12 results about "Inversion Time" patented technology

The invention discloses a sparse inversion time-frequency analysis method based on a semi-norm, and the method comprises the steps: designing a window function for original seismic data, so as to reduce the influence of a window truncation effect on time-frequency analysis; constructing a Fourier forward matrix and a windowed Fourier matrix according to the size of the window function and the size of the frequency spectrum; in-window seismic data extraction is carried out on sample points of single-channel seismic data, and a semi-norm constrained time-frequency spectrum sparse inversion objective function is constructed; performing time-frequency spectrum coefficient inversion on the time-frequency spectrum sparse inversion target function to obtain a sparse inversion time-frequency spectrum result of the seismic data in the window; and sliding the sample points of the seismic data to repeat the process, and performing time-frequency spectrum sparse inversion on all the seismic data to obtain a final time-frequency analysis result. According to the method, the time-frequency spectrum precision and the time-frequency focusing property of the seismic signal can be effectively improved, and a time-frequency analysis result with higher precision is provided for subsequent reservoir identification and oil and gas prediction work.

The invention relates to the technical field of geophysical exploration, and discloses a ground-air transient electromagnetic data fast inversion method based on a long and short term memory network, comprising the following steps: constructing a ground-air transient electromagnetic training data set, including generating resistivity and thickness parameters through a random stratified model, generating corresponding transient electromagnetic response data based on forward modeling simulation; preprocessing the training data set, wherein the preprocessing comprises normalization, time sequence remodeling and data set division; and constructing a CNN-BiLSTM-Attention hybrid neural network model, wherein the model comprises a CNN feature extraction module, a BiLSTM time sequence modeling module, an attention mechanism module and a Seq2Seq decoder module which are connected in sequence. According to the ground-air transient electromagnetic data rapid inversion method based on the long and short term memory network, reasoning is performed by using the trained network, the inversion time of a single measuring point can be shortened to a second level, a traditional several-day interpretation period is compressed to an hour level, and the response speed of geological disaster monitoring and exploration is greatly improved.

Systems and methods for contrast scanning are provided. In one example, a system includes a display device (234), one or more processors (204), and a memory (206) storing instructions executable by the one or more processors to obtain (304) a series of TI scout images, each TI scout image of the series of TI scout images obtained with a different inversion time; process (408) at least a portion of each TI scout image of the series of TI scout images to generate a similarity metric map that depicts a degree of statistical similarity between each pair of TI scout images of the series of TI scout images in a region of interest (ROI); predict (418, 422) an inversion time or an inversion time range based on the similarity metric map; and display (308) the predicted inversion time or the inversion time range on the display device.

The present application relates to a kind of cardiac magnetic resonance longitudinal relaxation time measurement method, medium and equipment.The measurement method includes the following steps: S1 uses MOLLI sequence respectively before and after scanning contrast;Specifically, in the before and after scanning contrast, by setting different initial inversion time TI, setting 3 groups of LL experiment LL1, LL2, LL3 triggered by continuous ECG, enough relaxation curve sampling points are obtained after fusion;And data is collected at the end of diastole of cardiac cycle, and 1 image is obtained per heartbeat;S2 image processing.This measurement method can realize single-pixel high-resolution T1 mapping under the breath holding of selection data acquisition and multi-LL experiment data fusion, give consideration to high accuracy, high resolution and high efficiency, and have good compatibility and practicality, provide new scheme for early diagnosis of heart disease, lesion quantification and prognosis evaluation, have wide clinical application prospect.

The invention relates to a river bank collapse detection method based on remote sensing inversion time sequence data. Comprising the steps that remote sensing inversion time series data formed by a target river bank in a preset detection time period are acquired, and the remote sensing inversion time series data comprise suspended sediment concentration time series data of a plurality of pixel point areas covered by the target river bank; identifying potential collapse areas from the plurality of pixel point areas according to all suspended sediment concentration time sequence data; screening out remote sensing inversion qualified data of the potential collapse area from the target suspended sediment concentration time sequence data according to the temporal and spatial variation characteristics of the target suspended sediment concentration time sequence data of the potential collapse area; determining a data quality index of the suspended sediment concentration of the potential collapse area under each detection time sequence according to an association relationship between the remote sensing observation total data and the remote sensing inversion qualified data of the potential collapse area in the preset detection time period; and performing collapse detection on the target river bank according to the data quality index. By adopting the method, the accuracy of river bank collapse detection is improved.

