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We explore the feasibility of surface-related multiple elimination by two-step separation where primaries and multiples are separated in the latent space of a convolutional autoencoder. First, we train a convolutional autoencoder to produce orthogonal embeddings of primaries and multiples. Second, we train another network to classify the latent space embedding of target data into respective wave types and decode predictions back to the data domain. Moreover, we propose an end-to-end workflow for the generation of realistic synthetic seismic data sufficient for knowledge transfer from training on synthetic to inference on field data. We evaluate the two-step separation approach in synthetic setup and highlight the strengths and weaknesses of using masks in encoder latent space for surface-related multiple elimination.

Oleg OvcharenkoFollow

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- Surface-related multiple elimination through orthogonal encoding in the latent space of convolutional autoencoder Oleg Ovcharenko¹, Anatoly Baumstein, and Erik Neumann², ExxonMobil Upstream Research Company Acknowledgements: Huseyin Denli, Joe Reilly and many other colleagues ¹presently at KAUST ²presently at ExxonMobil Production Deutschland GmbH
- Outline 2 Introduction • Problem • Value • Intuition behind the problem New ML multiple attenuation approach • Training data generation • Architectures Examples • Jaktopia synthetic data • NW Australia field data
- Surface-related multiples 3 https://www.youtube.com/watch?v=ua3r_KWn7bY&t=867s
- Echo in the data 4 Primaries Multiples Observed data
- Remove echo! 5 Primaries Multiples Observed data
- Remove echo! 6 Primaries Multiples Observed data
- Not that easy 7
- Conventional methods 8 https://www.youtube.com/watch?v=ua3r_KWn7bY&t=867s https://www.youtube.com/watch?v=EwjuhtKxkcQ
- What we aim to do 9 Primaries (P) Multiples (M) Data (D) Neural network (-s)
- Why do we need deep learning (DL)? 10 Why? • Advanced methods are costly (human efforts, time) • 2D out-of-plane effects • No totally accurate solution yet What? • Data-driven split of primaries and multiples based on previous experience Value? • Approximately correct at lower cost • Possible workaround of out-of-plane effects
- Why do we need deep learning (DL)? 11 Why? • Advanced methods are costly (human efforts, time) • 2D out-of-plane effects • No totally accurate solution yet What? • Data-driven split of primaries and multiples based on previous experience Value? • Approximately correct at lower cost • Possible workaround of out-of-plane effects The goal is to explore other ways
- Why do we need deep learning (DL)? 12 Why? • Advanced methods are costly (human efforts, time) • 2D out-of-plane effects • No totally accurate solution yet What? • Data-driven split of primaries and multiples based on previous experience Value? • Approximately correct at lower cost • Possible workaround of out-of-plane effects
- Intuition (physics) behind DL 13 Emulate Radon in latent space Encode waveform statistics ß Multiple shots ß Individual shots Train to … Given … Encode physics in the subsurface ß Individual CMPs after NMO
- What is training data? 14 Very synthetic synthetic (SS) Data-based synthetic (DS) Processed field data from nearby (DN) Processed field data from far away (DF) Realism Efforts SS DS DN DF
- 15 Unlimited amount Exact wave field separation Not limited by conventional Proximity to field required Data-based synthetic D P M Field Syn Closer look in examples
- 16 Field data RMS velocities PSTM or NMO stack Reflectivity Pseudo- density (in depth) Simulation with FS boundary condition and field data geometry Simulation with MIRROR boundary condition and field data geometry Data Primaries Multiples Interval velocities In depth Data-based synthetic Matched filter
- Two approaches 17 Separation in data domain Separation in encoded domain zD zP zM D pP pM D pP pM 1 2 Cats + Dogs Cats Dogs Cats + Dogs Cats Dogs Cats + Dogs in encoded domain
- Separation in data domain. U-Net 18 Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015. D pP pM U N E T P M L1 L1 1
- Tzinis, Efthymios, et al. "Two-Step Sound Source Separation: Training On Learned Latent Targets." ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. Separation in encoded domain 1. Orthogonal encoding 2. Separation in encoded space Two-stages: 2
- D P M D’ P’ M’ Df Df’ zP zM zD zDf mP mM Softmax zD zD E N C O D E R D E C O D E R Shared encoder to make primaries and multiples separable in the latent space L1 Df D P M Stage 1. Orthogonal encoding Field data Synthetic data Synthetic primaries Synthetic mutliples
- Stage 1. Orthogonal encoding 21 E N C O D E R D E C O D E R D D’ Encoded data
- Stage 1. Orthogonal encoding 22 mP + mM + mNA = 1 E N C O D E R D E C O D E R D D’ P M N/A Latent space masks
- mP mM Cross Entropy Loss P M N/A 3-classes: Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation. https://arxiv.org/abs/1809.07454 zD Stage 2. Separation in encoded space
- Two-stage separation (TSS) inference bird view 24 D zD Classifier E D mP’ = zP’ mM’ zD = pP pM zD zM’ Stage 1 Stage 2 1. Select field data 2. Make DS for training 3. Train orthogonal encoder 4. Train latent space classifier 5. Run inference on field data 6. *Asub 7. *Stack Complete workflow:
- • The model was generated by combining stratigraphic information with a rock physics model (𝑉!"#$% and porosity) and includes several different classes of AVO. • A gas cloud was introduced, creating imaging challenges underneath. Synthetic model 𝑛!"# = 1000 𝑛"$# = 480 𝑑!"# = 25.0 𝑚 𝑑"$# = 12.5 𝑚 𝑑% = 6.25 𝑚 source = band-limited spike Observed data
- Data-based synthetic 26 Same acquisition Smooth Vp Rho stretched respectively Acoustic modeling Vp Rho Source D = TPOW(D, 1) D /= max(abs(D))
- 27
- 28
- D P M UNet
- 30
- D P M TSS
- D P M What are we looking for?
- D P M Keep Remove What are we looking for?
- Raw predictions D P P1 P2 M1 M2 M 1 – Unet 2 – TSS
- Raw predictions D P P1 P2 M1 M2 M 1 – Unet 2 – TSS
- Subtract in stack domain (D – (PM - PP)) D P P1 P2 M1 M2 M 1 – Unet 2 – TSS
- Subtract in data domain (D - PP) D P P1 P2 M1 M2 M 1 – Unet 2 – TSS
- 38
- M RAW
- 40
- 41
- 42
- D P M
- 44
- 45
- D P M Reference stacks
- Full stack and after SRME
- SRME predicted multiples
- Multiples by SRME Unet TSS 49
- After de-multiple SRME / UNet / TSS 50
- Summary We developed a new data-driven ML-aided multiple attenuation method: • Produces estimates of primaries and multiples • Does not rely on conventional demultiple methods • Is not limited by incomplete acquisition • Delivers fast turnover > This is a proof-of-concept study
- Acknowledgements 52 Based on Geoscience Australia material: Arachnid 2D/W99ARA-019
- Summary We developed a new data-driven ML-aided multiple attenuation method: • Produces estimates of primaries and multiples • Does not rely on conventional demultiple methods • Is not limited by incomplete acquisition • Delivers fast turnover > This is a proof-of-concept study

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