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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:

Convolutional neural networks as aid in core lithofacies classification
Rafael Pires de Lima, Fnu Suriamin, Kurt J. Marfurt, et al.
Interpretation (2019) Vol. 7, Iss. 3, pp. SF27-SF40
Closed Access | Times Cited: 53

Showing 26-50 of 53 citing articles:

Generating a labeled data set to train machine learning algorithms for lithologic classification of drill cuttings
Daniela Becerra, Rafael Pires de Lima, Henry Galvis-Portilla, et al.
Interpretation (2022) Vol. 10, Iss. 3, pp. SE85-SE100
Closed Access | Times Cited: 8
Image recognition of carbonate fossils and abiotic particles based on deep convolutional neural network mode
Tao Ye, Zhidong Bao, Fukang Ma, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
Gated Recurrent Units for Lithofacies Classification Based on Seismic Inversion
Runhai Feng
Springer eBooks (2024), pp. 97-113
Closed Access | Times Cited: 1
A novel approach to classify lithology of reservoir formations using GrowNet and Deep‐Insight with physic‐based feature augmentation
Said Mousavi, Seyed Mojtaba Hosseini‐Nasab
Energy Science & Engineering (2024) Vol. 12, Iss. 10, pp. 4453-4477
Open Access | Times Cited: 1
Lithofacies Classification of Carbonate Reservoirs Using Advanced Machine Learning: A Case Study from a Southern Iraqi Oil Field
Mohammed A. Abbas, Watheq J. Al‐Mudhafar
(2021)
Closed Access | Times Cited: 9
Machine learning for geophysical characterization of brittleness: Tuscaloosa Marine Shale case study
Mark Mlella, Ming Ma, Rui Zhang, et al.
Interpretation (2020) Vol. 8, Iss. 3, pp. T589-T597
Closed Access | Times Cited: 9
Digitalization of Legacy Datasets and Machine Learning Regression Yields Insights for Reservoir Property Prediction and Submarine-Fan Evolution: A Subsurface Example From the Lewis Shale, Wyoming
Thomas Martin, Jared Tadla, Zane Jobe
The Sedimentary Record (2022) Vol. 20, Iss. 1
Open Access | Times Cited: 5
Assessing bivalve phylogeny using Deep Learning and Computer Vision approaches
Steffen Kiel
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 7
Deep Learning Applications in Geosciences: Insights into Ichnological Analysis
Korhan Ayrancı, Isa Eren Yildirim, Umair bin Waheed, et al.
Applied Sciences (2021) Vol. 11, Iss. 16, pp. 7736-7736
Open Access | Times Cited: 7
Carbonate texture identification using multi-layer perceptron neural network
Oltion Fociro, Ana Fociro, Redi Muçi, et al.
Open Geosciences (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 2
Accelerating core characterization and interpretation through deep learning with an application to legacy data sets
John Solum, Neal C. Auchter, O. Falivene, et al.
Interpretation (2022) Vol. 10, Iss. 3, pp. SE71-SE83
Closed Access | Times Cited: 4
Application of Artificial Neural Networks for Identification of Lithofacies by Processing of Core Drilling Data
Mingsheng Yang, Yuanbiao Hu, Baolin Liu, et al.
Applied Sciences (2023) Vol. 13, Iss. 21, pp. 11934-11934
Open Access | Times Cited: 2
Creating probabilistic 3D models of lithofluid facies using machine-learning algorithms
Saba Keynejad, Marc L. Sbar, Roy A. Johnson
Interpretation (2020) Vol. 8, Iss. 4, pp. T701-T714
Closed Access | Times Cited: 4
Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning
Mike Lyons
Journal of Computer Applications in Archaeology (2021) Vol. 4, Iss. 1, pp. 188-188
Open Access | Times Cited: 4
Prediction of TOC in Lishui–Jiaojiang Sag Using Geochemical Analysis, Well Logs, and Machine Learning
Xu Han, Dujie Hou, Xiong Cheng, et al.
Energies (2022) Vol. 15, Iss. 24, pp. 9480-9480
Open Access | Times Cited: 3
Characterization of seismic-scale petrofacies variability in the Arbuckle Group using supervised machine learning: Wellington Field, Kansas
Abidin B. Caf, David Lubo-Robles, Kurt J. Marfurt, et al.
Interpretation (2024) Vol. 12, Iss. 3, pp. T341-T354
Closed Access
Machine learning-based classification of petrofacies in fine laminated limestones
Gallileu Genesis Pereira de Sousa, Igor Fernandes Gomes, José Antônio Barbosa, et al.
Anais da Academia Brasileira de Ciências (2024) Vol. 96, Iss. 1
Open Access
Improving the classification of rock samples with diffusion probabilistic machine learning
Julián L. Gómez, E. Camilion
Interpretation (2024) Vol. 13, Iss. 1, pp. T153-T162
Closed Access
Fully Automated Carbonate Petrography Using Deep Convolutional Neural Networks
Ardiansyah Koeshidayatullah, Michele Morsilli, Daniel J. Lehrmann, et al.
EarthArXiv (California Digital Library) (2020)
Open Access | Times Cited: 3
Geochemical Biodegraded Oil Classification Using a Machine Learning Approach
Sizenando Bispo-Silva, Cleverson J.F. Oliveira, Gabriel de Alemar Barberes
Geosciences (2023) Vol. 13, Iss. 11, pp. 321-321
Open Access | Times Cited: 1
Convolutional neural networks: core interpretation with instance segmentation models
Rafael Pires de Lima, Fnu Suriamin
Elsevier eBooks (2022), pp. 117-140
Closed Access | Times Cited: 2
Interpretation of deep neural networks for carbonate thin section classification
Lukas Mosser, George Ghon, Gregor T. Baechle
Second International Meeting for Applied Geoscience & Energy (2022), pp. 371-375
Closed Access | Times Cited: 2
Recurrent autoencoder model for unsupervised seismic facies analysis
Yanhui Zhou, Wenchao Chen
Interpretation (2022) Vol. 10, Iss. 3, pp. T451-T460
Closed Access | Times Cited: 2
Machine learning for geophysical characterization of brittleness: Tuscaloosa Marine Shale case study
Mark Mlella, Ming Ma, Rui Zhang, et al.
(2020), pp. 1606-1610
Closed Access | Times Cited: 2
Summarized Applications of Machine Learning in Subsurface Geosciences
Shuvajit Bhattacharya
SpringerBriefs in petroleum geoscience & engineering (2021), pp. 123-165
Closed Access | Times Cited: 1

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