73 results about "Shear waves" patented technology

The invention relates to a method for forming (P1) a primary stack (30) comprising: a supply step (E1) of an initial stack (10) comprising an initial substrate (11) having a front face (Fav) comprising a central zone (Zc) and a peripheral contouring zone (Zdp), and a resonant thin layer (13) of piezoelectric material; and a protection step (E2), in which a protective thin layer (17) is deposited on the initial stack (10) and covering the peripheral contouring zone (Zdp); the protective thin layer (17) being of a material having an acoustic impedance greater than 20 MRayl for longitudinal waves and / or greater than 12 MRayl for shear waves. The invention also relates to a resonant electronic device (100) and a method (P2) for manufacturing a resonant electronic device (100) from such a primary stack (30). Figure 2

For shear wave imaging with ultrasound, the apparent pulse repetition frequency is increased by combining displacements from different lateral locations. Different combinations based on different shear wave velocities and corresponding time shifts and / or attenuations and corresponding scalings are tested to find a smooth displacement profile for the combination. Once the smooth displacement profile is found, the corresponding shear wave velocity is estimated or determined.

This specification relates to the field of compressed air energy storage technology, specifically disclosing a condition monitoring system and method for underground gas storage facilities. The system includes: an acoustic monitoring well located in the upper part of the underground gas storage facility, equipped with a multi-component shear wave detector for acquiring creep or deformation monitoring data of the roof and surrounding rock; a microseismic monitoring well located in the lower part of the underground gas storage facility, equipped with an underground microseismic detector array for acquiring rupture event monitoring data of the surrounding rock; a fault leakage monitoring device, including a tracer gas injector and a tracer gas detection mechanism, for acquiring surface gas concentration monitoring data; and a data processing device connected to a multi-dimensional monitoring network for performing spatiotemporal correlation and cross-validation analysis on the received multi-dimensional monitoring data to generate a fusion assessment result of the structural integrity status and sealing safety risk of the underground gas storage facility. The above solution enables high-precision monitoring and assessment of underground gas storage facilities.

The application discloses a viscoelasticity parameter inversion method, and belongs to the field of medical image processing and computational imaging. The method obtains the time-space image sequence data of the measured soft tissue after excitation, extracts the shear wave group velocity by using the time-of-flight method, and equivalently takes the shear wave group velocity as the phase velocity anchor point under the zero-frequency limit condition to determine the static shear modulus; the two-dimensional fast Fourier transform is used to extract the middle-high frequency band phase velocity dispersion curve; the rheological model of the measured soft tissue is constrained based on the static shear modulus, and the single-parameter fitting of the viscosity coefficient is combined with the middle-high frequency band phase velocity dispersion curve to obtain the viscoelasticity parameter.

This application discloses a diabetes detection system based on multi-sensor pulse characteristics, relating to the field of medical signal processing. The system includes: a pulse wave data acquisition module that uses a pressure sensor and a photoelectric sensor to acquire the user's wrist pressure pulse wave and fingertip photoplethysmography (PPG) pulse wave; a preprocessing module to obtain usable single-cycle pulse wave data; a signal fusion module that converts the pressure pulse wave signal and PPG pulse wave signal into two-dimensional image signals through continuous wavelet transform to retain time-frequency domain information, and processes the two two-dimensional images using non-subsampled shear wave transform and a pulse-coupled neural network; and a feature extraction and calculation module that extracts pulse wave image features through a ResNet network and finally classifies them using a random forest. This detection system improves the fusion effect without increasing computational load, achieving non-invasive diabetes detection using multiple sensors and improving detection accuracy.

