343 results about "Seismic trace" patented technology

A method for removing tube wave interference from optical fiber acoustic wave sensing seismic data, including: acquiring seismic wavefield data which contains a tube wave and is collected by an optical fiber acoustic wave sensing instrument; calculating a root-mean-square amplitude of the waveform data cut on the seismic trace as an amplitude normalization factor; performing normalization processing on the amplitude value; performing de-tail mean filtering processing on the normalized amplitude value along the travel time of the tube wave, to obtain a predicted amplitude value; performing tube wave interference removal processing on each seismic trace, and performing inverse normalization processing to obtain the seismic wavefield data without tube wave interference. The method effectively suppresses the tube wave interference in the optical fiber acoustic wave sensing seismic data. An apparatus for removing tube wave interference from optical fiber acoustic wave sensing seismic data, and a computer device are further provided.

The invention provides a deep coal seam fracture density azimuth seismic inversion method and device, and relates to the technical field of seismic exploration, and the method comprises the steps: obtaining pre-stack seismic trace set data and logging data of a research work area; constructing an elastic stiffness matrix of which each stratum is a VTI medium based on the logging data; processing the porosity of the coalbed methane reservoir in the logging data and an elastic stiffness matrix of the coalbed methane reservoir as a VTI medium according to a linear slip theory to obtain an elastic stiffness matrix of the coalbed methane reservoir as an orthotropic medium so as to determine an initial model parameter vector; combining a reflection coefficient approximation formula based on pre-stack seismic trace set data and orthoanisotropy to construct an objective function used for inverting model parameters; solving the target function to obtain a target model parameter vector of the research work area; and the target model parameters of the coal bed gas reservoir are processed by using the target prediction model to obtain the fracture density of the coal bed gas reservoir, so that an effective technical means is provided for inversion of the fracture density of the deep coal bed.

The invention discloses a pre-stack seismic elastic parameter inversion method based on a conditional diffusion model, and the method comprises the steps: obtaining a pre-stack CMP multi-angle seismic trace set and a well data low-frequency trend, mapping seismic data and an elastic parameter model to a potential space, training the conditional diffusion model in the potential space, carrying out the step-by-step denoising in order to recover the distribution of the elastic parameter, and carrying out the inversion of the elastic parameter. In the sampling process, Aki-Richards equation physical constraint is introduced, and uncertainty quantization is realized through transverse constraint and multiple sampling. According to the method, the spatial continuity of an inversion result can be ensured, uncertainty quantification can be realized, and reliable confidence evaluation is provided for geological interpretation. The method is wide in applicability and high in calculation efficiency, and the pre-stack elastic parameter inversion result with higher precision and higher credibility can be quickly obtained in actual three-dimensional seismic data processing.

The invention discloses a multidirectional earthquake river channel sand body prediction method. The problems that in the prior art, due to the fact that full-superposition earthquakes are adopted, signals are covered due to superposition, the distinguishing capacity is insufficient, and the prediction precision of narrow and small river channels is low are solved. The method comprises the following steps: S1, obtaining pre-stack seismic trace gather data and a sound wave impedance logging curve; s2, performing statistics to obtain the azimuth angle range of the pre-stack seismic gather as theta1-theta2; s3, acquiring a plurality of seismic bodies with different azimuth angles; s4, obtaining seismic bodies of a plurality of azimuth angles after normalization; s5, obtaining a synthetic seismic data volume; s6, acquiring a plurality of correlation coefficient data volumes; s7, obtaining a fusion seismic body; and S8, based on the obtained fused seismic body, extracting plane amplitude attributes of a target stratum, and realizing river channel sand body prediction. According to the multidirectional earthquake channel sand body prediction method, azimuth angle information of the pre-stack earthquake channel set is comprehensively utilized, the channel sand body prediction precision is improved, and the oil field exploration and development effects are improved.

The invention provides a diffracted wave separation method and device under non-uniform space-time sampling, and relates to the technical field of seismic prospecting, the method comprises the following steps: obtaining common offset seismic data of a target area, the common offset seismic data comprising fluctuation signals of a plurality of sampling points, the position intervals among the sampling points of different seismic traces are non-uniform, and / or the time intervals among different sampling points of the same seismic trace are non-uniform; calculating the plane wave slope of each sampling point of the common offset seismic data through a finite difference method to obtain plane wave slope data; determining a reflected wave travel time curve of each sampling point according to the plane wave slope data; and diffracted wave separation of the common offset seismic data is carried out according to the reflected wave travel time curve of each sampling point. According to the method, accurate diffracted wave separation under non-uniform space-time sampling is realized, and the seismic data analysis accuracy is improved.

