AI-Based 5D Seismic Interpolation

Use Case

Enhancing Subsurface Visibility using AI Generated 5D Seismic Volumes

Business Challenge

Legacy 3D seismic datasets often suffer from limited offset and azimuth coverage, noise interference, and uneven spatial sampling, which reduce their reliability for structural and attribute analysis.

Sparse spatial sampling is common, with poor inline and crossline density particularly in older datasets.
Offset and azimuth coverage is often limited, leading to suboptimal amplitude variation with offset (AVO) analysis.
High noise levels frequently degrade signal clarity and interpretability, especially in pre-stack domains.
Acquisition gaps and inconsistencies arise due to inaccessible terrains, acquisition design constraints, or budget limitations.

The AI Approach

To address these limitations, the client deployed an advanced seismic interpolation workflow powered by AI, domain-aware signal processing, and intelligent pattern recognition.

Fourier-based 5D interpolation was used to estimate missing traces across offset and azimuth dimensions using mathematical transforms.
ML-augmented trace prediction used trained models on valid gathers to identify seismic patterns and reconstruct data in under-sampled areas.
Noise suppression and amplitude balancing normalized amplitude distributions and improved signal-to-noise ratio (SNR) through pre-processing.
AVO compatible reconstruction ensured amplitude preservation to support rock property analysis and reservoir characterization workflows.

Project Deployment Overview

Input Data Used

Input data consisted of 3D SEG-Y cubes sourced from mature basin regions with sparse spatial sampling and offset limitations.

Final Output Generated

Reconstructed 5D seismic cubes were produced, featuring balanced offset-azimuth coverage and enhanced spatial continuity.

Deployment Platform

The solution was deployed on the SeisSense AI Engine, utilizing GPU-accelerated architecture for rapid and scalable computation.

Processing Scope

The end-to-end workflow included interpolation, denoising, amplitude balancing, and AVO-consistent conditioning for downstream analysis.

Business Outcomes & Value Unlocked

The AI enhanced seismic workflow delivered measurable operational improvements reducing redundant acquisition costs, enhancing subsurface imaging clarity, accelerating time-to-insight, and enabling data-driven decision-making across geophysical and reservoir teams.