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1.
Sci Rep ; 11(1): 16990, 2021 Aug 20.
Artículo en Inglés | MEDLINE | ID: mdl-34417535

RESUMEN

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. Pockmarks are densely clustered on the northern bank of the canyon and occur at a water depth of 500-900 m. In parallel, near-seafloor strata contain channel-fill deposits, channel lobes, meandering channel belts and overbank sediments deposited downslope of the submarine canyon. We propose that subsurface fluid migrates from relatively deep Cretaceous strata through shallow channel-fill deposits and lobes to latter seep out through the canyon and associated gullies. The new, reprocessed Fluid Cube meta-attribute confirms that fluids have seeped out through the eroded walls of the Waitaki Canyon, with such a seepage generating seafloor depressions in its northern bank. Our findings stress the importance of shallow reservoirs (channel-fill deposits and lobes) as potential repositories for fluid, hydrocarbons, or geothermal energy on continental margins across the world.

2.
Sci Rep ; 10(1): 14134, 2020 08 24.
Artículo en Inglés | MEDLINE | ID: mdl-32839502

RESUMEN

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. The responses, defined by a set of individual attributes, corresponding to the MTD, are computed from seismic volume and amalgamated them into a hybrid attribute. This generated new attribute, called as MTD Cube meta-attribute, does not only define the subsurface architecture of MTD distinctly but also reduces the human involvement thereby accelerating the process of interpretation. The system, after being fully trained, quality checked and validated, automatically delimits the structural geometry of MTDs within the Karewa prospect in northern Taranaki Basin off New Zealand, where MTDs are evidenced.

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