Pseudo3D
Transforming 2D seismic into Pseudo-3D volumes using cutting-edge machine learning techniques.
dGB Earth Sciences leverages advanced machine learning to transform sparse 2D seismic lines into high-resolution Pseudo-3D volumes. Our proprietary workflow unites classical structurally guided interpolation with deep learning and bandwidth extension, delivering detailed subsurface imaging at a fraction of the cost of new 3D seismic acquisition.
In a recent offshore Indonesia case study, the technique generated high-frequency volumes that enabled detailed regional geological interpretation and precise imaging of channel geometries — demonstrating how Pseudo3D reduces exploration uncertainty while supporting more efficient 3D survey planning.
Key Capabilities
Transform 2D into 3D
Convert sparse 2D seismic lines into dense Pseudo-3D volumes that support detailed regional interpretation.
Machine Learning Enhancement
Deep learning, combined with structurally guided interpolation and bandwidth extension, enhances seismic data quality and resolution.
Proven Results
Offshore Indonesia case studies demonstrate precise imaging of channel geometries and other geological features from legacy 2D data.
Cost-Effective Alternative
Pseudo3D delivers high-resolution subsurface understanding at a fraction of the cost of acquiring new 3D seismic — reducing exploration uncertainty and supporting efficient 3D survey planning.
The Workflow
Our Pseudo3D workflow integrates classical geophysical interpolation with modern AI to fill the spatial gaps between 2D lines. Structurally guided interpolation respects subsurface geometry, while deep learning models extend the bandwidth and detail of the resulting volumes — producing data that supports high-resolution interpretation, regional mapping, and quantitative analysis inside OpendTect.