Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs

ilustraciones, fotografías color, fotografías blanco y negro, tablas

Autores:: Leal Freitez, Jorge Alberto

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs
dc.title.translated.spa.fl_str_mv	Aprendizaje automático y atributos fractales aplicados a la detección y clasificación automática de planos geológicos en registros de imágenes resistivas
title	Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs
spellingShingle	Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs 550 - Ciencias de la tierra 500 - Ciencias naturales y matemáticas 000 - Ciencias de la computación, información y obras generales Oil and gas wells Machine learning Fractal dimension Lacunarity Borehole resistivity imaging Geologic planes automatic picking Automatic dip classification Hydrocarbon reservoirs Aprendizaje automático Dimensión fractal Lagunaridad Imágenes resistivas de pozo Planos geológicos Clasificación automática de buzamientos Yacimientos de hidrocarburos
title_short	Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs
title_full	Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs
title_fullStr	Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs
title_full_unstemmed	Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs
title_sort	Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs
dc.creator.fl_str_mv	Leal Freitez, Jorge Alberto
dc.contributor.advisor.none.fl_str_mv	Ochoa Gutiérrez, Luis Hernán
dc.contributor.author.none.fl_str_mv	Leal Freitez, Jorge Alberto
dc.subject.ddc.spa.fl_str_mv	550 - Ciencias de la tierra 500 - Ciencias naturales y matemáticas 000 - Ciencias de la computación, información y obras generales
topic	550 - Ciencias de la tierra 500 - Ciencias naturales y matemáticas 000 - Ciencias de la computación, información y obras generales Oil and gas wells Machine learning Fractal dimension Lacunarity Borehole resistivity imaging Geologic planes automatic picking Automatic dip classification Hydrocarbon reservoirs Aprendizaje automático Dimensión fractal Lagunaridad Imágenes resistivas de pozo Planos geológicos Clasificación automática de buzamientos Yacimientos de hidrocarburos
dc.subject.proposal.eng.fl_str_mv	Oil and gas wells Machine learning Fractal dimension Lacunarity Borehole resistivity imaging Geologic planes automatic picking Automatic dip classification Hydrocarbon reservoirs
dc.subject.proposal.spa.fl_str_mv	Aprendizaje automático Dimensión fractal Lagunaridad Imágenes resistivas de pozo Planos geológicos Clasificación automática de buzamientos Yacimientos de hidrocarburos
description	ilustraciones, fotografías color, fotografías blanco y negro, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-11-24T20:07:06Z
dc.date.available.none.fl_str_mv	2021-11-24T20:07:06Z
dc.date.issued.none.fl_str_mv	2021-11
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
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url	https://repositorio.unal.edu.co/handle/unal/80725 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Aggarwal, C., 2015, Data mining. The textbook, first edition. Springer, New York, 181-488pp. Allain, C., Cloitre, M., 1991, Characterizing the lacunarity of random and deterministic fractal sets. Physical Review A, 44(6), 3552 - 3553. https://doi.org/10.1103/PhysRevA.44.3552 Alpaydin, E., 2014, Introduction to machine learning, third edition. The MIT Press, Cambridge, 27-238pp. Al-Sit, W., Al-Nuaimy, W., Marelli, M., Al- Ataby, A., 2015, Visual texture for automated characterization of geological features in borehole televiewer imagery. Journal of Applied Geophysics, 119, 39-146pp. http://dx.doi.org/10.1016/j.jappgeo.2015.05.015 