99 results about "Frequency dispersion" patented technology

The invention relates to the technical field of underwater detection, and discloses an underwater comprehensive detection system. The system collects multi-dimensional acoustic data including sound wave reflection intensity, propagation time delay and frequency dispersion characteristics of a target water area through an underwater sonar array; dividing the detection area into a plurality of layered units according to a water attenuation coefficient and a substrate reflection characteristic of the acoustic data, and extracting an environment intrinsic parameter of each layered unit; constructing an acoustic propagation path model based on the environmental intrinsic parameters, and generating adaptive filtering parameters in combination with a preset noise suppression strategy; calling sonar array hardware configuration data, matching filtering parameters with an array beam forming mode, and calculating a detection sensitivity threshold value of each layered unit; and integrating the sensitivity threshold and the acoustic data, generating a three-dimensional acoustic characteristic spectrum of the target water area, and marking the space coordinates and confidence level of the abnormal area. The system can improve the comprehensiveness and accuracy of underwater detection, and meets the requirement of high-precision detection.

The invention provides an airfield pavement detection method based on an earthquake background noise imaging technology, and relates to the technical field of airfield pavement detection. Comprising the following steps: acquiring seismic background noise data of an airport pavement to be measured; preprocessing the seismic background noise data to obtain preprocessed noise data; carrying out cross-correlation calculation on the preprocessed noise data and carrying out empirical Green function extraction; according to the empirical Green function, a surface wave group velocity frequency dispersion curve is extracted by adopting spatial autocorrelation and multiple filtering methods; inverting shear wave velocity distribution of each layer of the pavement based on the surface wave group velocity dispersion curve, and constructing a three-dimensional velocity model; determining a shear wave velocity distribution diagram according to the three-dimensional velocity model; and determining a detection result of the airfield pavement to be detected according to the transverse wave velocity distribution diagram. According to the invention, the problems of low detection precision and poor real-time performance of a common detection technology in the prior art are solved.

The invention relates to the technical field of electrical engineering and intelligent fault diagnosis, and discloses a water pump motor fault diagnosis method and system based on current harmonic characteristics, and the method comprises the steps: collecting stator current, rotating speed and load signals; generating an alpha-beta current component through Clark transformation, constructing an analytic signal, extracting an instantaneous phase, and calculating an instantaneous frequency; a mapping relation is established in combination with the rotating speed, and the current signals are resampled to eliminate frequency dispersion; obtaining a high-resolution harmonic energy distribution diagram by adopting multi-scale synchronous compression wavelet transform; and extracting an energy concentration ratio, a peak offset and a bandwidth expansion coefficient in the characteristic frequency band, inputting the energy concentration ratio, the peak offset and the bandwidth expansion coefficient into the pre-trained multi-layer perceptron classifier, and outputting a fault type. According to the method, through high-precision time-frequency focusing and multi-dimensional feature fusion, the fault recognition accuracy and robustness are remarkably improved, and online diagnosis of multiple typical faults can be achieved only through current signals.

The invention relates to the technical field of oil and gas geophysical exploration, and particularly discloses a tight sandstone gas reservoir fluid mobility gradient attribute prediction method and system.The method comprises the steps that a reflection coefficient expression containing a fluid mobility item is constructed, and a formula for calculating fluid mobility through frequency dispersion attributes is deduced; constructing a fluid mobility gradient attribute related to the frequency and the incident angle of the seismic wave; the method comprises the following steps: establishing a double-pore rock physical model, generating a synthetic seismic record, applying frequency variation AVO inversion to the synthetic seismic record, and extracting a bulk modulus dispersion attribute and a shear modulus dispersion attribute; and calculating the fluid fluidity changing along with the frequency and the incident angle, and calculating the gradient attribute of the fluid fluidity based on a gradient attribute calculation formula and the fluid fluidity. According to the method, seismic wave frequency dispersion and attenuation response caused by permeability and gas saturation are fully utilized, the seismic gradient attribute of the fluid fluidity and the calculation method are constructed, and high-precision prediction of the fluid fluidity by applying seismic signal elasticity and frequency dispersion rock physical information is realized.

