Local frequency-based adaptive windowed coherence cube computation method and system

Abstract

The application provides a local frequency-based adaptive window coherence volume calculation method and system, which comprises the following steps: obtaining a seismic data volume, determining local frequency and local dip angle of each data sampling point; determining the vertical length of a coherence calculation window based on the local frequency of a current data sampling point, and determining the data sampling point positions of different seismic traces in the window for coherence calculation based on the local dip angle; extracting seismic data based on the data sampling point positions of different seismic traces in the window for coherence calculation to calculate a coherence value; and traversing each data sampling point to construct a coherence volume matched with the seismic data volume. The application can take into account vertical resolution and noise resistance, reduce the step-shaped false image generated by a fixed window and the coherence anomaly generated by formation occurrence changes, and improve the identification effect of geological discontinuous structures.

Description

Technical Field

[0001] This invention relates to the field of seismic data processing technology, specifically to an adaptive window coherence volume calculation method and system based on local frequency. Background Technology

[0002] In seismic exploration and data processing, coherence attributes can be used to characterize the waveform similarity between adjacent seismic traces to identify geological discontinuities such as faults. Related techniques typically employ fixed vertical windows for coherence calculations. However, the frequency of seismic data varies with both vertical and lateral positions, making it difficult for fixed vertical windows to adapt to spatial variations in frequency components. An excessively long vertical window reduces vertical resolution, aliasing stratigraphic phase axes from different times (or depths) and producing step-like artifacts; an excessively short vertical window results in poor noise resistance. Although the vertical window length can be adjusted based on the peak frequency obtained from spectral decomposition, this peak frequency is susceptible to noise fluctuations and is often unstable in low-amplitude regions such as weak reflection zones, near faults, or between phase axes, requiring further smoothing and resulting in lower reliability. Furthermore, actual subsurface strata often have varying dip angles; neglecting the influence of dip angle can easily lead to non-geological coherence anomalies, affecting the accurate identification of geological discontinuities. Summary of the Invention

[0003] To address the technical problems mentioned above, this invention provides an adaptive window coherence volume calculation method based on local frequency. This method can reduce the step-like artifacts caused by the overlapping of different strata within a fixed window and the coherence anomalies caused by changes in stratum attitude, thereby improving the characterization accuracy of geological discontinuities such as faults, unconformities, and river boundaries.

[0004] The adaptive window coherence volume calculation method based on local frequency provided by this invention includes the following steps:

[0005] Acquire a seismic data volume, which includes multiple data sampling points distributed along the vertical and horizontal directions;

[0006] Based on the earthquake data volume, the local frequency and local dip angle of each of the data sampling points are determined;

[0007] For any current data sampling point, the vertical length of the coherent calculation window is determined based on the local frequency of the current data sampling point;

[0008] Based on the local dip angle of the current data sampling point, determine the location of the data sampling point used for coherent calculation in each seismic trace within the coherent calculation window;

[0009] Based on the location of the data sampling points used for coherence calculation in each seismic trace within the coherence calculation window, seismic data are extracted and coherence values ​​are calculated, and the coherence value of the current data sampling point is also calculated.

[0010] By traversing multiple data sampling points, the coherence values ​​of each data sampling point are obtained, and a coherence volume matching the seismic data volume is constructed.

[0011] In some embodiments, determining the local frequency of each of the data sampling points based on the seismic data volume includes:

[0012] Based on the earthquake data volume, Hilbert transform is performed on the earthquake signals corresponding to each seismic trace in the earthquake data volume to obtain the corresponding Hilbert transform signals.

[0013] Based on the seismic signal and the Hilbert transform signal, calculate their derivatives along the vertical direction, respectively.

[0014] Based on the seismic signal, the Hilbert transform signal, and their derivatives along the vertical direction, and combined with shaping regularization, a frequency solution relationship containing the frequency to be determined is established.

[0015] Based on a preset smoothing operator, the conjugate gradient method is used to solve the frequency relationship to obtain the local frequency of each data sampling point.

[0016] In some embodiments, calculating the derivatives of the seismic signal and the Hilbert transform signal along the vertical direction includes:

[0017] The seismic signal and the Hilbert transform signal are respectively subjected to Fourier transform along the vertical direction to obtain the corresponding Fourier transform results;

[0018] In the frequency domain or wavenumber domain corresponding to the vertical direction, the Fourier transform result is compared with the corresponding differential factor. or Multiply;

[0019] Perform an inverse Fourier transform on the multiplication result to obtain the derivative of the seismic signal and the derivative of the Hilbert transform signal.

[0020] In some embodiments, the step of solving the frequency relationship using the conjugate gradient method includes:

[0021] Based on the frequency solution relationship after shaping and regularization, the frequency solution relationship is iteratively solved using the conjugate gradient method until a preset iteration termination condition is met. The preset iteration termination condition is that the number of iterations reaches a preset maximum number of iterations, or the convergence index of the current iteration is less than a preset accuracy threshold.

[0022] In some embodiments, the seismic data volume is a three-dimensional seismic data volume, the vertical direction is the time direction or the depth direction, and the horizontal direction includes mutually perpendicular transverse and longitudinal directions;

[0023] Determining the local dip angle of each data sampling point based on the seismic data volume includes:

[0024] Determine the local gradients of the seismic data volume along the vertical direction, the horizontal direction, and the longitudinal direction, respectively;

[0025] Construct the gradient structure tensor corresponding to each data sampling point based on the local gradients in the three directions;

[0026] The local tilt angle of each data sampling point is determined based on the gradient structure tensor.

[0027] The local gradient is determined by convolving the seismic data with the first derivative of the Gaussian convolution kernel.

[0028] In some embodiments, determining the local tilt angle of each data sampling point based on the gradient structure tensor includes:

[0029] The gradient structure tensor is Gaussian smoothed.

[0030] Eigenvalue decomposition is performed on the smoothed gradient structure tensor;

[0031] Based on the eigenvector corresponding to the maximum eigenvalue of the smoothed gradient structure tensor, the local tilt angle of the data sampling point in the horizontal and vertical directions is determined.

[0032] In some embodiments, determining the locations of data sampling points for coherence calculation within the coherence calculation window for different seismic traces based on the local dip angle of the current data sampling point includes:

[0033] The seismic trace where the current data sampling point is located is determined as the central seismic trace;

[0034] Determine the spatial offset of the neighboring seismic traces relative to the central seismic trace in the lateral and longitudinal directions for coherence calculation;

[0035] Based on the spatial offset and the local dip angle of the current data sampling point in the horizontal and vertical directions, the vertical sampling offset corresponding to each of the adjacent seismic traces is determined;

[0036] The location of data sampling points for coherence calculation of each of the adjacent seismic traces is determined based on the vertical sampling offset.

[0037] The coherence calculation window includes the central seismic trace and a plurality of neighboring seismic traces distributed around the central seismic trace.

[0038] In some embodiments, determining the vertical length of the coherent calculation window based on the local frequency of the current data sampling point includes:

[0039] The corresponding local period is determined based on the local frequency of the current data sampling point;

[0040] The vertical length of the coherence calculation window is determined based on the local period and the vertical sampling interval of the seismic data volume.

[0041] The number of sampling points corresponding to the vertical length of the coherent calculation window is determined based on the reciprocal of the product of the local frequency of the current data sampling point and the vertical sampling interval.

[0042] Based on the same inventive concept, the present invention also provides an adaptive window coherence volume computing system based on local frequency, comprising:

[0043] The data acquisition module is used to acquire seismic data volumes, which include multiple data sampling points distributed along the vertical and horizontal directions.

