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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Akhilesh Verma, Soumi Chaki, Aurobinda Routray, et al.
Journal of Applied Geophysics (2014) Vol. 111, pp. 141-155
Open Access | Times Cited: 13

Showing 13 citing articles:

Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models
Daniel Asante Otchere, Tarek Ganat, Raoof Gholami, et al.
Journal of Petroleum Science and Engineering (2020) Vol. 200, pp. 108182-108182
Closed Access | Times Cited: 355
Well-Log and Seismic Data Integration for Reservoir Characterization: A Signal Processing and Machine-Learning Perspective
Soumi Chaki, Aurobinda Routray, William K. Mohanty
IEEE Signal Processing Magazine (2018) Vol. 35, Iss. 2, pp. 72-81
Closed Access | Times Cited: 59
Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm
Yile Ao, Hongqi Li, Liping Zhu, et al.
Journal of Petroleum Science and Engineering (2018) Vol. 173, pp. 781-792
Closed Access | Times Cited: 55
A probabilistic neural network (PNN) based framework for lithology classification using seismic attributes
Soumi Chaki, Aurobinda Routray, William K. Mohanty
Journal of Applied Geophysics (2022) Vol. 199, pp. 104578-104578
Closed Access | Times Cited: 24
Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods
Hadi Fattahi, Sadegh Karimpouli
Computational Geosciences (2016) Vol. 20, Iss. 5, pp. 1075-1094
Closed Access | Times Cited: 29
A Diffusion Filter Based Scheme to Denoise Seismic Attributes and Improve Predicted Porosity Volume
Soumi Chaki, Aurobinda Routray, William K. Mohanty
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2017) Vol. 10, Iss. 12, pp. 5265-5274
Closed Access | Times Cited: 18
Sand fraction prediction from seismic attributes using optimized support vector regression in an oil reservoir
Mohammad Sadegh Amiri Bakhtiar, Ghasem Zargar, Mohammad Ali Riahi, et al.
Earth Science Informatics (2020) Vol. 13, Iss. 2, pp. 405-416
Closed Access | Times Cited: 6
Reservoir Characterization: A Machine Learning Approach
Soumi Chaki
arXiv (Cornell University) (2015)
Open Access | Times Cited: 5
Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS
Soumi Chaki, Aurobinda Routray, William K. Mohanty, et al.
(2015) Vol. 3, pp. 1-6
Open Access | Times Cited: 3
Information entropy as an identifier in rock mass structure determination using low-frequency radars
В. В. Набатов
Journal of Mining Science (2017) Vol. 53, Iss. 2, pp. 407-416
Closed Access | Times Cited: 2
Features denoising-based learning for porosity classification
Jatin Bedi, Durga Toshniwal
Neural Computing and Applications (2019) Vol. 32, Iss. 21, pp. 16519-16532
Closed Access | Times Cited: 2
Application of Machine Learning Algorithms for Petroleum Reservoir Characterization
Soumi Chaki, Aurobinda Routray, William K. Mohanty
(2022), pp. 6-20
Closed Access | Times Cited: 1
Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS
Soumi Chaki, Aurobinda Routray, William K. Mohanty, et al.
arXiv (Cornell University) (2016)
Open Access
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