Seismic classification: A Thalweg tracking/machine learning approach

Paul de Groot; Mike Pelissier; Marieke van Hout

doi:10.3997/1365-2397.fb2021020

ISSN: 0263-5046
E-ISSN: 1365-2397

Seismic classification: A Thalweg tracking/machine learning approach
Authors Paul de Groot¹, Mike Pelissier², Marieke van Hout¹
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Affiliations: ¹ dGB Earth Sciences BV ² Integrated GGRE Asia Sdn
Source: First Break, Volume 39, Issue 3, Mar 2021, p. 59 - 64
DOI: https://doi.org/10.3997/1365-2397.fb2021020
- Published online: 01 Mar 2021

Abstract

We describe a machine learning seismic classification workflow in which the thousands of class labels needed for training a deep graph are automatically generated from just a handful of manually picked seed positions. The class labels are generated by a Thalweg tracker. This special kind of connectivity filter grows a 3D body of user-defined size from a single seed position by adding only one point at a time. The user controls the size such that the tracker stays within one seismic class. The shape of the growing body is the main criterion for deciding when and where the tracker starts tracking another class. We present two examples. The first example is a 3D seismic facies classification of a setting with stacked meandering channels. We classify the target interval into eight seismic facies classes. In the second example, we extract turbidite channels from a two-pass gradient (i.e., second derivative) attribute volume.

EAGE Publications BV

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Seismic classification: A Thalweg tracking/machine learning approach

First Break 39, 59 (2021); https://doi.org/10.3997/1365-2397.fb2021020

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Seismic classification: A Thalweg tracking/machine learning approach

Abstract

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