[0044] A local frequency determination module is used to determine the local frequency of each of the data sampling points based on the seismic data volume.

[0045] The local dip angle determination module is used to determine the local dip angle of each of the data sampling points based on the seismic data volume;

[0046] The coherent calculation window determination module is used to determine the vertical length of the coherent calculation window for any current data sampling point based on the local frequency of the current data sampling point.

[0047] The sampling location determination module is used to determine the location of each seismic trace used for coherent calculation within the coherent calculation window based on the local dip angle of the current data sampling point.

[0048] The coherence value determination module is used to extract the corresponding seismic data from the seismic data volume based on the location of the data sampling points for coherence calculation of each seismic trace within the coherence calculation window, and to calculate the coherence value of the current data sampling point.

[0049] The coherence volume construction module is used to traverse the multiple data sampling points to obtain the coherence value of each data sampling point and construct a coherence volume corresponding to the seismic data volume.

[0050] In summary, the adaptive window coherence volume calculation method and system based on local frequency provided by this invention have at least the following technical effects: the coherence calculation window can change with the local frequency of the seismic signal and align different seismic traces along the dip direction of the local phase axis. While taking into account both vertical resolution and noise resistance, it reduces the step-like artifacts caused by fixed windows and the coherence anomalies caused by changes in stratigraphic attitude, making geological discontinuities such as faults, unconformities, and channel boundaries more accurately displayed in the coherence volume. Attached Figure Description

[0051] The accompanying drawings are provided to further illustrate the embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention.

[0052] Figure 1 This is a flowchart of an embodiment of the adaptive window coherence volume calculation method based on local frequency according to the present invention.

[0053] Figure 2 This is a schematic diagram of the horizontal arrangement of seismic traces in the coherence calculation window according to an embodiment of the present invention, wherein, Figure 2 (a), (b), and (c) in the figure show cases where the total number of seismic traces used for coherence calculation is 5, 9, and 13, respectively.

[0054] Figure 3 It is a vertical profile of actual seismic data.

[0055] Figure 4 The image shows the result of determining the adaptive window using a spectral decomposition method based on the S-transform, where... Figure 4 In the diagram, (a) is the peak frequency plot obtained from the S-transform. Figure 4 (b) in the diagram is the smoothed peak frequency plot. Figure 4 (c) in the figure is the vertical length distribution of the coherent calculation window obtained based on the smoothed peak frequency.

[0056] Figure 5 This is a result image of the local frequency and adaptive window determined using the method of this invention, wherein, Figure 5 (a) in the image is a local frequency map obtained based on integer regularization. Figure 5 (b) in the figure is a vertical length distribution diagram of the coherent calculation window obtained based on local frequency.

[0057] Figure 6 The results are coherence calculations of actual seismic data obtained using a formula based on variance. Figure 6 (a) in the figure is a fixed-window coherence result plot based on variance. Figure 6 (b) in the figure is the variance-based adaptive window coherence result diagram.

[0058] Figure 7 The results are coherence calculations of actual seismic data obtained using a coherence value calculation formula based on the maximum eigenvalue. Figure 7 (a) in the image shows the coherence results based on a fixed window with the largest eigenvalue. Figure 7 (b) in the figure is the coherence result of the adaptive window based on the maximum eigenvalue.

[0059] Figure 8 The results are coherence calculations of actual seismic data obtained using a coherence value calculation formula based on the variational principle. Figure 8 (a) in the figure is a fixed-window coherence result diagram based on the variational principle. Figure 8 (b) in the figure is the adaptive window coherence result diagram based on the variational principle.

[0060] Figure 9 This is a schematic diagram of the structure of the adaptive window coherence volume calculation system based on local frequency in an embodiment of the present invention.

[0061] Figure 10 This is a schematic diagram of the structure of the electronic device in an embodiment of the present invention.

[0062] Figure label:

[0063] 100. An adaptive window coherence volume computing system based on local frequency;

[0064] 110. Data Acquisition Module; 120. Local Frequency Determination Module; 125. Local Tilt Angle Determination Module; 130. Coherence Calculation Window Determination Module; 140. Sampling Position Determination Module; 150. Coherence Value Determination Module; 160. Coherence Volume Construction Module;

[0065] 210. Processor; 220. Memory; 230. Communication interface; 240. Communication bus. Detailed Implementation

[0066] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0067] refer to Figure 1 This invention provides an adaptive window coherence volume calculation method based on local frequency, which can be applied to seismic data interpretation to improve the identification of geological discontinuities such as faults, unconformities, and river boundaries. In some application scenarios, this method can also be used to identify underground fault zones at airport sites, providing coherence attribute data for analyzing the location, orientation, and extension trend of these fault zones.

[0068] The adaptive window coherence volume calculation method based on local frequency provided by this invention includes the following steps:

[0069] S1, acquire the seismic data volume, which includes multiple data sampling points distributed along the vertical and horizontal directions.

[0070] Specifically, the seismic data volume is formed by arranging multiple seismic traces according to their lateral spatial positions, and each seismic trace includes multiple seismic data points arranged vertically. Data sampling points are determined by both their lateral position and their vertical position within that seismic trace. After acquiring the seismic data volume, a data sampling point index is established according to the lateral and vertical positions, ensuring that each seismic data point has a corresponding data location, providing a unified spatial reference for subsequent point-by-point calculations and result backfilling.

[0071] S2, based on the seismic data volume, determine the local frequency and local dip angle of each of the data sampling points.

[0072] Specifically, for each data sampling point, based on the seismic data corresponding to that sampling point in the seismic data volume, a local frequency that characterizes the oscillation rate of the seismic signal is determined, and a local dip angle that characterizes the spatial extension direction of the seismic phase axis is determined. The local frequencies and local dip angles are associated and stored with the data index of the corresponding data sampling point, so that subsequent processing can retrieve the corresponding local frequencies and local dip angles based on the index of the current data sampling point.

[0073] S3, for any current data sampling point, determine the vertical length of the coherent calculation window based on the local frequency of the current data sampling point.

[0074] Specifically, any data sampling point is selected from multiple data sampling points as the current data sampling point, and the local frequency corresponding to its data index is read. According to a preset correspondence between the local frequency and the vertical length of the coherent calculation window, the local frequency is converted into the vertical length corresponding to the current data sampling point. Thus, data sampling points with different local frequencies can correspond to different vertical lengths of the coherent calculation window, without having to use the same fixed length for all data sampling points.

[0075] S4. Based on the local dip angle of the current data sampling point, determine the location of the data sampling point for coherent calculation of each seismic trace within the coherent calculation window.

[0076] Specifically, the local dip angle corresponding to the data index of the current data sampling point is read. Based on the extension direction of the in-phase axis represented by this local dip angle, the locations of data sampling points used for coherence calculation in different seismic traces within the coherence calculation window are determined. The locations of data sampling points used for coherence calculation in different seismic traces within the window are distributed along the local in-phase axis near the current data sampling point, so that data belonging to the same local in-phase axis in different seismic traces can correspond to each other during coherence calculation, thereby reducing coherence anomalies caused by stratigraphic attitude changes.

[0077] S5. Based on the data sampling point positions of different seismic traces used for coherence calculation within the coherence calculation window, extract the corresponding seismic data from the seismic data volume and calculate the coherence value of the current data sampling point.

[0078] Specifically, based on the vertical length determined in step S3, seismic data at the data sampling point locations of different seismic traces used for coherence calculation within the coherence calculation window determined in step S4 are extracted from the seismic data body, and the waveform similarity of the extracted seismic data is calculated to obtain the coherence value of the current data sampling point.

