677 results about "Shear waves" patented technology

The invention provides a multi-mode ultrasonic fusion pressure vessel weld defect nondestructive testing method and system, and relates to the technical field of nondestructive testing. According to the method, geometric parameters of a welding seam are obtained through three-dimensional laser scanning, and an optimal scanning parameter set is generated; driving ultrasonic phased array equipment to scan for one time and synchronously acquire shear wave full-matrix capture and longitudinal wave linear scanning data; performing energy flow angular spectrum analysis and envelope analysis on the bimodal data, extracting defect feature parameters and constructing a three-dimensional feature tensor; carrying out multi-dimensional feature fusion by adopting Tucker decomposition, and enhancing a core tensor through physical modeling; generating three types of defect indication diagrams including a defect existence possibility diagram, a defect relative scale diagram and a defect space orientation diagram from the enhanced feature tensor; and the three types of indication diagrams are visually presented for comprehensive interpretation of detection personnel. Through multi-modal data fusion and physical modeling enhancement, the defect identification accuracy and detection efficiency are remarkably improved, the false alarm rate is reduced, and reliable technical support is provided for pressure vessel welding seam safety detection.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

The invention discloses a high cut slope stability evaluation method based on multi-source data fusion, and particularly relates to the technical field of data analysis. By integrating topographic data, geological exploration data, remote sensing images, monitoring sensor data and geophysical detection data, an inversion algorithm is adopted to automatically extract abnormal structure features such as shear wave velocity sudden change and resistivity valley, the abnormal structure features and geological profiles are subjected to cross validation to generate an underground structure risk map layer, and a fusion model is combined with machine learning and a mechanical algorithm to obtain an underground structure risk map layer. According to the method, slope stability indexes including the safety coefficient and the sliding probability are comprehensively output, intelligent identification and grading of potential sliding dangerous areas are achieved, finally, an evaluation result is visually displayed in a three-dimensional GIS platform in a layer superposition mode, the identification precision and evaluation scientificity of slope hidden risks are remarkably improved, and the method is suitable for popularization and application. And a more reliable decision basis is provided for early warning and disposal of a high-risk slope.

The invention discloses a data center seismic isolation performance detection method based on AI intelligence, and particularly relates to the technical field of seismic isolation performance detection. Earthquake vibration characteristic data are collected in real time on a data center building and equipment, earthquake input characteristics are extracted in combination with a historical earthquake wave database, a seismic isolation performance prediction model is constructed and trained, a foundation shear wave velocity gradient abnormal value and a seismic isolation layer dynamic displacement response deviation ratio are further extracted, a deviation index is comprehensively calculated, and a seismic isolation performance prediction model is constructed and trained. And classification processing is carried out according to the deviation level, especially under the condition of a middle deviation level, foundation physical parameters are dynamically updated, and the model is optimized, so that the accuracy of the prediction model and the self-adaptive capability of the system are remarkably improved, and the shock isolation safety and the continuous operation capability of the data center in a strong shock environment are effectively guaranteed.

The invention relates to an anisotropic elastic medium Rayleigh surface wave frequency dispersion curve inversion method, which comprises the steps of collecting near-earth surface wave exploration seismic data, obtaining a Rayleigh surface wave frequency dispersion energy diagram based on a phase shift method, and extracting a frequency dispersion curve from the frequency dispersion energy diagram based on a convolutional neural network artificial intelligence method; constructing a near-surface anisotropic elastic parameter initial model; calculating to obtain a theoretical Rayleigh surface wave dispersion curve by using a Rayleigh surface wave dispersion curve forward modeling method according to the current elastic parameter model; designing a loss function according to the observed Rayleigh surface wave dispersion curve and the theoretically calculated Rayleigh surface wave dispersion curve, and calculating and judging whether the loss function reaches a set threshold value or converges to a minimum value; if yes, outputting a current near-surface shear wave velocity model; if the conditions are not met, updating the shear wave velocity model by adopting a quasi-Newton method; and outputting the near-surface shear wave velocity model and the sensitive function of the Rayleigh surface wave frequency dispersion curve to the anisotropic elastic parameter after the loss function meets the condition.

