19 results about "Velocity spectrum" patented technology

The invention belongs to the field of power equipment simulation calculation, and discloses a two-dimensional power equipment high-fidelity efficient transient flow field solving method under rectangular coordinates and a related device, and the method comprises the steps: solving an established power equipment two-dimensional model through a flow field full-order model, obtaining flow field horizontal velocity components and vertical velocity components of the fluid in the power equipment at multiple moments; forming a sample matrix by the horizontal velocity components and the vertical velocity components of the flow field at the multiple moments; performing singular value decomposition on the sample matrix to obtain a velocity basis function and a velocity spectrum coefficient at an initial moment; calculating velocity spectrum coefficients at different moments by using an established two-dimensional power equipment flow field low-order transient calculation model, the velocity primary function and the velocity spectrum coefficients at the initial moment; and carrying out linear superposition on the velocity basis function and the velocity spectrum coefficients at different moments to obtain an approximate field of the flow field. According to the method, the transient flow field of the power equipment can be efficiently solved on the premise of ensuring the accuracy of a calculation result.

The application discloses a method and device for identifying interlayer multiple wave velocity, which comprises the following steps: obtaining a velocity spectrum and a propagation path of the interlayer multiple wave in a stratum; picking up the velocity of a plurality of energy groups with energy exceeding a preset value from the velocity spectrum to obtain the propagation velocity and propagation time length of the plurality of primary waves; calculating the propagation velocity and propagation time length of the interlayer multiple wave corresponding to each primary wave step by step according to the propagation time length and propagation velocity of the plurality of primary waves, the propagation path of the interlayer multiple wave in the stratum, to obtain the propagation velocity and propagation time length of the low-order interlayer multiple wave; determining the layer position generating the low-order interlayer multiple wave and the order of the low-order interlayer multiple wave; and identifying the interlayer multiple wave according to the propagation velocity and propagation time length of the low-order interlayer multiple wave, the layer position generating the low-order interlayer multiple wave, the order of the low-order interlayer multiple wave and the velocity spectrum. The method effectively identifies the interlayer multiple wave and provides a basis and means for suppressing the interlayer multiple wave.

This disclosure discloses a velocity identification method and apparatus for interbed multiples, including: obtaining a velocity spectrum and propagation paths of interbed multiples in a stratum; collecting, from the velocity spectrum, velocities of a plurality of energy clusters, and obtaining propagation velocities and propagation duration of a plurality of primaries; calculating propagation velocities and propagation duration of an interbed multiples corresponding to each primary order by order, to obtain propagation velocities and propagation duration of low-order interbed multiples; and determining a horizon at which a low-order interbed multiples are generated and an order of the low-order interbed multiples; and identifying the interbed multiples based on the propagation velocities and the propagation duration of the low-order interbed multiples, the horizon at which the low-order interbed multiples are generated, and the order of the low-order interbed multiples, and in combination with the velocity spectrum.

The invention belongs to the technical field of seismic exploration, and particularly discloses a velocity spectrum label manufacturing method, which comprises the following steps: S1, acquiring a seismic stack velocity spectrum; s2, picking up speed label data; s3, speed prediction is carried out through an intelligent speed spectrum interpretation system, label abnormal points are positioned and corrected, and corrected speed label data are obtained; s4, calculating an energy extreme value of the corrected speed label data, and optimizing the corrected speed label data according to an energy extreme value curve and a set speed fine tuning parameter; and S5, further optimizing the optimized correction speed label data according to the energy extreme value curve and the set minimum time parameter of the increase point to obtain final optimized correction speed label data. According to the invention, the quantity and quality of the speed label data can be improved, and quality guarantee is provided for intelligent speed spectrum pickup. The method is suitable for velocity spectrum interpretation.