[0079] Specifically, the more similar the waveforms of different seismic traces at the data sampling points used for coherence calculation, the lower the coherence value of the current data sampling point; conversely, the greater the waveform differences, the higher the coherence value of the current data sampling point. Furthermore, since the data used for calculation has been positionally correlated according to the dip direction of the local phase axis, the obtained coherence value can more accurately reflect the waveform continuity between adjacent seismic traces.

[0080] S6. Traverse multiple data sampling points to obtain the coherence value of each data sampling point, and construct a coherence volume that matches the seismic data volume.

[0081] Specifically, after obtaining the coherence value of the current data sampling point, it is checked whether there are any unprocessed data sampling points in the seismic data volume. If so, the next unprocessed data sampling point is determined as the new current data sampling point, and steps S3 to S5 are repeated; if not, the traversal ends. The coherence values ​​obtained from the traversal are arranged according to the vertical and horizontal indices of the corresponding data sampling points in the original seismic data volume to form a coherence volume with the same spatial coordinate relationship as the original seismic data volume. The coherence volume can be output through vertical profiles, horizontal slices, or three-dimensional display to observe the location, orientation, and spatial continuity of high coherence anomalies.

[0082] In the above steps, the local frequency obtained in step S2 controls the vertical length in step S3, the local dip angle obtained in step S2 controls the corresponding sampling positions of different seismic traces in step S4, and steps S3 and S4 together define the window of seismic data used in step S5. The coherence value obtained in step S5 is then reconstructed into a coherence volume in step S6 according to the original spatial index. Therefore, adaptive windowing and dip correction are not independent post-processing steps, but rather work together in the data organization process for coherence value calculation.

[0083] In summary, this embodiment allows the coherence calculation window to vary with the local frequency of the seismic signal and aligns different seismic traces along the dip direction of the local phase axis. While taking into account both vertical resolution and noise resistance, it reduces the step-like artifacts caused by fixed windows and the coherence anomalies caused by changes in stratigraphic attitude, making geological discontinuities such as faults, unconformities, and channel boundaries more accurately displayed in the coherence volume.

[0084] In some embodiments, step S2 includes:

[0085] Based on the aforementioned seismic data volume, the seismic signals corresponding to each seismic trace within the seismic data volume are analyzed. Perform a Hilbert transform to obtain the corresponding Hilbert transform signal. ;

[0086] Based on the earthquake signal The Hilbert transform signal Calculate the derivatives of both along the vertical direction;

[0087] Based on the earthquake signal The Hilbert transform signal And the derivatives of both along the vertical direction, combined with shaping regularization, establish a frequency solution relationship that includes the frequency to be determined;

[0088] Based on a preset smoothing operator, the conjugate gradient method is used to solve the frequency relationship to obtain the local frequency of each data sampling point.

[0089] The smoothing operator allows the desired frequency at the current data sampling point to be constrained by other data sampling points in the local neighborhood. Outliers caused by noise or weak amplitude at a single sampling point cannot independently determine the solution result. The continuous frequency information contained in adjacent data sampling points can constrain the outlier, thereby reducing the probability of isolated frequency jumps in the local area.

[0090] This embodiment establishes frequency solution relationships and applies local continuity constraints, which can transform unstable point-by-point frequency calculations into global solutions using local neighborhood information. This makes the obtained local frequencies more continuous and stable while maintaining the local variation characteristics of the seismic signal, and reduces isolated frequency anomalies caused by noise or weak amplitude.

[0091] Optionally, the smoothing operator includes a triangular smoothing operator. The triangular smoothing operator can act on the frequency to be determined along the vertical direction and at least one lateral direction, so that the local frequency retains the local variation trend of the seismic data while suppressing isolated frequency jumps caused by noise.

[0092] Further, calculating the derivatives of the seismic signal and the Hilbert transform signal along the vertical direction includes:

[0093] For the earthquake signal and the Hilbert transform signal Perform Fourier transforms along the vertical direction respectively to obtain the corresponding Fourier transform results;

[0094] In the frequency domain or wavenumber domain corresponding to the vertical direction, the Fourier transform result is compared with the corresponding differential factor. or Multiply;

[0095] Perform an inverse Fourier transform on the multiplication result to obtain the derivative of the seismic signal. and the derivative of the Hilbert transform signal .

[0096] In this embodiment, determining the local frequency of each data sampling point based on the seismic data volume includes the following steps.

[0097] First, for any seismic trace in the seismic data volume, acquire the original seismic signal distributed along the vertical direction of that seismic trace. and the original seismic signal Perform a Hilbert transform to obtain the Hilbert transformed signal. The seismic signals were respectively... and the Hilbert transform signal Calculate the derivative along the vertical direction to obtain and Wherein, when the vertical direction is the time direction, respectively for and Perform a Fourier transform, and then compare the corresponding Fourier transform result with the differential factor in the frequency domain. Multiply, then perform an inverse Fourier transform on the result to obtain... and When the vertical direction is a spatial direction, the Fourier transform result and the differential factor are compared in the corresponding wavenumber domain. Multiply them, then perform an inverse Fourier transform to obtain the derivative in the corresponding direction.

[0098] In this embodiment, the seismic signal The Hilbert transform signal Then the corresponding complex number can be expressed as:

[0099] (Formula 1)

[0100] In the formula, Indicates a complex number; Represents the imaginary unit. This indicates the sampling time. The complex trace refers to a complex signal formed by combining an original seismic trace with its Hilbert transform signal.

[0101] Complex channels can also be represented in the form of instantaneous amplitude and instantaneous phase, that is:

[0102] (Formula 2)

[0103] In the formula, Indicates instantaneous amplitude. Indicates instantaneous phase, Represents the natural constant.

[0104] The instantaneous frequency is the derivative of the instantaneous phase with respect to time, that is:

[0105] (Formula 3)

[0106] In the formula, Indicates instantaneous frequency. Indicates instantaneous phase The derivative of , They represent , The corresponding derivative.

[0107] According to Formula 3, define a vector. and diagonal matrix ;vector The elements can be represented as diagonal matrix The diagonal elements can be represented as .

[0108] After constructing vectors and matrices from all sampling points, we have:

[0109] (Formula 4)

[0110] In the formula, Indicates the instantaneous frequency The frequency vector formed by these vectors.

[0111] Equation 4 is subjected to shaping regularization to construct a frequency solution relation containing the frequency to be determined:

[0112] (Formula 5)

[0113] (Formula 6)

[0114] In the formula, This represents the local frequency vector obtained after shaping and regularization. Indicates the scale parameter. This indicates the total number of samples in the seismic data volume. Indicates a data index. This represents a smoothing operator, typically a triangular smoothing operator. Represents the identity matrix, superscript This represents the transpose of a matrix.

[0115] In this embodiment, local frequencies are obtained through shaping regularization, which can constrain not only the vertical neighboring data sampling points of the current data sampling point, but also the horizontal neighboring data sampling points, thus ensuring the continuity and smoothness of frequency information within the local neighborhood.

[0116] In some embodiments, the step of solving the frequency relationship using the conjugate gradient method includes:

[0117] Based on the frequency solution relationship after shaping and regularization, the frequency solution relationship is iteratively solved using the conjugate gradient method until a preset iteration termination condition is met. The preset iteration termination condition is that the number of iterations reaches a preset maximum number of iterations, or the convergence index of the current iteration is less than a preset accuracy threshold.

