146 results about "Frequency dispersion" patented technology

The invention relates to the technical field of model construction, in particular to a method for constructing an effective dielectric constant model of a carbon fiber composite material. The method comprises the following steps: collecting a sample, carrying out electromagnetic orthogonal frequency sweep analysis, generating a spectrum distribution map, obtaining internal field distribution mapping data through boundary topology recombination, carrying out interval correlation analysis on the internal field data, generating a spatial distribution function, and calibrating a phase resonance point to obtain a frequency characteristic spectrum. Performing field region classification and interactive coupling evaluation according to the frequency characteristic spectrum, deducing a node link structure, performing internal and external field interactive tensor projection and constraint embedding processing based on the node link structure, constructing a composite field model, generating a dielectric frequency dispersion curve through dielectric characteristic inversion, and performing modeling based on frequency dispersion curve data to obtain an effective dielectric constant model. According to the invention, the more accurate construction method of the carbon fiber composite material effective dielectric constant model is realized.

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 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 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 discloses a microwave photon beam forming network architecture based on a switch switching type optical adjustable true delay line and a phased array radar system thereof. According to the framework, microwave signals are modulated in an optical domain, and the characteristics of a plurality of adjustable delay paths in a single-channel switch switching type delay line are utilized, so that delay difference adjustment of multiple paths of optical signals is realized, the number of delay lines and delay units is greatly reduced, and meanwhile, high-precision and broadband beam-strabismus-free beam forming is supported; through two-stage or multi-stage delay configuration, the network can flexibly meet the broadband delay compensation requirements of different array antenna units, has low complexity and strong dynamic reconfigurable capability, is suitable for realization of a large-scale photonic integrated chip, reduces the number of delay lines by about 50%, does not generate frequency dispersion, and is suitable for large-scale photonic integrated chips. The system complexity, the cost and the power consumption can be remarkably reduced, and the method is particularly suitable for broadband phased array systems of millimeter waves, satellite communication, radar detection and the like.

The invention discloses a method and a device for inverting a high-order frequency dispersion curve of a seafloor node Schelt wave. The method comprises the following steps: determining the distance between reference points according to seismic data arrangement information, and setting a plurality of reference points on a target section where a seismic survey line is located; according to the maximum offset distance and the minimum offset distance, common receiving point sub-gathers are screened from a common receiving point Schelt channel set of each seabed node, sub-gathers are intercepted from the common receiving point sub-gathers according to the reference point position information and the maximum distance, and the sub-gathers with the length not smaller than the minimum length form the gathers of the reference points; determining a frequency dispersion energy spectrum of each sub-gather in the reference point gather to obtain a superposed frequency dispersion energy spectrum of the reference point; and picking up a high-order frequency dispersion curve by superposing a frequency dispersion energy spectrum, and carrying out seismic inversion modeling. According to the method, through screening of available trace gathers of reference points and determination and superposition of frequency dispersion energy spectrums, an energy-enhanced superposed frequency dispersion energy spectrum is obtained, so that high-resolution seismic modeling can be realized based on extracted high-order frequency dispersion curve inversion.

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 a physical condition embedded seismic surface wave intelligent inversion method and device and a medium, and the method comprises the steps: obtaining the seismic surface wave data of an active source or a passive source to be inverted, and carrying out the extraction through a frequency dispersion analysis method, and obtaining an observation frequency dispersion curve of the seismic surface wave data; constructing an initial shear wave velocity model, obtaining a theoretical frequency dispersion curve and a sensitivity matrix of the initial shear wave velocity model, and calculating physical condition embedding of the observation frequency dispersion curve based on the theoretical frequency dispersion curve and the sensitivity matrix of the theoretical frequency dispersion curve; based on an initial shear wave velocity model and physical condition embedding, an inversion model is used for iterative solution to obtain an inversion shear wave velocity model, and the device and the medium are used for implementing the method. Compared with the prior art, the method has the advantages that the problem that an initial model is easy to fall into a local minimum value due to manual selection is avoided, the inversion precision of the seismic surface waves is improved, the limitation of fixed input or output dimensions is eliminated, and the applicability of the inversion method in processing actual seismic surface wave data is remarkably improved.