The invention discloses a fine velocity modeling method based on interval velocity inversion. The method comprises the following steps: carrying out collaborative modeling by utilizing an earthquake stacking velocity spectrum and a ground fitting velocity curve to obtain an interval velocity inversion initial model, determining a velocity anomalous body in an overlying stratum of a target stratum, carrying out tracking interpretation to obtain a structural interpretation layer, and taking the structural interpretation layer and the target stratum as control horizon in an inversion process, so as to obtain an interval velocity inversion model; and carrying out interval velocity inversion by utilizing depth information of a control horizon, a ground fitting velocity curve and an interval velocity inversion initial model to obtain interval velocities of a structural interpretation layer and a target layer, and meanwhile, fusing the interval velocities with an initial interval velocity of an overlying stratum of the structural interpretation layer to obtain a complete stratum interval velocity. The method not only strictly monitors the input data, but also considers the influence of the velocity anomalous body on interval velocity inversion, the interval velocity obtained by the method better conforms to geological conditions, the obtained structural map is higher in precision, and the success rate of well drilling development is improved.

The invention discloses a remnant multiple construction and attenuation method based on Rayleigh integral forward modeling, and the method comprises the following steps: S1, building a high-resolution pre-stack time migration velocity spectrum, picking up multiple velocity, and building a multiple migration velocity field; s2, calculating three-dimensional pre-stack time migration based on a multiple migration velocity field; s3, constructing a residual multiple record based on time domain Rayleigh integral forward modeling; and S4, residual multiple suppression based on curvelet domain least square filtering. Through multiple record construction and adaptive subtraction processing of the method, residual multiples in the data can be further eliminated, and the signal-to-noise ratio of seismic data is remarkably improved. The method is beneficial supplement to a conventional multiple attenuation method, and a new way is provided for multiple suppression.

A processing unit analyzes a reception signal to calculate, for every plurality of receiving antennas, a velocity spectrum in which a frequency is associated with a velocity at which the phase of the reception signal is changed at every cycle period. The processing unit extracts, as a group of identical object peaks, peaks that are generated on a velocity spectrum due to an identical object and that are identical in number to transmitting antennas. The processing unit determines, for each of the plurality of peaks constituting the group of identical object peaks, whether there is power variation among a plurality of the receiving antennas. The processing unit calculates an orientation of the object except for virtual receiving antennas included in a plurality of virtual receiving antennas and formed by the transmitting antennas corresponding to the peaks determined to involve the power variation.

The present invention belongs to the field of radar technology, and relates to a deception interference identification method, device and electronic device based on speed spectrum. The method includes the following steps: the radar determines the generated Doppler frequency according to the real speed of the real target; the radar transmits a set of broadband pulse signals; samples each pulse signal and performs Fourier transform in the distance dimension; for each distance unit, performs Fourier transform between pulses, and determines the Doppler spectrum of a single distance unit as a speed spectrum; searches for the maximum peak point of the speed spectrum of the current single distance unit; sets the reference value of the maximum peak point as a threshold; counts the number of speed points above the threshold; compares the number of speed points above the threshold with the set threshold, and determines the identification result of the current detection target. The present invention uses broadband signal transmission to identify speed deception interference, and can effectively identify the real target and speed deception interference based on the shape difference between the speed spectrum of the real target and the speed spectrum formed by the deception interference.

The invention relates to the technical field of seismic data processing, in particular to a velocity spectrum pickup method and device based on target detection. The method comprises the following steps: converting pre-acquired velocity spectrum data into picture data; pre-picked tv pairs and corresponding velocity spectrum picture data are adopted as sample labels, and model training is performed on the YOLOv7 network to extract picture features; performing model processing on the velocity spectrum picture data to be processed by using the trained YOLOv7 network to output pickup result data obtained by reasoning; and correcting the pickup result data to avoid pickup multiples so as to obtain final pickup result data. The method can better adapt to the speed spectrum pickup work of data with different signal-to-noise ratios, the pickup result is close to the manual label pickup style, manual intervention is not needed in the whole process, compared with manual pickup, the pickup accuracy is close to that of a manual pickup method, and the pickup efficiency of an automatic pickup method is remarkably improved.

This invention provides an unsupervised automatic velocity spectrum picking method, electronic device, storage medium, and apparatus based on K-means clustering, belonging to the field of seismic data processing technology. The automatic picking method includes: manually picking multiple time-velocity pairs and generating prediction lines, as well as generating first and second constraint lines; cleaning the data and performing K-means clustering on the cleaned data to obtain multiple near-surface cluster centers, mid-level cluster centers, and deep cluster centers; performing least-squares linear fitting on the multiple near-surface cluster centers to obtain multiple near-surface time-velocity pairs; calculating the percentage of the average velocity of the multiple deep cluster centers to the velocity value of the corresponding point on the prediction line, and correcting the deep cluster centers based on this percentage to obtain multiple deep time-velocity pairs. This method can effectively reduce the density of velocity control points and manual picking time, and has picking accuracy comparable to manual picking, thereby reducing data processing costs.