The present application belongs to the technical field of oil seismic exploration, and discloses a near-surface modeling method and system based on microseismic spectral ratio interface imaging constraint, wherein the method comprises the following steps: collecting continuous microseismic data of a set time length in the whole work area and performing pretreatment; performing spectral ratio calculation on the pretreated microseismic data, smoothing the spectral ratio, and obtaining peak frequency from the smoothed spectral ratio; obtaining the shear wave velocity structure from the microseismic signal inversion, converting the peak frequency under each station into interface depth according to the shear wave velocity structure, and obtaining the interface depth information of the whole work area; taking the interface depth information as a constraint condition, adding the first arrival wave travel time tomography inversion, and completing the near-surface velocity modeling. The present application provides fine lithology interface constraint, makes up for the defect of insufficient micro-logging spatial sampling, improves the vertical resolution and lateral continuity of the near-surface velocity model, and solves the problem of insufficient accuracy of the near-surface model caused by lack of constraint in complex areas.

This invention discloses a cross-well shear wave velocity prediction method based on GR lithology bin alignment. Addressing the problems of large inter-well lithological differences, inconsistent engineering parameter distributions, and insufficient utilization of multi-source features in existing cross-well prediction methods, this method first collects drilling engineering parameters and logging data from multiple wells. After preprocessing, the data is divided into training and test sets using the well retention method. The optimal GR lithology bin alignment strategy is selected from the training set to achieve inter-well lithology alignment. Based on this, the absolute and relative deviation characteristics of engineering parameters are calculated, and drilling logging features such as P-wave transit time and density are integrated to construct a complete prediction feature set. Finally, the LightGBM regression algorithm is used to establish a cross-well shear wave velocity prediction model, which is optimized using an early stopping mechanism before outputting the prediction results. Practical application shows that the coefficient of determination R0 is significantly improved. 2 The accuracy reached 0.91, which is more than 18% higher than that of traditional modeling methods, and can effectively achieve high-precision and rapid prediction under the condition of no shear wave logging in new wells.

A method that includes disposing an acoustic logging tool in a borehole. The acoustic logging tool comprises one or more transmitters and one or more receivers. The method may further comprise taking one or more acquisitions with the acoustic logging tool as the acoustic logging tool traverses through the borehole, creating an azimuth rotation angle array of one or more angles, from the one or more acquisitions, and applying a trial angle selected from the one or more angles of the azimuth rotation angle array to calculate a fast shear waveform.

An elasticity measurement method, a matching method based on an elasticity image and an ultrasonic imaging system, the elasticity measurement method comprising: acquiring at least two frames of shear wave elasticity images of a target tissue based on shear wave elasticity imaging; selecting at least one first shear wave elasticity image from the at least two frames of shear wave elasticity images; determining a first target region in the at least one first shear wave elasticity image, and obtaining a first elasticity measurement result according to an elasticity measurement value of the first target region; and determining a second target region in at least one second shear wave elasticity image from the at least two frames of shear wave elasticity images other than the first shear wave elasticity image based on the first target region, and obtaining a second elasticity measurement result according to an elasticity measurement value of the second target region. The present application can automatically measure multiple frames of shear wave elasticity images without the user determining the target region frame by frame, thereby simplifying the operation process.

This invention discloses a method, apparatus, and storage medium for determining tissue operation parameters based on a detection device. The method includes: under a preset pressure condition, controlling a transmitting module to send a first excitation signal to a second piezoelectric layer, whereby the second piezoelectric layer converts the first excitation signal into a first ultrasonic signal, and controlling the second piezoelectric layer to emit the first ultrasonic signal towards the tissue to generate a shear wave; controlling the transmitting module to send a second excitation signal to the second piezoelectric layer, whereby the second piezoelectric layer converts the second excitation signal into a second ultrasonic signal, and controlling the second piezoelectric layer to emit the second ultrasonic signal towards the tissue to track the shear wave; controlling a receiving module to receive the echo signal of the second ultrasonic signal, and obtaining ultrasonic imaging data and shear wave elastography data of the tissue based on the echo signal; and determining the operation parameters for operating the tissue. The determined operation parameters have high accuracy, thereby greatly improving the precision of the operation.

