For variable selection to binary response regression, stochastic search variable selection and Bayesian Lasso have recently been popular. However, these two variable selection methods suffer from heavy computation burden caused by hyperparameter tuning and by matrix inversions, especially when the number of covariates is large. Therefore, this article incorporates the componenetwise Gibbs sampl...

in this research we have studied tabriz temperature inversion using radio-sound information, during the 2004-2008 periods in daily, monthly and seasonally scales. produced data associated with temperature inversion daily of the surface (2 meters) to 700 h.pa. several different measures are used, including the height of the base and the height of the top of the inversion, the temperature at the ...

Seismic inversion is an established approach to model the geophysical characteristics of oil and gas reservoirs, being one of the basis of the decision making process in the oil&gas exploration industry. However, the required accuracy levels can only be attained by dealing and processing significant amounts of data, often leading to consequently long execution times. To overcome this issue and ...

Present-day high quality 3D acquisition can give us lower frequencies and longer offsets with which to invert. However, the computational costs involved in handling this data explosion are tremendous. Therefore, recent developments in full-waveform inversion have been geared towards reducing the computational costs involved. A key aspect of several approaches that have been proposed is a dramat...

We have developed an efficient stochastic AVA inversion technique that works directly in a fine-scale stratigraphic grid, and is conditioned by well data and multiple seismic angle stacks. We use a Bayesian framework and a linearized, weak contrast approximation of the Zoeppritz equation to construct a joint log-Gaussian posterior distribution for Pand S-wave impedances. We apply a Sequential G...

An important problem when using Stochastic Inversion Transduction Grammars is their computational cost. More specifically, when dealing with corpora such as Europarl only one iteration of the estimation algorithm becomes prohibitive. In this work, we apply a reduction of the cost by taking profit of the bracketing information in parsed corpora and show machine translation results obtained with ...

Seismic inversion algorithms have been playing a key role in the characterization of oil and gas reservoirs, where a high accuracy is often required to support the decision about the optimal well locations. Since these algorithms usually rely on computer simulations that generate, process and store significant amounts of data, their usage is often limited by their long execution times. In fact,...

An inversion technique comprising stochastic search and regularized gradient optimization was developed to solve the atmospheric source characterization problem. The inverse problem comprises retrieving the spatial coordinates, source strength, and the wind speed and wind direction at the source, given certain receptor locations and concentration values at these receptor locations. The Gaussian...

Abstract We consider the Ensemble Kalman Inversion which has been recently introduced as an efficient, gradient-free optimization method to estimate unknown parameters in inverse setting. In case of large data sets, becomes computationally infeasible misfit needs be evaluated for each particle iteration. Here, randomised algorithms like stochastic gradient descent have demonstrated successfully...