Monitoring and detecting energy resources


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.


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

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
March 6, 2023
We approach the representation learning task by tackling the hubness problem.
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
March 6, 2023
We propose DeepMVC – a unified framework which includes many recent methods as instances.
New Visual Intelligence paper accepted to NeurIPS
September 23, 2022
ProtoVAE explainability paper by Srishti Gautam and co-authors is published to NeurIPS 2022.