Abstract: The disclosure provides at compounds of Formula I, compositions comprising the same, and methods of using the same, including use in treating a disease and/or a symptom of a disease caused by a coronavirus.

Abstract: Provided are a structure with a screw, a screw fixing structure and a display device. The structure with a screw includes a limiting structure, an assembly member, an elastic member and a screw, the limiting structure is connected to the assembly member and provided with an operating hole, the assembly member is provided with a through hole for accommodating the screw and the elastic member, an abutting step is provided in the through hole, an end of the elastic member abuts against the abutting step and the other end of the elastic member abuts against the screw, a head portion of the screw has an operating portion and an abutting portion, the abutting portion abuts against the limiting structure, and the operating portion is exposed through the operating hole.

Abstract: The present invention is related to a method for reconstructing at least one trace in a seismic image of a common receiver and time domain, the image comprising traces in time domain with seismic data and one or more traces to be reconstructed. A first aspect of the invention is a method that is characterized by a specific use of a convolutional neural network trained under an unsupervised learning approach with a modified receptive field. A second aspect of the invention is a deblending method based on the use of a reconstructing method according to the first aspect of the invention applied to a denoising step of a deblending process allowing a very effective data acquisition while keeping a high quality output data sets after being processed according to the first and/or second aspects of the invention.

Abstract: A computer-implemented method for obtaining reconstructed seismic data for determining a subsurface feature, includes: determining an initial training velocity model, training a machine learning model based on first training seismic data and second training seismic data generated from the training velocity model, the first training seismic data corresponding to one or more first frequencies, the second training seismic data corresponding to one or more second frequencies lower than the one or more first frequencies, obtaining, based on measured seismic data and the machine learning model, reconstructed seismic data corresponding to the one or more second frequencies, generating a velocity model based on the measured seismic data, the reconstructed seismic data, and a full waveform inversion (FWI), and when the generated velocity model does not satisfy a preset condition, updating the training velocity model based on the generated velocity model, to obtain updated reconstructed seismic data for determining a s

Abstract: A computer-implemented method for determining a subsurface feature, includes: determining a first velocity model based on an initial velocity model; generating a second velocity model based on measured seismic data at one or more first frequencies, the first velocity model, and a full waveform inversion (FWI); and in response to the second velocity model not satisfying a preset condition, performing a seismic forward simulation on the second velocity model to generate simulated seismic data at one or more second frequencies lower than the one or more first frequencies; updating the first velocity model based on the simulated seismic data at the one or more second frequencies; and updating the second velocity model based on the measured seismic data at the one or more first frequencies, the updated first velocity model, and the FWI, to determine the subsurface feature.

Abstract: A computer-implemented method for determining a subsurface feature, includes: determining a first velocity model based on an initial velocity model; generating a second velocity model based on measured seismic data at one or more first frequencies, the first velocity model, and a full waveform inversion (FWI); and in response to the second velocity model not satisfying a preset condition, performing a seismic forward simulation on the second velocity model to generate simulated seismic data at one or more second frequencies lower than the one or more first frequencies; updating the first velocity model based on the simulated seismic data at the one or more second frequencies; and updating the second velocity model based on the measured seismic data at the one or more first frequencies, the updated first velocity model, and the FWI, to determine the subsurface feature.

Abstract: A computer-implemented method for obtaining reconstructed seismic data for determining a subsurface feature, includes: determining an initial training velocity model, training a machine learning model based on first training seismic data and second training seismic data generated from the training velocity model, the first training seismic data corresponding to one or more first frequencies, the second training seismic data corresponding to one or more second frequencies lower than the one or more first frequencies, obtaining, based on measured seismic data and the machine learning model, reconstructed seismic data corresponding to the one or more second frequencies, generating a velocity model based on the measured seismic data, the reconstructed seismic data, and a full waveform inversion (FWI), and when the generated velocity model does not satisfy a preset condition, updating the training velocity model based on the generated velocity model, to obtain updated reconstructed seismic data for determining a s

Abstract: A full wave inversion (FWI) may utilize an Amplitude-Frequency-Differentiation (AFD) or a Phase-Frequency-Differentiation (PFD) operation to form a velocity model of a subterranean formation utilizing recovered low wavenumber data. Received seismic data is processes to isolate two data signals at different frequencies. In an AFD operation, the two data signals are summed and the data of the envelope of the summed function is used for the FWI. In a PFD operation, the phase data of the quotient of the two data signals is used for the FWI. The FWI proceeds iteratively utilizing either the AFD or PFD data or with single frequency data until the cost function of the AFD or PFD is satisfied.

Abstract: A memory-efficient Q-RTM computer method and apparatus for imaging seismic data is described. A seismic image may be formed from a memory-efficient Q-RTM module utilizing received attenuated seismic data. Seismic data is processed by the memory-efficient Q-RTM module to compensate for amplitude attenuation and phase velocity dispersion simultaneously during back-propagation in RTM. A negative quality factor, Q, is obtained by modifying the wave equation to compensate for amplitude attenuation. One or more dispersion optimization terms introduced to a wave equation for compensation of Q effects on the phase, solved by a finite difference algorithm, compensate for phase velocity change and further adjust amplitude attenuation compensation.

