Results 81 to 90 of about 3,297,474 (208)
Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.Abstract Physics‐informed neural networks (PINNs) integrate physical constraints with neural architectures and leverage their nonlinear fitting capabilities to solve complex inverse problems. Tomography serves as a classic example, aiming to reconstruct subsurface velocity models to improve seismic exploration.
Yonghao Wang +3 more
wiley +1 more source
Latent representation learning in physics-informed neural networks for full waveform inversion
Full waveform inversion (FWI), a state-of-the-art seismic inversion algorithm, comprises an iterative data-fitting process to recover high-resolution Earth’s properties (e.g., velocity).
Alkhalifah, Tariq Ali +2 more
core +1 more source
Reflection full waveform inversion [PDF]
, 2018 The Full Waveform Inversion (FWI) gradient is composed of a low wavenumber tomographic component and a high wavenumber migration component. A successful application of FWI requires that the low wavenumber parts of the model be recovered before the high
Irabor, Kenneth Otabor
core +1 more source
GeoFWI: A Large Velocity Model Data Set for Benchmarking Full Waveform Inversion Using Deep Learning
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.Abstract Full waveform inversion (FWI) plays an increasingly important role in the field of seismic imaging due to its strong ability to estimate subsurface properties. Specifically, data‐driven FWI (DDFWI) establishes a straightforward mapping relationship between seismic data and the corresponding velocity model, yielding promising results.
Chao Li +5 more
wiley +1 more source
Reflection intensity waveform inversion
, 2020 Traditional iteration-based full-waveform inversion (FWI) methods encounter serious challenges if the initial velocity model is far from the true model or if the observed data are lacking low-frequency content.
Liu, Yike +4 more
core +1 more source
Transient Wave‐Based Data Assimilation for Localizing Multiple Leaks in Water Pipe Networks
Water Resources Research, Volume 62, Issue 4, April 2026.Abstract Due to aging without timely renewal, hidden leaks continually occur in urban water distribution systems. Time‐domain full‐waveform inversion is a robust and flexible method for localizing multiple leaks in water pipe networks. However, as a deterministic estimator, this method assumes precise knowledge of model parameters and only gives a ...
Qiuru Chen +3 more
wiley +1 more source
Full-waveform inversion-using phase encoded all order multiples
, 2021 Full waveform inversion (FWI) inherently can handle all wave kinds of waves, such as turning wave, primary, and multiples. However, when an initial model is far from the true model, data containing multiples can increase nonlinearity and FWI tends to ...
Zhendong Zhang (11433308) +3 more
core +1 more source
, 2007 International audienceP276 Multiparameter Full-Waveform Inversion for Velocity and Attenuation – Refining the Imaging of a Sedimentary Basin M. Malinowski* (Institute of Geophysics PAS) A. Ribodetti (Geosciences Azur CNRS UMR) & S.
Malinowski, M. +2 more
core +4 more sources
Resolving high frequency anomalies of gas cloud using full waveform inversion
Annals of Geophysics, 2016 High resolution models with structurally improved results significant to the physical properties of rocks in geologically complex areas require advance modeling methodologies.
Srichand Prajapati, Deva Ghosh
doaj +1 more source
OPTIMAL CODING OF BLENDED SEISMIC SOURCES FOR 2D FULL WAVEFORM INVERSION IN TIME
CT&F Ciencia, Tecnología & FuturoFull Waveform Inversion (FWI) schemes are gradually becoming more common in the oil and gas industry, as a new tool for studying complex geological zones, based on their reliability for estimating velocity models.
Flórez, Katherine-A. +2 more
doaj +1 more source