banner

Machine Learning, Deep Learning and Implementation Language in Geological Field

Yongzhang Zhou, Jun Wang, Renguang Zuo, Fan Xiao, Wenjie Shen, Shugong Wang

Abstract


Geological big data is growing exponentially. Only by developing intelligent data processing methods can we catch up with the extraordinary growth of big data. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Machine learning has become the frontier hotspot of geological big data research. It will make geological big data winged and change geology. Machine learning is a training process of model derived from data, and it eventually gives a decision oriented to a certain performance measurement. Deep learning is an important subclass of machine learning research. It learns more useful features by building machine learning models with many hidden layers and massive training data, so as to improve the accuracy of classification or prediction at last. Convolutional neural network algorithm is one of the most commonly used deep learning algorithms. It is widely used in image recognition and speech analysis. Python language plays an increasingly important role in the field of science. Scikit-Learn is a bank related to machine learning, which provides algorithms such as data preprocessing, classification, regression, clustering, prediction and model analysis. Keras is a deep learning bank based on Theano/Tensorflow, which can be applied to build a simple artificial neural network.


Keywords


Geological Big Data; Machine Learning; Deep Learning; Artificial Neural Network; Intelligent Geology; Python

Full Text:

PDF

References


Aryafar A, Moeini H. Application of continuous restricted Boltzmann machine to detect multivariate anomalies from stream sediment geochemical data, Korit, East of Iran. Journal of Mining and Environment 2017; 8(4): 673–682. doi: 10.22044/JME.2017.966.

Bianco S, Buzzelli M, Mazzini D, et al. Deep learning for logo recognition. Neurocomputing 2017; 245: 23–30. doi: 10.1016/j.neucom.2017.03.051.

Brenden M, Ruslan S, Joshua B. Human-level concept learning through probabilistic program induction. Science 2015; 350(6266): 1332–1338. doi: 10.1126/science.aab3050.

Carranza EJM, Laborte AG. Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Computers & Geosciences 2015; 74: 60–70. doi: 10.1016/j.cageo.2014.10.004.

de Mulder EFJ, Cheng Q, Agterberg F, et al. New and game-changing developments in geochemical exploration. Episodes 2016; 39(1): 70–71.

Han S, Li M, Ren Q, et al. Intelligent determination and data mining for tectonic settings of basalts based on big data methods (in Chinese). Acta Petrologica Sinica 2018; 34(11): 3207–3216.

Hinton GE, Osindero S, Teh Y. A fast learning algorithm for deep belief nets. Neural Computation 2006; 18(7): 1527–1554. doi: 10.1162/neco.2006.18.7.1527.

Hinton GE, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 2012; 29(6): 82–97. doi: 10.1109/MSP.2012.2205597.

Jiao S, Zhou Y, Zhang Q, et al. Study on intelligent discrimination of tectonic settings based on global gabbro data from GEOROC (in Chinese). Acta Petrologica Sinica 2018; 34(11): 3189–3194.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521 (7553): 436–444.

Liu Y, Zhu L, Zhou Y. Application of Convolutional Neural Networkin prospecting prediction of ore deposits: Taking the Zhaojikou Pb-Zn ore deposit in Anhui province as a case (in Chinese). Acta Petrologica Sinica 2018; 34(11): 3217–3224.

Mayer-Schonberger V, Cukier K. Big data: A revolution that will transform how we live, work and think. New York: Houghton Mifflin Harcourt Publishing Company. 2013.

Ross ZE, Meier MA, Hauksson E. P-wave arrival picking and first-motion polarity determination with deep learning. Journal of Geophysical Research: Solid Earth 2018; 123(6): 5120–5129. doi: 10.1029 /2017JB015251.

Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks 2015; 61: 85–117. doi: 10.48550/arXiv.1404.7828.

Wang H, Luo J, Wang J, et al. Quantitative classification and metallogenic prognosis of basic-ultrabasic rocks based on big data: Taking its application in Beishan area for example (in Chinese). Acta Petrologica Sinica 2018; 34 (11): 3195–3206.

Xu S, Zhou Y. Artificial intelligence identification of ore minerals under microscope based on deep learning algorithm (in Chinese). Acta Petrologica Sinica 2018; 34 (11): 3244–3252.

Yosinski J, Clune J, Bengio Y, et al. How transferable are features in deep neural networks? Advances in Neural Information Processing Systems 2014; 27: 3320–3328. doi: 10.48550/arXiv.1411.1792.

Zhang Q, Zhou Y. Big data will lead to a profound revolution in the field of geological science (in Chinese). Scientia Geologica Sinica 2017; 52 (3): 637–648. doi: 10.12017/dzkx.2017.041.

Zhang Y, Li M, Han S. Automatic identification and classification in lithology based on deep learning in rock images (in Chinese). Acta Petrologica Sinica 2018; 34(2): 333–342.

Zhou Y, Chen S, Zhang Q, et al. Advances and prospects of big data and mathematical geoscience (in Chinese). Acta Petrologica Sinica 2018; 34(2): 256–263.

Zhou YZ, Zhang LJ, Zhang AD, et al. Big data mining & machine learning in geoscience (in Chinese). Guangzhou: Sun Yat-sen University Press. 2018. p. 1–360.




DOI: http://dx.doi.org/10.32629/jai.v4i1.479

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Yongzhang Zhou

License URL: https://creativecommons.org/licenses/by-nc/4.0