaining the denoised gather based on the original gather and the reconstructed trace array includes: subtracting the reconstructed trace array from the original gather to obtain the denoised gather.

8. An apparatus for predicting and suppressing high-density three-dimensional surface waves, characterized in that, including: A data acquisition unit for obtaining three-dimensional shot gather data and obtaining a geophone stake number arrangement array based on the three-dimensional shot gather data; A parameter setting unit for setting grid parameters and obtaining the original gather; A data processing unit for synthesizing new traces that meet the adjacent conditions within the grid based on the geophone stake number arrangement array and the grid parameters, and obtaining the dispersion spectrum of the new traces; reconstructing surface wave noise based on the dispersion spectrum of the new traces to obtain a reconstructed trace array; A denoising unit for obtaining the denoised gather based on the original gather and the reconstructed trace array.

9. An electronic device, characterized in that, including a memory and a processor, the memory is used to store one or more computer instructions, wherein when the one or more computer instructions are executed by the processor, the method for high-density three-dimensional surface wave prediction and suppression as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and when the computer program is executed by one or more processors, the method for high-density three-dimensional surface wave prediction and suppression as described in any one of claims 1-7 is implemented.

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