A viscoelasticity measurement method (100, 700) and an ultrasonic measurement system (1000), the viscoelasticity measurement method (100, 700) comprising: emitting an acoustic radiation force pulse to a subject to be measured to generate a shear wave in a region of interest of the subject to be measured (S110); emitting a first ultrasonic wave to track the shear wave to the region of interest and receiving a first ultrasonic echo of the region of interest to obtain first ultrasonic echo data (S120); obtaining a shear wave signal of the region of interest from the first ultrasonic echo data (S130); extracting shear wave components of at least two different frequencies from the shear wave signal (S140); determining phase velocities of the shear wave respectively according to the shear wave components of the at least two different frequencies (S150); determining viscosity information reflecting viscosity of the region of interest according to the at least two phase velocities (S160); and outputting the viscosity information (S170). The viscosity information of the region of interest can be determined by one viscoelasticity measurement.

This invention discloses a seismic treatment effectiveness evaluation system for thick loess sites, belonging to the fields of geotechnical engineering and earthquake engineering technology. The system comprehensively evaluates the seismic treatment effect by acquiring dynamic characteristic parameters and seismic response parameters of thick loess sites before and after seismic treatment. The system includes a site foundation parameter acquisition module, a dynamic characteristic parameter acquisition module, a loess dynamic degradation characteristic analysis module, a seismic response simulation module, a seismic treatment effectiveness quantification module, and an evaluation result output module. Specifically, it analyzes the loess dynamic degradation characteristics based on parameters such as shear wave velocity, shear modulus, and damping ratio, and acquires seismic response results such as surface acceleration, shear strain, and site amplification factor under the same seismic motion input conditions. By normalizing and weighting the dynamic degradation improvement index and the seismic response improvement index, a comprehensive seismic treatment effectiveness index is constructed, enabling quantitative evaluation and classification of seismic treatment effectiveness.

Embodiments of the present application disclose a dispersion correction method and device for dipole acoustic logging and a computing device. The method comprises: obtaining monopole logging data and dipole logging data of a target depth formation, calculating a first shear wave velocity based on the monopole logging data, and calculating a second shear wave velocity based on the dipole logging data; if a relative error between the second shear wave velocity and the first shear wave velocity is greater than an error threshold, generating initial dispersion data according to the dipole logging data; extracting a characteristic dispersion curve from the initial dispersion data; determining a target forward dispersion curve that is most matched with the characteristic dispersion curve from forward dispersion curves output by a dispersion forward model; and generating a dispersion correction result based on the target forward dispersion curve. The present application can improve the dispersion correction accuracy and efficiency of dipole acoustic logging.

The application discloses a tight sandstone gas-bearing reservoir prediction method and device based on pre-stack inversion. The method comprises the following steps: based on a well with a shear wave velocity curve in a research area, a sample well containing a plurality of shear wave velocity correlation curves and the shear wave velocity curve is obtained; a selected BP neural network is trained to obtain a shear wave velocity curve prediction model, and the shear wave velocity curve of the well is predicted; pre-stack seismic data bodies in the research area are divided into offset ranges for stacking to obtain a plurality of sets of post-stack seismic data bodies with different offsets; through well-seismic calibration, a wavelet of each set of post-stack seismic data bodies is obtained; based on the plurality of sets of post-stack seismic data bodies and the wavelet, a velocity field model of a target layer of the tight sandstone and a vertical and shear wave velocity curve of the well, the vertical and shear wave velocity ratio data body of the target layer is obtained through inversion; and in combination with a vertical and shear wave velocity ratio gas layer identification lower limit, the gas-bearing reservoir in the target layer is identified. The method realizes efficient and high-precision identification of the tight sandstone gas-bearing reservoir.

