This work introduces composite functions to compute distortion in volumetric seismic data.
Several loss functions, such as those based on L p-functions, ignore the structure of 3-D …

This work presents a method for volumetric seismic data compression by coupling a 3-D
convolution-based autoencoder to a generative adversarial network (GAN). The main …

Dados sísmicos s~ ao mapeamentos da subsuperfície terrestre que têm como objetivo
representar as características geofísicas da região onde eles foram obtidos de forma que …

In this work, we propose an approach to tackle the problem of three-dimensional post-stack
seismic data compression using multi-resolution deep autoencoders, by training one …

In this paper, we present a deep learning approach for lossy compression of 3D post-stack
seismic data. The network is feed with two-dimensional 32-bits slices from the original …

In a geophysical exploration survey, thousands of geophones are deployed where each
geophone must transmit hundreds of recording over a narrow band channel to a fusion …

Seismic data compression is highly demanded to reduce the cost for transmission and
storage due to an enormous volume of collected data. This paper presents a prediction …

We approach the problem of 3-D poststack seismic data compression by training a model
based on a deep autoencoder. Our network architecture is trained to consider the similarity …

Algorithms for low-rank seismic data denoising or interpolation have generally focused on
improving the aggregate signal to noise ratio (SNR) in the filtered or reconstructed data …

Due to the limitations imposed by acquisition cost, obstacles, and inaccessible regions, the
originally acquired seismic data are often sparsely or irregularly sampled in space, which …