The invention discloses an arc suppression coil grounding fault positioning method based on a transformer substation line protection device, particularly relates to the technical field of system fault measurement, and is used for solving the problem of insufficient positioning precision caused by neglecting the nonlinear characteristic of a fault point arc channel in an existing fault positioning method. A specific frequency current signal is injected into a fault phase, the phase relation between fundamental wave and harmonic wave components in current collected by a line protection device is analyzed to obtain a phase coherence coefficient, a fault line distribution parameter model is established to obtain theoretical reference information, and a spatial gradient sequence is generated by calculating actual measurement and theoretical spatial entropy values. Identifying a line section corresponding to the abnormal abrupt change point, and finally determining a grounding fault point according to the spatial position of the line section with significant difference. The method can effectively overcome the influence of nonlinear attenuation and frequency dispersion effect on the positioning precision, and achieves the precise measurement and positioning of a single-phase grounding fault.

The invention discloses a cable fault detection method based on a data transmission current differential signal, and relates to the technical field of cable fault point positioning, and the method comprises the steps: obtaining a cable channel fault feature vector through calculating the time delay domain feature and frequency domain envelope distortion degree of an enhanced differential signal at a head end and a tail end; and inputting the cable channel fault feature vector into the trained baseline model to identify whether the to-be-detected cable has a fault, when identifying that the to-be-detected cable has a fault, carrying out joint time-frequency analysis on the enhanced differential signals at the head end and the tail end, obtaining sudden change features at the head end and the tail end, solving relative time delay data of the sudden change features at the head end and the tail end on a time-frequency plane, and determining the fault of the to-be-detected cable. And calculating the accurate position of the fault point of the to-be-detected cable in combination with a frequency variation propagation model. According to the method, the positioning deviation caused by the frequency dispersion characteristic of the cable is effectively corrected through a double-end cooperative detection method, and the accuracy of fault positioning is improved.

The invention belongs to the technical field of surface wave frequency dispersion inversion, and particularly discloses a surface wave multi-modal frequency dispersion curve probability inversion method based on modal self-matching, which comprises the following steps of: dividing apparent frequency dispersion data points into two types, namely a basic modal data set and a higher modal data set; constructing a Bayesian equation for inverting multi-modal dispersion data in a non-likelihood inference method framework; establishing the similarity between the distance measurement quantized frequency dispersion data and the simulation sample, and appointing a method suitable for sampling; and representing the uncertainty of a multi-modal dispersion data inversion result (namely a shear wave velocity profile) based on the obtained posterior sample. According to the method, the uncertainty of the shear wave velocity profile obtained by inverting the Rayleigh wave multi-mode dispersion curve can be well quantified, the identifiability of the shear wave velocity profile is improved, and a wrong inversion result caused by misidentification of the mode is avoided by automatically matching the mode number.

The invention provides a micro-motion exploration data processing method and system for automatic partitioning of a two-dimensional dense array, a medium, equipment and a product, and relates to the technical field of micro-motion exploration, and the method comprises the steps: obtaining the spatial position coordinates of stations in the two-dimensional array and the waveform data of the stations, and carrying out the batch preprocessing of the waveform data of the stations; based on the spatial position coordinates of the stations, performing k nearest neighbor rolling partition on the stations; carrying out frequency dispersion energy spectrum calculation on the stations in each partition based on the station waveform data after batch preprocessing, and picking up the frequency dispersion energy spectrum by adopting an automatic correction picking-up method; a data processing modular workflow for data import, data preprocessing, station partitioning and frequency dispersion calculation is established for modular processing. According to the method, the data processing efficiency and effect of two-dimensional dense array micro-motion exploration can be remarkably improved.

The invention discloses a seismic wave velocity change analysis method under a hydraulic fracturing condition, and belongs to the technical field of seismic wave propagation analysis. The method comprises the following steps: S1, acquiring data by using a plurality of seismic stations and removing transient signals by using a long and short window method; s2, calculating a cross-correlation function between the two stations; s3, extracting a tail wave signal and estimating relative velocity change by using mobile window cross-spectrum analysis; s4, extracting a group velocity frequency dispersion curve based on a cross-correlation function, and constructing a two-dimensional velocity model before and after fracturing; and S5, generating a speed image through inversion, and calculating a difference image to reveal speed change characteristics. According to the method, the tail wave sensitivity and the moving window cross-spectrum analysis are combined, the speed change and the anisotropy characteristics of the speed change are accurately quantified, the method is suitable for the complex geological environment, and a new interpretation framework is provided for the influence of hydraulic fracturing on the underground structure.