[0118] The preset iteration termination condition may include: the number of iterations reaches a preset maximum number of iterations, or the convergence index of the current iteration is less than a preset accuracy threshold. In this embodiment, the preset maximum number of iterations can be 100, and the preset accuracy threshold can be [missing information]. The convergence metric can be the ratio of the current gradient inner product to the initial gradient inner product. Iteration stops and the local frequency is output when any termination condition is met.

[0119] The smoothing operator is a triangular smoothing operator. The weights of the triangular smoothing operator gradually decrease from the neighborhood center towards both sides. Therefore, nearby data sampling points have a greater impact on the current frequency result, while the influence of more distant data sampling points gradually decreases. This weight distribution can suppress isolated noise while avoiding excessive blurring of local frequency boundaries caused by uniform averaging.

[0120] In this embodiment, the process of solving the frequency relationship using the conjugate gradient method is as follows:

[0121] Step 1: Using scale parameters right and Normalization yields:

[0122] and (Formula 7)

[0123] In the formula: , They represent the normalized values ​​respectively. and .

[0124] Step 2: Initialize parameters: auxiliary vector Local frequency residual vector Regularization coefficient Precision error Number of iterations Fletcher–Reeves conjugate coefficient .

[0125] Step 3: Iterate

[0126] calculate

[0127] ; (Formula 8)

[0128] ; (Formula 9)

[0129] ; (Formula 10)

[0130] ; (Formula 11)

[0131] . (Formula 12)

[0132] if ,but

[0133] , ; (Formula 13)

[0134] otherwise

[0135] . (Formula 14)

[0136] like or ,but

[0137] Terminate the loop;

[0138] otherwise

[0139] calculate

[0140] ; (Formula 15)

[0141] ; (Formula 16)

[0142] ; (Formula 17)

[0143] ; (Formula 18)

[0144] ; (Formula 19)

[0145] ; (Formula 20)

[0146] . (Formula 21)

[0147] End if

[0148] . (Formula 22)

[0149] Step 4: Output .

[0150] In step 3 above, , , Representing local frequencies respectively Auxiliary vectors residual vector The gradient vector; , , Representing local frequencies respectively Auxiliary vectors residual vector The corresponding search direction; Represents a scalar determined by the gradient vector; Indicates the iteration step size; This represents the history used to calculate the conjugate coefficients in the next iteration. value; This represents the initial gradient inner product value; This indicates the current iteration number.

[0151] In some embodiments, the seismic data volume is a three-dimensional seismic data volume, the vertical direction is the time direction or the depth direction, and the horizontal direction includes mutually perpendicular transverse and longitudinal directions;

[0152] Determining the local dip angle of each data sampling point based on the seismic data volume includes:

[0153] Determine the local gradients of the seismic data volume along the vertical direction, the horizontal direction, and the longitudinal direction, respectively;

[0154] Construct the gradient structure tensor corresponding to each data sampling point based on the local gradients in the three directions;

[0155] The local tilt angle of each data sampling point is determined based on the gradient structure tensor.

[0156] The local gradients mentioned above are determined by convolving seismic data with the first derivative of a Gaussian convolution kernel.

[0157] Specifically, the three-dimensional seismic data volume is represented as a function of the vertical coordinate and two horizontal spatial coordinates. The local gradients of the seismic data are calculated along the vertical, horizontal, and longitudinal directions, respectively, and a gradient structure tensor is constructed using the local gradients in these three directions.

[0158] In this embodiment, it is assumed that the three-dimensional seismic data volume is represented as a function of the vertical coordinate and two horizontal spatial coordinates, and the function is considered along the three coordinate directions ( , , Local change calculations are performed to obtain the local gradient in the corresponding direction.

[0159] First, calculate the local gradient of the seismic data volume in the three coordinate directions. The calculation formula is as follows:

[0160] (Formula 23)

[0161] In the formula, , , These represent the earthquake data in , , Local gradient in the direction; This represents the convolution operation. Represents a three-dimensional seismic data volume. The time axis is the coordinate. and These represent the coordinates in the horizontal spatial direction, The Gaussian convolution kernel can be represented as:

[0162] (Formula 24)

[0163] In the formula, To indicate the coordinate direction, you can take... , or , This represents the standard deviation in the corresponding direction; standard deviation , , The value of is generally between 2 and 6; This represents an exponential function.

[0164] That is, the local gradients in the corresponding directions are obtained by convolving the seismic data volume with the first derivative of the Gaussian convolution kernels along the vertical, horizontal and longitudinal directions respectively.

[0165] Based on this, the local gradient vector at each data sampling point can be constructed. :

[0166] (Formula 25)

[0167] In the formula, , and These represent the data sampling points at... , and Directional index.

[0168] Its gradient structure tensor is:

[0169] (Formula 26)

[0170] This embodiment constructs a gradient structure tensor using local gradients in three directions, which can comprehensively describe the local spatial orientation of the three-dimensional seismic phase axis.

[0171] Further, determining the local tilt angle of each data sampling point based on the gradient structure tensor includes:

[0172] The gradient structure tensor is Gaussian smoothed.

[0173] Eigenvalue decomposition is performed on the smoothed gradient structure tensor;

[0174] Based on the eigenvector corresponding to the maximum eigenvalue of the smoothed gradient structure tensor, the local tilt angle of the data sampling point in the horizontal and vertical directions is determined.

[0175] Specifically, the first derivative of the Gaussian convolution kernel is used to determine the local gradients in three directions. To improve the stability of the gradient structure tensor, Gaussian smoothing is applied to each tensor element along the three coordinate directions. Eigenvalue decomposition is then performed on the smoothed gradient structure tensor, and the eigenvector corresponding to the largest eigenvalue is obtained. This eigenvector represents the normal direction of the local seismic phase axis, and the local dip angles in the two lateral directions are determined by the ratio between its vertical component and the two lateral components.

[0176] Gaussian convolution assigns higher weights to data points closer to the current sampling point, thus preserving local structural orientation while suppressing high-frequency random noise. Smoothing the tensor elements weakens gradient direction fluctuations caused by random noise by the dominant directional information within the neighborhood. The eigenvector corresponding to the largest eigenvalue represents the direction of the highest concentration of tensor energy, which corresponds to the normal direction of the strongest changes in local seismic data. The local dip angle is then obtained from the ratio of the horizontal to vertical components of this eigenvector, giving the dip angle a clear geometric origin.

[0177] To improve the reliability of the calculation results, Gaussian convolution kernels are applied along different directions to the dataset composed of each element of the gradient structure tensor of all data sampling points to perform Gaussian smoothing, resulting in a smoothed gradient structure tensor. ,

[0178] (Formula 27)

[0179] In the formula, This indicates local Gaussian smoothing. For the Gaussian convolution kernel used in the above equation, The value of is generally between 2 and 16. and The value of is generally between 2 and 6.

[0180] At each data sampling point Perform eigenvalue decomposition to obtain the eigenvector corresponding to the largest eigenvalue. ,

[0181] (Formula 28)

[0182] In the formula: , , These represent the components of the eigenvector in the t, x, and y directions, respectively.

[0183] The local tilt angle in the spatial direction of each data sampling point can then be expressed as:

[0184] (Formula 29)

[0185] In the formula: , These represent the local tilt angles in the x and y directions, respectively.

[0186] This embodiment improves the robustness and spatial continuity of the local tilt angle to random noise by using Gaussian smoothing, tensor eigenvalue decomposition, and maximum eigenvector orientation.

