Fast modelling of Airborne EM data using "Smart Interpretation"

…by I•GIS

Presented at the 2015 AGU meeting in San Fransisco

Smart Interpretation – application of machine learning in geological interpretation of AEM data

Torben Bach 1, Rikke Jakobsen1, Tom Martlev Pallesen1, Mats Lundh Gulbrandsen2, Thomas Mejer Hansen2, Anne-Sophie Høyer3, Flemming Jørgensen3

1. GeoScene3D Team, I-GIS, Risskov, Denmark2. Niels-Bohr Institute, Computational Geoscience, University of Copenhagen, Denmark3. Geological Survey of Denmark and Greenland (GEUS), Denmark

The ERGO project: Effective High-Resolution Geological Modeling

…by I•GISOutline

Presentation outline

• Motivation behind and Introduction to “Smart Interpretation”

• Workflow when modelling with “Smart Interpretation”

• Case Example, Gotland, Sweden

• Summary and outlook

Introduction Workflow Test Case Summing Up

…by I•GISMotivation

Motivation for Smart Interpretation (SI)• Observations:

• Large AEM surveys - enormous amount of data.• One the one hand - manual interpretation is time consuming• On the other hand - geophysical resistivity is not necessarily linked to geological formation or

lithology • A Geological expert is needed.

• Inspiration: Seismic Auto-picker, used daily as a standard part of modelling of seismic data in O&G

• Goal: Develop a practical and usable tool for assisting the Geologist


Autumn Spring

20 50 ohmm

Sand and Clay have overlapping resistivitiesSeasonal variation is reflected in resistivities

…by I•GISSI - Theory

Steps• Infer a statistical model h(d|M)• Solve the problem: d = f (M).• Perform predictions dpred with uncertainty

Mpred

dpred

f(M)

h(dpred|Mpred)

+/- 1 std.

M

d

Our Toolbox• Standard Gaussian based inversion theory – with a twist…**

Benefits compared to other Machine Learning techniques:• Tools for analysing parametric covariances and interdependencies• A measure of uncertainty on the estimates• Very fast !

**See ”Smart Interpretation - Automatic geological interpretations based on supervised statistical models” byGulbrandsen, Cordua , Bach and Hansen, currently subitted and in review for ”Computational Geosciences”



M

Geophysical Data (M)



M d

Geophysical Data (M)

Geological Knowledge (d)



M d

h(d,M)

Geophysical Data (M) Statistical Model

h(d,M)




M d

h(d,M)

Mpred

dpred

Geophysical Data (M) Statistical Model

h(d,M)

Geophysical Data Elsewhere

Mpred Predicted Geology with uncertainty

h(dpred|Mpred)



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1:Add manual interpretation

2:Run SI Locally3:Apply

algorithm globally

4:Evalute and QC result


Workflow in Production

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Groundwater mapping on the Island of Gotland

Courtesy Peter Dahlquist, SGU

Test Case


…by I•GISTest Setup


The Geologists• Geologist 1: Using normal manual modelling• Geologist 2: Using SI assisted manual modelling

Limestone

Marlstone

Clay- and marlstone

The Geology

Sharp boundary

Diffuse Zone

The Test• Compare ”Manual Model” to ”Model generated using 10% as input to SI”

• Compare ”Manual Model” to ”SI assisted Model”

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Reference Model

The manual model

…by I•GISTest: Manual Model


Surface 2Surface 1

Geologist 1 – a standard manual model

• Evenly distributed mesh of manual interpretation points• Surfaces dipping trend towards the south-east• Abrupt high in north-west

…by I•GISTest: Manual Model


The Geologist avoids couplings and artifacts in data

Difuse ZoneInterpreted

The Geologist models the ”pinch out” of the ”diffuse” layer

Geologist 1 – a standard manual model

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TEST 1

Throw away 90% of the Geologists input

– and run Smart Interpretation

…by I•GISTest: SI using 10% of Manual Model


• General trend in surfaces is reproduced• Higher small scale variation due to the increased amount of interpretation points

Surface 2Surface 1Manual Manual

MANUAL

…by I•GISTest: SI using 10% of Manual Model



Surface 2Surface 1Manual 10% of manual points, 1688 SI points generated

Manual 10% of manual points, 1653 SI points generated

Smart Interpretation

…by I•GISTest: Reduced Model 10%


Geologist 1 Remove 90% of interpretation points – and run SI

10% Manual + SI26 man.points, 1653 SI.points

Difference

Surface 1264 points

343 points

Surface 2

Manual Model+/- 10 m

26 man.points, 1688 SI.points

…by I•GIS

Manual

Test: SI using 10% of Manual Model


Manual

MANUAL

…by I•GIS

Manual

Test: SI using 10% of Manual Model


10% of manual points

Manual10% of manual points

Couplings only partly managed

Difuse ZoneIs managed

Pinch Out is managed


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TEST 2

A model build using Smart Interpretation

…by I•GISTest: SI Assisted Model



Surface 2Surface 1

Manual Model Manual Model

MANUAL




Surface 2Surface 1

Manual Model Manual ModelSI Assisted Model SI Assisted Model


…by I•GIS

Manual

Manual

Test: SI Assisted Model


MANUAL

…by I•GIS

Manual

Manual

Test: SI Assisted Model


SI Assisted Model

SI Assisted Model

Couplings are managed

Difuse ZoneIs managed

Pinch Out is managed




Summary• The theoretical framework derived from Gaussian based inversion techniques

• It is very fast• calculation uncertainty

• Test case shows ability to map couplings and diffuse geological boundaries• More interpretation points -> more variation in the generated surfaces• Implemented in production software GeoScene3D

Looking ahead…• Currently underway

• developments toward looking for “structures” in data• other attribute types, e.g. coherency• other datatypes included in SI

Come and join us

…by I•GIS

Thank You !

Science

Fast modelling of Airborne EM data using "Smart Interpretation"