The invention is suitable for the technical field of geophysical exploration, and provides a rock physics-driven time-frequency domain longitudinal wave velocity dispersion attribute prediction method, which comprises the following steps of: establishing a rock physics model; under the driving of the rock physical model, inverting the porosity of the microfractures in the well, calculating the skeleton elastic modulus of the rock containing the fractures, and predicting a frequency-dependent longitudinal wave velocity logging curve; constructing a time-frequency domain longitudinal wave velocity seismic inversion initial model; constructing a TF-AVO theoretical formula, developing a time-frequency domain longitudinal wave velocity seismic inversion method based on an L-BFGS algorithm, and performing longitudinal wave velocity inversion by taking the initial model as prior information; analyzing the relative change of the frequency-varying longitudinal wave speed under different frequencies, and quantitatively predicting the frequency dispersion attribute of the longitudinal wave speed; and on the basis of logging data, calibrating a longitudinal wave velocity dispersion attribute prediction result, and carrying out reservoir oil and gas detection. The method has higher precision and resolution, the stability and reliability of longitudinal wave velocity dispersion attribute prediction are remarkably improved, and the dependence on strong amplitude response is overcome.

The invention relates to the field of power system state monitoring, and discloses a power equipment fault diagnosis system suitable for a complex power grid environment, and the system comprises a propagation medium model building unit which is used for dynamically correcting a model through benign transient waves; the transient traveling wave synchronous acquisition unit is used for synchronously acquiring traveling wave data at high precision; the fault positioning unit is used for determining a fault position based on a time difference residual minimization principle; and the fault property and equipment state analysis unit is used for carrying out deep analysis by comparing actually measured and theoretical reflected wave path spectrums and frequency dispersion characteristics. The method comprises the following steps: constructing and dynamically correcting a transient wave propagation medium model; synchronously acquiring transient traveling waves excited by an electrical event; calculating a fault physical position; the propagation characteristics of the transient waves are analyzed to determine fault properties and device states. According to the invention, the electrical asymmetry hidden danger can be identified and positioned by actively injecting the pure-sequence traveling wave probe, and the combination of passive diagnosis and active early warning is realized.

The invention relates to the technical field of ultrasonic signal processing, in particular to an ultrasonic data processing method and system and electronic equipment, and the method comprises the steps: obtaining an original ultrasonic echo signal, and carrying out the time gain compensation, band-pass filtering and down-sampling preprocessing; extracting dynamic frequency dispersion characteristic parameters based on Coann class time-frequency distribution; constructing a depth self-adaptive phase compensation model, and correcting nonlinear phase distortion segment by segment; hilbert transform is adopted to extract an envelope, and after logarithmic compression and linear mapping are carried out, image reconstruction is completed by utilizing delay summation beam forming and an adaptive contrast enhancement algorithm. According to the method, the imaging definition is improved through cooperation of frequency dispersion modeling and phase correction, and the method has good depth adaptability and image visualization effect and is suitable for high-precision image acquisition requirements in scenes such as medical ultrasonic imaging and industrial nondestructive testing.

The invention relates to the technical field of geophysical exploration, and discloses a common station pair coherent superposition enhancement method for active source seismic surface waves with a low signal-to-noise ratio. Comprising the following steps: generating a Green function according to a relative relationship between a seismic source position and a station pair: when the station pair is located at the same side of a seismic source, calculating the cross-correlation of seismic records of two stations in the station pair, and generating a cross-correlation Green function; when the station pair is located on the different side of the seismic source, the cross correlation between the seismic records of the two stations in the station pair and the seismic record of the reference station at the seismic source is calculated, then convolution calculation is carried out, and a convolution Green function is generated; frequency dispersion energy diagrams are respectively calculated based on the superposed cross-correlation Green function and convolution Green function; and superposing the two types of frequency dispersion energy diagrams to generate a final frequency dispersion diagram for extracting a frequency dispersion curve. According to the active source seismic surface wave signal co-station pair coherent superposition enhancement method provided by the invention, the surface wave signal-to-noise ratio and the frequency dispersion diagram quality are effectively improved on the premise that the data acquisition cost is not increased.