The invention discloses a method for predicting a favorable area of a thin-layer stacked sand body, and relates to the technical field of exploration and development of oil and gas reservoirs, and the method comprises the steps: generating a synthetic seismic record through well-seismic combination by using interval transit time and a density curve, aligning the synthetic seismic record with an actual seismic record, and building a three-dimensional frame model of a target layer of a research area; performing abnormal value elimination and standardization processing on the logging curve, making a statistical histogram, comparing peak separation degrees of sandstone and mudstone, and selecting the most sensitive curve to distinguish the sandstone and mudstone; and realizing well-side seismic trace waveform dynamic clustering analysis through singular value decomposition, and determining the number of effective samples. Based on the result of waveform indication simulation, the method is combined with the optimized seismic attribute to predict the thin-layer stacked sand body together, a systematic method is provided, the multiplicity of solutions of predicting the thin-layer stacked favorable sand body through the seismic attribute and the uncertainty of depicting the sand body by the sedimentary facies in the area with few wells can be reduced, and the prediction accuracy of the thin-layer stacked favorable sand body is improved. Therefore, the precision and reliability of thin-layer stacked sand body prediction are improved.

The invention relates to the technical field of seismic exploration, and provides a five-dimensional seismic data prediction method and device. The method comprises the following steps: carrying out inclination angle imaging enhancement processing on five-dimensional seismic data; carrying out azimuth time window analysis based on the inclination angle imaging data, and calculating coherent attributes; carrying out gradient mode attributes on the basis of coherent attributes, and carrying out orientation attribute fusion; five-dimensional phase control step-by-step inversion is carried out under fusion attribute constraint; and predicting an oil and gas reservoir of the fractured volcanic rock based on an inversion result. According to the method, the fractured oil and gas reservoir is predicted based on the five-dimensional seismic data, interrelation between adjacent seismic traces is considered, and underground medium parameters are accurately obtained in combination with seismic sensitive attributes and a multi-trace inversion method, so that the problems that single attribute prediction is high in multiplicity of solutions and space description of a fracture system is not comprehensive enough are solved, and the prediction accuracy of the fractured oil and gas reservoir is improved. And meanwhile, the seismic inversion stability and continuity are improved, and high-precision fractured reservoir prediction is realized.

The present application belongs to the technical field of geophysical well logging data processing, and particularly relates to a multi-scale dual-branch neural network well logging curve reconstruction method based on seismic constraint, which comprises reading in well-seismic and interpreted horizon data, target interval sublayering and facies belt division based on three-dimensional seismic interpretation, pyramid multi-scale training set sample construction by fusing multi-curve and well flanking seismic trace information, residual-Transformer dual-branch neural network training model establishment taking a composite loss function as the core, three-stage progressive training strategy and reconstruction of target well missing section and result evaluation. The method still maintains high stability and robustness under the condition of complex heterogeneous reservoirs; on the basis of seismic data constraint, geological sublayering and facies belt division and other interpretation information are fused, so that the reconstruction result is more in line with geological laws; and the obtained reconstructed curve provides reliable support for re-interpretation of old data and full use of incomplete well logging data.

The invention discloses a seismic wave velocity model inversion method based on time-space coupling deep learning, and the method achieves a time-space coupling mechanism through a Fourier neural operator framework of a wavelet transform-attention mechanism, and supports the reconstruction of a spatial two-dimensional velocity model from a time sequence track of multiple seismic sources. Firstly, the local time-frequency representation of each seismic time-domain trajectory is extracted by adopting continuous wavelet transform, and then a wavelet time-frequency spectrogram is coded through a convolution feature extractor. Then, information from multiple sources is fused at each receiver point location through a multi-head attention mechanism, enhancing correlation between multiple sources. And finally, projecting the fused features into a two-dimensional Fourier neural operator to efficiently learn space mapping and reconstruct a bottom-layer velocity model based on sound velocity. According to the method, the time sequence characteristic and the spatial distribution characteristic of the seismic channel are fully utilized, the precision and robustness of an inversion result are improved, and a more reliable speed model is provided for geological structure interpretation and acoustic imaging.