The application discloses a method for obtaining soil strength indexes based on soil layer geophysical prospecting data, and comprises the following steps: S1, obtaining the test depth and equivalent shear wave velocity of different measuring points in a target site; obtaining the drilling samples of each measuring point at the corresponding depth, obtaining the corresponding internal friction angle and soil type; preprocessing the collected data to obtain a comprehensive data set; S2, selecting the test depth and equivalent shear wave velocity in the comprehensive data set as basic features, constructing derived features, performing feature standardization, and obtaining a data set; dividing the data set into a training set and a test set; S3, based on the training set obtained in S2, optimizing and training the soil classification model and the internal friction angle prediction model; S4, obtaining the soil strength index internal friction angle based on the soil classification model and the internal friction angle prediction model obtained by the training in S3. The method of the application realizes high-precision prediction of soil strength indexes by constructing derived features and introducing physical range constraints.

Provided are systems and methods of delivering an exogenous payload to a cell in a tissue of a subject, including: administering an exogenous payload to the subject; administering a sonoactive agent to the subject, and applying an acoustic radiation force (ARF) to the subject, thereby generating shear waves in the tissue of the subject, wherein the acoustic radiation force enhances delivery of the exogenous payload to the cell in the tissue of an organ of the subject.

The application discloses a method for determining a complex well section shear wave slowness curve based on local response and long-range continuous features, and belongs to the technical field of geophysical well logging and oil and gas field exploration and development. The method comprises the following steps: collecting data of a training well group, and establishing a mapping relationship between input features and shear wave slowness; constructing an initial residual sequence by using a difference between actual measurement and estimation of the shear wave slowness; intercepting a continuous response window along a well depth direction, extracting long-range continuous features, and splicing the long-range continuous features with the input features in a feature dimension to form an enhanced feature set, and establishing a mapping relationship between the enhanced feature set and the initial residual sequence; collecting input features of a target well to be predicted, determining a basic shear wave slowness value and a residual compensation value according to the above mapping relationship, superimposing the basic shear wave slowness value and the residual compensation value point by point, and then performing reverse standardization to obtain a shear wave slowness curve. The method can simultaneously consider local petrophysical response of a current depth point and a continuous change rule in the well depth direction, and a high-quality shear wave slowness curve is obtained.

The application discloses a shearing device, which comprises a casing, a baffle, a probe and a first driving assembly. The casing is arranged in a pre-drilled hole to form a detection channel, and at least one opening is arranged along the axial length direction of the casing. The probe is arranged in the casing, and the opening is used to ensure that the shear wave generated by a seismic source can pass through the casing and be received by the probe, thereby avoiding the influence or even shielding of the casing on the wave velocity propagation. The baffle is slidably arranged at the opening of the casing along the axial direction of the casing. The first driving assembly can drive the probe and the baffle to move up and down in the casing along the axial direction, so that segmented shear wave velocity tests at different depths can be realized. The positional relationship between the probe and the baffle is limited as follows: (the horizontal distance between the probe and the bottom side of the baffle, the vertical distance between the probe and the bottom side of the baffle, and the repose angle of the accumulated slope body), so that the baffle can block part of the falling material during the upward movement of the probe, the probe is ensured to be located in an area that is not covered by the falling cone, and the probe is prevented from being buried.

The present application relates to the technical field of oil reservoir fracture attribute description, and particularly relates to a fracture attribute fusion method based on fractured low-permeability oil reservoirs. The method comprises the following steps: establishing a fracture intensity model by combining well data with seismic data, establishing a fracture network model according to the fracture intensity model, obtaining a natural fracture attribute volume, fitting to obtain shear wave time difference data, first establishing a density model, a longitudinal wave model and a shear wave model, then establishing a rock mechanics parameter model, establishing an overburden pressure and pore pressure model according to the overburden pressure and the pore pressure of the overlying strata of a single well, establishing a ground stress model and simulating and calculating artificial fracture attribute data according to the rock mechanics parameter model and the overburden pressure and pore pressure model; fusing the artificial fracture attribute data to the natural fracture attribute volume, obtaining a fused fracture attribute volume and establishing a target geological model, and simulating and calculating fracturing artificial fracture parameters. The method provided by the present application fuses the fracture attributes of natural fractures and artificial fractures, and improves the description accuracy of fracture attributes.

