Norwegian version of this page

Disputation: Aina Juell Bugge

Doctoral candidate Aina Juell Bugge at the Department of Geosciences, Faculty of Mathematics and Natural Sciences, is defending the thesis Aspects of automated seismic interpretation for the degree of Philosophiae Doctor.

Aina Juell Bugge. Photo: Simula

Aina Juell Bugge. Photo: Simula

The University of Oslo is currently closed, and disputations will therefore be streamed directly using Zoom. The host will moderate the digital issues while the defense moderator chairs the defense.

Ex auditorio questions: The defense moderator will invite ex auditorio questions, and these can be submitted either in writing or orally by clicking "Participants -> Raise Your Hand".

Click here to participate in the defense (opens at 08.45 on May 5)

Downlaod Zoom from here

 

Trial lecture - time and place

Machine learning applied to geophysical well log data for rock- and fluid-property estimation | Digital recording of the trial lecture

Conferral summary

Seismic reflection data provide images of the Earth’s subsurface, from which geologists can map out geological structures of interest and interpret the geological evolution. The mapping of geological structures in seismic images is a time-consuming and often manual process. This dissertation deals with workflows for automatic interpretation of the seismic data. The candidate has developed digital tools and automated workflows that extract qualitative and quantitative information from seismic data. The workflows are based on signal processing, image processing and machine learning algorithms.

Main research findings

Popular scientific article about Bugge’s dissertation:

Aspects of automated seismic interpretation

To understand the Earth’s subsurface, geoscientists map out geological structures of interest and interpret its geological evolution from seismic images. These images are generated through acquisition and processing of seismic reflection data, where pressure waves are emitted and sent into the ground, and reflected back to the surface when hitting different geological strata.

Image may contain: Map, Text, Line, Ecoregion, Slope.
The figure illustrates a manually interpreted, complex seismic image from the Barents Sea, north of Norway, with geological sequences deposited during different stages of the geological evolution in the area. The geological sequences represent units of relatively conformable seismic reflections, i.e. time intervals of similar sedimentation conditions, governed by sediment supply and relative sea level.
Credit: Lundin Norway AS

The interpretation of seismic images is a labor intensive and integrated process that requires geophysical experience and an intuitive geological understanding. While manual interpretation of seismic data often is essential in order to accumulate knowledge and build an understanding of the subsurface, some elements of the interpretation workflow can be tedious and partly trivial.

In this thesis, we present data-driven methods that seek to integrate data science and geoscience in order to address different aspects of automated seismic interpretation. These methods, based on digital tools from image processing, signal processing and machine learning, can be used to automate the interpretive workflow and to extract qualitative and quantitative information from seismic data without the need for manual user-interaction.

Photo and other information:

Press photo: Aina Juell Bugge, portrait; 500px. Photo: Simula

Other photo material: Figure with description and credit as specified in the article above, size 1000px.

Published Apr. 21, 2020 9:21 AM - Last modified June 9, 2020 3:03 PM