See the sea_floor

How to “See” theHow to “See” the
Sea-FloorSea-Floor
Frank W. Bentrem,Frank W. Bentrem,
Data Science FellowData Science Fellow
Insight Data ScienceInsight Data Science
New York, New YorkNew York, New York
Collaborator:Collaborator:
Will AveraWill Avera
Naval Research LaboratoryNaval Research Laboratory
Stennis Space Center,Stennis Space Center,
MississippiMississippi

Which planet has been mostWhich planet has been most
mapped in the greatest detail?mapped in the greatest detail?

• Hint: It’s not Earth (at 100m resolution)

• Answer: Mars (then Venus, then Earth)
Copley, J. The Conversation, 2014
“Just how little do we know about the ocean floor?”
https://theconversation.com/just-how-little-do-we-know-
about-the-ocean-floor-32751

• Satellites use light
– Great for atmospheric gases
– Not so good for sea-water

• Satellites use light
– Great for atmospheric gases
– Not so good for sea-water
• Sonar uses sound
– Limited range in air (bats)
– Long range under water (dolphins, whales)

Outline
●
“Seeing” through hearing
– Turning sound into images
●
Data mining for sediment type
– Finding most likely model parameters
– Use an idea from materials science
●
Finding boundaries
– Image segmentation
– Use an idea from statistical mechanics

●
Seeing with Light
– Light has a source
(sun, flashlight)
– Light travels as waves
(electromagnetic)
– The eyes sense light
– Brain interprets
“Seeing” the Sea-Floor

●
Seeing with Light
– Light has a source
(sun, flashlight)
– Light travels as waves
(electromagnetic)
– The eyes sense light
– Brain interprets
●
“Seeing” with Sound
– Sound has a source
(sonar, speaker)
– Sound travels as
waves (pressure)
– Microphones record
sound
– Computer interprets
“Seeing” the Sea-Floor

Illuminating the Sea-Floor
●
Loud echoes show
as bright spots
●
Shadows and soft
echoes show as dark
spots
●
Sand ripples
●
The Navy searches
for mines
●
Need to identify
sediment
imgur.com/user/higgsbos0n

●
Forming beams
– Time-syncing
●
Sonar speak
(Ensonifying the
Ocean Floor)
●
Echo strength falls
with angle
●
Echo curve indicates
sediment type
Illuminating the Sea-Floor

Sediment Types
• Mud
• Fine Sand
• Coarse Sand
• Gravel/Rock
4 < ϕ
1.5 < ϕ < 4
-1 < ϕ < 1.5
ϕ < -1
Uses mean-grain-size scale, ϕ = -log2(d /d0),
where d is mean-grain diameter and d0 = 1 mm

Scattering Model
●
Backscattering Strength (BS) is:
BS = 10 log(Iin / Iout),
where I is intensity at sea-floor Iin
Iout

Scattering Model
●
Backscattering Strength (BS) is:
BS = 10 log(Iin / Iout),
where I is intensity at sea-floor
●
Ocean Bottom Backscatter Model
by Jackson, D.R., et al., APL-UW TR 9407
APL-UW High-Frequency Ocean Environmental Acoustic
Models Handbook
●
Considered valid for frequencies in the range 10-100 kHz
Iin
Iout

Scattering Model
● BS(θ ) = 10 log(σr + σv),
where
– σr is scattering cross section at rough sea-floor
boundary, and
– σv is scattering cross section from sea-floor volume
●
BS is a function of angle θ , frequency, and 6
other geoacoustic parameters
BS(θ ) = f (a1,a2,a3,a4,a5,a6)
Iin
Iout

Scattering Model
●
Geoacoustic parameters
– a1, density ratio
– a2, sound speed ratio
– a3, loss parameter
– a4, volume parameter
– a5, spectral exponent
– a6, spectral strength
●
Find most likely sediment type (Yikes!)
●
Play hotter/colder game?
} Parameterized by ϕ

Simulated AnnealingSimulated Annealing
S (Cost)
ΔS
Parameter to optimize
Best Fit

Annealing
www.youtube.com/watch?v=GCAjCVMvkBY

Field Test
●
SediMap®
●
Gulf of Mexico
●
5 days
●
Core samples yielded
mud
●
SediMap® indicated
0.5 km2 patch of sand
●
More samples
showed ...

