Problem
The operator's geoscience team was strong, but it was bottlenecked by manual interpretation cycles. Every new prospect needed a senior interpreter to walk every line, flag faults, and reconcile against well data — work that scales linearly with headcount and not at all with software.
Approach
WAVERITY's core insight: most of the interpretive judgment that takes the longest is repetitive pattern recognition over high-dimensional signal. We applied a model architecture trained on a domain-specific dataset of interpreted volumes to produce a first-pass interpretation that a senior geophysicist reviews and corrects.
Build
eiLink validated the geophysical assumptions (resolution limits, fault geometry, processing artifacts). DRL built the data pipeline, the ML inference layer, and the interactive review UI that lets the senior interpreter accept, reject, or correct each flagged feature in seconds.
Result
The first-pass false-positive fault rate landed at 11% — comfortably within target — so reviewers spent their time confirming real structure rather than chasing noise.
What's next
WAVERITY is rolling out the same workflow with two more operators in 2026 and adding amplitude-variation-with-offset (AVO) inference as a separate module.