Arizabalo, R., Oleschko, K., Gabor, K., Lozada, M., Castrejón, R., Ronquillo, G., 2006, Lacunarity of geophysical well logs in the Cantarell oil field, Gulf of Mexico. Geofísica International, 45(2), 99-105pp. Assous, S., Elkington, P., Clark, S., Whetton, J., 2013, Automated detection of planar geological feature in borehole image. Society of Exploration Geophysicists, 79 (1). D11-D19pp. https://doi.org/10.1190/geo2013-0189.1 Asquith, G., Krygowski, D., 2004, Basic well log analysis, second edition. The American Association of Petroleum Geologist, Tulsa, 31pp. Arora, N., Sarvani, G., 2017, A review paper on Gabor filter algorithm & its application. IJARECE, 6 (9), 1003-1007pp. doi:10.17148/IJARCCE.2017.6492 Awad, M., Khanna, R., 2015, Efficient learning machines, second edition. Apress Open, Berkeley, 14-17pp. Ayad, A., Amrani, M., Bakkali, S., 2019, Quantification of the disturbances of phosphate series using the box-counting method on geoelectrical images (Sidi Chennane, Morocco). International Journal of Geophysics, 2019(12), 1-12. https://doi.org/10.1155/2019/2565430 Barnsley, M., 1993, Fractals Everywhere, second edition. Morgan Kaufmann, Atlanta, 171pp. Bloem, P., 2010, Machine learning and fractal geometry. M.Sc. Thesis, University of Amsterdam. iii-8pp. Boggs, S., 2009, Petrology of sedimentary rocks, second edition. Cambridge University Press, Cambridge, 194-314pp. Boggs, S., 2014, Principles of sedimentology and stratigraphy, fifth edition. Pearson Educational Limited, Edinburgh, 76-135pp. Brownlee, J., 2016, What is a Confusion Matrix in Machine Learning. Machine Learning Mastery, 18 November 2016, https://machinelearningmastery.com/confusion-matrix-machine-learning/ (accessed 6 June 2020). Burger, W., Burge, M., 2009, Principles of digital image processing. Fundamental techniques, first edition. Springer, Hagenberg, 57-122pp. Burger, W., Burge, M., 2009, Principles of digital image processing. Core algorithms, first edition. Springer, Hagenberg, 110pp. Changchun, Z., Ge, S., 2002, A Hough transform-based method for fast detection of fixed period sinusoidal curves in images. Signal Processing 6th International Conference, 909-912pp. DOI: 10.1109/ICOSP.2002.1181204 Cheng, G., Guo, W., 2017, Rock images classification by using deep convolutional neural network. Journal of Physiscs, 887, 1-7pp. DOI: 10.1088/1742-6596/887/1/012089 Conway, D., Myles, J., 2012, Machine learning for hackers, first edition. O’Reilly, Sebastopol, 17pp. Davis, G., Reynolds, S., Kluth, C., 2012, Structural geology of rocks and regions, third edition. John Wiley and Sons, Hoboken, 786pp. Desarda, A., 2019, Understanding AdaBoost. Towards Data Science, 17 January 2019, https://towardsdatascience.com/understanding-adaboost-2f94f22d5bfe (accessed 7 June 2020). Ellis, D., Singer, J., 2008, Well logging for earth scientists, second edition. Springer, Ridgefield, 20pp. Geron, A., 2019, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, second edition. O’Reilly Media Inc., Sebastopol, 177pp. Glander, S., 2018, Machine Learning Basics - Gradient Boosting & XGBoost. Shirin's playgRound, 29 November 2018, https://www.shirin-glander.de/2018/11/ml_basics_gbm/ (accessed 8 June 2020). Han, J., Kamber, M., Pei, J., 2012, Data mining. Concepts and techniques, third edition. Morgan Kaufmann, Waltham, 254-460pp. Harvey, A., Fotopoulos, G., 2016, Geological mapping using machine learning algorithms. The international Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI (B8), 423-430pp. DOI:10.5194/ISPRS-ARCHIVES-XLI-B8-423-2016 He, C., Wang, W., 2010, A PCNN-Based Edge Detection Algorithm for Rock Fracture Images, 2010 Symposium on Photonics and Optoelectronics, 