The invention discloses a data processing method and system for micro-motion detection array station distribution, and relates to the technical field of micro-motion detection, and the method comprises the steps: according to the actually measured starting point coordinates and the azimuth angle of a measuring line, setting the measuring point distance, and the radius and number of nested triangle circumcircles, generating field station coordinates in batches; collecting and preprocessing station waveform data; calculating a spatial autocorrelation coefficient of any center station and a non-center station; dBSCAN clustering is carried out based on the station distance; performing spatial averaging on the spatial autocorrelation coefficient according to a clustering result; fitting the averaged spatial autocorrelation coefficient and a zero-order Bessel function to obtain a frequency dispersion spectrum, picking up a surface wave phase velocity in the frequency dispersion spectrum, and calculating an apparent shear wave velocity; inverting the shear wave velocity of the waveform data by adopting a self-adaptive gradient inversion strategy; and generating a velocity comprehensive profile of a depth domain and a frequency domain according to the surface wave phase velocity, the apparent shear wave velocity and the inverted shear wave velocity. The invention provides a new method for station distribution to data processing for micro-motion detection.

The invention belongs to the technical field of geotechnical engineering testing and site investigation, and provides a shear wave velocity estimation method based on a piezocone penetration test, which can accurately and conveniently estimate the shear wave velocity. The method specifically comprises the following steps of S1, carrying out a pore pressure static sounding test on a plurality of drill holes in a site, and obtaining pore pressure static sounding basic parameters and shear wave velocity of each drill hole tested in the depth direction, S2, interpreting derived parameters according to the pore pressure static sounding basic parameters, S3, carrying out soil layer division, soil type identification and soil body type judgment, and S4, carrying out soil layer classification, soil type identification and soil body type judgment. S4, selecting data tested by the same soil body type as an analysis object, performing fitting analysis on the scatter diagrams of the cone tip resistance and the shear wave velocity of the piezocone penetration test to obtain a fitting formula, and S5, obtaining normalized cone tip resistance and normalized shear wave velocity based on the cone tip resistance and the shear wave velocity, substituting the normalized cone tip resistance and the normalized shear wave velocity into the fitting formula in the step S4, and introducing a soil classification index to obtain a soil classification result. And obtaining a calculation expression of the optimal estimation shear wave.

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 medical elastography and material multi-scale mechanical measurement, in particular to an elastography method and system based on an instantaneous scattering displacement field, and the method comprises the steps: driving a passive / active method scattering displacement field excitation device through a frequency domain controllable sine wave signal, and generating a scattering displacement field on a target material, an instantaneous displacement field is obtained through high spatial resolution measurement, then sliding window local interception, two-dimensional autocorrelation calculation and nonlinear fitting based on the Rayleigh / shear wave spatial autocorrelation theory are performed on the single-frame instantaneous displacement field, shear wave velocity distribution is obtained, and then the viscoelasticity modulus of a target area is inverted to achieve comprehensive characterization of the viscoelasticity of the material. According to the scheme, the single-frame scattering displacement field is excited and collected, and the elasticity inversion is performed based on the instantaneous displacement field, so that the dependence of traditional elasticity imaging on high-frame-rate equipment is broken through, the material viscoelasticity evaluation can be efficiently completed at low cost, and the application prospect in the fields of clinical diagnosis and mechanical measurement is wide.

The invention discloses an accurate acupuncture positioning method under ultrasonic guidance, which relates to the technical field of medical health and comprises the following steps: acquiring a two-dimensional or three-dimensional ultrasonic image of a target area by utilizing ultrasonic image acquisition equipment; the Young modulus of the target area is calculated by measuring the shear wave propagation speed of the tissue of the target area; a sensor module is adopted to monitor the interaction force between the puncture needle and the tissue in real time, and mechanical data are obtained; according to Young modulus and modulus variance obtained through calculation of the shear wave elastography image and mechanical data fed back by a sensor, a time sequence feature vector is constructed; performing dynamic modeling and prediction on the tissue state in the puncture process by using a long-short-term memory neural network; the path offset of the needle head is judged, a correction signal is generated through a PID controller, and the posture or direction of the needle head is adjusted in real time; the problem of artifacts in a traditional ultrasonic image can be solved, more accurate acupuncture path planning is provided, and the method adapts to the change of tissue elasticity in real time.

The invention discloses a transmission tower vulnerability analysis method and system based on seismic simulation, and belongs to the technical field of electric power facility disaster prevention, and the method comprises the steps: correcting bedrock seismic oscillation parameters based on equivalent shear wave velocity, and generating a high-precision earth surface PGA distribution diagram; automatically positioning a key node layer through eigenvalue decomposition, reducing the order of the three-dimensional model of the power transmission tower into a two-dimensional series multi-degree-of-freedom model, and outputting a layer mass vector, a condensation stiffness matrix and rod piece bending stiffness; performing time-history analysis by using the corrected PGA-driven reduced-order model to generate an earthquake damage probability; the multi-source remote sensing data and the PGA are fused, the landslide space probability is output through a deep learning model, and the impact strength and the landslide kinetic energy are calculated to judge the landslide damage probability; and based on the dual-threshold condition triggering probability union set, calculating a comprehensive damage probability, and generating a three-dimensional vulnerable curved surface. According to the method, the problems of high missing report rate and low calculation efficiency of single disaster assessment are solved, and minute-level accurate early warning of the composite disaster risk of the power transmission tower is realized.