The invention discloses a fire-fighting pipeline water leakage identification method and system based on multi-modal data processing, and relates to the field of pipeline water leakage identification, and the method comprises the steps: firstly, carrying out the fusion of sound and vibration waveforms and geographic coordinate data, and carrying out the decoupling of a complex broadband water leakage signal into a series of narrowband intrinsic mode components through employing a variational mode decomposition technology; on this basis, a spatial topology construction technology based on a GPS is introduced, and an accurate physical path provided by geographic coordinates is used as a spatial constraint vector to construct a closed-loop feedback mechanism of acoustics and geographic information. Spatial constraint is applied to adaptive group velocity matching under a frequency dispersion effect, and actual group velocities of different frequency bands propagating in a tube are dynamically calculated. And finally, combining a multi-modal weighted fusion strategy, and integrating the corrected distance difference of each frequency band to obtain an accurate positioning result, thereby realizing effective compensation and correction of the frequency dispersion error in the inhomogeneous medium by using multi-modal data.

The invention discloses a zero-dispersion nonlinear MEMS accelerometer for monitoring construction stress of a floating fan platform, and belongs to the technical field of ocean engineering structure monitoring. The zero-frequency-dispersion nonlinear MEMS accelerometer comprises a mass block, an amplification beam pair assembly, an electrode system and resonant beams arranged on the two sides of the mass block in a bilateral symmetry mode, and the mass block is connected with the resonant beams through the amplification beam pair assembly. According to the invention, adjustable static nonlinearity is introduced in the capacitance excitation process of the double-end clamped resonant beam, and the adjustable static nonlinearity is matched and counteracted with the geometric nonlinearity of the resonant beam, so that the resonator enters a zero-frequency dispersion working area within a preset excitation range, and therefore, under the working conditions of construction load disturbance, structural vibration amplitude fluctuation and the like, the resonance efficiency of the resonator is improved. Frequency response information related to structural dynamic characteristics can still be stably output, and a high-stability perception basis is provided for inversion analysis of welding residual stress and structural health states.

The present application relates to the technical field of task scheduling processing, in particular to a task scheduling monitoring method and system based on a signal monitoring machine, comprising the following steps: capturing a mixed sound pressure signal generated by underwater base station and vehicle communication through a submerged signal monitoring machine, separating the direct wave and reflected wave components; calculating the current sound pressure channel quality score according to the frequency dispersion index of the direct wave component and the time delay dispersion degree of the reflected wave component; setting an adaptive threshold, triggering a multi-level decision response based on the deviation degree of the channel quality score and the adaptive threshold, and generating a channel degradation alarm at the same time. The present application optimizes the task resource allocation strategy while ensuring the reliability of communication, and is suitable for various complex operation scenarios such as underwater unmanned platform, emergency communication and ocean monitoring.

The application discloses a component fatigue micro-damage early identification method based on a nonlinear static component, and particularly relates to the technical field of material nondestructive testing and structural health monitoring, which comprises the following steps: arranging an excitation probe and a receiving probe on the surface of a component to be measured; selecting an excitation frequency based on a frequency dispersion curve and generating a Hanning window modulated wave; collecting a propagation response signal; extracting a static component amplitude A and a fundamental wave amplitude A through time-frequency processing; calculating a nonlinear static parameter beta=A / A²; further calculating a first derivative S(N) and a second derivative ΔS(N) of beta with respect to a fatigue loading cycle N; and identifying a dislocation dominant stage, a dislocation saturation or rearrangement stage, a micro-crack initiation stage and a macro-crack formation stage according to the first derivative S(N) and the second derivative ΔS(N). The application can be used for early stage identification of the evolution process of component fatigue micro-damage.

