Chapter 10 Self-Organizing Map (SOM) network for tracking horizons and classifying seismic traces

Zhang, Lin; Quieren, John; Schuelke, James S.

doi:10.1016/s0950-1401(01)80024-0

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…et al 1994;Fournier and Derain 1995;Johann, Castro and Barroso 2001;Saggaf, Toksöz and Marhoon 2003). Other techniques are support vector machines (Li and Castagna 2004), unsupervised techniques such as self-organising maps (SOMs) (Kohonen 2001;Taner et al 2001;Zhang, Quieren and Schuelke 2001;Coléou, Poupon and Azbel 2003;, 2004a,b, 2007Roy and Marfurt 2011), and, recently, generative topographic mapping (Roy et al 2014;Chopra and Marfurt 2014). In fact, one of the most attractive applications of SOMs in typical problems regarding seismic facies analysis has been performed by Saraswat and Sen (2012).…”

Section: Introductionmentioning

confidence: 99%

“…Other techniques are support vector machines (Li and Castagna ), unsupervised techniques such as self‐organising maps (SOMs) (Kohonen ; Taner et al . ; Zhang, Quieren and Schuelke ; Coléou, Poupon and Azbel ; Osorio, Matos and Johann ; Matos, Osorio and Johann , ,b, ; Roy, Matos and Marfurt ; Roy, Marfurt and Matos ; Roy and Marfurt ), and, recently, generative topographic mapping (Roy et al . ; Chopra and Marfurt ).…”

Section: Introductionmentioning

confidence: 99%

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Seismic facies analysis through musical attributes

Amendola

Gabbriellini

Dell’Aversana

et al. 2017

Geophysical Prospecting

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Seismic facies analysis is a well‐established technique in the workflow followed by seismic interpreters. Typically, huge volumes of seismic data are scanned to derive maps of interesting features and find particular patterns, correlating them with the subsurface lithology and the lateral changes in the reservoir. In this paper, we show how seismic facies analysis can be accomplished in an effective and complementary way to the usual one. Our idea is to translate the seismic data in the musical domain through a process called sonification, mainly based on a very accurate time–frequency analysis of the original seismic signals. From these sonified seismic data, we extract several original musical attributes for seismic facies analysis, and we show that they can capture and explain underlying stratigraphic and structural features. Moreover, we introduce a complete workflow for seismic facies analysis starting exclusively from musical attributes, based on state‐of‐the‐art machine learning computational techniques applied to the classification of the aforementioned musical attributes. We apply this workflow to two case studies: a sub‐salt two‐dimensional seismic section and a three‐dimensional seismic cube. Seismic facies analysis through musical attributes proves to be very useful in enhancing the interpretation of complicated structural features and in anticipating the presence of hydrocarbon‐bearing layers.

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