Rafael Pires de Lima

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Youzuo LinAssociate Professor, University of North Carolina at Chapel HillPatvirtintas el. paštas unc.edu
David DuarteOvintivPatvirtintas el. paštas ovintiv.com
Charles NicholsonUniversity of OklahomaPatvirtintas el. paštas ou.edu
Roger SlattProfessor of Petroleum Geology and Geophysics, University of OklahomaPatvirtintas el. paštas ou.edu
FNU SuriaminOklahoma Geological Survey, University of OklahomaPatvirtintas el. paštas ou.edu
Morteza KarimzadehAsst. Prof. in Geography; Affiliate, Computer Science, University of Colorado BoulderPatvirtintas el. paštas colorado.edu
Matthew J. PranterDirector, School of Geosciences; Eberly Family Chair; Professor-Geosciences, University of OklahomaPatvirtintas el. paštas ou.edu
Behzad VahediPhD Student, University of Colorado BoulderPatvirtintas el. paštas colorado.edu
Javier TellezAssistant Professor of Geosciences, Colorado Mesa UniversityPatvirtintas el. paštas coloradomesa.edu
Walter N MeierNational Snow and Ice Data CenterPatvirtintas el. paštas colorado.edu
Nick HughesNorwegian Meteorological InstitutePatvirtintas el. paštas met.no
Marcos Vinícius FerreiraServiço Geológico do Brasil, CPRMPatvirtintas el. paštas cprm.gov.br
Karelia La MarcaPhD. student, University of OklahomaPatvirtintas el. paštas ou.edu
Oriol FaliveneShell - Subsurface Data and DigitalPatvirtintas el. paštas shell.com
Rachel W. ClarkUniversity of HoustonPatvirtintas el. paštas central.uh.edu
Henry A Galvis-PortillaUniversity of CalgaryPatvirtintas el. paštas ucalgary.ca
Sumit VermaEndeavor Energy ResourcesPatvirtintas el. paštas alumni.ou.edu
Daniela BecerraUniversity of CalgaryPatvirtintas el. paštas ucalgary.ca
Francisco Ferreira de CamposPesquisador em Geociências, Serviço Geológico do BrasilPatvirtintas el. paštas sgb.gov.br
Lennon Infante-PaezPh.D. Student in Geophysics, The University of OklahomaPatvirtintas el. paštas ou.edu

Stebėti

Rafael Pires de Lima

Data Scientist, VerAI Discoveries

Patvirtintas el. paštas ver-ai.com

Deep Learning Data Science Earth Observation Geophysics


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Convolutional neural network for remote-sensing scene classification: Transfer learning analysis R Pires de Lima, K Marfurt Remote Sensing 12 (1), 86, 2019	282	2019
Deep convolutional neural networks as a geological image classification tool RP de Lima, A Bonar, DD Coronado, K Marfurt, C Nicholson The Sedimentary Record 17 (2), 4-9, 2019	75	2019
Convolutional neural networks as aid in core lithofacies classification R Pires de Lima, F Suriamin, KJ Marfurt, MJ Pranter Interpretation 7 (3), 1-50, 2019	72*	2019
Petrographic microfacies classification with deep convolutional neural networks RP de Lima, D Duarte, C Nicholson, R Slatt, KJ Marfurt Computers & Geosciences 142, 104481, 2020	69	2020
Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms R Pires de Lima, Y Chen, Y Lin Space Weather 18 (2), e2019SW002399, 2020	40	2020
Normal or abnormal? Machine learning for the leakage detection in carbon sequestration projects using pressure field data S Sinha, RP de Lima, Y Lin, AY Sun, N Symons, R Pawar, G Guthrie International Journal of Greenhouse Gas Control 103, 103189, 2020	39	2020
Convolutional neural networks as an aid to biostratigraphy and micropaleontology: a test on Late Paleozoic microfossils R PIRES DE LIMA, KF Welch, JE Barrick, KJ Marfurt, R Burkhalter, ... Palaios 35 (9), 391-402, 2020	30	2020
Principal component analysis and K-means analysis of airborne gamma-ray spectrometry surveys RP de Lima, KJ Marfurt SEG Technical Program Expanded Abstracts 2018, 2277-2281, 2018	27	2018
Pretraining Convolutional Neural Networks for Mudstone Petrographic Thin-Section Image Classification R Pires de Lima, D Duarte Geosciences 11 (8), 336, 2021	21	2021
Progress and Challenges in Deep Learning Analysis of Geoscience Images RP De Lima, K Marfurt, D Duarte, A Bonar 81st EAGE Conference and Exhibition 2019 2019 (1), 1-5, 2019	21	2019
Deep convolutional neural networks as an estimator of porosity in thin-section images for unconventional reservoirs D Duarte-Coronado, J Tellez-Rodriguez, R Pires de Lima, K Marfurt, ... SEG Technical Program Expanded Abstracts 2019, 3181-3184, 2019	13	2019
Lithofacies identification in cores using deep learning segmentation and the role of geoscientists: Turbidite deposits (Gulf of Mexico and North Sea) O Falivene, NC Auchter, RP de Lima, L Kleipool, JG Solum, P Zarian, ... AAPG Bulletin 106 (7), 1357-1372, 2022	10	2022
Statistical controls on induced seismicity S Sinha, Y Wen, R Pires De Lima, K Marfurt Unconventional Resources Technology Conference, Houston, Texas, 23-25 July …, 2018	9	2018
Generating a labeled data set to train machine learning algorithms for lithologic classification of drill cuttings D Becerra, R Pires de Lima, H Galvis-Portilla, CR Clarkson Interpretation 10 (3), SE85-SE100, 2022	8	2022
Leak Detection in Carbon Sequestration Projects Using Machine Learning Methods: Cranfield Site, Mississippi, USA S Sinha, R Pires De Lima, Y Lin, A Y Sun, N Symon, R Pawar, G Guthrie SPE Annual Technical Conference and Exhibition, 2020	8	2020
Transforming seismic data into pseudo-RGB images to predict CO2 leakage using pre-learned convolutional neural networks weights R Pires de Lima, Y Lin, KJ Marfurt SEG Technical Program Expanded Abstracts 2019, 2368-2372, 2019	8	2019
Geophysical data integration and machine learning for multi-target leakage estimation in geologic carbon sequestration RP de Lima, Y Lin SEG Technical Program Expanded Abstracts 2019, 2333-2337, 2019	8	2019
Model Ensemble With Dropout for Uncertainty Estimation in Sea Ice Segmentation Using Sentinel-1 SAR RP de Lima, M Karimzadeh IEEE Transactions on Geoscience and Remote Sensing 61, 1-15, 2023	6	2023
Comparison of clustering techniques to define chemofacies in mississippian rocks in the STACK Play, Oklahoma D Duarte, R Lima, R Slatt, K Marfurt American association of petroleum geologists search and discovery 42523, 2020	6	2020
Machine learning applications for geoscience problems RAP LIMA	6	2019

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