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A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey

Authors: Oliveira, M; Avalone, M; Vital Brazil, E; Salles Civitarese, D;

A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey

Abstract

In the past decades, machine learning algorithms have been developed for accelerating the seismic interpretation process. For this purpose, benchmark availability for algorithm improvement in computational geophysics is imperative. However, in geosciences, the supply of open benchmark datasets is very limited. The purpose of our study was to evaluate a CNN algorithm response in comparison to an OpendTect tool for seismic horizon interpolation, and in addition, offer a benchmark dataset for further algorithms evaluation.

Keywords

machine learning, seismic, seismic interpretation

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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