2010, 1-4pp. 10.1109/SOPO.2010.5504347. Joseph, R., 2018, Grid Search for model tuning. Towards Data Science, 29 December 2018, https://towardsdatascience.com/grid-search-for-model-tuning-3319b259367e (accessed 8 June 2020). Khan, J., 2019, Guide to image inpainting: using machine learning to edit and correct defects in photos. Medium Heartbeat, 7 August 2019, https://heartbeat.fritz.ai/guide-to-image-inpainting-using-machine-learning-to-edit-and-correct-defects-in-photos-3c1b0e13bbd0 (accessed 5 June 2019). Koehrsen, W., 2018, Improving the Random Forest in Python Part 1. Towards Data Science, 6 January 2018. https://towardsdatascience.com/improving-random-forest-in-python-part-1-893916666cd (accessed 10 January 2020). Leal, J., Ochoa, L., Contreras, C., 2018, Automatic identification of calcareous lithologies using support vector machines, borehole logs and fractal dimension of borehole electrical imaging. Earth Sciences Research Journal, 22(2), 75-82pp. https://doi.org/10.15446/esrj.v22n2.68320 Leal, J., Ochoa, L., Garcia, G., 2016, Identification of natural fractures using resistive image logs, fractal dimension and support vector machines. Ingeniería e Investigación, 36(3), 125-132pp. https://doi.org/10.15446/ing.investig.v36n3.56198 Li, J., Sun, C., Du, Q., 2006, A new box-counting method for estimation of image fractal dimension. International Conference on Image Processing, 2006, 3029-3032. DOI: 10.1109/ICIP.2006.313005. Lisle, R., 2004, Geological structures and maps. A practical guide, third edition. Elsevier, Oxford, 2pp. Luthi, S., 2001, Geological well logs. Their use in reservoir modeling, first edition. Springer, Berlin, 53pp. Mandelbrot, B., 1983, The fractal geometry of nature, second edition. W. H. Freeman and Company, New York, 14pp. Maynberg, O., Kush, G., 2013, Airborne crown density estimation. International Society For Photogrammetry And Remote Sensing, 2 (49), 49-54pp. https://doi.org/10.5194/isprsannals-II-3-W3-49-2013 Moreno, G., García, O., 2006, Quantitative characterization of fracture patterns with circular windows and fractal analysis., Geología Colombiana, (31), 73-74pp. Morton, D., Woods, A., 1992, Development geology reference manual. AAPG Methods in exploration V10., Tulsa, 174pp. Neer, K., Mathur, S., 2015, An improved method of edge detection based on Gabor wavelet transform. Recent Advances in Electrical Engineering and Electronic Devices, 184-191pp. Nelson, R., 2001, Geologic analysis of naturally fractured reservoirs, second edition. Gulf Professional Publishing, Woburn, 23pp. Nichols, G., 2009, Sedimentology and stratigraphy, second edition. Willey-Blackwell, Chichester, 66-88pp. Ochoa, L., Niño, L., Vargas, C., 2018, Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques. DYNA, 85 (204), 161-168pp. https://doi.org/10.15446/dyna.v85n204.68408 Oppenheimer, A., 2018, ¡Sálvese quien pueda! EL trabajo del futuro en la era de la automatización, primera edición. Penguin Random House Group Editorial, Ciudad de México, 6pp. Park, S., Kim, Y., Ryoo, C. Sanderson, D., 2010, Fractal analysis of the evolution of a fracture network in a granite outcrop, SE Korea. Geosciences Journal, 14(1), 201-215pp. https://doi.org/10.1007/s12303-010-0019-z Parker, J., 2011, Algorithms for image processing and computer vision, second edition. John Wiley and Sons, Indianapolis, 85pp. Plotnick, R., Garner, R., Hargrove, W., Prestegaard, K., Perlmutter, M., 1996, Lacunarity analysis: A general technique for the analysis of spatial patterns. Physical Review E, 53(5461), 5461-5468. https://doi.org/10.1103/PhysRevE.53.5461 Pratt, W., 2007, Digital image processing, fourth edition. John Wiley and Sons, Los Altos, 421pp. Quan, Y., Xu, Y., Sun, Y., Luo, Y., 2014, Lacunarity analysis on image patterns for texture classification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, The United States Of America, 23-28 June. DOI: 10.1109/CVPR.2014.28 Quintanilla, C., Cacau, D., Dos Santos, R., Ribeiro, E., Leta, F., Gonzalez, E., 2017, Improving accuracy of automatic fracture detection in borehole images with deep learning and GPUs. 