Slides for my talk at SEG Workshop in Manama, Bahrain, December 2017. We introduce an approach to extrapolate for missing low-frequency data from frequency representation of multi-offset seismic data. Meaning that data on multiple high-frequencies is used to infer a single low-frequency for each receiver. In the end, we demonstrate a preliminary example of building an initial model for FWI from the extrapolated data.
5. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
FWI without low frequencies
Modifications of misfit/gradient
(Warner et al., 2015; Leeuwen & Herrmann, 2014; Métivier et al., 2016)
(Alkhalifah, 2015, 2016; Kazei, et al., 2016)
etc…
Pros:
- Established workflow
- Relative robustness
Cons:
- Computational costs
- Prone to event mismatching
6. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
FWI without low frequencies
Extrapolation of low-frequency data
Pros:
- Cheaper computations
Cons:
- Not well explored robustness
- Wavefield approximations
(Smith et al., 2008; Hu et al., 2014; Li & Demanet, 2015, 2016)
etc…
7. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
Low-frequency extrapolation
Beat tone inversion
(Hu et al., 2014)
Bandwidth extension for atomic events
(Li & Demanet, 2015, 2016)
Bandwidth extension using
Continuous Wavelet Transform
(Smith et al., 2008)
This work: Low-frequency data extrapolation using Artificial Neural Network
14. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
Neural Networks pros and cons
- Lots of parameters
- Hard to interpret
- Comp. costs for training
+ Good for highly-nonlinear problems
+ Good for large inputs
+ Data-driven
+ Easy to implement and parallelize
Not a magic wand, use with care
Pros Cons
26. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
Random model generation
- Random Gaussian field
- Flat bathymetry
- Fixed model size
- Permissible velocity range
- Use data for each src-rec pair
Sampling multidimensional model space
The more diverse data is - the better
27. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
Random model generation
- Random Gaussian field
- Flat bathymetry
- Fixed model size
- Permissible velocity range
- Use data for each src-rec pair
Sampling multidimensional model space
The more diverse data is - the better
28. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
Random model generation
- Random Gaussian field
- Flat bathymetry
- Fixed model size
- Permissible velocity range
- Use data for each src-rec pair
Sampling multidimensional model space
The more diverse data is - the better
29. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
Main idea
High-frequency data for
random velocity
models
Low-frequency data for
random velocity
models
Predict data on single low-frequency from multiple high-frequency data
NSRC*NFREQ training
samples from a single
random velocity model
47. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
Computational facts
48000
total
960 240
NN: 3 hidden layers
- 2 * N inputs
- 2 * N outputs
- 1 * N outputs
Batch size: 1024
Learning rate: 0.005
Optimizer: Adam
Weight regularization: 0.005
NVIDIA Quadro K2200TensorFlow 1.3.0Python 3.6 Keras 2.0.5Matlab R2016b
Initialization “xavier”
(Glorot & Bengio, 2010)
Training time ~ 5 min
Prediction time ~ 5 sec
Training data generation
~ 40 min on 24 cores
51. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
• Phase is predicted better than amplitude
• Lower frequencies are better predicted
• Model generator is crucial
• Current network type and architecture are not optimal
Next steps:
• Improve data generator
• Search for optimal configuration
• Explore stability
• Compare with other techniques
Conclusions
52. oleg.ovcharenko@kaust.edu.saLow-frequency data extrapolation
The End
Automated fault detection
(Araya-Polo et al., 2017)
Salt body picking
(Guillen et al., 2017)
Mapping reservoirs on
migrated seismic
(Bougher, 2016)
Facies classification
and reservoir properties prediction
(Hall, 2017; Ahmed et al., 2010)
Event detection
(Akram et al., 2017)
Interpolation of missing data
(Jia and Ma, 2017)
Denoising
(Zhang et al, 2017)
Some NN applications in geophysics
Inversion of seismic, DC data etc.
(Röth, Tarantola, 1994;
Neyamadpour et al., 2009)