Tools for Automated Rock Description

References

1. Balsam, W. L., Deaton, B. C., and Damuth, J. E.
   [1999]. Evaluating optical lightness as a proxy for carbonate content in marine sediment cores. Marine Geology, 161, 141–153. Retrieved from www.elsevier.nlrlocatermargeo
   [Google Scholar]
2. Baraboshkin, E. E., Ismailova, L. S., Orlov, D. M., Zhukovskaya, E. A., Kalmykov, G. A., Khotylev, O. V., Baraboshkin, E. Y., and Koroteev, D. A.
   [2020]. Deep convolutions for in-depth automated rock typing. Computers & Geosciences, 135, 104330. https://doi.org/10.1016/j.cageo.2019.104330
   [Google Scholar]
3. Bradski, G.
   [2000]. The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 120, 122–125.
   [Google Scholar]
4. Breiman, L.
   [2001]. Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   [Google Scholar]
5. Chollet, F.
   [2017]. Deep learning with python. Manning Publications Company.
   [Google Scholar]
6. Comaniciu, D., and Meer, P.
   [2002]. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619. https://doi.org/10.1109/34.1000236
   [Google Scholar]
7. Dalal, N., and Triggs, B.
   [2005]. Histograms of Oriented Gradients for Human Detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1, 886–893. https://doi.org/10.1109/CVPR.2005.177
   [Google Scholar]
8. Duda, R. O., Hart, P. E., and Stork, D. G.
   [2001]. Pattern Classification.
   [Google Scholar]
9. Egorov, D.
   [2019, September 2]. Extraction of Petrophysical Information and Formation Heterogeneity Estimation from Core Photographs by Clustering Algorithms. https://doi.org/10.3997/2214-4609.201902190
   [Google Scholar]
10. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J.
    [2008]. LIBLINEAR: A Library for Large Linear Classification. In Journal of Machine Learning Research (Vol. 9). Retrieved from http://www.csie.ntu.edu.tw/
    [Google Scholar]
11. Fawcett, T.
    [2006]. An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
    [Google Scholar]
12. Hall, J., Ponzi, M., Gonfalini, M., and Maletti, G.
    [1996]. Automatic Extraction And Characterisation Of Geological Features And Textures From Borehole Images And Core Photographs. SPWLA 37th Annual Logging Symposium, 1–13. New Orlean, Louisiana.
    [Google Scholar]
13. Hallsworth, C. R., and Knox, R. W. O.
    [1999]. BGS Rock Classification Scheme. Classification of sediments and sedimentary rocks. 3.
    [Google Scholar]
14. Hunter, J. D.
    [2007]. Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55
    [Google Scholar]
15. Intrator, N.
    [1990]. A Neural Network for Feature Extraction. Advances in Neural Information Processing Systems, 719–728. Retrieved from https://papers.nips.cc/paper/244-a-neural-network-for-feature-extraction.pdf
    [Google Scholar]
16. Khasanov, I. I.
    [2014]. Rock color distribution analysis of core images (in Russian). OIL AND GAS GEOLOGY, (5), 33–39.
    [Google Scholar]
17. Lloyd, S. P.
    [1982]. Least Squares Quantization in PCM. In IEEE TRANSACTIONS ON INFORMATION THEORY (Vol. 28). Retrieved from http://www-evasion.imag.fr/people/Franck.Hetroy/Teaching/ProjetsImage/2007/Bib/lloyd-1982.pdf
    [Google Scholar]
18. Lowe, D. G.
    [2004]. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
    [Google Scholar]
19. Macqueen, J., and Macqueen, J.
    [1967]. Some methods for classification and analysis of multivariate observations. IN 5-TH BERKELEY SYMPOSIUM ON MATHEMATICAL STATISTICS AND PROBABILITY, 281--297. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.8619
    [Google Scholar]
20. MartínAbadi, AshishAgarwal
    , Paul Barham, E. B., ZhifengChen, CraigCitro, Greg S. Corrado, A. D., JeffreyDean, MatthieuDevin, Sanjay Ghemawat, I. G., AndrewHarp, GeoffreyIrving, MichaelIsard, Rafal Jozefowicz, Y. J., LukaszKaiser, ManjunathKudlur, JoshLevenberg, Dan Mané, M. S., RajatMonga, SherryMoore, DerekMurray, Chris Olah, J. S., … Zheng, X. [2015]. TensorFlow: Large-scale machine learning on heterogeneous systems. Software Available from Tensorflow.Org.
    [Google Scholar]
21. Metz, C. E.
    [1978]. Basic Principles of ROC analysis. Seminars in Nuclear Medicine, 8(4), 283–298. https://doi.org/10.1016/S0001-2998(78)80014-2
    [Google Scholar]
22. Patel, A. K., Chatterjee, S., and Gorai, A. K.
    [2017]. Development of machine vision-based ore classification model using support vector machine (SVM) algorithm. Arabian Journal of Geosciences, 10(5). https://doi.org/10.1007/s12517-017-2909-0
    [Google Scholar]
23. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, É.
    [2011]. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(Oct), 2825–2830. Retrieved from http://jmlr.org/papers/v12/pedregosa11a.html
    [Google Scholar]
24. Perarnau, A.
    [2011]. Use of Core Photo Data in Petrophysical Analysis. 1–6.
    [Google Scholar]
25. Prince, C. M., and Chitale, J.
    [2008]. Core Image Analysis : Reliable Pay Estimation in Thin-Bedded Reservoir Units. Image Processing, 1–6.
    [Google Scholar]
26. Thomas, A., Rider, M., Curtis, A., and MacArthur, A.
    [2011]. Automated lithology extraction from core photographs. First Break, 29(6), 103–109. Retrieved from https://www.geos.ed.ac.uk/homes/acurtis/Thomas_etal_FirstBreak_2011.pdf
    [Google Scholar]
27. Tipping, M. E., and Bishop, C. M.
    [2006]. Mixtures of probabilistic principal component analysers. In Neural Computation (Vol. 11). Retrieved from MIT Press website: http://www.miketipping.com/papers.htm
    [Google Scholar]
28. Travis, O. E.
    [2006]. A guide to NumPy. USA: Trelgol Publishing.
    [Google Scholar]
29. van der Walt, S.
    [2005]. Automated stratigraphic classification and feature detection from images of borehole cores (University of Stellenbosch). https://doi.org/10.1.1.463.876
    [Google Scholar]
30. van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T.
    [2014]. scikit-image: image processing in Python. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453
    [Google Scholar]
31. Van Rossum, G., and Drake, F. L.
    [2011]. The Python language reference manual : for Python version 3.2. Network Theory Ltd.
    [Google Scholar]
32. Wentworth, C. K.
    [1922]. A Scale of Grade and Class Terms for Clastic Sediments. The Journal of Geology, 30, 377–392. Retrieved from http://www.jstor.org/stable/10.2307/30063207
    [Google Scholar]
33. Wiatowski, T., and Bolcskei, H.
    [2018]. A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction. IEEE Transactions on Information Theory, 64(3), 1845–1866. https://doi.org/10.1109/TIT.2017.2776228
    [Google Scholar]
34. Wieling, I. S.
    [2013]. Facies and permeability prediction based on analysis of core images. Retrieved from https://repository.tudelft.nl/islandora/object/uuid%3A9b6bd4b0-1001-4d9b-a6eb-7761bc3b2309
    [Google Scholar]