The invention provides a petroleum drilling geomechanical characteristic estimation method based on deep learning, and aims to solve the problems of high measurement cost and poor real-time performance of shear wave velocity Vs in traditional geomechanical analysis by fusing logging while drilling LWD data and real-time drilling engineering parameters. According to the method, a Transformer model is adopted to carry out depth correction on logging-while-drilling data, depth offset of a drill bit and a logging sensor is eliminated, and real-time drilling parameters such as torque T, bit pressure WOB and drilling speed ROP are combined and input into a multi-layer perceptron MLP network to predict the shear wave speed Vs. And further calculating key geomechanical parameters based on the predicted shear wave velocity Vs. In practical application, the method improves the shear wave velocity prediction accuracy to 97.2%, the mean absolute error MAE of the shear modulus is reduced from 0.186 to 0.059, and the bulk modulus is reduced from 0.189 to 0.040. The method can be used for outputting stratum elastic parameters, optimizing bit pressure, pre-warning well wall instability and adjusting a well track, well drilling safety and efficiency are improved, and meanwhile dependence on an expensive well logging technology is reduced.

The invention relates to the technical field of optical coherence elastography, in particular to a microscopic optical coherence elastography method and system.The method comprises the steps that a collection trigger signal is generated, a vibration control signal and a position control signal are output at the same time, and a target excitation system corresponding to a to-be-detected sample is determined; triggering the line scanning camera by collecting a trigger signal, collecting a line scanning image of the to-be-tested sample, and adjusting the position of the to-be-tested sample in the sample table so as to place the to-be-tested sample at a target test position; controlling a bending actuator or an ultrasonic transducer to vibrate by using the vibration control signal so as to excite low-frequency shear waves or ultrasonic waves in the sample to be detected; the to-be-detected sample is scanned through the position control signal to obtain propagation information of low-frequency shear waves or ultrasonic waves, and the pixel-level biomechanical properties of the to-be-detected sample are reconstructed by using a near-field full-wave inversion algorithm, so that the microstructure of a deeper tissue can be observed, the imaging speed is relatively high, and the detection performance of small inclusions is improved.

The invention relates to the technical field of computer modeling, in particular to an earthquake resistance and disaster reduction simulation system for a liquefiable foundation. The system comprises a cyclic stress ratio field analysis unit, an excess pore pressure ratio evolution analysis unit and a stress coupling correction unit, the cyclic stress ratio field analysis unit is used for generating a cyclic stress ratio field by using a depth reduction relation; the excess pore pressure ratio evolution analysis unit is used for deducing an excess pore pressure ratio and establishing an evolution relation of the excess pore pressure ratio along with time and depth; and the stress coupling correction unit is used for updating the effective vertical stress value based on the excess pore pressure ratio, synchronously correcting the shear modulus according to the coupling relationship between the shear wave velocity and the excess pore pressure ratio, and feeding back the corrected shear modulus to the excess pore pressure ratio evolution analysis unit. According to the method, the scientificity and engineering applicability of earthquake resistance and disaster reduction of the liquefiable foundation are effectively improved.

The invention relates to the technical field of medical care, in particular to a massage method for avoiding tissue damage through flexible mechanical conduction by Taoist massage, and discloses a massage method named'massage method for avoiding tissue damage through flexible mechanical conduction by Taoist massage '. The invention aims to solve the problems that the existing massage technology is easy to cause tissue damage due to force application rigidity, and the safety, the accuracy and the treatment depth of the massage technology are limited. The method is characterized in that a closed-loop system of tissue stress dynamic dispersion, layer-controlled shear wave guidance and biological impedance coupling feedback is adopted, traditional rigid force is converted into flexible conduction of low-frequency and small-amplitude transverse shear waves, and layer-by-layer, dispersion-type and non-invasive penetration of mechanical energy is achieved. By the adoption of the technical scheme, tissue damage can be effectively avoided, and the deep treatment effect and safety are improved.