Abstract: Method for reconstructing subsurface Q depth profiles from common offset gathers (92) of reflection seismic data by performing migration (40), ray tracing (100), CDP-to-surface takeoff angle finding (96, 98), kernel matrix construction (110), depth-to-time conversion and wavelet stretching correction (80), source amplitude spectrum fitting, centroid frequency shift calculation (90), and box-constrained optimization (120).

Abstract: A memory-efficient Q-RTM computer method and apparatus for imaging seismic data is described. A seismic image may be formed from a memory-efficient Q-RTM module utilizing received attenuated seismic data. Seismic data is processed by the memory-efficient Q-RTM module to compensate for amplitude attenuation and phase velocity dispersion simultaneously during back-propagation in RTM. A negative quality factor, Q, is obtained by modifying the wave equation to compensate for amplitude attenuation. One or more dispersion optimization terms introduced to a wave equation for compensation of Q effects on the phase, solved by a finite difference algorithm, compensate for phase velocity change and further adjust amplitude attenuation compensation.

Abstract: A full wave inversion (FWI) may utilize an Amplitude-Frequency-Differentiation (AFD) or a Phase-Frequency-Differentiation (PFD) operation to form a velocity model of a subterranean formation utilizing recovered low wavenumber data. Received seismic data is processes to isolate two data signals at different frequencies. In an AFD operation, the two data signals are summed and the data of the envelope of the summed function is used for the FWI. In a PFD operation, the phase data of the quotient of the two data signals is used for the FWI. The FWI proceeds iteratively utilizing either the AFD or PFD data or with single frequency data until the cost function of the AFD or PFD is satisfied.

Abstract: A full wave inversion (FWI) may utilize an Amplitude-Frequency-Differentiation (AFD) or a Phase-Frequency-Differentiation (PFD) operation to form a velocity model of a subterranean formation utilizing recovered low wavenumber data. Received seismic data is processes to isolate two data signals at different frequencies. In an AFD operation, the two data signals are summed and the data of the envelope of the summed function is used for the FWI. In a PFD operation, the phase data of the quotient of the two data signals is used for the FWI. The FWI proceeds iteratively utilizing either the AFD or PFD data or with single frequency data until the cost function of the AFD or PFD is satisfied.

Abstract: Method for reconstructing subsurface Q depth profiles from common offset gathers (92) of reflection seismic data by performing migration (40), ray tracing (100), CDP-to-Data Domain surface takeoff angle finding (96, 98), kernel matrix construction (110), depth-to-time conversion and wavelet stretching correction (80), source amplitude spectrum fitting, centroid frequency shift calculation (90), and box-constrained optimization (120).

Abstract: Method for reconstructing subsurface profiles for seismic velocity or other geophysical properties from recorded seismic data. In one embodiment, a starting model of seismic velocity is assumed (10). The computational domain is divided into two (or more) subdomains by horizontal planes based on an analysis of velocity model (30), and the allowed maximum grid size for each subdomain is determined (50). Auxiliary perfectly matched layers (PML's) are attached to each planar interface between subdomains (80), e.g. two PML's on each side of the interface between the coarse and fine subdomains. Simulated seismic data are computed using the SG-DO technique (100-230). The simulated seismic data are compared to the recorded seismic data, then the residual is calculated (240) and used to update the model (320). The method may be iterated until the model is suitably converged (260).

Abstract: Method for reconstructing subsurface Q models (110) from seismic data (10) by performing ray-based (60), centroid frequency shift (50) Q tomography. The seismic source waveform's amplitude spectrum is approximated by a frequency-weighted exponential function of frequency (40), having two parameters to adjust to fit the frequency shift data, thereby providing a better fit to various asymmetric source amplitude spectra. Box constraints may be used in the optimization routine, and a multi-index active-set method used in velocity tomography is a preferred technique for implementing the box constraints (100).

Abstract: Method and system is described for reducing sensitivity imbalance issues and/or implements target-oriented tomography to enhance tomographic inversion for velocity model building. The method may include performing a preparation stage to construct a measurement vector from seismic data and a kernel matrix from ray-path information; performing a sensitivity optimization stage to generate a data weighting vector; and performing a property optimization stage to reconstruct a subsurface model of one or more geophysical properties.

Abstract: Method for reconstructing subsurface Q models (110) from seismic data (10) by performing ray-based (60), centroid frequency shift (50) Q tomography. The seismic source waveform's amplitude spectrum is approximated by a frequency-weighted exponential function of frequency (40), having two parameters to adjust to fit the frequency shift data, thereby providing a better fit to various asymmetric source amplitude spectra. Box constraints may be used in the optimization routine, and a multi-index active-set method used in velocity tomography is a preferred technique for implementing the box constraints (100).