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 345-350pp. DOI: 10.1109/SIBGRAPI.2017.52. Raghupathy, K., 2004, Curve tracing and curve detection in images. M.Sc. Thesis, Cornell University. pp. ii. Ranjay, K., 2017, Computer vision: Foundation and Applications, first edition. Stanford University, Stanford, 17pp. Rider, M., 2000, The geological interpretation of well logs, second edition. Rider – French Consulting Ltd., Sutherland, 67pp. Roy, A., Perfect, E., Dunne, W., Mackay, L., 2007, Fractal characterization of fracture networks. An improved box-counting technique. Journal of Geophysical Research, (112), 1-2pp. https://doi.org/10.1029/2006JB004582 Russell, S., Norvig, P., 2010, Artificial intelligence a modern approach, third edition. Prentice Hall, Upper Saddle River, 698-764pp. Sadeghi, B., Madeni, N., Carranza, E., 2014, Combination of geostatistical simulation and fractal modeling for mineral resource classification. Journal of Geochemical Exploration, 149(10), 59-73pp. http://dx.doi.org/10.1016/j.gexplo.2014.11.007 Schlager, W., 2004, Fractal nature of stratigraphic sequences. GeoScience World, 32(3), 185-188pp. https://doi.org/10.1130/G20253.1 Schlumberger, 2013, FMI-HD High-definition formation microimager. Schlumberger brochure, 4pp. Schlumberger, 1999, Geologic Applications of Dipmeter and Borehole Images. Schlumberger Educational Services, 31-322pp. Schott, M., 2019, Random forest algorithm for machine learning. Medium, 25 April 2019, https://medium.com/capital-one-tech/random-forest-algorithm-for-machine-learning-c4b2c8cc9feb (accessed 10 April 2020). Shapiro, L., Stockman, G., 2001, Computer Vision. The University of Washington, 107-332pp. Singh, H., 2018, Understanding Gradient Boosting Machines. Towards Data Science, 3 November 2018, https://towardsdatascience.com/understanding-gradient-boosting-machines-9be756fe76ab (accessed 8 June 2020). Singh, V., 2019, Model-based feature importance. Towards data sciences, 3 January 2019, https://towardsdatascience.com/model-based-feature-importance-d4f6fb2ad403 (accessed 31 July 2020). Tan, T., Stainbach M., Kumar, V., 2006, Introduction to data mining, first edition. Pearson Addison-Wesley, Boston, 297-598pp. Telea, A., 2004, An image inpainting technique based on the fast marching method. Journal of Graphic Tools, 9 (1), 25-36pp. https://doi.org/10.1080/10867651.2004.10487596 Turcotte, D., 1997, Fractal and chaos in geology and geophysics, second edition. Cambridge University, Cambridge, 166pp. Twiss, R., Moores, E., 2006, Structural geology, second edition. W. H. Freeman and Company, New York, 50pp. Vasiloudis, T., 2019, Block-distributed Gradient Boosted Trees. Theodore Vasiloudis, 26 August 2019, http://tvas.me/articles/2019/08/26/Block-Distributed-Gradient-Boosted-Trees.html (accessed 15 November 2020). Vivas, M., 1992, A techniques for inter well description by applying geostatistic and fractal geometry methods to well logs and core data. Doctoral dissertation, University of Oklahoma, 16pp. Wang, W., Liao, H., Huang, Y., 2007, Rock fractured tracing based on image processing and SVM. Third International Conference of Natural Computation, 1, 632-635pp. 10.1109/ICNC.2007.643 Weatherford, 2014, Compact microimager. Weatherford brochure, 1-4pp.
