Search Results for author:
Found 10 papers, 5 papers with code
Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework
We use Bayesian Evidential Learning (BEL), a Monte Carlo-based training approach, to optimize the design of a 4D temperature field monitoring experiment.
A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area
The uncertainty range of the posterior WHPA distribution is affected by the number and position of data sources (injection wells).
Bayesian inference of 1D activity profiles from segmented gamma scanning of a heterogeneous radioactive waste drum
We present a Bayesian approach to probabilistically infer vertical activity profiles within a radioactive waste drum from segmented gamma scanning (SGS) measurements.
Bayesian Inference
Data Analysis, Statistics and Probability
Instrumentation and Detectors
Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder
When solving inverse problems in geophysical imaging, deep generative models (DGMs) may be used to enforce the solution to display highly structured spatial patterns which are supported by independent information (e. g. the geological setting) of the subsurface.
Geophysics
Pixel-wise Conditioned Generative Adversarial Networks for Image Synthesis and Completion
We investigate the influence of this regularization term on the quality of the generated images and the fulfillment of the given pixel constraints.
Pixel-wise Conditioning of Generative Adversarial Networks
In this paper, we study the effectiveness of conditioning GANs by adding an explicit regularization term to enforce pixel-wise conditions when very few pixel values are provided.
Dilated Spatial Generative Adversarial Networks for Ergodic Image Generation
In combination with convolutional (for the discriminator) and de-convolutional (for the generator) layers, they are particularly suitable for image generation, especially of natural scenes.
Gradient-based deterministic inversion of geophysical data with Generative Adversarial Networks: is it feasible?
Global probabilistic inversion within the latent space learned by Generative Adversarial Networks (GAN) has been recently demonstrated (Laloy et al., 2018).
Geophysics
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media.
Training-image based geostatistical inversion using a spatial generative adversarial neural network
After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds.