A method and a magnetic resonance imaging (MRI) system improve an MRI signal from a magnetization of interest. The method includes performing an MRI pulse sequence containing three consecutive radio frequency (RF) elements, namely, a first element that is an inversion-recovery pulse sequence characterized by a time of inversion, a second element that is an image-encoding pulse sequence starting at the time of inversion with an excitation RF pulse followed by an image-encoding gradient and a data sampling. The second element is followed by a third element. An MRI signal generated by the object and sampled by image readout blocks applied to the object by the MRI system during each repetition time, is acquired. From the MRI signal, an image of the object is reconstructed. The third element is a modified driven-equilibrium (mDE) pulse sequence configured for achieving a conversion of a transverse magnetization component.

Systems and methods are provided for contrast scans. In one example, a system includes a display device, one or more processors, and memory storing instructions executable by the one or more processors to obtain a series of TI scout images, each TI scout image in the series of TI scout images obtained with a different inversion time; process at least a portion of each TI-scout image in the series of TI scout images to generate a similarity metric plot that depicts a degree of statistical similarity between each pair of TI scout images of the series of TI scout images in a region of interest (ROI); predict an inversion time or a range of inversion times based on the similarity metric plot; and display the predicted inversion time or the range of inversion times on the display device.

This invention discloses a real-time inversion method for seepage edge analysis in dams based on a physical information neural network, belonging to the field of water conservancy engineering safety monitoring technology. The method includes: collecting and preprocessing multi-source monitoring data on seepage pressure, temperature, and deformation; constructing a seepage-temperature-deformation coupling mechanism model; constructing a physical information neural network, embedding the partial differential equations of seepage mechanics as physical constraints into the loss function, and using hard constraint boundary conditions; lightweighting the model and deploying it on edge computing nodes to achieve real-time seepage field inversion; and performing streamline tracing and comprehensive anomaly index calculation based on the inversion results to identify abnormal seepage paths and provide graded early warnings. This invention achieves technical effects such as an inversion accuracy R²=0.97, low equation residuals, short single inversion time (seconds), and anomaly identification accuracy of 94%, and can be widely applied to seepage safety monitoring of reservoirs, dams, dikes, and tailings ponds.

The application relates to a method for determining inversion time in blood vessel imaging, a magnetic resonance imaging method and a device. The method for determining inversion time in blood vessel imaging comprises the following steps: selecting an imaging position from a blood vessel imaging area; at the imaging position, collecting at least two blood vessel images with different inversion times by using an inversion time searching technology; obtaining an inversion time corresponding to a blood vessel image with optimal blood vessel display; and determining a time point for setting a longitudinal magnetization to zero according to the inversion time corresponding to the blood vessel image with optimal blood vessel display. The magnetic resonance imaging method comprises the following steps: determining an inversion time based on the method for determining inversion time in blood vessel imaging; applying a regional selection inversion pulse at an imaging position; and applying a target imaging sequence at the imaging position after the inversion time to obtain a target magnetic resonance image. The method can optimize the inversion time, thereby obtaining optimal blood vessel display.

The invention provides an improved UNet ground penetrating radar-based conductivity and relative dielectric constant inversion method. The method comprises the following steps of S1, constructing an underground medium model and generating a conductivity numerical diagram and a relative dielectric constant distribution diagram; s2, generating a B-scan image by adopting gprMax, and performing preprocessing, normalization and size unification on the B-scan image, the conductivity numerical diagram and the relative dielectric constant distribution diagram to form an image data set; s3, inputting the image data set image into a DEP-UNet network model based on a convolutional neural network; s4, setting the training set scale, the learning rate and the number of iterations, training the DEP-UNet network model, and meanwhile, storing training optimal model parameters; and S5, outputting a predicted conductivity numerical diagram and a relative dielectric constant distribution diagram through the DEP-UNet network model. According to the method, the inversion time of the ground penetrating radar can be remarkably shortened, and the inversion precision is improved.

This invention discloses a SCADA incident inversion method, apparatus, equipment, and medium, relating to the field of monitoring technology. The method includes: determining an inversion time interval based on an alarm event and a preset inversion duration; acquiring historical data within the inversion time interval, and processing unexpected or missing data using a preset data repair strategy to obtain a continuous time-series target historical data sequence; based on the target historical data sequence, and combining the correlation between historical data and SCADA screen configuration, reconstructing and rendering the monitoring screen state at the target time according to a set time step, generating time-series image frames; and synthesizing the time-series image frames into an incident inversion playback video. This enables intuitive restoration and controllable playback of the incident process within a specified time period of the SCADA system, making the incident inversion process more intuitive and visual, while also possessing video-level playback control capabilities, effectively improving the efficiency and intuitiveness of incident analysis.