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dc.format.extent.spa.fl_str_mv	136 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Doctorado en Geociencias
dc.publisher.department.spa.fl_str_mv	Departamento de Geociencias
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ochoa Gutiérrez, Luis Hernán8958273ddc0a568fd20c71c9669775a7Leal Freitez, Jorge Alberto483d92cd2769638663f63f3f91b975662021-11-24T20:07:06Z2021-11-24T20:07:06Z2021-11https://repositorio.unal.edu.co/handle/unal/80725Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías color, fotografías blanco y negro, tablasThe purpose of this research is to develop a methodology for automatic detection and classification of geologic planes in wireline acquired resistivity imaging. The methodology involves the application of bioinspired image filters, machine learning, and elements of fractal theory. In upstream activities of hydrocarbon reservoirs, the analysis and interpretation of resistivity imaging is a time-consuming and repetitive task, usually manually performed by specialized geologists. Delay in this task may result in operational problems, causing economic losses during field operations. In order to solve this issue, the present research proposes automatic extraction of geologic features in borehole resistivity imaging shortly after the image log is acquired. Features are initially extracted from pixels analysis of the dynamic-normalized images, employing a set of computer vision techniques to detect edges and sinusoids. Afterward, these sinusoids are classified using statistical measures and machine learning. During training and classification stages, machines employ fractal dimension, lacunarity, and their derived statistic parameters (fractal attributes); the fractal information is complemented with resistivity, gamma rays, and photoelectric factor logs to improve the accuracy during classification. In both clastic and carbonate environments, the outcome consists of a set of classified geologic planes, precise enough to recognize patterns in the structural and stratigraphic dips of the drilled sequence. Academically, the main novelty of this research is the integration of fractal theory and machine learning, aiming at automatizing interpretation of resistivity imaging in hydrocarbon producer wells. (Text taken from source)El propósito de esta investigación es desarrollar una metodología para la detección y clasificación automática de planos geológicos en imágenes resistivas adquirida con cable. La metodología involucra aplicación de filtros bioinspirados, aprendizaje automático y elementos de la teoría fractal. En las actividades aguas arriba de yacimientos de hidrocarburos, el análisis e interpretación de imágenes resistivas es una tarea repetitiva, que requiere mucho tiempo y generalmente realizada manualmente por geólogos especializados. La demora en esta tarea puede resultar en problemas operativos, causando pérdidas económicas durante las operaciones de campo. Para resolver este problema, la presente investigación propone la extracción automática de características geológicas en las imágenes de resistividad del pozo poco después de que se adquiere el registro de imágenes. Las características se extraen inicialmente del análisis de píxeles de las imágenes dinámicas normalizadas, empleando un conjunto de técnicas de visión por computadora para detectar bordes y sinusoides. Posteriormente, estas sinusoides se clasifican utilizando medidas estadísticas y aprendizaje automático. Durante las etapas de entrenamiento y clasificación, las máquinas emplean la dimensión fractal, lagunaridad y sus parámetros estadísticos derivados (atributos fractales); la información fractal se complementa con registros de resistividad, rayos gamma y factor fotoeléctrico para mejorar la precisión durante la clasificación. Tanto en ambientes clásticos como en carbonatos, el resultado consiste en un conjunto de planos geológicos clasificados, lo suficientemente precisos como para reconocer patrones en los buzamientos estructurales y estratigráficos de la secuencia perforada. Académicamente, la principal novedad de esta investigación es la integración de la teoría fractal y aprendizaje automático, con el objetivo de automatizar la interpretación de imágenes resistivas en pozos productores de hidrocarburosDoctoradoDoctor en GeocienciasSistemas Inteligentes Aplicados a las Geociencias136 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Doctorado en GeocienciasDepartamento de GeocienciasFacultad de CienciasUniversidad Nacional de Colombia - Sede Bogotá550 - Ciencias de la tierra500 - Ciencias naturales y matemáticas000 - Ciencias de la computación, información y