The invention discloses an electronic equipment impact function failure evaluation method based on a pseudo-velocity spectrum, and belongs to the field of electronic equipment function failure evaluation in an impact environment. The implementation method comprises the following steps: establishing a nonlinear structure impact dynamic model with viscous damping according to geometric and functional characteristics of electronic equipment; and setting a function failure criterion, determining the dominant frequency of the equipment by a numerical method, enabling the equipment to be equivalent to a single-degree-of-freedom structure, calculating equal failure reference parameters by a critical static load, and drawing a function failure reference boundary. According to the impact environment, selecting an impact waveform coefficient, and calculating the critical impact load of the equipment impact function failure under different impact waveforms and different signal frequencies. And analyzing pseudo velocity spectrum characteristics of all critical impact loads, correcting equivalent failure reference parameters, and establishing an equipment impact function failure boundary containing upper and lower boundaries. And according to the impact function failure boundary of the electronic equipment, realizing impact function failure evaluation of the electronic equipment. The method has the advantages of low cost, high precision, high speed and the like.

The application discloses a method and device for determining a velocity spectrum and a computer device. The method comprises the following steps: obtaining seismic data of an exploration area, determining an initial layer velocity field in a depth domain based on the seismic data; determining a reference scanning layer in a subsurface medium layer, obtaining scanning parameters corresponding to the reference scanning layer, generating a scanning velocity field of the reference scanning layer by using the scanning parameters, and determining a common imaging point migration gather and a depth migration profile based on the scanning velocity field; processing the common imaging point migration gather and the depth migration profile to obtain a depth correction parameter of a reflection point, correcting an initial depth of the reflection point by using the depth correction parameter, and obtaining a reference depth of the reflection point; determining an energy distribution of the reflection point located at the reference depth, determining a velocity spectrum of the reference scanning layer by using the energy distribution of the reflection point, and determining a velocity spectrum of the subsurface medium layer of the exploration area by using the velocity spectrum of the reference scanning layer. The method can improve the accuracy and the precision of the determined velocity spectrum.

The present application relates to a multi-scale establishment method for the velocity field of multi-channel seismic data. Multiple velocity spectra are made from data with different offsets and data in different frequency bands, and the multiple made velocity spectra are combined to form a new velocity spectrum. This method has strong practicability, high efficiency and good effect, and can efficiently and accurately obtain the velocity field of ultra-high resolution multi-channel seismic data. The innovative velocity picking point library of this method has determined time values and velocity values, but the intervals of different velocity picking points are variable; based on the imaging quality corresponding to the velocity, the optimization of velocity picking points is carried out, thereby determining the optimal velocity field of ultra-high resolution multi-channel seismic data.

The present invention provides a method for extracting a velocity spectrum from DAS data, comprising the following steps: dividing the full-well-section DAS data into a plurality of data blocks according to a set depth interval, and obtaining a frequency-wavenumber diagram corresponding to each data block; performing grid division on each frequency-wavenumber diagram; obtaining the power spectrum of each grid; setting a plurality of slope lines with different slope values passing through the origin of the frequency-wavenumber diagram, and each slope value is within a preset range of slope values; for each frequency-wavenumber diagram, calculating the integral value of each set slope line, where the integral value is the weighted average of the length value of the slope line within each grid it passes through and the power spectrum within that grid; using the slope value of the slope line corresponding to the maximum integral value as the static velocity value corresponding to the data block where it is located; splicing the static velocity values corresponding to each data block to obtain the static velocity spectrum corresponding to the full-well-section DAS data; and obtaining the up-travel wave and down-travel wave velocity spectra in the wellbore according to the static velocity spectrum and the fluid velocity in the wellbore.