The invention provides a tissue viscoelasticity calculation method and system based on wavelet transform, and the method comprises the steps: exciting and generating shear waves at a target region of a tissue, and extracting shear wave information; carrying out wavelet transformation on the shear wave information, and extracting frequency domain information in the shear wave information, so as to obtain time-frequency domain information of shear waves; taking any point on the transverse tangent plane as a target point, and performing cross-correlation between the time-frequency domain information of the target point and the time-frequency domain information of the other points by using a cross-correlation algorithm to obtain phase differences between the target point and the other points under different frequencies; determining phase velocities of shear waves under different frequencies according to phase differences between the time-frequency domain signals of the target point and the time-frequency domain signals of other points under different frequencies; and fitting the phase velocities of the shear waves under different frequencies to obtain a viscosity value and an elasticity value corresponding to the target area of the tissue. The method does not need to collect data for many times, reduces errors caused by data collection, and improves the mechanical characteristic calculation precision of the tissue.

The invention relates to the technical field of full-focusing phased arrays, and discloses an on-line automatic bar flaw detection method and device based on a full-focusing phased array technology. According to the method, after water immersion type coupling is carried out, array elements of a concave array phased array probe are excited in a plane wave emission mode, multi-angle acoustic beams are formed, and full-matrix echo data are captured; for transverse wave detection, the sound field uniformity is improved by adopting a grouped alternate emission mechanism. Full-domain dynamic focusing is realized through coherent superposition processing, and real-time alarm and mark spraying are carried out on standard exceeding defects in combination with encoder data. The device comprises an off-line automatic calibrator, a conveying roller way, a steel splitting instrument platform, a pinch roll system, a lifting transverse moving platform, an ultrasonic detection host, a mark spraying system, a water circulation system and a PLC system, and efficient and accurate on-line bar flaw detection is achieved.

The invention discloses a nuclear power station in-service equipment physical performance monitoring method based on EMAT, and belongs to the field of nuclear power station equipment service performance detection. The method comprises the following steps: simultaneously exciting ultrasonic transverse and longitudinal waves by using an EMAT probe; the EMAT sensor is designed to resist high temperature; ultrasonic sound velocity correction in a high-temperature environment; key physical performance parameters such as elastic modulus and Poisson's ratio of in-service equipment in a high-temperature environment are continuously monitored by utilizing sound velocity data of transverse waves and longitudinal waves excited by an EMAT probe. Based on the electromagnetic ultrasonic principle, the method can effectively monitor the elasticity modulus, Poisson's ratio and other key physical performance parameters of the in-service equipment of the nuclear power station at high temperature, corrects the measurement error caused by high temperature, provides an important basis for the health management of the equipment of the nuclear power station, and ensures the feasibility and safety of the equipment.

The invention discloses a tight sandstone gas reservoir logging scale fracture development evaluation method, and relates to the field of earth exploration physics, and the method comprises the steps: carrying out the inversion based on a complex pore structure high-angle fracture type rock physical model and actual logging data through employing a simulated annealing particle swarm algorithm, and obtaining two groups of fracture density parameters in a well; respectively calculating crack density identification factors under different rotation angles; based on a correlation coefficient between the Poisson impedance and the fracture density under different rotation angles, determining an optimal rotation angle when the correlation coefficient reaches a maximum value; calculating a crack density identification factor under the optimal rotation angle by combining longitudinal wave impedance and transverse wave impedance data; and applying the fracture density identification factor under the optimal rotation angle to actual logging data, and representing fracture development. The method can effectively distinguish and quantify multiple groups of high-angle fractures developed in the tight sandstone reservoir, significantly improve the prediction precision and reliability of the fractured reservoir, and provide a key basis for reservoir sweet spot prediction and well location deployment.

The invention relates to the technical field of unconventional shale oil and gas resource exploration, in particular to a shale shear wave velocity estimation method considering clay directional arrangement influence. According to the technical scheme, a shale compaction model is constructed, a shale directional arrangement background medium is formed, an orientation distribution function is used for simulating the elastic tensor of the background medium under different directional arrangement degrees, and the anisotropic speed of the background medium is calculated; calculating a longitudinal wave speed and a transverse wave speed in an equivalent isotropic background medium according to the anisotropic speed, and then calculating a bulk modulus and a shear modulus of the equivalent isotropic background medium; and adding clay-related pores and sand-related pores into an equivalent isotropic rock matrix by using an Xu-White model to obtain a bulk modulus and a shear modulus of dry shale, and finally simulating the shear wave velocity of the shale under the influence of directional arrangement of the clay. According to the invention, a new rock physical model is established to simulate the influence of clay directional arrangement, pore morphology, mineral and fluid properties on longitudinal and transverse wave velocities in shale.

