We exploit the high memory bandwidth of AI-customized Cerebras CS-2 systems for seismic
processing. By leveraging low-rank matrix approximation, we fit memory-hungry seismic …

Geophysical measurements such as seismic datasets contain valuable information that
originate from areas of interest in the subsurface; these seismic reflections are, however …

Reconstructing the details of subsurface structures deep beneath complex overburden
structures, such as subsalt, remains a challenge for seismic imaging. Over the past few …

Multidimensional deconvolution constitutes an essential operation in a variety of
geophysical scenarios at different scales ranging from reservoir to crustal, as it appears in …

Pre-stack AVO inversion of seismic data is a modeling tool for estimating subsurface elastic
properties. Our focus is on the model-based inversion method where then unknown …

Marchenko methods are based on integral representations which express Green's functions
for virtual sources and/or receivers in the subsurface in terms of the reflection response at …

Characterizing the elastic properties in deep-buried reservoirs beneath complex overburden
structures remains challenging for seismic inversion. Elastic full-waveform inversion (FWI) is …

ABSTRACT A novel factorization-based, low-rank regularization method is introduced to
solve multidimensional deconvolution (MDD) problems in the frequency domain. In this …

We develop a novel elastic amplitude-variation-with-offset (AVO) inversion process to
estimate the fluid saturation and effective pressure or variations in these properties from time …

Among single-component inversions, vertical gravity gradient data have been proven to be
sufficient to obtain reasonable density distribution. However, the increasing numbers of …