Atlas Deep Geo is a small technical team developing physics-guided reconstruction methods. We work with operators on real datasets to turn sparse subsurface observations into probabilistic 3D volumes — P10, P50, P90 — with calibrated uncertainty.
Every problem we tackle is a variant of the same one: sparse or missing observations in a known geometry, reconstructed with a learned geological prior. The observation type differs. The mask geometry differs. The method does not.
Residual learning on a classical smooth prior. Horizon flattening. Full-extent slabs. Ensemble uncertainty quantification.
How it works →2D-to-3D reconstruction is the current focus. Infill, obscured-zone imaging, and footprint removal are research directions we pick up as POC engagements arise.
The ORCA framework →Penobscot. Glacier monitoring. Real datasets, honest validation, documented limits.
View studies →The work runs through POC engagements. A customer brings us a specific reconstruction question and a real dataset; we work through it together; the result is a deliverable for them and (when appropriate) a published case study for us. As workflows mature across multiple engagements on the same problem class, customers who use them often will want to run them in-house — license-style packaging is the eventual destination, not the starting point.
RBF interpolation gives one answer. Sparse inversion gives one answer. The ORCA framework gives a distribution of answers — each geologically plausible — with the spread across realizations quantifying the irreducible uncertainty from limited observations. That changes the conversation from "here is our best estimate" to "here is the range of structures consistent with your data."