[0187] In some embodiments, determining the vertical length of the coherent calculation window based on the local frequency of the current data sampling point includes:

[0188] The corresponding local period is determined based on the local frequency of the current data sampling point;

[0189] The vertical length of the coherence calculation window is determined based on the local period and the vertical sampling interval of the seismic data volume.

[0190] The number of sampling points corresponding to the vertical length of the coherent calculation window is determined based on the reciprocal of the product of the local frequency of the current data sampling point and the vertical sampling interval.

[0191] Specifically, if the local frequency of the current data sampling point is Vertical sampling interval is The local period is The number of sampling points corresponding to the vertical window According to Confirmed. When the vertical direction is the depth direction, The corresponding interval should be replaced with the depth sampling interval. The corresponding local wavenumber is then used. In actual calculations, the calculation result can be converted into an integer number of sampling points according to a preset rounding rule; when the current data sampling point needs to be the center of the window, the number of sampling points can also be adjusted to an odd number. The rounding method, the odd number adjustment method, and the upper and lower limits of the window length can be preset according to the data sampling interval and the target construction scale.

[0192] It should be noted that the output of the frequency solution is the local angular frequency. At that time, it can be done according to The local frequency was obtained by conversion in terms of vertical coordinates per week. Then utilize local frequencies Determine the vertical length of the local period and coherence calculation window. This ensures that the frequency unit and window length calculation are consistent.

[0193] Local frequency and local period are reciprocals of each other, so frequency changes can be directly converted into vertical scale changes. The local period is then converted into the number of sampling points using the vertical sampling interval, so that the physical period length corresponds to the number of window points in the discrete seismic data. Therefore, the number of window points corresponding to each data sampling point is determined by the local frequency of that point, rather than by a uniform empirical parameter.

[0194] The number of vertical sampling points in the coherence calculation window can be expressed as:

[0195] (Formula 30)

[0196] In the formula, This indicates the number of sampling points corresponding to the coherence calculation window in the vertical direction. This represents the local frequency of the current data sampling point. This indicates the vertical sampling interval of the seismic data volume.

[0197] In this embodiment, the vertical length of the coherent calculation window is determined based on the local period. A shorter window can be used in the high-frequency region to maintain vertical resolution, while a longer window can be used in the low-frequency region to improve noise resistance. This avoids the problem that a fixed window cannot adapt to changes in the frequency of seismic data. It is beneficial to preserve the vertical details of small-scale geological discontinuities and to stably display the continuous response of geological structures in low-frequency, weak signal regions.

[0198] In some embodiments, determining the locations of data sampling points for coherence calculation within the coherence calculation window for different seismic traces based on the local dip angle of the current data sampling point includes:

[0199] The seismic trace where the current data sampling point is located is determined as the central seismic trace;

[0200] Determine the spatial offset of the neighboring seismic traces relative to the central seismic trace in the lateral and longitudinal directions for coherence calculation;

[0201] Based on the spatial offset and the local dip angle of the current data sampling point in the horizontal and vertical directions, the vertical sampling offset corresponding to each of the adjacent seismic traces is determined;

[0202] The location of data sampling points for coherence calculation of each of the adjacent seismic traces is determined based on the vertical sampling offset.

[0203] The coherence calculation window includes the central seismic trace and a plurality of neighboring seismic traces distributed around the central seismic trace. It should be noted that, in this invention, the number of seismic traces used for coherence calculation is determined based on actual testing.

[0204] refer to Figure 2 Specifically, the seismic trace where the current data sampling point is located is taken as the central seismic trace, and neighboring seismic traces are selected around the central seismic trace. For the j-th neighboring seismic trace, its spatial offset relative to the central seismic trace in the first lateral direction and the second lateral direction is determined respectively. The two spatial offsets are multiplied by the local dip angle in the corresponding direction and summed to obtain the vertical sampling offset of the neighboring seismic trace.

[0205] According to the stated vertical sampling offset, the vertical sampling positions of adjacent seismic traces are moved to the same phase axis position corresponding to the central seismic trace. When the vertical sampling offset is a non-integer sampling interval, the seismic data at the corresponding position can be obtained through linear interpolation, quadratic convolution interpolation, or Sinc interpolation. The seismic traces used for calculation can be selected as 5, 9, or 13 traces depending on the shape of the data network.

[0206] Here, the lateral spatial offset represents the spatial distance between adjacent seismic traces and the central seismic trace, and the local dip angle represents the vertical change corresponding to a unit lateral distance. The vertical sampling offset obtained by multiplying the two is the vertical position difference of the same local phase axis between two seismic traces. After determining the data sampling positions of different seismic traces for coherence calculation based on this position difference, data belonging to the same local phase axis in different seismic traces can correspond to each other during coherence calculation.

[0207] In this embodiment, the coherence calculation formula based on tilt angle correction is as follows:

[0208] Coherence calculation formula based on variance

[0209] (Formula 31)

[0210] In the formula, This represents the coherence result obtained based on variance calculation; Indicates the time direction coordinate. The time sampling interval is... Indicates along direction and Local tilt angle of direction, and They represent the first The spatial coordinates of the path relative to the center of the window. This indicates the vertical length of the calculation window. ( (representing the local frequency at the calculation location). Indicates the index of the time sampling point. This indicates the total number of seismic traces within the window. Generally, 5, 9, or 13 seismic traces are selected for calculation. Figure 2 As shown, Indicating the index of the seismic trace, This displays the earthquake data within the window. The standard track is represented by the following formula:

[0211] (Formula 32)

[0212] Correcting adjacent seismic traces based on spatial offsets in two lateral directions and local dip angles can align in-phase information on dipped strata within a window, reducing the non-geological impact of stratum dip angles on coherence values. This allows highly coherent anomalies to more effectively characterize faults or other real geological discontinuities, improving the geological indicative power of coherence properties.

[0213] Furthermore, in other embodiments of the present invention, a coherence calculation method based on the maximum eigenvalue can also be employed. A data matrix is ​​constructed from the dip-corrected windowed seismic data, and then a covariance matrix is ​​constructed from the data matrix. The maximum eigenvalue of the covariance matrix is ​​determined, and the coherence value is determined based on the relationship between the maximum eigenvalue and the total energy of the covariance matrix. The maximum eigenvalue can be quickly obtained using the power method.

[0214] In another implementation, a coherence calculation method based on variational principles is employed. The corresponding coherence values ​​are obtained by solving for the matching relationship that minimizes the overall difference between each seismic trace and the standard trace. All three coherence calculation methods described above use adaptive vertical window and dip-corrected windowed seismic data as input, and can be selected based on the required computational accuracy and efficiency.

[0215] Variance-based methods reflect the consistency of each seismic trace through amplitude dispersion, directly characterizing the deviation of each trace within the window from the standard trace. Maximum eigenvalue-based methods reflect the presence of a dominant common signal by the proportion of energy occupied by the principal eigenvalues ​​of the covariance matrix. Variational principle-based methods obtain the optimal matching relationship between each seismic trace and the standard trace through overall optimization. Although these three methods employ different mathematical metrics, they all use dip-corrected in-phase data as the comparison object, thus reducing the interference of formation dip on coherence results.

[0216] The coherence calculation formula based on the largest eigenvalue

[0217] First, construct the covariance matrix after local tilt correction. :

[0218] (Formula 33)

[0219] In the formula, This represents a matrix composed of seismic data within the window, with dimensions of... The matrix elements are .

[0220] Covariance matrix The largest eigenvalue is The largest eigenvalue can be quickly obtained using the power method. The formula for calculating coherence based on the largest eigenvalue can be written as:

[0221] (Formula 34)

[0222] In the formula: This represents the coherence calculation result determined based on the largest eigenvalue.

