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of 35
pro vyhledávání: '"Chaki, Soumi"'
Spiking Neural Networks (SNNs) are biologically inspired alternatives to conventional Artificial Neural Networks (ANNs). Despite promising preliminary results, the trade-offs in the training of SNNs in a distributed scheme are not well understood. He
Externí odkaz:
http://arxiv.org/abs/2303.00928
Publikováno v:
In Journal of Applied Geophysics April 2022 199
Autor:
Chaki, Soumi, Verma, Akhilesh Kumar, Routray, Aurobinda, Mohanty, William K., Jenamani, Mamata
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical pr
Externí odkaz:
http://arxiv.org/abs/1612.01349
A novel multiclassSVM based framework to classify lithology from well logs: a real-world application
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divide
Externí odkaz:
http://arxiv.org/abs/1612.00840
Autor:
Chaki, Soumi, Verma, Akhilesh Kumar, Routray, Aurobinda, Mohanty, William K., Jenamani, Mamata
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurfac
Externí odkaz:
http://arxiv.org/abs/1612.00841
This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve prediction problem. The proposed two-stage Domain Knowledge based Fuzz
Externí odkaz:
http://arxiv.org/abs/1612.00585
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This paper proposes a complete framework consisting pre-processing, modeling, and post-processing stages to carry out well tops guided prediction of a reservoir property (sand fraction) from three seismic attributes (seismic impedance, instantaneous
Externí odkaz:
http://arxiv.org/abs/1509.07079
In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked
Externí odkaz:
http://arxiv.org/abs/1509.07074
This paper presents a novel pre-processing scheme to improve the prediction of sand fraction from multiple seismic attributes such as seismic impedance, amplitude and frequency using machine learning and information filtering. The available well logs
Externí odkaz:
http://arxiv.org/abs/1509.07065