The invention relates to the field of oil and gas geophysical exploration, and particularly discloses a reservoir physical property parameter seismic inversion evaluation method and device, and the method comprises the steps: building a rock physical model based on the geological features of a work area reservoir; based on the rock physical model, sequentially carrying out inversion test on seismic traces beside a modeling well, a non-modeling well and all wells in a work area to determine parameters of the rock physical model and an inversion algorithm; utilizing the rock physical model and an inversion algorithm to perform seismic inversion on the physical property parameters of the work area reservoir to obtain an inversion result; and extracting a work area well connection profile and reservoir section plane attributes from the inversion result, and then performing analysis. According to the reservoir physical property parameter seismic inversion evaluation method, on the basis of problems needing to be noticed in the reservoir physical property parameter seismic inversion process of the rock physical model and the quality control mode, the trial and error processes in the technical inversion process can be reduced, the working efficiency of reservoir physical property inversion is improved, and the technical practicability is improved.

The invention provides a seismic trace gather far offset energy preserving processing method, which comprises the following steps of: 1, decompiling field seismic data, and performing first arrival picking and trace gather extraction processing; 2, performing high-order dynamic correction processing on the extracted gather data to improve the influence of conventional dynamic correction stretching on the far offset; step 3, performing space-time variation cutting on gather first arrival waves and interference waves by adopting control points; step 4, performing offset resolution improvement processing on the gather data, and further improving the frequency dispersion problem of the far-offset data; and step 5, carrying out deconvolution, superposition and migration processing on the far-offset information preserving gather to obtain far-offset energy preserving seismic result data. According to the seismic trace gather far-offset energy maintaining processing method, recovery and maintenance of far-offset energy in seismic trace gather data can be realized, the imaging quality of seismic data of middle and shallow layers is improved, and recovery and maintenance of far-offset information are realized.

The present invention discloses a method and device for removing strong seismic reflections and shielding. The method comprises: calculating weighted average seismic trace data based on the seismic trace data of a current seismic trace and adjacent seismic traces in seismic data; extracting seismic atomic parameters corresponding to the location of the strongest amplitude based on the weighted average seismic trace data; and subtracting the seismic atoms corresponding to the seismic atomic parameters from the weighted average seismic trace data to obtain seismic trace data for the current seismic trace after removing strong reflections. The present invention removes strong seismic reflections and avoids shielding weak useful signals, thereby significantly improving the accuracy of identifying strong reflection isotropic axes. This method has important guiding significance for the exploration and development of oil and gas reservoirs hidden below strong seismic reflection axes.

Examples of methods and systems are disclosed. The methods include, obtaining a seismic dataset regarding a subsurface region of interest, wherein the seismic dataset comprises a plurality of time-domain seismic traces. The methods also include determining a plurality of single-frequency volumes from the plurality of time-domain seismic traces. The methods further include determining a frequency ratio volume based, at least in part, on the plurality of single-frequency volumes. The methods still further include extracting a three-dimensional (3D) geological body based, at least in part, on the frequency ratio volume. The methods further include determining a drilling target in the subsurface region based on the 3D geological body.

Systems and methods for detecting shallow subsurface anomalies in a subsurface formation include obtaining seismic data for the subsurface formation; forming one or more seismic gathers by sorting seismic traces from the seismic data into a plurality of bins based on a midpoint and an offset. For bins of the plurality of bins iteratively, encoding a pair of discrete time shifts which are represented by a single binary variable per seismic trace; determining cross-correlations between the seismic traces in the bins to form an objective function based on the encoded discrete time shifts; and determining, by a quantum annealing machine, a set of discrete time-shifts representing the residual refraction statics that maximize stack power for the bins by maximizing the objective function, wherein a next iteration is initialized with a different set of time-shifts than a current iteration. Refraction-based surface-consistent phase corrections are performed for each seismic trace.

The invention discloses a robust full-waveform inversion method of a first-arrival constraint physical embedded recurrent neural network, which comprises the following steps of: solving a wave equation through forward propagation of the physical embedded recurrent neural network, generating a forward shot gather record, screening out a seismic trace with high matching degree with observation data by using first-arrival information, and participating in loss calculation to obtain a seismic trace with high matching degree with observation data; and along with the proceeding of the inversion process, the range of seismic traces participating in the inversion is gradually expanded, it is ensured that the inversion result gradually tends to a globally optimal solution, back propagation is carried out by using space-time field gradient information recorded by forward propagation of the recurrent neural network, the speed parameter is further optimized, and the inversion precision is improved. According to the method, the physical process of seismic wave propagation is embedded into the recurrent neural network, the first arrival constraint and the progressive inversion strategy are introduced, the dependence of the FWI on the initial model is remarkably reduced, and the underground velocity field structure can still be accurately reconstructed even if the initial model and a real model have remarkable deviation.