The present application relates to the technical field of machining tools, and proposes a tool life real-time monitoring and early warning method and system. First, the original acoustic emission signal of the tool working site is obtained, adaptive cavitation noise cancellation processing is performed, and comparison index calculation is performed based on the processed signal, including entropy change and kurtosis calculation, frequency dispersion-attenuation coupling analysis is performed in combination with short-time Fourier transform, and frequency dispersion slope change rate and energy attenuation ratio are extracted. Subsequently, the current tool individualized threshold vector identified by the deep learning model is obtained from the cloud, compared with the comparison index, the life remaining proportion is determined, and early warning is implemented. Through millisecond acoustic emission signal analysis, microscopic secondary detection and cloud model self-learning, the present application realizes millisecond response, extremely low false alarm rate and seamless system integration under the premise of no shutdown and unattended, and breaks through the traditional fixed tool changing period, and approaches the real service life limit of the tool.

The application discloses a coal seam roof sandstone aquifer water enrichment evaluation method based on three-dimensional seismic, which comprises the following steps: S10, collecting drilling data, well logging data and high-precision three-dimensional seismic data of a research area; S20, inverting pre-stack seismic data to obtain a P-S wave velocity ratio of the roof sandstone aquifer; S30, performing time-frequency analysis on post-stack seismic data and performing spectral equalization on frequency domain seismic data; S40, inverting a longitudinal wave frequency dispersion attribute of a target layer according to a reflection coefficient approximate formula and the time-frequency spectrum of the post-stack seismic data after the spectral equalization; S50, using an entropy weight method to calculate weight values of the P-S wave velocity ratio and the longitudinal wave frequency dispersion attribute; and S60, constructing a water enrichment indication factor calculation formula and evaluating water enrichment of the sandstone aquifer according to the water enrichment indication factor. The method is based on the theoretical basis of seismic rock physics, has low cost, high operability, more statistical significance, can quickly and accurately evaluate water enrichment of the coal seam roof sandstone aquifer, and provides technical support for development and utilization of coal resources.

The invention relates to the technical field of roadbed cavity detection, in particular to a roadbed cavity intelligent detection method based on multi-feature fusion and deep learning and related equipment. The method comprises the following steps: acquiring road-base pulse signal data and preprocessing to form a preprocessed signal sequence; multi-dimensional features such as amplitude attenuation and frequency dispersion change are extracted based on a cavity physical effect model, and a high-discrimination fusion feature vector is generated through feature fusion and dimension reduction; and training a deep learning cavity recognition model by using the fusion features to realize automatic recognition of the cavity existence probability of the to-be-detected road section. And finally, spatial positioning and risk assessment are performed by combining cross-correlation analysis and frequency dispersion curve inversion, so that high-precision detection and reliable judgment of the roadbed cavity are realized. According to the invention, the accuracy, efficiency and intelligent degree of roadbed cavity detection are improved.

The application provides a formation fracturing detection method and device, a storage medium and a processor, and belongs to the technical field of well logging. The method obtains a frequency spectrum array of original array waveform data, calculates slowness values of each acoustic mode, then obtains a frequency dispersion curve greater than a preset resolution according to iterative operation of hierarchical clustering and threshold division, projects the time difference and the frequency of the frequency dispersion curve to a time difference axis by using a time difference-frequency projection method to obtain a time difference-frequency projection curve after fracturing, overlaps the time difference-frequency projection curve after fracturing with a time difference-frequency projection curve before fracturing to obtain a time difference change value at the lowest frequency, and finally determines the degree of formation fracturing according to the time difference change value at the lowest frequency to obtain a formation fracturing detection result, so that the change of formation shear wave velocity caused by fracturing and the degree of formation fracturing can be accurately distinguished.

The invention belongs to the technical field of nondestructive testing, and relates to a phase group velocity matching frequency dispersion removal method. The method comprises the following steps: S1, exciting a signal in a propagation medium, collecting a propagation signal, removing a frequency spectrum of an excitation signal in the propagation signal, and carrying out normalization processing; s2, solving a nonlinear frequency-wave number spectrum; s3, performing linear mapping according to the propagation medium frequency dispersion curve to obtain a linear frequency-wave number corresponding relation; s4, setting equal-interval wave number vectors, and performing linear mapping from the nonlinear wave number spectrum to obtain a linear wave number spectrum; s5, converting the linear wave number spectrum into a new normalized frequency spectrum according to a wave number-frequency corresponding relation, and adding an excitation signal frequency spectrum component to obtain a frequency spectrum of a non-dispersion propagation signal; and S6, performing calculation to obtain a frequency-dispersion-free reconstructed time domain signal. According to the method, the wave packet of the target mode can be extracted and separated, the frequency dispersion signal is recovered to the non-frequency dispersion signal, the signal quality and accuracy are improved, and the method is suitable for scenes such as structure health of ultrasonic detection.

