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UnCRtainTS: Uncertainty quantification for cloud removal in optical satellite time series
Clouds and haze often occlude optical satellite images, hindering continuous, dense
monitoring of the Earth's surface. Although modern deep learning methods can implicitly …
monitoring of the Earth's surface. Although modern deep learning methods can implicitly …
A comprehensive review of seismic inversion based on neural networks
Seismic inversion is one of the fundamental techniques for solving geophysics problems. To
obtain the elastic parameters or petrophysical parameters, it is necessary to establish a …
obtain the elastic parameters or petrophysical parameters, it is necessary to establish a …
Fast Bayesian linearized inversion with an efficient dimension reduction strategy
Bayesian linearized inversion (BLI) stands out as an exceptional stochastic inversion
method in the realms of geophysics and remote sensing. It excels in estimating inversion …
method in the realms of geophysics and remote sensing. It excels in estimating inversion …
FMG_INV, a fast multi-Gaussian inversion method integrating well-log and seismic data
High-resolution prestack inversion combining the well-logging and seismic data is a
significant geophysical task and can be achieved by two kinds of stochastic inversion …
significant geophysical task and can be achieved by two kinds of stochastic inversion …
High-fidelity permeability and porosity prediction using deep learning with the self-attention mechanism
Accurate estimation of reservoir parameters (eg, permeability and porosity) helps to
understand the movement of underground fluids. However, reservoir parameters are usually …
understand the movement of underground fluids. However, reservoir parameters are usually …
[HTML][HTML] Uncertainty quantification in autoencoders predictions: Applications in aerodynamics
A data-driven model is compared to classical equation-driven approaches to investigate its
ability to predict quantity of interest and their uncertainty when studying airfoil aerodynamics …
ability to predict quantity of interest and their uncertainty when studying airfoil aerodynamics …
A generic model of global earthquake rupture characteristics revealed by machine learning
Rupture processes of global large earthquakes have been observed to exhibit great
variability, whereas recent studies suggest that the average rupture behavior could be …
variability, whereas recent studies suggest that the average rupture behavior could be …
Stochastic facies inversion with prior sampling by conditional generative adversarial networks based on training image
Probabilistic methods for geophysical inverse problems allow the use of arbitrarily complex
prior information in principle. Geostatistical techniques, such as multiple-point statistics …
prior information in principle. Geostatistical techniques, such as multiple-point statistics …
[HTML][HTML] Deep clustering in subglacial radar reflectance reveals subglacial lakes
Ice-penetrating radar (IPR) imaging is a valuable tool for observing the internal structure and
bottom of ice sheets. Subglacial water bodies, also known as subglacial lakes, generally …
bottom of ice sheets. Subglacial water bodies, also known as subglacial lakes, generally …
Ensemble Smoother with Fully Convolutional VAE for seismic facies inversion
Seismic facies inversion is an important process in the oil and gas industry to estimate
subsurface geological facies or rock types based on seismic data. Recently, the Ensemble …
subsurface geological facies or rock types based on seismic data. Recently, the Ensemble …