Interactive seismic facies classification of stack and AVO data using textural attributes and neural networks

Autor:	Steve R. May, John Eastwood, Brian P. West, Christine Rossen
Rok vydání:	2001
Předmět:	Artificial neural network business.industry Computer science Probabilistic logic Pattern recognition computer.software_genre Class (biology) Texture (geology) Seismic analysis Naive Bayes classifier Facies Artificial intelligence Data mining business computer TRACE (psycholinguistics)
Zdroj:	SEG Technical Program Expanded Abstracts 2001.
DOI:	10.1190/1.1816683
Popis:	We present an interactive method for volume-based seismic facies mapping using seismic textural attributes and probabilistic neural networks. Textural analysis can quantitatively describe many aspects of the classic seismic facies description preformed by the interpreter. Stratigraphically-steered seismic texture is a quantitative, multi-trace (imagebased) attribute that mimics the visual process of the seismic interpreter more effectively than traditional trace-based attribute analyses do. Probabilistic neural networks (PNNs) are parallel implementations of a standard Bayesian classifier that can efficiently perform pattern classification. A primary advantage of the PNN is that it does not require extensive training. In the case of seismic analysis, a reliable seismic facies classification can occur with as little as one example per facies class.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::eb22fafc4fbb2f7afad5e4260aef62c0 https://doi.org/10.1190/1.1816683 Zobrazit plný text záznamu