The invention provides a pipeline long-distance guided wave detection system and a detection method thereof, and relates to the technical field of guided wave detection. Torsional mode signals and longitudinal mode signals reflected by guided waves in a target pipeline are collected; the frequency dispersion characteristic of the torsional mode signal is corrected, and the echo characteristic of the torsional mode is determined through the amplitude difference and the phase offset of the corrected torsional mode signal in the circumferential sensor array; compensating the axial symmetry vibration amplitude in the longitudinal modal signal through the attenuation coefficient of the axial propagation of the guided wave in the pipeline structure body and the correlation constraint of the intensity of the guided wave between the axially adjacent sensors, and extracting the echo characteristics of the longitudinal modal; multi-mode guided wave cooperative detection is conducted on the target pipeline according to the echo features of the torsional mode and the longitudinal mode, the defect echo features of the target pipeline are obtained, the defect position of the inner wall of the target pipeline is positioned based on the defect echo features, multi-mode guided wave cooperative detection can be achieved, and therefore the pipeline defect detection precision is improved.

The invention relates to the technical field of image processing and deep learning, in particular to an automatic surface wave inversion method and system, and the method comprises the steps: receiving original seismic information, sequentially carrying out the mean value removal, trend removal and spectrum whitening preprocessing of surface wave information, calculating a single-component noise cross-correlation function and a nine-component noise cross-correlation function based on the preprocessed data, and obtaining a single-component noise cross-correlation function and a nine-component noise cross-correlation function; and based on a time-frequency analysis method, calculating a surface wave group velocity and a velocity frequency dispersion energy diagram through the cross-correlation function, automatically extracting a frequency dispersion curve from the velocity frequency dispersion energy diagram by using an artificial intelligence frequency dispersion extraction method, screening frequency dispersion files of which effective frequency dispersion points and signal-to-noise ratios meet a preset standard, and calculating the surface wave group velocity and the velocity frequency dispersion energy diagram through the cross-correlation function. And inverting the parameters of the three-dimensional shear wave velocity model based on the underground interface constraint three-dimensional shear wave velocity inversion method to obtain a three-dimensional shear wave velocity model. The problems that in the prior art, efficiency is low, the operation process is complex, and the professional threshold is high are solved.

The invention provides a radar sea surface target detection method and system. The method comprises the following steps: removing the influence of noise in a radar echo signal by adopting an improved wavelet threshold denoising method; according to the method, sea clutters are preprocessed in combination with a TQWT algorithm based on clutter reconstruction and a TQWT parameter adaptive calculation process, time-frequency analysis is carried out on preprocessed signals, two time-frequency features including the maximum singular value proportion and the frequency dispersion are proposed, and a support vector machine is used for classifying sea clutter units and target units; and optimizing the optimal transformation order in the SVD-FRFT algorithm by adopting an improved Raccoon longnose optimization algorithm to further process the target unit. The method is suitable for a low-speed small target scene in the sea surface, the influence of the radar on sea clutters in sea surface target detection is overcome, and the target detection performance is improved.

The invention relates to the technical field of nondestructive testing, and particularly provides a fuel rod cladding damage detection method based on multiple diffraction guided wave probability imaging, which comprises the following steps: arranging a pair of signal receiving and transmitting devices at two ends of a to-be-detected area of a fuel rod cladding for transmitting an input signal f (t) and receiving and acquiring wave field data; performing narrowband signal extraction on the obtained wave field data, and extracting a wave packet signal h (t) when frequency dispersion does not occur in each spiral path according to spiral guided wave propagation characteristics; calculating a signal difference coefficient SDC of each spiral path by comparing the difference between f (t) and h (t); calculating the damage probability of each point position in the detection area by using the SDC; and judging the damage probability of all the point positions in the detection area through the damage probability threshold, and determining the damage position. According to the invention, only one pair of signal receiving and transmitting devices is arranged, so that the defects of the scanning area in the fuel rod cladding can be accurately detected and positioned by utilizing spiral guided waves.

