Data reduction is increasingly being applied to scientific data for numerical simulations,
scientific visualizations and data analyses. It is most often used to lower I/O and storage …

The article proposes the use of PCA transformation carried out with the use of a neural
network as a method for compress data obtained from geodetic measurements. In this study …

This paper presents a new algorithm for the lossy compression of scalar data defined on 2D
or 3D regular grids, with topological control. Certain techniques allow users to control the …

The data storage burden resulting from large climate model simulations continues to grow.
While lossy data compression methods can alleviate this burden, they introduce the …

We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing
the least-squares inversion in seismic imaging. The IDL is proposed to overcome the …

Elastic full-waveform inversion (FWI) estimates the subsurface velocity model by inverting
multi-component seismic data. Most of the current elastic FWI solves the elastic wave …

With the increasing use of seismic waveform inversion and imaging, the scale of numerical
simulations of seismic waves has been increasing. Concomitantly, the resulting extremely …

Distributed big data in scientific computing presents a major I/O performance bottleneck
when exploiting data paral-lelism. Consumer and producer compute nodes are often …

Scientific simulations on high-performance computing (HPC) systems generate vast
amounts of floating-point data that need to be reduced in order to lower the storage and I/O …

Huffman encoding and arithmetic coding algorithms have shown great potential in the field
of image compression. These algorithms are the origin of current image compression …