This invention belongs to the field of seismic exploration technology, specifically disclosing a method for creating velocity spectrum tags, including the following steps: S1, acquiring the seismic stacked velocity spectrum; S2, picking velocity tag data; S3, performing velocity prediction through an intelligent velocity spectrum interpretation system, locating tag anomalies, and correcting them to obtain corrected velocity tag data; S4, calculating the energy extrema of the corrected velocity tag data, and optimizing the corrected velocity tag data based on the energy extrema curve and set velocity fine-tuning parameters; S5, further optimizing the optimized corrected velocity tag data based on the energy extrema curve and set minimum time parameters for adding points to obtain the final optimized corrected velocity tag data. This invention can improve the quantity and quality of velocity tag data, providing quality assurance for intelligent velocity spectrum picking. This invention is applicable to velocity spectrum interpretation.

The present invention relates to a millimeter-wave radar multipath range and velocity fast estimation method, characterized by comprising: 1) obtaining a beat frequency signal; 2) obtaining a multipath component range-velocity spectrum using a fast Fourier transform; 3) preliminarily estimating multipath component parameters using a peak detection algorithm and determining a pre-estimated parameter input set; 4) estimating the multipath component parameters using a high-precision channel parameter estimation algorithm, generating a reconstructed signal, and calculating a residual signal; 5) determining whether parameter estimation has been performed for all paths in the input set; if so, executing step 6); otherwise, switching to the next path and repeating step 4 using the residual signal as the input signal; and 6) determining whether a power difference is less than a threshold; if so, outputting an estimation result; otherwise, using the residual signal as the input of a peak detection algorithm and re-executing steps 3) and 6). Compared with the prior art, the present invention has the advantages of ensuring high-precision estimation while reducing the time complexity of the algorithm.

Embodiments of the invention disclose a frequency domain fault diagnosis method and apparatus, a computer device and a medium. The method comprises the steps of receiving acceleration data transmitted by a sensor in a to-be-diagnosed device; acquiring acceleration frequency spectrum data and / or speed frequency spectrum data according to the acceleration data; fault frequency bands are divided according to a preset frequency band division rule, and an RMS value in each fault frequency band is calculated; and judging whether the equipment has a fault according to the RMS value in each fault frequency band and generating a fault type. According to the frequency domain fault diagnosis method and device, the computer equipment and the medium provided by the embodiment of the invention, the method can be suitable for different equipment, the frequency domain fault diagnosis efficiency is high, and the diagnosis result is more accurate.

The invention provides a radar velocity spectrum intelligent pickup method based on adaptive multistage Otsu constraint and energy clustering analysis. The method specifically comprises the following steps: step 1, determining a constraint block region through a dynamic window; step 2, adaptively adjusting a blocking range according to a local maximum between-class variance; step 3, independently calculating Otsu threshold constraint cleaning for the divided regions; 4, performing energy clustering analysis to automatically pick up a time-speed pair; step 5, performing least square regression fitting according to a picking result in the step 4; step 6, optimizing a time-speed pair by using the superposed energy and Euclidean distance joint loss function; and step 7, outputting a speed spectrum intelligent pickup result. The method can effectively reduce the labor and time cost consumed in manual picking, can be integrated in a radar signal processing system, and achieves the automatic high-efficiency and high-precision speed spectrum picking.

The invention discloses a semi-supervised time domain speed modeling method and device based on expert experience constraint. The method comprises the following steps: acquiring an initial velocity function of an extracted seismic gather velocity spectrum constructed according to expert experience, and obtaining initial velocity functions of velocity spectrums of other seismic gathers in a research area through interpolation; based on an initial velocity function of a velocity spectrum, performing extrapolation according to a set rule to obtain a candidate region; for the velocity spectrum of each seismic trace set, identifying an energy cluster from the candidate region, and tracking the energy maximum position of the energy cluster to obtain a velocity function; and performing time interpolation on the velocity function, and obtaining a time domain velocity model of the research area according to the velocity function after interpolation of each seismic trace set. Through the semi-supervised time domain speed modeling method based on expert experience constraint, the influence of interference noise and multiple wave energy groups can be effectively avoided, the energy group pickup precision is ensured, the intellectualization and automation of time domain speed analysis are realized, the manpower is liberated, and the quality and efficiency are improved.