The invention discloses an offshore field two-dimensional seismic data rapid imaging method, and relates to the technical field of seismic exploration data processing, and the method comprises the steps: obtaining SEGD format original seismic data of a target region from a seismic collection system, combining the data of each shot of each measuring line, decoding the data into internal available data, and obtaining a to-be-processed data body; and loading an observation system and assigning seismic trace header parameters according to actual acquisition parameters based on the decompiled to-be-processed data volume. According to the invention, through a multi-stage denoising and signal protection strategy, effective suppression of various sea interferences is realized, a frequency division multi-channel statistical abnormal amplitude suppression technology is adopted, zero-phase high-pass filtering is combined, low-frequency effective signals are protected while low-frequency strong-energy background noise is removed, and the noise suppression effect is improved. The direct wave cutting step further eliminates strong energy interference under a long cable condition, highlights an effective reflection signal of middle and far offset data, forms a progressive signal-to-noise ratio improvement mechanism from original data to final imaging, and improves the interpretability of seismic data.

The invention belongs to the technical field of geophysical logging data processing, and particularly relates to a multi-scale double-branch neural network logging curve reconstruction method based on seismic constraints. Comprising the steps of reading in well seismic and interpreting horizon data, performing target layer section layering and phase belt division based on three-dimensional seismic interpretation, fusing multiple curves and well-side seismic channel information to construct a pyramid multi-scale training set sample, establishing a residual-Transformer double-branch neural network training model by taking a composite loss function as a core, and constructing a multi-scale training set sample based on the residual-Transformer double-branch neural network training model. And a three-stage progressive training strategy is adopted, and target well missing section reconstruction and result evaluation are carried out. According to the method, high stability and robustness are still kept under the condition of a complex heterogeneous reservoir; interpretation information such as geological layering and phase belt division is fused on the basis of seismic data constraints, so that a reconstruction result better conforms to geological laws; and the obtained reconstruction curve provides reliable support for re-explanation of old data and full utilization of incomplete logging data.

The invention provides a seismic fault identification method and system based on multi-attribute collaboration and spectral domain graph enhancement, and relates to the technical field of seismic exploration, and the method comprises the steps: carrying out the all-directional dip angle and azimuth angle scanning of a seismic data volume, extracting multi-scale guide field information through combining structure tensor decomposition, and executing anisotropic diffusion filtering; calculating characteristic value distribution through a characteristic value coherence algorithm based on the filtering data volume, and determining the structural consistency difference of adjacent seismic traces to obtain a fracture coherence attribute data volume; carrying out azimuth gather sorting and pre-stack time migration processing on the seismic data volume, extracting seismic wave dynamic response characteristics, carrying out Fourier series expansion on the azimuth change rate to obtain a crack indication information data volume, splicing the data volume and executing multi-scale three-dimensional convolution solution, and constructing a spatial dependency graph through a spectral clustering algorithm; spectral domain enhancement features are obtained through spectral domain graph transformation and frequency selective filtering reconstruction, and morphological connectivity analysis is executed to obtain a crack prediction result.

A computer-implemented method includes: accessing wireline data and vertical seismic profiling (VSP) data; correlating logged velocity from the wireline data with velocity data from the VSP data to calibrate the logged velocity; determining, based on, at least in part, the calibrated logged velocity, a range of incidence angles for acquiring seismic traces sufficient to map a formation depth at the geo-exploration site using pairs of acoustic emitter and acoustic receiver placed at a surface of the geo-exploration site; and determining a range of offsets between the acoustic emitter and the acoustic receiver of each pair so that the acoustic receiver can acquire seismic traces sufficient to map the formation depth at the geo-exploration site; and comparing the range of angles and the range of offsets with acquisition parameters of a planned seismic survey to determine whether the planned seismic survey can map as deep as the formation depth.