obras generalesOil and gas wellsMachine learningFractal dimensionLacunarityBorehole resistivity imagingGeologic planesautomatic pickingAutomatic dip classificationHydrocarbon reservoirsAprendizaje automáticoDimensión fractalLagunaridadImágenes resistivas de pozoPlanos geológicosClasificación automática de buzamientosYacimientos de hidrocarburosMachine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logsAprendizaje automático y atributos fractales aplicados a la detección y clasificación automática de planos geológicos en registros de imágenes resistivasTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAggarwal, C., 2015, Data mining. The textbook, first edition. Springer, New York, 181-488pp.Allain, C., Cloitre, M., 1991, Characterizing the lacunarity of random and deterministic fractal sets. Physical Review A, 44(6), 3552 - 3553. https://doi.org/10.1103/PhysRevA.44.3552Alpaydin, E., 2014, Introduction to machine learning, third edition. The MIT Press, Cambridge, 27-238pp.Al-Sit, W., Al-Nuaimy, W., Marelli, M., Al- Ataby, A., 2015, Visual texture for automated characterization of geological features in borehole televiewer imagery. Journal of Applied Geophysics, 119, 39-146pp. http://dx.doi.org/10.1016/j.jappgeo.2015.05.015Arizabalo, R., Oleschko, K., Gabor, K., Lozada, M., Castrejón, R., Ronquillo, G., 2006, Lacunarity of geophysical well logs in the Cantarell oil field, Gulf of Mexico. Geofísica International, 45(2), 99-105pp.Assous, S., Elkington, P., Clark, S., Whetton, J., 2013, Automated detection of planar geological feature in borehole image. Society of Exploration Geophysicists, 79 (1). D11-D19pp. https://doi.org/10.1190/geo2013-0189.1Asquith, G., Krygowski, D., 2004, Basic well log analysis, second edition. The American Association of Petroleum Geologist, Tulsa, 31pp.Arora, N., Sarvani, G., 2017, A review paper on Gabor filter algorithm & its application. IJARECE, 6 (9), 1003-1007pp. doi:10.17148/IJARCCE.2017.6492Awad, M., Khanna, R., 2015, Efficient learning machines, second edition. Apress Open, Berkeley, 14-17pp.Ayad, A., Amrani, M., Bakkali, S., 2019, Quantification of the disturbances of phosphate series using the box-counting method on geoelectrical images (Sidi Chennane, Morocco). International Journal of Geophysics, 2019(12), 1-12. https://doi.org/10.1155/2019/2565430Barnsley, M., 1993, Fractals Everywhere, second edition. Morgan Kaufmann, Atlanta, 171pp.Bloem, P., 2010, Machine learning and fractal geometry. M.Sc. Thesis, University of Amsterdam. iii-8pp.Boggs, S., 2009, Petrology of sedimentary rocks, second edition. Cambridge University Press, Cambridge, 194-314pp.Boggs, S., 2014, Principles of sedimentology and stratigraphy, fifth edition. Pearson Educational Limited, Edinburgh, 76-135pp.Brownlee, J., 2016, What is a Confusion Matrix in Machine Learning. Machine Learning Mastery, 18 November 2016, https://machinelearningmastery.com/confusion-matrix-machine-learning/ (accessed 6 June 2020).Burger, W., Burge, M., 2009, Principles of digital image processing. Fundamental techniques, first edition. Springer, Hagenberg, 57-122pp.Burger, W., Burge, M., 2009, Principles of digital image processing. Core algorithms, first edition. Springer, Hagenberg, 110pp.Changchun, Z., Ge, S., 2002, A Hough transform-based method for fast detection of fixed period sinusoidal curves in images. Signal Processing 6th International Conference, 909-912pp. DOI: 10.1109/ICOSP.2002.1181204Cheng, G., Guo, W., 2017, Rock images classification by using deep convolutional neural network. 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Weatherford brochure, 1-4pp.AdministradoresBibliotecariosConsejerosEstudiantesGrupos comunitariosInvestigadoresMaestrosMedios de comunicaciónPadres y familiasPersonal de apoyo escolarProveedores de ayuda financiera para estudiantesPúblico generalReceptores de fondos federales y solicitantesResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/80725/1/license.txt8153f7789df02f0a4c9e079953658ab2MD51ORIGINAL389194.2021.pdf389194.2021.pdfTesis de Doctorado en Geocienciasapplication/pdf7657799https://repositorio.unal.edu.co/bitstream/unal/80725/3/389194.2021.pdfc15191fb90275942b398288627e39c8dMD53THUMBNAIL389194.2021.pdf.jpg389194.2021.pdf.jpgGenerated Thumbnailimage/jpeg5457https://repositorio.unal.edu.co/bitstream/unal/80725/4/389194.2021.pdf.jpg9f9f776ab5631268dea5e0fb990c85caMD54unal/80725oai:repositorio.unal.edu.co:unal/807252024-08-01 23:10:34.706Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

Machine learning and fractal attributes applied to automatic detection and classification of geologic planes in resistivity image logs

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