355 citations · 468 across the 28 of their papers we have counts for
6 papers · 1 filter
Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
Rodrigo S. Luna, Thiago H. N. Coelho, Luiz S. L. Neto +8
This chapter discusses how a data-driven machine learning approach can reproduce key aspects of the physical behavior of multiphase flows in complex geological formations. We propo…
Accelerated Full Waveform Inversion by Deep Compressed Learning
Maayan Gelboim, Amir Adler, Mauricio Araya-Polo
We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as computational cost mitigation approach. Given modern seismic acquisition system…
Learning CO plume migration in faulted reservoirs with Graph Neural Networks
Xin Ju, François P. Hamon, Gege Wen +3
Deep-learning-based surrogate models provide an efficient complement to numerical simulations for subsurface flow problems such as CO geological storage. Accurately capturing t…
Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO Plumes via Deep Learning
Adrian Celaya, Bertrand Denel, Yen Sun +2
We introduce three algorithms that invert simulated gravity data to 3D subsurface rock/flow properties. The first algorithm is a data-driven, deep learning-based approach, the seco…
Deep Neural Network Learning with Second-Order Optimizers -- a Practical Study with a Stochastic Quasi-Gauss-Newton Method
Christopher Thiele, Mauricio Araya-Polo, Detlef Hohl
Training in supervised deep learning is computationally demanding, and the convergence behavior is usually not fully understood. We introduce and study a second-order stochastic qu…
Learning with a Wasserstein Loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi +2
Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we deve…