.png){kind=icon}
Return
Full waveform inversion based on the ensemble Kalman filter method using uniform sampling without replacement
.png){kind=icon}
PRE
AI
DOI：10.1016/j.scib.2019.01.021.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Full waveform inversion (FWI) has been increasingly more and more important in seismology to better understand the interior structure of the Earth. FWI, by taking advantage of both the traveltime and amplitude in the data, provides high-resolution model parameters of the earth which can produce images with high resolution. However, this inversion method conventionally suffers from non-uniqueness due to many local minima of the objective function and large computing costs. In this study, we propose a new FWI method in a semi-random framework by integrating the ensemble Kalman filter and uniform sampling without replacement. Numerical results demonstrate that the new method can achieve high-resolution results and a wider convergence domain. Accordingly, the new method overcomes the disadvantage of conventional FWIs that depend strongly on the initial model. (C) 2019 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.
Keywords:
Data assimilation
Ensemble Kalman filter
Uniform sampling without replacement
Full waveform inversion
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
21.1
Papers:
5.1K
Citations:
2.2W
Organization
Cited Papers
Glutamate and kainate increase intracellular sodium activity in leech neuropile glial cells.png)
Glia
GliaIF0
PREAI
ShareSaveImproved decadal climate prediction in the North Atlantic using EnOI-assimilated initial conditionSCIENCE BULLETIN
OAAI
SCIENCE BULLETINIF21.1
OAAIShareSaveShareSaveExtracellular Matrix Optimization for Enhanced Physiological Relevance in Hepatic Tissue-ChipsPolymers
OAAI
PolymersIF0
OAAIShareSaveShareSaveShareSaveShareSave
View more