The invention provides a three-dimensional scattering imaging method, device and equipment and a storage medium, and relates to the technical field of seismic wave imaging, and the method comprises the steps: collecting seismic wave data through a three-component detector, carrying out the polarization analysis, obtaining the actual path data of scattered waves, constructing a common scattering point gather of each preset underground scattering point, and obtaining a common scattering point gather of each preset underground scattering point; when the common scattering point gather is constructed, the amplitude is adaptively weighted according to the consistency of theoretical path data and actual path data of a seismic trace, and then imaging is performed based on the common scattering point gather to obtain an imaging result including the underground scatterer. According to the method, by introducing path consistency weighting, the common scattering point gather quality and the imaging precision are remarkably improved.

The invention belongs to the technical field of seismic exploration, and discloses a seismic common reflection point intelligent superposition imaging method under three-dimensional structure feature constraint, which comprises the following steps of: 1, constructing a three-dimensional structure geometric model of a target area, and extracting structure features to generate a structure feature constraint field; 2, dividing the target area into a structure gentle area and a structure sudden change area according to the structure feature constraint field, and selecting a wide azimuth gather for the structure gentle area to carry out dynamic correction and superposition so as to obtain a first superposition result; selecting an optimal narrow azimuth gather subset in a narrow azimuth range along the structure trend for a structure mutation region; 3, superposing seismic traces in the subset of the optimal narrow azimuth trace gather according to a self-adaptive weight coefficient driven by a structural feature to obtain a second superposition result; and a fourth step of fusing the first superposition result and the second superposition result to obtain seismic common reflection point superposition imaging. According to the method, partition differentiation processing is constrained according to structural features, and imaging better conforms to real geologic features.

This invention provides a method for calculating the minimum delay time between two seismic traces: Calculating a first error value between the two seismic traces under at least three first delay times (either preset or obtained after the last iteration update); fitting an error function between the delay time and the error value; calculating a second delay time and a second error value at the extreme point of the error function; if the first difference between the second delay time and a threshold delay time of any of the first delay times is greater than or equal to a first threshold, or the second difference between the second error value and the first error value is greater than or equal to a second threshold, then the second delay time is used to replace one of the edge delay times in the current first delay time. All the above steps are repeated until the first difference is less than the first threshold and the second difference is less than the second threshold, and this second delay time is determined as the minimum delay time between the two seismic traces. This invention improves the calculation speed for calculating the minimum delay time between two seismic traces.

The present invention belongs to the technical field of petroleum and natural gas exploration. Provided are an intelligent seismic high-frequency sequence interpretation method and apparatus using logging information. The method comprises: inputting, into a sequence stratigraphy prediction model in a current state, seismic attribute data of determined point locations, which are determined on the basis of a sequential path, along a well-tie seismic trace of a known conditioning well in a target block, so as to obtain high-frequency sequence stratigraphy data of the determined point locations; and determining whether any high-frequency sequence stratigraphy data of a point location in the target block has not been obtained, and if any high-frequency sequence stratigraphy data of a point location in the target block has not been obtained, adding the high-frequency sequence stratigraphy data of the determined point locations to sequential simulation conditioning data, using updated sequential simulation conditioning data to construct a learning sample to train the sequence stratigraphy prediction model in the current state, and then jumping to the previous step to perform high-frequency sequence stratigraphy prediction on the next determined point location determined on the basis of the sequential path. The method can be applied to onshore early-exploration blocks and offshore sparse-well blocks, etc., so as to realize high-frequency sequence interpretation of these blocks with high efficiency, low costs and high accuracy.

The present invention provides a prestack inversion method and device based on a gradient optimizer, relating to the technical field of prestack inversion, including: obtaining prestack angle gather data, horizon data, and elastic parameters of a target well log in a target work area; determining a solution space for the elastic parameters in the target work area based on the elastic parameters and horizon data; constructing a first objective function for inverting the elastic parameters in the target work area based on the prestack angle gather data of the target work area and the Zoeppritz equation; determining a first population within the solution space for solving the elastic parameters of a target seismic trace; iteratively updating the first population using a gradient optimizer to determine an optimal solution for the elastic parameters of the target seismic trace based on the angle gather data of the target seismic trace and the first objective function; and constructing an elastic parameter profile of the target work area based on the optimal elastic parameter solutions of multiple seismic traces. The application of the gradient optimizer enables the method of the present invention to simultaneously balance the convergence speed and inversion accuracy of prestack inversion.

