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