The invention relates to the technical field of mortar strength detection, in particular to a penetration type mortar strength detection device. According to the device, gravel attribute parameters corresponding to mortar to be detected are obtained through detection, the porosity of gravel is obtained through calculation based on the gravel attribute parameters, gravel liquefaction test of shear wave velocity is conducted on the gravel based on the porosity of the gravel, the liquefaction resistance strength of the gravel is obtained, and mortar preparation is conducted on the gravel meeting the mortar preparation requirement. The method comprises the following steps: detecting a mortar solidification sample based on a penetration type mortar strength detection instrument to obtain mortar strength, carrying out stability detection on the mortar solidification sample based on a transient dynamic method to obtain an integrity detection result of the mortar solidification sample, and evaluating the stability of the mortar solidification sample according to an analysis result to obtain a stability evaluation result. The quality of the mortar solidification sample is evaluated based on the mortar strength and stability evaluation result, and the accuracy and reliability of the quality detection result of the mortar solidification sample can be ensured according to the mortar attribute information.

The invention provides an azimuth transverse wave remote detection data processing method, system and device and a medium. The method comprises the steps that an instrument is adopted to collect novel four-component transverse wave data (xx, xy, yx and yy) of a target well section; under a coordinate system (x, y) of the instrument, synthesizing an XX reflected wave signal with an angle defined relative to the x-axis direction of the instrument by using the orthogonal dipole four-component reflected signal, and defining the XX reflected wave signal as an angle when the reflected wave signal reaches the maximum amplitude; obtaining two cross component signals xy and yx by using novel four-component shear wave data (xx, xy, yx and yy), performing sliding windowing on the cross component signals xy and yx, performing Fourier transform on the signals in the window to obtain phase spectrums, and comparing the phase spectrums of the two cross component signals to obtain an actual phase difference; according to a novel four-component data combination mode, changing the orientation of the reflector, theoretically calculating the theoretical phase difference between the two cross component signals xy and yx, and establishing a rule chart of the cross component signals along with the orientation change of the reflector outside the well based on the theoretical phase difference; and judging the real azimuth angle of the reflector according to the rule plate.

The invention discloses a steel pressure vessel outer wall surface crack detection method based on array surface waves, which comprises the following steps: S1, arranging a phased array probe on a detection object and carrying out scanning detection to obtain a detection signal and a detection image feature; s2, fan-shaped scanning parameters and array surface wave focusing rules of the phased array are set; s3, analyzing the detection signal and the detection image features, and calculating the crack length and the wall thickness of the pressure vessel according to the time of the crack opening end reflection pulse f1, the crack tip reflection pulse f2 and the conversion shear wave reflection pulse f3; s4, confirming the crack and correcting the length of the crack; according to the method, the detection efficiency and the quantitative detection capability of the surface opening crack defect are greatly improved.

The invention belongs to the technical field of seismic exploration, and particularly discloses an elastic wave vector seismic exploration data static correction method, which comprises the following steps: S1, establishing a longitudinal wave micro-logging surface velocity model; s2, establishing a three-component micro-logging shear wave surface velocity model; s3, performing low-frequency static correction on the longitudinal wave seismic data; s4, performing low-frequency static correction on the transverse wave seismic data; s5, performing low-frequency static correction on the converted wave seismic data; s6, calculating residual static correction values of shot points and detection points of the longitudinal wave field, the converted wave field and the transverse wave field; and S7, calculating total static correction values of shot points and detection points of the longitudinal wave field, the converted wave field and the transverse wave field. According to the method, the static correction problem of various wave field seismic data in elastic wave vector seismic exploration is effectively solved, and the calculation efficiency and the imaging precision are improved. The method is suitable for static correction of seismic exploration data.

The invention relates to the technical field of geological exploration, in particular to a deep water ultra-shallow natural gas reservoir rock physical shear wave velocity prediction method, which comprises the following steps: calculating the equivalent bulk modulus, shear modulus and density of a mixed matrix of various minerals according to the shale content; calculating the bulk modulus and the shear modulus of the skeleton; calculating the bulk modulus and the shear modulus of the saturated rock through the model, and calculating the density of the saturated rock through the density of the rock matrix; distributing longitudinal and transverse velocities in a suspension state and a bearing state, and calculating equivalent longitudinal and transverse wave velocities; taking the longitudinal wave velocity as a known condition, and constructing an inversion objective function by using the equivalent longitudinal wave velocity; comparing the actual longitudinal wave speed with the calculated longitudinal wave speed; and realizing nonlinear inversion of the shear wave velocity according to an algorithm, and obtaining the shear wave velocity of the work area to be inverted. According to the method, rock physical models in a particle suspension state and a bearing state are considered at the same time, and shear wave velocity prediction and direct inversion of the shear wave velocity of the deepwater ultra-shallow natural gas reservoir are realized.

