Numerical analysis of a deep learning formulation of multi-parameter elastic full waveform inversion

2020 ◽  
Author(s):  
Tianze Zhang ◽  
Kristopher A. Innanen ◽  
Jian Sun ◽  
Daniel O. Trad
Keyword(s):  
Deep Learning ◽  
Numerical Analysis ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform

Layered low-frequency extrapolation with deep learning in full-waveform inversion

2021 ◽  
Author(s):  
Yao Liu ◽  
Baodi Liu ◽  
Jianping Huang ◽  
Jun Wang ◽  
Honglong Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Full Waveform

Deep-learning seismic full-waveform inversion for realistic structural models

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. R31-R44
Author(s):  
Bin Liu ◽  
Senlin Yang ◽  
Yuxiao Ren ◽  
Xinji Xu ◽  
Peng Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Waveform Inversion ◽  
Structural Models ◽  
Velocity Model ◽  
Dense Layer ◽  
Full Waveform Inversion ◽  
Model Inversion ◽  
Full Waveform ◽  
Inversion Methods ◽  
The Common

Velocity model inversion is one of the most important tasks in seismic exploration. Full-waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion methods, but it heavily depends on initial models and is computationally expensive. In recent years, a large number of deep-learning (DL)-based velocity model inversion methods have been proposed. One critical component in those DL-based methods is a large training set containing different velocity models. We have developed a method to construct a realistic structural model for the DL network. Our compressional-wave velocity model building method for creating dense-layer/fault/salt body models can automatically construct a large number of models without much human effort, which is very meaningful for DL networks. Moreover, to improve the inversion result on these realistic structural models, instead of only using the common-shot gather, we also extract features from the common-receiver gather as well. Through a large number of realistic structural models, reasonable data acquisition methods, and appropriate network setups, a more generalized result can be obtained through our proposed inversion framework, which has been demonstrated to be effective on the independent testing data set. The results of dense-layer models, fault models, and salt body models that we compared and analyzed demonstrate the reliability of our method and also provide practical guidelines for choosing optimal inversion strategies in realistic situations.

Incorporating Acquisition Geometry in Deep Learning-Based Full Waveform Inversion

2021 ◽  
Author(s):  
H. Hashemi ◽  
M. Saadat ◽  
M. Nabi Bidhendi ◽  
P. De Groot
Keyword(s):  
Deep Learning ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform
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