Energy

Monitoring and detecting energy resources

Background

Data from the subsurface of the earth such as borehole imagery and seismic data is especially important in energy exploration. Automated interpretation of this complex data has great potential to be useful for tasks like monitoring and detecting natural resources.

Main objective

The innovations in energy innovation area will achieve robust and reliable methods for automatic analysis of complex imagery from the digital subsurface for more efficient and detailed energy exploration.

Challenges

The amount and quality of labelled training data is a challenge in this field. Existing interpretations are not made for machine learning purposes. Hence, the annotation quality for the task is unknown and interpretations are generally incomplete. Generating realistic simulated data is difficult as simulated data tends to be too simple. For many of these problems context and dependencies, through exploitation of prior knowledge of the geology, dependencies in space and time or results derived from existing solutions could improve predictions.

Visual Intelligence is advancing deep learning to overcome these challenges.

Highlighted publications

Using Machine Learning to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images
June 21, 2022
Developing artificial intelligence methods to help pathologists in analysis of whole slide images for cancer treatment and detection.
Principle of Relevant Information for Graph Sparsification
May 20, 2022
How can we remove the redundant or less-informative edges in a graph without changing its main structural properties?
Detection and classification of fish species from acoustic data
March 7, 2022
Using deep learning to assess fish stocks from acoustic images.