4 citations · 17 across the 20 of their papers we have counts for
8 papers · 1 filter
Machine Learning for Airborne Electromagnetic Data Inversion: a Bootstrapped Approach
Ophir Greif, Bas Peters, Michael S. McMillan +2
Aircraft-based surveying to collect airborne electromagnetic data is a key method to image large swaths of the Earth's surface in pursuit of better knowledge of aquifer systems. De…
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data
Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentat…
Inverting airborne electromagnetic data with machine learning
Michael S. McMillan, Bas Peters, Ophir Greif +2
This study focuses on inverting time-domain airborne electromagnetic data in 2D by training a neural-network to understand the relationship between data and conductivity, thereby r…
Fully reversible neural networks for large-scale 3D seismic horizon tracking
Bas Peters, Eldad Haber
Tracking a horizon in seismic images or 3D volumes is an integral part of seismic interpretation. The last few decades saw progress in using neural networks for this task, starting…
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing
Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface cha…
Does shallow geological knowledge help neural-networks to predict deep units?
Bas Peters, Eldad Haber, Justin Granek
Geological interpretation of seismic images is a visual task that can be automated by training neural networks. While neural networks have shown to be effective at various interpre…
Neural-networks for geophysicists and their application to seismic data interpretation
Bas Peters, Eldad Haber, Justin Granek
Neural-networks have seen a surge of interest for the interpretation of seismic images during the last few years. Network-based learning methods can provide fast and accurate autom…
Multi-resolution neural networks for tracking seismic horizons from few training images
Bas Peters, Justin Granek, Eldad Haber
Detecting a specific horizon in seismic images is a valuable tool for geological interpretation. Because hand-picking the locations of the horizon is a time-consuming process, auto…