[0223] Coherence calculation formula based on variational principle:

[0224] (Formula 35)

[0225] In the formula, in the formula: This represents the coherence calculation results determined based on the variational principle; Let be the amplitude matching coefficient of the j-th seismic trace relative to the standard trace. It can be represented as:

[0226] (Formula 36)

[0227] The other variables are the same The meaning is the same as in the text.

[0228] It should be noted that in this invention, the coherence value calculation method can be selected according to the scale of the processed data, the noise level, and the accuracy requirements. While maintaining the combined effects of adaptive window and tilt correction, it can also take into account computational efficiency, amplitude difference sensitivity, and the ability to identify the dominant in-phase signal. This allows the method to adapt to the coherence attribute calculation needs of seismic data of different scales, signal-to-noise ratios, and tectonic scales.

[0229] refer to Figures 3 to 8 This verification embodiment uses actual seismic data to verify the adaptive window coherence volume calculation method. Figure 3 The vertical profile of the actual seismic data used is shown. The in-phase axes in this profile have different local frequency characteristics at different spatial locations and contain lateral discontinuities that need to be identified through coherence attributes. Therefore, it can be used to compare the processing results of different window determination methods and different coherence value calculation methods.

[0230] Figure 4 The diagram shows the results of determining the adaptive window using the S-transform-based spectral decomposition method as a comparative example. Specifically, the S-transform is first used to determine the peak frequency of each data sampling point, then the peak frequencies are smoothed, and the vertical length of the coherent calculation window is calculated from the smoothed peak frequencies. Figure 4 (a) in the diagram is named the peak frequency diagram obtained by the S-transform. Figure 4 (b) in the diagram is named the smoothed peak frequency plot. Figure 4 (c) is named the vertical length distribution diagram of the coherent calculation window obtained based on the smoothed peak frequency. This comparative example is used to illustrate that when the window is directly determined by the point-by-point peak frequency, additional smoothing processing is usually required to reduce local abrupt changes in the frequency results. It should be noted that the S-transform described is only used as a comparison method and is not a preferred embodiment of the present invention for determining local frequencies.

[0231] Figure 5 This illustration shows the local frequencies obtained using shaping regularization in this invention, and the corresponding vertical length distribution of the coherent calculation window. Specifically, it shows... Figure 5 (a) in the image is named the local frequency map obtained based on integer regularization. Figure 5 (b) is named the vertical length distribution diagram of the coherent calculation window obtained based on local frequency. Figure 5 The local frequencies shown in (a) are directly obtained from the constraint frequency solution relation containing the smoothing operator. Figure 5 The vertical length of the coherence calculation window shown in (b) is determined based on the local frequency, local period, and vertical sampling interval. Since the local continuity constraint has been incorporated into the frequency solution process, the local frequency and window length can be continuously adjusted with the frequency changes of the seismic signal, without having to smooth the frequency results obtained point by point as an independent post-processing object.

[0232] refer to Figure 6 When using the variance-based coherence value calculation formula, Figure 6 (a) in the image is named the fixed-window coherence result plot based on variance. Figure 6 (b) is named the variance-based adaptive window coherence result map. As can be seen from the comparison, the adaptive window uses a statistical scale that matches the local period for different frequency regions, thereby reducing the step-like artifacts or stratigraphic correlation artifacts caused by the mismatch between the fixed window scale and the local seismic signal, and making the coherence anomalies at lateral discontinuities more concentrated.

[0233] refer to Figure 7 When using the coherence value calculation formula based on the maximum eigenvalue, Figure 7 (a) is named the fixed-window coherence result map based on the maximum eigenvalue. Figure 7 (b) is named the coherence result map based on the adaptive window of the maximum eigenvalue. The adaptive window changes the vertical sample range used to construct the covariance matrix according to the local frequency of different data sampling points, so that the common signal energy represented by the maximum eigenvalue is adapted to the local period of the current position, thereby reducing the coherence anomalies caused by the mixing of adjacent reflection layer signals by the fixed window.

[0234] refer to Figure 8When using the coherence value calculation formula based on the variational principle, Figure 8 (a) in the figure is named the fixed-window coherence result plot based on the variational principle. Figure 8 (b) is named the coherence result map based on the variational principle of adaptive window. The adaptive window limits the range of data used to calculate the coherence value based on the variational principle, so that the seismic trace data participating in the coherence calculation matches the local period of the current data sampling point, which helps to improve the characterization accuracy of discontinuity boundaries.

[0235] comprehensive Figures 6 to 8 It is evident that the adaptive window does not depend on a specific coherence calculation criterion. Regardless of whether a calculation method based on variance, maximum eigenvalue, or variational principle is used, the vertical length of the coherence calculation window can be determined first according to local frequency, then the vertical sampling positions of different seismic traces can be corrected according to local dip angle, and finally the coherence value can be determined using the corrected windowed seismic data. Therefore, the adaptive window and dip correction constitute a preliminary data organization process that can be combined with various coherence measurement methods.

[0236] In summary, the comparison results of actual seismic data show that the local frequencies obtained based on shaping regularization can provide continuously changing adaptive vertical windows for each data sampling point. When combined with dip correction, this window can be applied to various coherence calculation methods and can reduce the step-like artifacts and coherence anomalies caused by fixed windows, making the discontinuous structures such as faults in seismic data more accurate and improving the structural traceability of coherent bodies in vertical profiles, horizontal slices, and three-dimensional space.

[0237] In the execution of the adaptive window coherence volume calculation method based on local frequency provided by the present invention, this embodiment uses... Figure 3 The three-dimensional seismic data volume containing the actual seismic data shown is the processing object. The time direction is used as the vertical direction, and the perpendicularly intersecting survey lines are used as the first and second lateral directions. Seismic amplitude data, time sampling intervals, trace spacing in the two lateral directions, and the three-dimensional index of each data sampling point are read to obtain a set of data sampling points characterized by the vertical sampling position and the two lateral positions.

[0238] First, the local frequencies of the entire seismic data volume are calculated. For each seismic trace, a Hilbert transform is performed on the seismic signal to obtain the Hilbert-transformed signal. The derivatives of the seismic signal and the Hilbert-transformed signal along the vertical direction are calculated separately, and a vector is constructed based on the seismic signal, the Hilbert-transformed signal, and their derivatives. and diagonal matrix Then, a frequency solution relationship containing the frequency to be determined is constructed using shaping regularization; then, the frequency solution relationship is solved using a preset triangular smoothing operator and the conjugate gradient method to obtain the local frequency of each data sampling point.

[0239] During the solution process, the maximum number of iterations can be preset to 100, and the preset precision threshold can be set to... The local frequencies are output when any termination condition is met. The above processing is performed on each seismic trace in the seismic data volume to obtain the local frequency volume corresponding to the spatial index of the seismic data volume. An example result is shown below. Figure 5 As shown in (a) of the diagram. Figure 4 The peak frequency of the S-transform, the peak frequency after smoothing, and their corresponding window lengths shown in (a) to (c) are only used as comparison results.

[0240] Next, the local dip angle of the entire seismic data volume is calculated. The seismic data volume is convolved with the first derivative of Gaussian convolution kernels along the vertical, first lateral, and second lateral directions to obtain local gradients in these three directions. A local gradient vector and a gradient structure tensor are constructed at each data sampling point. Gaussian smoothing is applied to each element of the gradient structure tensor, and eigenvalue decomposition is performed on the smoothed gradient structure tensor. Based on the eigenvector corresponding to the largest eigenvalue of the smoothed gradient structure tensor, the local dip angle of the data sampling point in the lateral and vertical directions is determined.

