Elastic impedance based facies classification using support vector machine and deep learning
نویسندگان
چکیده
منابع مشابه
Support Vector Machine Based Facies Classification Using Seismic Attributes in an Oil Field of Iran
Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases...
متن کاملsupport vector machine based facies classification using seismic attributes in an oil field of iran
seismic facies analysis (sfa) aims to classify similar seismic traces based on amplitude, phase,frequency, and other seismic attributes. sfa has proven useful in interpreting seismic data, allowingsignificant information on subsurface geological structures to be extracted. while facies analysis hasbeen widely investigated through unsupervised-classification-based studies, there are few casesass...
متن کاملSupport Vector Machine-based Facies Classification Using Seismic Attributes in an Oil Field of Iran
Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases...
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Background: Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.Objective: In this study, we aimed to evaluate the efficiency of discrete wavelet transfo...
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ژورنال
عنوان ژورنال: Geophysical Prospecting
سال: 2018
ISSN: 0016-8025,1365-2478
DOI: 10.1111/1365-2478.12682