The invention discloses a rock-soil body internal defect identification method based on wave field inversion, and the method comprises the following steps: S1, carrying out the wave field numerical simulation of a target region through a coarse grid discretization model, and generating distorted wave field data containing a numerical frequency dispersion effect; s2, frequency dispersion characteristic fragments related to defects are extracted from the distorted wave field data; s3, matching the frequency dispersion characteristic fragments with a pre-stored or dynamically generated defect characteristic template library, and determining a defect candidate area; s4, when matching fails, decomposing a scattering unit based on distortion wave field residual data, generating a new defect template through parameterized folding recombination, and updating the new defect template to a template library; s5, replacing the coarse grid numerical solution in the candidate region with an analytic wave field solution or a mixed solution of a newly generated template, and inverting defect parameters; through core technologies of coarse grid efficient calculation, dynamic template adaptive generation, physical constraint strong verification and the like, leap-forward progress is realized in the aspects of defect identification efficiency, complex form adaptability, environment robustness and the like.

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 discloses a wave-absorbing material design method based on neural network guidance, and belongs to the field of intelligent material design. The method comprises the following steps: selecting actually measured magnetic conductivity frequency dispersion data of a magnetic material as a boundary condition, constructing a parameter space containing magnetic conductivity, dielectric constant, material thickness and frequency, and calculating a reflection loss RL value through a transmission line model; gPU acceleration tensor operation is adopted to generate a three-dimensional parameter space, and discretization processing is carried out on the parameter space; implementing a double-task screening strategy to extract an optimal dielectric constant combination and an effective region boundary; constructing a neural network model with physical constraints, and inputting thickness and frequency prediction dielectric constant parameters; and generating solution set distribution to guide material design through inversion verification. A physical model and deep learning are fused, an Einstein summation method and a broadcast mechanism are adopted to realize hundred million-level parameter parallel calculation, the problem of multi-parameter coupling optimization is solved, the physical rationality of output parameters is ensured through a softplus activation function, and the design efficiency and precision of the magnetic wave-absorbing material are remarkably improved.

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 ultrasonic guided wave nondestructive testing, and particularly discloses an arched concrete filled steel tube bridge detection method based on ultrasonic guided wave frequency dispersion characteristics, which adopts a theoretical derivation-simulation verification-experimental analysis method, and comprises the following steps: firstly, establishing a fluctuation characteristic equation based on a rotating coordinate system and a semi-analytical finite element method; deriving a guided wave frequency dispersion equation, obtaining a frequency dispersion curve through numerical calculation, and determining an optimal monitoring mode and an excitation frequency range; a three-dimensional finite element model is constructed for wave field numerical simulation, and the reliability of the model is verified through comparison of a theoretical solution and a simulation solution; then carrying out an ultrasonic guided wave detection experiment, collecting a time domain signal and extracting an actually measured frequency dispersion characteristic; and finally establishing theoretical prediction through time-frequency analysis, comparing numerical simulation with experimental data, and verifying the accuracy of a theoretical model in combination with error analysis. The method can accurately detect the defects of the arched concrete-filled steel tube bridge, and improves the reliability and practicability of complex structure damage identification.

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.

A method for detecting natural gas reservoirs using amplitude-frequency modulation is disclosed. The method comprises the following steps: acquiring seismic data; decomposing the seismic data using time-frequency analysis techniques to obtain a number of data subsets; performing wave impedance inversion on each of the data subsets using sparse pulse inversion techniques; calculating the mean square error function of the wave impedance for each frequency band; and performing amplitude-frequency modulation on the seismic data using the mean square error function. The present application provides a method for detecting natural gas reservoirs using amplitude-frequency modulation. This method uses a multi-scale inversion method based on time-frequency analysis to extract an indicator factor related to frequency dispersion. This factor is used to automatically modulate reflection intensity, thereby weakening the influence of other factors on reflection intensity, highlighting seismic reflection characteristics related to gas content, and enhancing the accuracy of geophysical detection of gas-bearing reservoirs.

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 invention relates to a near-surface imaging method and system for wave equation seismic surface wave full-frequency dispersion spectrum inversion, comprising: collecting data acquisition information of a target area and inputting the data acquisition information into a grid subdivision model to obtain an observation surface wave ground acquisition system; solving the elastic wave equation to obtain simulated surface wave data in the ground acquisition system; calculating an accompanying earthquake source based on the observed surface wave data and the simulated surface wave data; cross-correlating the forward wavefield of the actual earthquake source with the reverse wavefield of the accompanying earthquake source to calculate the gradient of an updated seismic wave velocity model, and updating a network subdivision model based on the model update gradient; inputting the data to be measured into the updated grid subdivision model to obtain near-surface imaging results from wave equation seismic surface wave full-frequency dispersion spectrum inversion. Because the surface wave dispersion spectrum is used as inversion data, the method avoids manually picking dispersion curves, reduces human errors, and improves the efficiency of large-scale seismic data processing.

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.