Results 1 to 10 of about 4,186 (258)
Full-waveform inversion imaging of the human brain [PDF]
npj Digital Medicine, 2020 Magnetic resonance imaging and X-ray computed tomography provide the two principal methods available for imaging the brain at high spatial resolution, but these methods are not easily portable and cannot be applied safely to all patients.
Lluís Guasch +4 more
doaj +8 more sources
Full waveform inversion for arterial viscoelasticity. [PDF]
Phys Med Biol, 2023 Abstract Objective . Arterial viscosity is emerging as an important biomarker, in addition to the widely used arterial elasticity. This paper presents an approach to estimate arterial viscoelasticity using shear wave elastography (SWE). Approach .
Roy T, Guddati MN.
europepmc +3 more sources
Accelerated Full Waveform Inversion by Deep Compressed Learning [PDF]
SensorsWe propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as a computational cost mitigation approach. Given modern seismic acquisition systems, the data (as an input for FWI) required for an industrial-strength ...
Maayan Gelboim +2 more
doaj +2 more sources
Full-waveform inversion reveals diverse origins of lower mantle positive wave speed anomalies [PDF]
Scientific ReportsDetermining Earth’s structure is paramount to unravel its interior dynamics. Seismic tomography reveals positive wave speed anomalies throughout the mantle that spatially correlate with the expected locations of subducted slabs. This correlation has been
Thomas L. A. Schouten +6 more
doaj +2 more sources
An empirical study of large-scale data-driven full waveform inversion [PDF]
Scientific ReportsThis paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has ...
Peng Jin +7 more
doaj +2 more sources
Full Waveform Inversion and the Truncated Newton Method [PDF]
SIAM Journal of Scientific Computing, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jean Virieux +2 more
exaly +6 more sources
Bayesian full-waveform inversion with realistic priors [PDF]
Geophysics, 2021 ABSTRACT Seismic full-waveform inversion (FWI) uses full seismic records to estimate the subsurface velocity structure. This requires a highly nonlinear and nonunique inverse problem to be solved; therefore, Bayesian methods have been used to quantify uncertainties in the solution.
Xin Zhang, Andrew Curtis
exaly +3 more sources
Progressive plug and play full waveform inversion with multitask learning [PDF]
Scientific ReportsThe quality of low-frequency data and the initial model are crucial for robust seismic full waveform inversion (FWI). Some existing deep learning works only address one to improve the inversion accuracy.
Benwen Zhang, Linrong Wang
doaj +2 more sources
Full-intensity waveform inversion [PDF]
Geophysics, 2018 ABSTRACT Many full-waveform inversion schemes are based on the iterative perturbation theory to fit the observed waveforms. When the observed waveforms lack low frequencies, those schemes may encounter convergence problems due to cycle skipping when the initial velocity model is far from the true model. To mitigate this difficulty, we
Yike Liu, Yingcai Zheng, Bin He
exaly +3 more sources
Variational full-waveform inversion [PDF]
Geophysical Journal International, 2020 SUMMARY Seismic full-waveform inversion (FWI) can produce high-resolution images of the Earth’s subsurface. Since full-waveform modelling is significantly nonlinear with respect to velocities, Monte Carlo methods have been used to assess image uncertainties.
Xin Zhang, Andrew Curtis
openaire +2 more sources