SediMap®
●
Validation criteria was set
●
Testing period – passed!
●
Formally transitioned to the Naval
Oceanographic Office
●
Discussed other needs with the NAVOCEANO
geologists
●
Agreed to a follow-on project

Sediment Boundaries
●
Geologists:
– Can you do our work
for us?
– Find boundaries for
sediment regions
– Btw: Make it real-time
●
Image Segmentation
– Existing algorithms
●
Fast or texture-based
– Need: Fast algorithm
with texture

Intensity Image
• Acoustic imagery can
be represented as an
intensity matrix
• Low numbers --- low
intensity (black)
• High numbers ---
high intensity (white)
• What to do?
1 2 4 2 3 3 4 3 2 3
2 3 2 2 3 4 3 3 4 3
3 3 4 4 5 4 4 4 3 4
3 3 3 4 4 4 5 4 4 4
3 3 3 3 4 5 5 6 5 6
2 3 4 3 3 4 5 6 5 6
3 4 3 4 5 6 7 6 7 8
3 4 4 5 4 5 5 6 7 6
4 5 5 6 6 7 8 7 8 7
Pixel Matrix

●
North and south poles
●
Arrow from south to
north defines
orientation
●
Domains in some
materials behave as
bar magnets
●
Domain behavior
determines type
Bar Magnets
Image from Wikipedia

Types of Magnetism
●
Ferromagnetism
●
align with no field
●
Anti-ferromagnetism
●
alternate with no field
●
Paramagnetism
●
align with field (weak
attraction)
●
Diamagnetism
●
align against field (weak
repulsion)
↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑
↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑
↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑
↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑
↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑
↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓
↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑
↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓
↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑
↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓↑↓

Ising Model
●
Replace arrows with
1's and -1's
●
Used to study
disordered systems
●
E is energy
●
H is magnetic field
●
J is coupling constant
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
Ferromagnet
1-1 1-1 1-1 1-1
-1 1-1 1-1 1-1 1
1-1 1-1 1-1 1-1
-1 1-1 1-1 1-1 1
1-1 1-1 1-1 1-1
Anti-ferromagnet
E=−H ∑
i
si−J ∑
nn
si s j

Ising Model
●
H = 0, J > 0
●
H = 0, J < 0
●
H > 0, J = 0
●
H < 0, J = 0
●
Ferromagnetism
●
Anti-Ferromagnetism
●
Paramagnetism
●
Diamagnetism
E=−H ∑
i
si− J ∑
nn
si s j

Potts Model
●
Ising model
– s = -1, 1
●
Potts model is a generalization
– s = 0, 1, 2, 3,...,(q-1)
● δ(s i,s j) = 1 if s i = s j
● δ(s i,s j) = 0 otherwise
E=− H ∑
i
si− J ∑
nn
δ (si ,s j)

Segment Categories
White Black
Light Gray Dark Gray
Intensity
Bright Dark
Texture
UniformVariable

Segment Categories
H > 0, J > 0 H < 0, J > 0
H > 0, J < 0 H < 0, J < 0
H
Paramagnetic Diamagnetic
J
Ferromagnetic
Anti-
ferromagnetic

Segmentation Process
●
Example: q = 10
●
s = 0,1,2,3,4,5,6,7,8,9
●
For each pixel
– If s = [0,4], H > 0
• otherwise H < 0
– If s for at least one
of the nearest-
neighbor pixels = s,
J < 0
• otherwise J > 0
●
Minimize the energy
1 2 4 2 3 3 4
2 3 2 2 3 4 3
3 3 4 4 5 4 4
3 3 3 4 4 4 5
3 3 3 3 4 5 5
2 3 4 3 3 4 5
3 4 3 4 5 6 7
H > 0, J < 0H > 0, J < 0

Example
Original
Image
Previous
Method

Example
Original
Image
Previous
Method
Potts
Method

Example
Original
Image
Previous
Method
Potts
Method
Sky
Brush

Example
●
Sidescan Sonar
●
Finds sand
ripples
●
Processes in
real-time (linear)
●
2 Patents issued
●
1 Patent
pending

SummarySummary
• Sound can be processed to make detailed
images.
• Sea-floor sediment can be predicted using
a scattering model with simulated
annealing.
• Sediment boundaries can be rapidly
determined using a novel image
segmentation algorithm.

My JourneyMy Journey
Frank Bentrem
Scientific Computing, Ph.D.
Polymer Simulations
Acoustic Remote Sensing
Teaching Physics
Quantitative Finance
Data Science Fellowship

See the sea_floor

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