The embodiment of the present application provides a kind of shear wave time difference prediction method and device, belong to petroleum exploration development technical field.The method comprises: obtaining well logging sample data as the training data set of prediction model;The training data set is preprocessed, data screening is carried out based on importance analysis, data grouping is carried out based on kurtosis and skewness, and the processed training data set is obtained;The processed training data set is input into the neural network of CNN and LSTM hybrid building respectively for training, and the shear wave time difference prediction model is obtained;Obtain the well logging data of the shear wave time difference to be predicted;The well logging data is preprocessed, well logging data grouping is carried out based on kurtosis and skewness, and the processed well logging data is obtained;The processed well logging data is respectively input into the shear wave time difference prediction model, and the shear wave time difference is obtained.The shear wave time difference prediction method and device of the present application have the advantages of high processing efficiency, high prediction accuracy and strong regional applicability.

The present application belongs to the technical field of image recognition, and particularly relates to a diamond quality detection method and system based on image recognition, comprising the following steps: obtaining a diamond image to be detected, calculating the structure tensor in the local neighborhood of each pixel of the diamond image, constructing an initial structure guide map according to the eigenvalue distribution, and recording the difference between the maximum eigenvalue and the minimum eigenvalue of the structure tensor as an anisotropy degree map; performing multi-scale shear wave transformation on the diamond image, extracting high-frequency subband coefficients at each scale to calculate local energy, and fusing the local energy at each scale to obtain a multi-scale edge saliency map; and fusing the initial structure guide map and the multi-scale edge saliency map to generate a composite guide map. The present application avoids the edge blurring phenomenon caused by traditional filtering, and improves the completeness of diamond surface defect extraction and the accuracy of overall quality detection.

Certain aspects of the present disclosure provide a method for providing stacked multi-component seismic while drilling (SWD) seismic waveforms. The method includes obtaining multi-component SWD seismic pressure and shear waveforms associated with a planned well. The method includes pre-processing, at the at least one processor, the multi-component seismic SWD waveforms to generate an ordered subset of filtered SWD seismic waveforms and determining, based on a maximum eigenvalue associated with first and maximum correlation matrices of the pressure and shear waveform components, a number of the subset of filtered SWD seismic waveforms to stack. The method includes estimating a first arrival time (FAT) and sending the stacked number of filtered SWD seismic waveforms to a surface equipment, centered around the estimated FAT.

This invention provides a shear wave impact pad and operating method for shallow seismic exploration, comprising a load-bearing block, a wedge, and two symmetrically arranged inclined plates. The load-bearing block is connected to the wedge, which is positioned between the two inclined plates, with its sidewall slidingly engaged with the inner wall of the inclined plate. When the load-bearing block is subjected to a vertical impact force, the wedge moves downward and pushes the two inclined plates symmetrically outward along the sliding engagement surface, thereby converting the vertical impact force into a lateral shear force. This invention uses the load-bearing block, wedge, and symmetrically arranged inclined plates to form a linkage and reversing structure for converting vertical force into lateral force. Vertical impact drives the wedge downward, simultaneously pushing the two inclined plates symmetrically outward to generate bidirectional equal-intensity shear waves, replacing the traditional side-impact plate operation method, improving shear wave conversion efficiency, and enabling shear wave excitation with minimal source-receiver distance, thus meeting the operational requirements of shallow seismic exploration.

The application relates to a converted wave shear wave splitting calculation method and system, equipment and a storage medium, and belongs to the technical field of oil and natural gas geophysical exploration. The method comprises the following steps: respectively pre-processing, sorting and dynamic correction of converted wave radial component data and converted wave tangential component data; synchronously dynamic correcting radial component gather data and tangential component gather data; respectively performing azimuthal division on the obtained radial component data and tangential component data after synchronization; based on the azimuthal division obtained common receiver point radial component and tangential component azimuthal division gather, a phase inversion method and a phase-constrained least square method, the shear wave splitting is calculated according to a vertical layer. The application combines the advantages of the phase inversion method and the least square method, has lower requirements on input data, is suitable for the situation of low signal-to-noise ratio data and irregular acquisition data, and improves the shear wave splitting calculation precision.

