Submarine canyon systems focusing sub-surface fluid in the Canterbury Basin, South Island, New Zealand.

This work uses a high-quality 3D seismic volume from offshore Canterbury Basin, New Zealand, to investigate how submarine canyon systems can focus sub-surface fluid. The seismic volume was structurally conditioned to improve the contrast in seismic reflections, preserving their lateral continuity. It reveals multiple pockmarks, eroded gullies and intra-slope lobe complexes occurring in association with the Waitaki Submarine Canyon.

A machine learning tool for interpretation of Mass Transport Deposits from seismic data.

Machine learning is a tool that allows machines or intelligent systems to learn and get equipped to solve complex problems in predicting reliable outcome. The learning process consists of a set of computer algorithms that are employed to a small segment of data with a view to speed up realistic interpretation from entire data without extensive human intervention. Here we present an approach of supervised learning based on artificial neural network to automate the process of delineating structural distribution of Mass Transport Deposit (MTD) from 3D reflection seismic data.