The invention relates to a multi-source information collaborative intelligent compaction monitoring system based on dynamic compensation, which comprises a data acquisition module, a displacement acquisition module, a data processing module, a shear wave velocity acquisition module and a soil body compaction degree value prediction model construction module, an intelligent compaction index, a soil pressure peak value in a soil body and an acceleration peak-to-peak value in the soil body are extracted through a data processing module, soil body displacement data are obtained through a displacement obtaining module, coordinates of a bending element sensor are corrected through a shear wave velocity obtaining module according to the soil body displacement data, and then the shear wave velocity is calculated. And the compaction degree of the soil body is obtained through an on-site sand filling method experiment. A neural network model is constructed by taking a soil pressure peak value in a soil body, an acceleration peak-to-peak value in the soil body, transverse and vertical shear wave velocities, soil body displacement data and an intelligent compaction index as input, and accurate prediction of the compaction degree is realized. According to the method, the precision, the real-time performance and the applicability of compaction quality evaluation are remarkably improved.

One or more embodiments of the invention provide a Scholte wave frequency dispersion curve adaptive inversion method and a related device, and the method comprises the steps: obtaining and processing the original seismic data of a target seabed, and obtaining a Scholte wave frequency dispersion curve; constructing an initial shear wave velocity model through linear inversion based on a Schlte wave frequency dispersion curve; and performing fitting optimization on the Schlte wave frequency dispersion curve based on the initial shear wave velocity model to obtain a two-dimensional shear wave velocity profile. According to the technical scheme, the problem that a traditional linear inversion method depends on an initial model is solved, the calculation efficiency is remarkably improved, the problem of local extremum is avoided, and an automatic high-precision inversion solution suitable for batch data processing is provided for submarine seismic exploration.

The invention belongs to the technical field of geotechnical engineering test and investigation, and provides a method for selecting an optimal shear wave velocity prediction model according to field demand evaluation, which can quantitatively evaluate the accuracy and rationality of a plurality of shear wave velocity prediction models and select the optimal prediction model. A reference value is provided for accurate estimation of the shear wave velocity and geotechnical engineering test and investigation; according to the technical scheme, the method comprises the following steps: evaluating and selecting a shear wave velocity optimal prediction model according to field requirements; the method comprises the following steps: determining a performance angle target area alpha'according to field requirements, respectively calculating mean values of center rates i and deviation angles alpha of various types of models for multiple types of shear wave velocity prediction models, performing comparative analysis to evaluate the accuracy and rationality of the shear wave velocity prediction models, and selecting an optimal shear wave velocity prediction model.

The invention belongs to the technical field of medical imaging, and provides a tissue stress measurement method and system based on a reverberation shear wave field, and the method comprises the steps: applying steady-state harmonic excitation, and obtaining the reverberation shear wave field of a target tissue; determining a stress direction according to the prior information of the target tissue, and constructing a stress direction vector; constructing an analytic operator, and extracting an intermediate characteristic quantity of the reverberation shear wave field; and according to the intermediate characteristic quantity, combining stress direction inversion to reconstruct spatial stress distribution. According to the method, a complex reverberation wave field is modeled into superposition of multi-direction propagation wavelets by utilizing anisotropic propagation characteristics of shear waves and combining a traveling wave expansion model, and characteristic quantities related to local stress and material parameters are directly extracted from the reverberation wave field by designing a wave field operator with physical significance; the three technical problems existing in a traditional shear wave elastic imaging technology are solved, the dependence of a traditional method on a single propagation direction and wave pattern controllability is broken through, and in-situ, in-vivo and non-invasive quantification is achieved.

Some aspects of the described technology may include a method, including inducing a wave field in a biological sample, the wave field comprising waves having a wave frequency, scanning the sample at a scanning velocity to perform a plurality of spatial measurements of the sample, determining spatial data from the plurality of spatial measurements, amplitude demodulating the spatial data based on the wave frequency and the scanning velocity, and generating displacement data for the biological sample based on the amplitude demodulated spatial data.