[0241] Then, the first data sampling point to be processed is selected from the seismic data volume as the current data sampling point, and the local frequency of this point is read. If the frequency solution result is a local angular frequency, it is first converted to a local frequency; then, the number of vertical sampling points in the coherence calculation window is determined based on the local frequency and the time sampling interval. The number of sampling points is rounded and adjusted to an odd number so that the current data sampling point is located in the center of the window; when the calculated value exceeds the preset window range, it is limited to between the preset lower limit and the preset upper limit. After performing this conversion on each data sampling point, the following is obtained: Figure 5 The vertical length distribution of the coherent calculation window, shown in (b) in the figure, varies continuously with the local frequency.

[0242] This embodiment selects Figure 2 The arrangement of the nine seismic traces shown in (b) involves using the seismic trace containing the current data sampling point as the central trace and selecting eight neighboring seismic traces around it. For each neighboring seismic trace, its spatial offset relative to the central seismic trace in two lateral directions is read, and the corresponding vertical sampling offset is calculated by combining it with the two local dip angles of the current data sampling point. The location of the data sampling point for coherence calculation of each neighboring seismic trace is determined according to the vertical sampling offset; when the data sampling point is located between two vertical sampling points, the seismic amplitude value at that location is obtained by interpolation. Thus, nine dip-corrected window seismic data are formed within the vertical length range determined in step S3.

[0243] Next, a variance-based coherence calculation method is used to calculate the coherence value of the current data sampling point. A standard trace is obtained from the 9-channel seismic data, and the coherence value is determined based on the amplitude differences between each seismic trace and the standard trace. Alternatively, a covariance matrix can be constructed from the dip-corrected data matrix, and the coherence value can be determined based on the largest eigenvalue; or a variational principle-based method can be used to determine the coherence value. For the same current data sampling point, only one of the coherence calculation methods needs to be selected to obtain the coherence value corresponding to that data sampling point.

[0244] After obtaining the coherence value of the current data sampling point, write the coherence value to the same coherence value storage location as the spatial index of the current data sampling point, and check if there is a next unprocessed data sampling point. If there is a next unprocessed data sampling point, update the current data sampling point, and repeat the window length determination, dip correction, and coherence value calculation; until all data sampling points in the seismic data volume are processed. For data sampling points located at the boundary of the seismic data volume and adjacent to seismic traces or with insufficient vertical sampling points, the window can be reduced to the effective data range, or a preset boundary extension method can be used to supplement the window data.

[0245] After traversal, all coherence values ​​are arranged according to the vertical coordinates and two horizontal coordinates of the original seismic data volume to obtain a three-dimensional coherence volume. Vertical profiles, horizontal slices, or three-dimensional anomalies can be extracted from the three-dimensional coherence volume to continuously track the spatial distribution of highly coherent anomalies. Figure 6 (a) and (b) Figure 7 (a) and (b) in the middle and Figure 8 Figures (a) and (b) show a comparison of the results of the three coherence calculation methods using a fixed window versus the adaptive window of this scheme, respectively. The comparison results show that this scheme can reduce the step-like artifacts or stratigraphic correlation artifacts caused by the fixed window and make the high coherence anomalies at geological discontinuities more concentrated.

[0246] In the application scenario of identifying underground fault zones at airport sites, the aforementioned three-dimensional coherence volume can be jointly interpreted with regional geological data, borehole data, other geophysical exploration results, or field investigation results to analyze whether highly coherent anomalies with spatial continuity correspond to underground fault zones, and to assist in determining the location, orientation, and extension trend of suspected fault zones. It should be noted that this application scenario is only one application of this method, and highly coherent anomalies themselves are not necessarily equivalent to underground fault structures.

[0247] like Figure 9As shown, based on the same inventive concept, the present invention also provides an adaptive window coherence volume calculation system 100 based on local frequency, which includes a data acquisition module 110, a local frequency determination module 120, a local tilt angle determination module 125, a coherence calculation window determination module 130, a sampling position determination module 140, a coherence value determination module 150, and a coherence volume construction module 160. The modules can be connected through a communication bus to realize signal transmission.

[0248] The system includes the following modules: a data acquisition module 110 acquires a seismic data volume comprising multiple data sampling points distributed along the vertical and horizontal directions; a local frequency determination module 120 determines the local frequency of each data sampling point based on the seismic data volume; a local dip angle determination module 125 determines the local dip angle of each data sampling point based on the seismic data volume; a coherence calculation window determination module 130 determines the vertical length of a coherence calculation window for any current data sampling point based on its local frequency; a sampling position determination module 140 determines the positions of data sampling points used for coherence calculation within the coherence calculation window for each seismic trace based on its local dip angle; and a coherence value determination module 150 extracts corresponding seismic data from the seismic data volume based on the positions of the data sampling points used for coherence calculation within the coherence calculation window for each seismic trace, and calculates the coherence value of the current data sampling point. The coherence volume construction module 160 is used to traverse the plurality of data sampling points to obtain the coherence value of each data sampling point and construct a coherence volume corresponding to the seismic data volume.

[0249] Specifically, the data acquisition module 110 reads the seismic data volume and its sampling parameters, and transmits the seismic data volume to the local frequency determination module 120 and the local dip angle determination module 125. The local frequency determination module 120 determines the local frequency of each data sampling point based on the seismic data volume and transmits the local frequency to the coherence calculation window determination module 130; the local dip angle determination module 125 determines the local dip angle of each data sampling point based on the seismic data volume and transmits the local dip angle to the sampling position determination module 140. The coherence calculation window determination module 130 determines the vertical length of the coherence calculation window based on the local frequency of the current data sampling point. The sampling position determination module 140 determines the position of each seismic trace within the coherence calculation window used for coherence calculation based on the local dip angle of the current data sampling point. The coherence value determination module 150 extracts the corresponding seismic data from the seismic data volume based on the position of each seismic trace within the coherence calculation window used for coherence calculation, and calculates the coherence value of the current data sampling point. The coherence volume construction module 160 traverses multiple data sampling points and organizes the corresponding coherence values ​​according to the spatial index of each data sampling point to construct the coherence volume.

[0250] The system described above can be implemented by an electronic device having a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, it causes the electronic device to perform the steps of the method embodiments described above. Each module can be a functional module implemented by a computer program, or it can be implemented by a processor, a graphics processor, a field-programmable gate array (FPGA), or a combination thereof.

[0251] refer to Figure 10 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. The electronic device provided in this embodiment includes a processor 210 and a memory 220. The memory 220 stores machine-readable instructions executable by the processor 210. When the machine-readable instructions are executed by the processor 210, they perform the steps in the adaptive window coherence volume calculation method based on local frequency described in any of the preceding embodiments. At least one processor 210 and one memory 220 are provided.

[0252] In this embodiment of the invention, the electronic device further includes a communication interface 230 and a communication bus 240, wherein the processor 210, the memory 220, and the communication interface 230 are interconnected via the communication bus 240. The communication bus 240 can be divided into an address bus, a data bus, a control bus, etc., for ease of representation. Figure 3 The communication bus 240 is represented by a single thick line, but this does not mean that there is only one communication bus 240 or only one type of communication bus 240. The processor 210 can also be called a controller, and there is no restriction on the name.