The application discloses a kind of inorganic cementitious material setting process automatic monitoring method and device based on transverse wave, it is related to civil engineering material performance monitoring technical field, comprising the following steps: S1, obtains the monitoring data of transverse wave pulse from stirring to hardening whole process through inorganic cementitious material;S2, noise data and transverse wave data are screened from monitoring data;S3, according to the twice mutation of propagation time judges initial setting and final setting state.The inorganic cementitious material setting process automatic monitoring method and device based on transverse wave, by real-time monitoring the fluctuation data of transverse wave through newly-poured inorganic cementitious material, effective transverse wave data is screened from monitoring data, and the first wave propagation time of transverse wave is extracted from transverse wave data, to determine the age of initial setting and final setting state change according to the twice sudden decrease of propagation time, realize the nondestructive, continuous, high-precision monitoring of setting whole process.

This application discloses a method, apparatus, device, and storage medium for predicting shear wave velocity, belonging to the field of seismic exploration technology. The shear wave velocity prediction method provided in this application first determines multiple characteristic curves based on geological parameters, core test results, and well logging data from multiple wells. Since these multiple characteristic curves include both original and augmented characteristic curves from the well logging data, the amount of data for model training can be increased, thereby improving the model's generalization ability. Furthermore, the method also determines a first characteristic curve and a second characteristic curve related to shear wave velocity from these multiple characteristic curves, and trains the model based on the first characteristic curve and multiple second characteristic curves. Because the characteristic curves used for training have a high correlation with shear wave velocity, using these characteristic curves as training data can guide and constrain the model's training direction, thereby further improving the model's accuracy and generalization ability. Due to the improved accuracy and generalization ability of the model, the accuracy of shear wave velocity prediction can be increased when using this model for prediction.

This invention discloses a method, apparatus, and medium for cross-dimensional Bayesian inversion of Rayleigh surface wave dispersion curves, relating to the field of surface wave exploration technology. This invention introduces a Dix-type physically guided soft prior, using a reference shear wave velocity profile as a probability anchor point to construct a Gaussian probability field around this profile. This guides the cross-dimensional Bayesian inversion to quickly enter a high-probability physically reasonable region in a probabilistic sense, thereby upgrading the Dix non-iterative inversion results from "fixed initial values / hard constraints" to "Gaussian probability anchor point soft priors." Simultaneously, based on the formation depth value corresponding to the subsurface shear wave velocity parameters to be inverted, a depth-related prior standard deviation of the Gaussian probability field is set to ensure the soft prior operates within the physical boundaries. This makes the final inversion results naturally biased towards the physically reasonable model region in a probabilistic sense, and physical boundary constraints are applied, eliminating systematic biases in the Dix inversion and ultimately obtaining inversion results consistent with actual geology.

This invention proposes a directional excitation device, system, and seismic data acquisition method for a gas explosion seismic source. The device includes: a gas storage mechanism comprising a cylindrical cavity and centrally symmetrically arranged weak points at both ends of the cylindrical cavity; an excitation mechanism, airtightly connected to the gas storage mechanism, for controlled detonation of explosive gas in the gas storage mechanism to release it outward through the weak points; and a sealing mechanism, detachably airtightly connected to the excitation mechanism, for injecting explosive gas into the cylindrical cavity of the gas storage mechanism through the excitation mechanism. This invention, by setting weak points at both ends of the cylindrical cavity, causes the gas to be ejected from the weak points as the detonation point during the explosion process, thereby causing the cylindrical cavity to twist and generating seismic torsional shear waves. The device of this invention is small in size and has minimal horizontal extension, requiring a smaller wellhead, making well drilling easier, and significantly improving the construction efficiency of exciting seismic waves.