Data-Driven Sampling

PhD Candidate Trygve Olav Fossum

Data-Driven SamplingWhat Robots Can Do For Ocean Science

Trygve Olav Fossum

Department of Marine Technology

Norwegian University of Science and Technology

http://ntnu.edu/employees/trygve.o.fossum

[email protected]

http://ntnu.edu/employees/trygve.o.fossum

MotivationTraditional practice:


Sampling the upper ocean

Remote Sensing

Drifters / Profiling floats

Fixed Moorings (time series stations)

Ship based and USV

Propelled and Glider AUVs

Coastal Networks

UAV and balloons

The tools to study the upper ocean


Ocean Models

B

U

O

Y

S

Ship

based

Remote Sensing

Gilder AUVs

Propelled AUVs

Ocean

sampling

Experiment design should be driven by

models and remote sensing, and follow

the value of information concept. The data

should be collected using autonomous

resources, supplemented with data

traditional in-situ assets.

Ocean sampling is dependent on a range

of sources to “fill the gaps”

FLOATERS


AUVsInstruments and measurements from

AUVs (Autonomous Underwater

Vehicles).


Data-Driven

sampling

Combine ocean models with robotic- and

remote sensing in order to render an

accurate representation of the ocean.

Use data-driven sampling to strategize

sampling efforts.


Adaptive vs. Non-adaptive


Sense Act

Sense Model Plan Act

Adaptive / Data-Driven Approach:

Sense Model Plan Act

Field

campaignResults from ENTiCE campaign. The

project set out to map and understand the

productive Froan archipelago.

Ocean Models

B

U

O

Y

S

Ship

based

Remote Sensing

Gilder AUVs

Propelled AUVs


Temperature

dynamicsTemperature is related to a number of

ocean phenomena and events; upwelling,

frontal zones, internal waves, local

circulation and turbulence, eddies and

biomass accumulation.

Idea: Use temperature variation, as

predicted by model, as a indicator of

dynamically active regions and as a way to

understand and approach model deviation

and shortcomings.

Surface temperature from SINMOD (ocean model) at

the Froan archipelago (Mid – Norway)

The date:


Internal

dynamicsTemperature is related to a number of

ocean phenomena and events; upwelling,

frontal zones, internal waves, local

circulation and turbulence, eddies and

biomass accumulation.

Idea: Use temperature variation, as

predicted by model, as a indicator of

dynamically active regions and as a way to

understand and approach model deviation

and shortcomings.

3D temperature structures and internal dynamics

Salinity layers (Isopycnals)


Finding a

suitable

survey areaAnalyze the dynamics in the model and

calculate the most interesting area.

A map of the empirical temperature variance


Data-Driven

sampling

Combine ocean models with robotic- and

remote sensing in order to render an

accurate representation of the ocean.

Use data-driven sampling to strategize

sampling efforts.


Information driven planning with AUV: Diving into ocean ecosystems – Trygve Fossum NTNU 2016

Data interpretation

Spatial reconstruction based on:

1-Gaussian Fields

2-“kriging” – spatial interpolationPhD Candidate Trygve Olav Fossum

Assimilation

in proxy modelFormulate a surrogate ocean model based

on a Gaussian Process and assimilate

online towards this proxy model.


Algorithm and

WaypointsUse an objective function to find locations

that have high variance and gradients.


Adaptive

behaviorResults from ENTiCE campaign. The

project set out to map and understand the

productive Froan archipelago.


Comparing

with OMDComparing SINMOD (ocean model) data

with in-situ measurements across different

depths and distance.


ConclusionThe ENTiCE project is one step in the right direction

– Combines robotic sampling and ocean models.

Ocean models as the starting point for ocean

sampling.

More have to be done towards assimilation of data

into the high fidelity model (SINMOD).

The first step is to adjust parameters and work

scenario based.

AUVs can efficiently find and provide the in-situ data.

Has to be accompanied by data from other sources

(remote sensing, buoy, ships, etc.)


AcknowledgementsThe entire ENTiCE project team.

Special thanks to: Jo Eidsvik, Ingrid Ellingsen, Morten

Alver, Geir Johnsen, Martin Ludvigsen and Kanna Rajan.

ENTiCE is funded by the Research Council of Norway.


Data-Driven Sampling

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