Scaling the “memory wall” for multi-dimensional seismic processing with algebraic compression on cerebras cs-2 systems
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 …
processing. By leveraging low-rank matrix approximation, we fit memory-hungry seismic …
Stochastic multi-dimensional deconvolution
Geophysical measurements such as seismic datasets contain valuable information that
originate from areas of interest in the subsurface; these seismic reflections are, however …
originate from areas of interest in the subsurface; these seismic reflections are, however …
Target-oriented high-resolution elastic full-waveform inversion with an elastic redatuming method
Y Li, T Alkhalifah - Geophysics, 2022 - library.seg.org
Characterizing the elastic properties in deep-buried reservoirs beneath complex overburden
structures remains challenging for seismic inversion. Elastic full-waveform inversion (FWI) is …
structures remains challenging for seismic inversion. Elastic full-waveform inversion (FWI) is …
Frequency-domain convolutive bounded component analysis algorithm for the blind separation of dependent sources
X Luo, Z Zhang, T Gong - IEEE Transactions on Instrumentation …, 2023 - ieeexplore.ieee.org
Aiming at the problem of dependent source separation in complex mechanical systems, the
highly universal frequency-domain convolutive bounded component analysis (FDCBCA) …
highly universal frequency-domain convolutive bounded component analysis (FDCBCA) …
A physics-aware, low-rank regularization for multidimensional deconvolution
This paper presents a novel factorization-based, low-rank regularization method for solving
multidimensional deconvolution problems in the frequency domain. In this approach, each …
multidimensional deconvolution problems in the frequency domain. In this approach, each …
Steering customized ai architectures for hpc scientific applications
AI hardware technologies have revolutionized computational science. While they have been
mostly used to accelerate deep learning training and inference models for machine learning …
mostly used to accelerate deep learning training and inference models for machine learning …
Solving multi-dimensional deconvolution via a nuclear-norm regularized least-squares approach
Summary Multi-dimensional deconvolution (MDD), a data processing technique stemming
from the Green's function representation theorem, is commonly solved as a linear least …
from the Green's function representation theorem, is commonly solved as a linear least …
Data-driven suppression of short-period multiples from laterally varying thin-layered overburden structures
Marchenko multiple elimination methods remove all orders of overburden-generated
internal multiples in a data-driven way. In the presence of thin beds, however, these …
internal multiples in a data-driven way. In the presence of thin beds, however, these …
Large-scale Marchenko imaging with distance-aware matrix reordering, tile low-rank compression, and mixed-precision computations
A variety of wave-equation-based seismic processing algorithms rely on the repeated
application of the Multi-Dimensional Convolution (MDC) operator. For large-scale 3D …
application of the Multi-Dimensional Convolution (MDC) operator. For large-scale 3D …
MicroDeblur: Image Motion Deblurring on Microcontroller-based Vision Systems
S Lee - Proceedings of the 22nd International Conference on …, 2023 - dl.acm.org
This paper introduces MicroDeblur, an on-device image motion deblur solution for resource-
constrained microcontroller-based vision systems. Although motion blurs caused by the …
constrained microcontroller-based vision systems. Although motion blurs caused by the …