The invention discloses a complex reservoir rock full-band sound wave velocity prediction and attenuation analysis method considering a pore structure and a multi-wave flow mechanism, and relates to the technical field of petrophysics and petroleum logging, the method comprises the following steps: constructing a multi-component digital rock based on X-ray CT and QEMSCAN, and extracting macropores; microcosmic pores are extracted through a high-resolution MAPS image, and a complex pore structure model is constructed; analyzing the static acoustic characteristics of the dry digital rock by adopting an elastic finite element method; on the basis, the influence of wave-induced flow in different scale spaces is considered, a new wave-induced flow mechanism of static flow-jet flow-Biot flow is provided, and a longitudinal wave and transverse wave speed prediction method of the rock with the complex pore structure is formed. According to the method, on the basis of digital rock, theoretical rock physics are fused, the rock sound wave velocity estimation method considering the pore structure and the microcosmic wave-induced flow mechanism is given, the frequency dispersion and attenuation characteristics of the rock sound wave velocity estimation method are determined, and a foundation is laid for subsequent reservoir logging evaluation.

The invention provides a multi-wave-field surface modeling method and device, and the method comprises the steps: carrying out the preprocessing of seismic data, and obtaining the seismic surface wave information and the first arrival time of a first arrival wave; acquiring a frequency dispersion curve by using the surface wave signal, and constructing an initial surface wave velocity model based on the frequency dispersion curve; determining a reliable depth and transverse and longitudinal wave velocity conversion relation of surface wave inversion, and obtaining a longitudinal wave velocity model in a constraint range; and the longitudinal wave velocity model is used for the first-arrival wave travel time chromatography to invert the initial velocity model, velocity updating iteration is carried out according to the first-arrival time and the initial velocity model, and a near-surface velocity model after shallow constraint is obtained when model parameters converge. According to the method, the near-surface model is inversed by using the surface wave frequency dispersion characteristics, the initial model of the first arrival wave tomography inversion is introduced and shallow constraint is carried out, and then fine optimization iteration is carried out, so that the high-precision near-surface velocity model and improved seismic imaging are obtained, the near-surface velocity modeling precision is improved, and the accuracy of the near-surface velocity modeling is improved. And the method is flexible, efficient, low in cost and suitable for large-scale popularization and application.

The invention discloses a surface wave frequency dispersion image generation method, a medium, equipment and a product, and relates to the field of surface wave exploration, and the method comprises the steps: obtaining time domain surface wave signals received by a geophone, and arranging the time domain surface wave signals according to spatial positions to form a gather; performing S transformation on the time domain surface wave signal of each seismic channel to obtain a complex value time-frequency spectrum; based on the time-frequency spectrum, extracting a complex frequency spectrum corresponding to main energy on each frequency point of each seismic channel, and performing normalization processing to obtain a phase spectrum; selecting a plurality of heuristic phase velocities in a preset phase velocity scanning range, performing coherent superposition on phase spectrums corresponding to all seismic traces for each frequency and the heuristic phase velocity, and constructing an energy function of a frequency-phase velocity domain; and when the tentative phase velocity is consistent with the actual surface wave phase velocity, obtaining a surface wave phase velocity frequency dispersion energy image based on an energy function of a frequency-phase velocity domain. According to the invention, the anti-interference capability and stability of surface wave frequency dispersion imaging under complex geology or noise conditions can be improved.

The invention discloses an intelligent control method, device and system for a water supply station and a medium. The method comprises the steps that acoustic environment data of a plurality of sensing nodes are obtained, the sensing nodes are arranged on a water supply network, and the acoustic environment data are used for monitoring the operation state of the water supply network; performing anomaly identification on the acoustic environment data to obtain an identification result; under the condition that the identification result comprises abnormal signals, a first leakage interval is determined according to the abnormal signals of the multiple sensing nodes, and the first leakage interval comprises a distance interval between two adjacent target sensing nodes; performing frequency dispersion characteristic analysis on the target environment data of the target sensing node to obtain a second leakage position; and generating a control strategy of the water supply pipe network where the second leakage position is located. According to the embodiment of the invention, the water supply pipe network and the water supply station can be monitored in real time, the fault position can be accurately positioned under the condition that the water supply pipe network breaks down, and the water supply station is controlled to supply water intelligently.