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Dual-band generative learning for low-frequency extrapolation in seismic land data
Jan. 17, 2022•0 likes•112 views
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The presence of low-frequency energy in seismic data can help mitigate cycle-skipping problems in full-waveform inversion. Unfortunately, the generation and recording of low-frequency signals in seismic exploration remains a non-trivial task. Extrapolation of missing low-frequency content in field data might be addressed in a data-driven framework. In particular, deep learning models trained on synthetic data could be used for inference on the field data. Such an implementation of switching application domains remains challenging. We, therefore, propose the concept of generative dual-band learning to facilitate the knowledge transfer between synthetic and field seismic data applications of low-frequency data extrapolation. We first explain the two-step procedure for training a generative adversarial network (GAN) that extrapolates low frequencies. Then, we describe the workflow for synthetic dataset generation. Finally, we explore the feasibility of the dual-band learning concept on real near-surface land data acquired in Saudi Arabia.
Oleg OvcharenkoFollow
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Dual-band generative learning for low-frequency extrapolation in seismic land data
- Saudi Aramco: Public Dual-band learning for improved knowledge transfer applied to land data O. Ovcharenko, V. Kazei, D. Peter, I. Silvestrov, A. Bakulin, T. Alkhalifah 1 Denver, CO, 2021 © Saudi Arabian Oil Company, 2021
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public About 2 PhD Candidate at SMI group at King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Oleg Ovcharenko Олег Овчаренко
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Challenge and solution 3 Deep learning inference = building a solution as a combination of learned kernels (basis functions) Training dataset != application dataset Synthetic Real
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Challenge and solution 4 Domain adaptation ~~ train on one dataset apply to another Deep learning inference = building a solution as a combination of learned kernels (basis functions) Training dataset != application dataset Synthetic Real Solutions: 1. Make datasets similar in the data domain 2. Make datasets similar inside the network
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Challenge and solution 5 Domain adaptation ~~ train on one dataset apply to another Deep learning inference = building a solution as a combination of learned kernels (basis functions) Training dataset != application dataset Synthetic Real Solutions: 1. Make datasets similar in the data domain 2. Make datasets similar inside the network
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Low-frequency extrapolation 6 Why low-frequencies? Converge to better minimum Encode large-scale structures Attenuate less Why not available? Costly to acquire Complex machinery Noise
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Overlapping bands 7
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Overlapping bands 8
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public 9 Overlapping bands 5 < High < 15 Hz 5 < Mid < 10 Hz 0 < Low < 5 Hz
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Data 10
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Data 11 Full-offset common-receiver gather
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public ”Marine-like” setup 12 Extracted 128 “field shots” Full band < 15 Hz
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Synthetic data 13 Central well-logs Survey design for synthetic data generation 256 models 4 shots / model mesh 100 x 500 5 m spacing
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Comparing datasets 14
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Single training pair 15 Synthetic Field High Mid Low 128 128 5 < High < 15 Hz 5 < Mid < 10 Hz 0 < Low < 5 Hz 1000 training shots Spacing: 10 m x 8 ms 1280 m 1 sec Scaled to [-1, 1]
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Single training pair 16 Synthetic Field High Mid Low 128 128 5 < High < 15 Hz 5 < Mid < 10 Hz 0 < Low < 5 Hz 1000 training shots Spacing: 10 m x 8 ms 1280 m 1 sec Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data *p - predicted HS MS LS HF MF LF Scaled to [-1, 1]
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Training strategy 17 Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data *p - predicted
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Training strategy 18 0. Learn synthetic to synthetic mapping. Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data *p - predicted
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Training strategy 19 0. Learn synthetic to synthetic mapping. 1. Pass synthetic data where low and mid frequencies are known Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data *p - predicted
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Training strategy 20 0. Learn synthetic to synthetic mapping. 1. Pass synthetic data where low and mid frequencies are known 2. Pass field data where only mid frequencies are known Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data *p - predicted
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Training strategy 21 0. Learn synthetic to synthetic mapping. 1. Pass synthetic data where low and mid frequencies are known 2. Pass field data where only mid frequencies are known 3. Discriminator evaluates whether the combination of predicted low-mid pair from field data matches the one from synthetic. Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data *p - predicted
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Training strategy 22 0. Learn synthetic to synthetic mapping. 1. Pass synthetic data where low and mid frequencies are known 2. Pass field data where only mid frequencies are known 3. Discriminator evaluates whether the combination of predicted low-mid pair from field data matches the one from synthetic. Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data *p - predicted
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Training strategy 23 0. Learn synthetic to synthetic mapping. 1. Pass synthetic data where low and mid frequencies are known 2. Pass field data where only mid frequencies are known 3. Discriminator evaluates whether the combination of predicted low-mid pair from field data matches the one from synthetic. Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data *p - predicted
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public One prediction 24 Input Spectrum HF MF LF LFp
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public What if we don’t use the dual-band approach? 25 < 5 Hz HS+MS Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data HF HS+MS +HF LF Input < 3 Hz MF HS UNet UNet GAN Target Target
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public What if we don’t use the dual-band approach? 26 < 5 Hz HS+MS Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data HF HS+MS +HF LF Input < 3 Hz MF HS UNet UNet GAN Target Target
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public What if we don’t use the dual-band approach? 27 < 5 Hz HS+MS Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data HF HS+MS +HF LF Input < 3 Hz MF HS UNet UNet GAN Target Target
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public What if we don’t use the dual-band approach? 28 < 5 Hz HS+MS Legend: HS, MS, LS – high-, mid- and low-frequency synthetic data HF, MF, LF – high-, mid- and low-frequency field data HF HS+MS +HF LF Input < 3 Hz MF HS UNet UNet GAN Target Target
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public A few more examples 29 HS+MS HS+MS +HF LF HS 1. 2. 3. 4. 5. 6. Samples
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Conclusions 30 1. Training on synthetic and field data at the same time 2. Intermediate frequency band to pivot generator and discriminator 3. Discriminator presumably measures ratio between channels 4. The method improves inference for low-frequency extrapolation when applied to land data but further research needed to quantify performance
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Acknowledgements 31 We thank KAUST and Saudi Aramco for funding this work. As well as SMI and SWAG groups from KAUST for frutiful discussions
- Saudi Aramco: Public oleg.ovcharenko@kaust.edu.sa Saudi Aramco: Public Conclusions 32 1. Training on synthetic and field data at the same time 2. Intermediate frequency band to pivot generator and discriminator 3. Discriminator presumably measures ratio between channels 4. The method improves inference for low-frequency extrapolation when applied to land data but further research needed to quantify performance
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