The present invention proposes a method and device for quality control of node seismic acquisition continuous data segmentation. The method performs quality control inspection on the segmentation of node seismic acquisition continuous data from multiple angles, such as judging whether the seismic acquisition excitation time and the node reception time are synchronized, whether the excitation source type and excitation content during seismic acquisition excitation meet the requirements, whether there is GPS drift in the daily inspection data of the node, whether the seismic trace header of the seismic data lacks preset parameter information, and whether there are multiple strong phases in the superposition of the trace set processed by linear dynamic correction, so as to ensure the accuracy of the node segmentation data and thus ensure the correctness of the collected seismic data.

The present invention relates to a method, device, medium and equipment for high-resolution seismic inversion of thin-layer structures. The method comprises the following steps: constructing a thin interbedded sand body model, forward-synthesizing seismic records based on the thin interbedded sand body model to obtain seismic traces s; forming training data from the seismic traces s and impedance data z to prepare a training set {s, z} T and validation set {s,z} V ; The training set is used to train a neural network model, and the validation set is used to validate the neural network model; the neural network model is used to obtain predicted impedance, and the predicted impedance is converted into a reflection coefficient sequence; the reflection coefficient sequence is convolved with the forward wavelet to generate a seismic record, and the generated seismic record and the input seismic trace set constitute a supervisory term constraint model to drive the inversion process.

The application discloses a frequency-space domain alternating iteration seismic random noise suppression method and system, transforms a seismic signal from a time-space domain to a frequency-space domain, and obtains a frequency slice by taking data of a fixed frequency domain index; arranges the frequency slice into a Hankel matrix and performs singular value decomposition, selects singular values and corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; obtains the reconstructed frequency slice, and takes the next frequency slice to complete spatial direction denoising; takes single seismic trace data of a fixed space domain index in the frequency-space domain data, arranges the single seismic trace data into a Hankel matrix and performs singular value decomposition, selects singular values and corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; then, the reconstructed single seismic trace data is obtained through anti-diagonal average, the next seismic trace data is taken, the obtained frequency-space domain data after denoising is transformed back to the time-space domain, and a signal after noise suppression is obtained.

The application discloses a method for extracting and correcting the time difference of synchronous seismic data collection while digging, which comprises the following steps: 1, establishing a seismic data collection system while digging, reading all seismic wave data in the collection system and filtering; 2, performing velocity analysis on the filtered seismic wave to obtain the propagation velocity of the seismic direct wave while digging; 3, using the seismic wave propagation velocity original data to correct the direct wave time difference of each seismic trace, and eliminating the influence of the seismic wave propagation time difference; 4, calculating the model trace data of each collection substation through the data after the time difference correction; 5, determining the reference substation according to the model trace data of each collection substation and the data of each seismic trace in the corresponding collection substation; and 6, calculating the time difference of the corresponding collection substation according to the cross-correlation time difference between the model trace of the reference substation and the model trace of each collection substation, and correcting the original data.

This invention belongs to the field of seismic exploration, specifically relating to a method for calculating minimum offset and delay based on direct waves and seafloor reflected waves. The method includes: selecting single-shot seismic data from a flat seafloor location, picking the direct wave travel time, and picking the seafloor reflected travel time of the first data trace at the single-shot location; performing regression fitting on the picked direct wave travel time to fit the direct wave equation, and obtaining the seawater velocity and initial minimum offset based on the direct wave equation; setting an initial delay time range, and calculating the seafloor depth h for each seismic trace at different delay times based on the seawater velocity, initial minimum offset, and seafloor reflected travel time; calculating the 2h variance based on the seafloor depth of the seismic trace, and obtaining the minimum offset and delay time. This invention can quickly obtain the minimum offset and delay time, achieving accurate positioning of seismic data, and the method is simple and easy to implement.

The application discloses a multiple wave prediction method and device, a computing device and a storage medium. The method comprises the following steps: acquiring frequency domain data of any shot point at any seismic trace; generating a seismic data matrix according to the frequency domain data, the seismic data matrix being an upper triangular matrix with a main diagonal line of 0, and any non-first column of the seismic data matrix containing frequency domain data of at least one seismic trace of the same shot point; constructing a multiple wave matrix, the multiple wave matrix being an upper triangular matrix with a main diagonal line of 0, and any non-first column of the multiple wave matrix containing a multiple wave model of at least one seismic trace of the same shot point; establishing a functional relationship between the multiple wave matrix and the seismic data matrix; and obtaining model frequency domain data corresponding to any multiple wave model in the multiple wave matrix according to the functional relationship. According to the scheme, the prediction efficiency of the multiple wave can be improved under the premise of ensuring the prediction accuracy of the multiple wave.