Deep Convolutions for In-Depth Automated Rock Typing

The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.

[1]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[2]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  I. Varfolomeev,et al.  Integrated Study of Thin Sections: Optical Petrography and Electron Microscopy (Russian) , 2016 .

[4]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[5]  M. Hallett.  Transcranial Magnetic Stimulation: A Primer , 2007, Neuron.

[6]  Andreas Holzinger,et al.  Augmentor: An Image Augmentation Library for Machine Learning , 2017, J. Open Source Softw..

[7]  Saifon Daungkaew,et al.  Applications of NMR Logs and Borehole Images to the Evaluation of Laminated Deepwater Reservoirs , 2007 .

[8]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[9]  Christopher M. Prince,et al.  CORE IMAGE ANALYSIS: RELIABLE PAY ESTIMATION IN THIN-BEDDED RESERVOIR UNITS , 2008 .

[10]  Sunil Arya,et al.  An optimal algorithm for approximate nearest neighbor searching fixed dimensions , 1998, JACM.

[11]  Zhongliang Wu,et al.  Temporal variation of noise correlation function (NCF) in Beijing and surroundings: Its relation with climate events and implications , 2009 .

[12]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[13]  François Chollet,et al.  Deep Learning with Python , 2017 .

[14]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

[17]  I. S. Wieling.  Facies and permeability prediction based on analysis of core images , 2013 .

[18]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[19]  C. Wentworth.  A Scale of Grade and Class Terms for Clastic Sediments , 1922, The Journal of Geology.

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[21]  Leslie N. Smith,et al.  Cyclical Learning Rates for Training Neural Networks , 2015, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[22]  C. Metz.  Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[23]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[24]  Snehamoy Chatterjee,et al.  Development of machine vision-based ore classification model using support vector machine (SVM) algorithm , 2017, Arabian Journal of Geosciences.

[25]  Andrew Curtis,et al.  Automated lithology extraction from core photographs , 2011 .

[26]  Sergey I. Nikolenko,et al.  Adapting Convolutional Neural Networks for Geographical Domain Shift , 2019, ArXiv.

[27]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[28]  Alexandr A. Kalinin,et al.  Albumentations: fast and flexible image augmentations , 2018, Inf..

[29]  Tal Hassner,et al.  Age and Gender Estimation of Unfiltered Faces , 2014, IEEE Transactions on Information Forensics and Security.

[30]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[31]  Herbert Freeman,et al.  Machine Vision for Three-Dimensional Scenes , 1990 .

[32]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[33]  Graham W. Taylor,et al.  Improved Regularization of Convolutional Neural Networks with Cutout , 2017, ArXiv.

[34]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[35]  Guido van Rossum,et al.  The Python language reference manual: for Python version 3.2 , 2011 .

[36]  Hossein Memarian,et al.  Lithological classification and chemical component estimation based on the visual features of crushed rock samples , 2017, Arabian Journal of Geosciences.

[37]  Snehamoy Chatterjee.  Vision-based rock-type classification of limestone using multi-class support vector machine , 2012, Applied Intelligence.

[38]  Snehamoy Chatterjee,et al.  Image-based quality monitoring system of limestone ore grades , 2010, Comput. Ind..

[39]  Snehamoy Chatterjee,et al.  Rock-type classification of an iron ore deposit using digital image analysis technique , 2008 .

[40]  Travis E. Oliphant,et al.  Guide to NumPy , 2015 .

[41]  Dmitry Koroteev,et al.  Core photos lithological interpretation using neural networks , 2018 .

[42]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[43]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[44]  Luis Hernan Ochoa,et al.  Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging , 2018 .

[45]  Snehamoy Chatterjee,et al.  Development of online machine vision system using support vector regression (SVR) algorithm for grade prediction of iron ores , 2017, 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA).

[46]  Dmitry Koroteev,et al.  Core photo lithological interpretation based on computer analyses , 2018 .

[47]  John Scott Bridle,et al.  Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition , 1989, NATO Neurocomputing.

[48]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[49]  Jean-Pierre Leduc,et al.  Improved Formation Evaluation In Thin Beds Using Petrophysical Images , 2004 .