[0253] In this embodiment, memory 220 stores instructions executable by at least one processor 210. By executing the instructions stored in memory 220, at least one processor 210 can perform the steps in the adaptive window coherence volume calculation method based on local frequency discussed above. Processor 210 can implement... Figure 9 The functions of each module in the device shown.

[0254] The processor 210 is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in the memory 220 and calling data stored in the memory 220, the various functions of the device and the data processing are monitored as a whole.

[0255] In one possible design, processor 210 may include one or more processing units. Processor 210 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, processor 210 and memory 220 may be implemented on the same chip or on separate chips.

[0256] Processor 210 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the adaptive window coherence volume calculation method based on local frequency disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0257] Memory 220, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 220 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 220 can be any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 220 may also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0258] By designing and programming the processor 210, the code corresponding to the adaptive window coherence volume calculation method based on local frequency described in the foregoing embodiments can be embedded into the chip, thereby enabling the chip to execute the code during runtime. Figure 1The steps of the adaptive window coherence volume calculation method based on local frequency in the illustrated embodiment are described below. How to design and program the processor 210 is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0259] Furthermore, this application also provides a computer-readable storage medium storing computer-executable instructions. When executed by processor 210, these instructions are used to implement the adaptive window coherence volume calculation method based on local frequency described in any of the preceding embodiments; therefore, they will not be repeated here. Additionally, the beneficial effects of using the same method will not be repeated here either. For technical details not disclosed in the embodiments of the computer storage medium involved in this invention, please refer to the description of the method embodiments of this invention.

[0260] In some possible implementations, various aspects of the adaptive window coherence volume calculation method based on local frequency provided in this application can also be implemented in the form of a program product, which includes program code. When the program product is run on a device, the program code is used to cause the control device to perform the steps in the adaptive window coherence volume calculation method based on local frequency based on various exemplary embodiments of this application described above.

[0261] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0262] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products based on embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a mechanism for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0263] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0264] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0265] Additionally, any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order based on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0266] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for calculating coherent volume using an adaptive window based on local frequencies, characterized in that, Includes the following steps: Acquire a seismic data volume, which includes multiple data sampling points distributed along the vertical and horizontal directions; Based on the earthquake data volume, the local frequency and local dip angle of each of the data sampling points are determined; For any current data sampling point, the vertical length of the coherent calculation window is determined based on the local frequency of the current data sampling point; Based on the local dip angle of the current data sampling point, determine the location of the data sampling point used for coherent calculation in each seismic trace within the coherent calculation window; Based on the location of the data sampling points for coherence calculation of each seismic trace within the coherence calculation window, the corresponding seismic data is extracted from the seismic data volume, and the coherence value of the current data sampling point is calculated. By traversing multiple data sampling points, the coherence values ​​of each data sampling point are obtained, and a coherence volume matching the seismic data volume is constructed.

2. The adaptive window coherence volume calculation method based on local frequency according to claim 1, characterized in that, The determination of the local frequency of each data sampling point based on the seismic data volume includes: Based on the earthquake data volume, Hilbert transform is performed on the earthquake signals corresponding to each seismic trace in the earthquake data volume to obtain the corresponding Hilbert transform signals. Based on the seismic signal and the Hilbert transform signal, calculate their derivatives along the vertical direction, respectively. Based on the seismic signal, the Hilbert transform signal, and their derivatives along the vertical direction, and combined with shaping regularization, a frequency solution relationship containing the frequency to be determined is established. Based on a preset smoothing operator, the conjugate gradient method is used to solve the frequency relationship to obtain the local frequency of each data sampling point.

3. The adaptive window coherence volume calculation method based on local frequency according to claim 2, characterized in that, The smoothing operator is a triangular smoothing operator.

4. The adaptive window coherence volume calculation method based on local frequency according to claim 2, characterized in that, The process of solving the frequency relationship using the conjugate gradient method includes: Based on the frequency solution relationship after the shaping and regularization process, the frequency solution relationship is iteratively solved using the conjugate gradient method until the preset iteration termination condition is met. The preset iteration termination condition is that the number of iterations reaches the preset maximum number of iterations, or the convergence index of the current iteration is less than the preset accuracy threshold.

5. The adaptive window coherence volume calculation method based on local frequency according to claim 1, characterized in that, The seismic data volume is a three-dimensional seismic data volume, the vertical direction is the time direction or the depth direction, and the horizontal direction includes mutually perpendicular transverse and longitudinal directions. Determining the local dip angle of each data sampling point based on the seismic data volume includes: Determine the local gradients of the seismic data volume along the vertical direction, the horizontal direction, and the longitudinal direction, respectively; Construct the gradient structure tensor corresponding to each data sampling point based on the local gradients in the three directions; The local tilt angle of each data sampling point is determined based on the gradient structure tensor. The local gradient is determined by convolving the seismic data with the first derivative of the Gaussian convolution kernel.

6. The adaptive window coherence volume calculation method based on local frequency according to claim 5, characterized in that, Determining the local tilt angle of each data sampling point based on the gradient structure tensor includes: The gradient structure tensor is Gaussian smoothed. Eigenvalue decomposition is performed on the smoothed gradient structure tensor; Based on the eigenvector corresponding to the maximum eigenvalue of the smoothed gradient structure tensor, the local tilt angle of the data sampling point in the horizontal and vertical directions is determined.

7. The adaptive window coherence volume calculation method based on local frequency according to claim 6, characterized in that, The step of determining the locations of data sampling points for coherence calculation within the coherence calculation window for different seismic traces based on the local dip angle of the current data sampling point includes: The seismic trace where the current data sampling point is located is determined as the central seismic trace; Determine the spatial offset of the neighboring seismic traces relative to the central seismic trace in the lateral and longitudinal directions for coherence calculation; Based on the spatial offset and the local dip angle of the current data sampling point in the horizontal and vertical directions, the vertical sampling offset corresponding to each of the adjacent seismic traces is determined; The location of data sampling points for coherence calculation of each of the adjacent seismic traces is determined based on the vertical sampling offset. The coherence calculation window includes the central seismic trace and a plurality of neighboring seismic traces distributed around the central seismic trace.

8. The adaptive window coherence volume calculation method based on local frequency according to any one of claims 1 to 7, characterized in that, The step of determining the vertical length of the coherent calculation window based on the local frequency of the current data sampling point includes: The corresponding local period is determined based on the local frequency of the current data sampling point; The vertical length of the coherence calculation window is determined based on the local period and the vertical sampling interval of the seismic data volume. The number of sampling points corresponding to the vertical length of the coherent calculation window is determined based on the reciprocal of the product of the local frequency of the current data sampling point and the vertical sampling interval.

9. An adaptive window coherence volume computing system based on local frequency, characterized in that, include: The data acquisition module is used to acquire seismic data volumes, which include multiple data sampling points distributed along the vertical and horizontal directions. A local frequency determination module is used to determine the local frequency of each of the data sampling points based on the seismic data volume. The local dip angle determination module is used to determine the local dip angle of each of the data sampling points based on the seismic data volume; The coherent calculation window determination module is used to determine the vertical length of the coherent calculation window for any current data sampling point based on the local frequency of the current data sampling point. The sampling location determination module is used to determine the location of each seismic trace used for coherent calculation within the coherent calculation window based on the local dip angle of the current data sampling point. The coherence value determination module is used to extract the corresponding seismic data from the seismic data volume based on the location of the data sampling points for coherence calculation of each seismic trace within the coherence calculation window, and to calculate the coherence value of the current data sampling point. The coherence volume construction module is used to traverse the multiple data sampling points to obtain the coherence value of each data sampling point and construct a coherence volume corresponding to the seismic data volume.

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