Deep learning in computational mechanics: a review
L Herrmann, S Kollmannsberger - Computational Mechanics, 2024 - Springer
The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
Sensing prior constraints in deep neural networks for solving exploration geophysical problems
One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …
process of analyzing and interpreting geophysical field data that are typically acquired at the …
Deep learning for seismic inverse problems: Toward the acceleration of geophysical analysis workflows
Seismic inversion is a fundamental tool in geophysical analysis, providing a window into
Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for …
Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for …
Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysis
The determination of subsurface elastic property models is crucial in quantitative seismic
data processing and interpretation. This problem is commonly solved by deterministic …
data processing and interpretation. This problem is commonly solved by deterministic …
A comprehensive review of seismic inversion based on neural networks
M Li, XS Yan, M Zhang - Earth Science Informatics, 2023 - Springer
Seismic inversion is one of the fundamental techniques for solving geophysics problems. To
obtain the elastic parameters or petrophysical parameters, it is necessary to establish a …
obtain the elastic parameters or petrophysical parameters, it is necessary to establish a …
OpenFWI: Large-scale multi-structural benchmark datasets for full waveform inversion
Full waveform inversion (FWI) is widely used in geophysics to reconstruct high-resolution
velocity maps from seismic data. The recent success of data-driven FWI methods results in a …
velocity maps from seismic data. The recent success of data-driven FWI methods results in a …
Deep learning reservoir porosity prediction based on multilayer long short-term memory network
The cost of obtaining a complete porosity value using traditional coring methods is relatively
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …
Reparameterized full-waveform inversion using deep neural networks
Q He, Y Wang - Geophysics, 2021 - library.seg.org
Full-waveform inversion (FWI) is a powerful method for providing a high-resolution
description of the subsurface. However, the misfit function of the conventional FWI method …
description of the subsurface. However, the misfit function of the conventional FWI method …
Seismic data reconstruction using deep bidirectional long short-term memory with skip connections
Due to environmental and economic constraints on their acquisition, seismic data are
always irregularly sampled and include bad or missing traces, which can cause problems for …
always irregularly sampled and include bad or missing traces, which can cause problems for …
Physics-driven self-supervised learning system for seismic velocity inversion
Seismic velocity inversion plays a vital role in various applied seismology processes. A
series of deep learning methods have been developed that rely purely on manually …
series of deep learning methods have been developed that rely purely on manually …