ML Research Engineer
Quamer Nasim
DeepKapha / EarthScan (formerly)
Quamer contributed to early EarthScan research on raster log digitisation and FMI-log correlation while at IIT Kharagpur. His work informed the architecture choices that became the ES Raster Digitizer pipeline.
M. Quamer Nassim contributed to EarthScan's early research line on automated well-log digitisation and FMI-based correlation, including the convolutional architecture that became the basis for the production ES Raster Digitizer pipeline.
His co-authored work covers:
- Automated well-log correlation using traditional + FMI logs (EAGE Workshop on Borehole Geology in Asia Pacific) — combining multi-modal log streams with deep image segmentation to detect formation boundaries, fractures, and breakouts.
- EarthAdaptNet — the deep-learning architecture for seismic facies analysis that pairs domain adaptation with attention-based segmentation.
This profile exists to attribute Quamer's contributions to the EarthScan research record. Newer work on the platform is led by the current EarthScan team.
