Machine Learning in Geology: Challenges and Prospects
DOI:
https://doi.org/10.54097/hset.v44i.7162Keywords:
machine learning; geology.Abstract
Machine Learning methods, along with high-performance computing, is creating chances for data intensive science in various domains such as Geology. Known for its precision and efficiency, Machine learning has renovated many fields of science thoroughly. However, when it comes to Geology, Machine Learning methods aren’t fully applied, lacking an overall frame to arrange those works with scattered aims and methods. This paper presents a comprehensive review of research focusing on application of Machine Learning methods in Geology studies. The works analyzed were categorized in three aspects of Geology studies: mineral prospecting and mapping, land-cover monitoring; geochemical anomalies detection; earthquake monitoring and geological sample identification. The selection and classification of the presented articles demonstrate how Geology benefits from Machine Learning methods.
Downloads
References
W. R. Muehlberger, Earth Science[J]. Science, 1966, 152 (3724):950-951.
S. F. Huang, X. H. Liu, Thinking about the application of geological big data and geological information development[J]. China Mining Magazine, 2016, 25(08):166-170.
L. N. Huynh, Y. Lee, R. K. Balan, DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications[A]. In: The International Conference, 2017[C].
Q. Zhang, X. Y. Jia, Z. Wu, J. R. Wang, S. T. Jiao, W. F. Chen, Big data will lead to a great change in Geological Science Research[A].2015 China Geoscience union annual meeting, Beijing, China, 2015 [C].
Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by backpropagating errors. Nature 323, 533–536.
Friedl, M.A., Brodley, C.E., 1997. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61, 399–409.
Guo, L., Chehata, N., Mallet, C., Boukir, S., 2011. Relevance of airborne lidar and multispectral image data for urban scene classification using random forests. ISPRS J. Photogramm. Remote Sens. 66, 56–66.
Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32.
Cortes, Corinna; Vladimir Vapnik (1995). "Support-Vector Networks". Machine Learning. 20 (3): 273–297.
Brown, W.M., Gedeon, T.D., Groves, D.I., Barnes, R.G., 2000. Artificial neural networks: a new method for mineral prospectivity mapping. Aust. J. Earth Sci. 47, 757–770.
Porwal, A., Carranza, E.J.M., Hale, M., 2003. Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India. Nat. Resour. Res. 12, 155–171.
Rodriguez-Galiano, V.F.; Chica-Olmo, M.; Chica-Rivas, M. (2014). Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain. International Journal of Geographical Information Science, 28(7), 1336–1354.
Carranza, Emmanuel John M.; Laborte, Alice G. (2016). Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines). Natural Resources Research, 25(1), 35–50.
Foody, G.M., and A. Mathur, 2004a. A relative evaluation of multiclass image classification by support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42(6):1335–1343
Abedi, M., Norouzi, G.H., Bahroudi, A., 2012. Support vector machine for multi-classification of mineral prospectivity areas. Comput. Geosci. 46, 272–283.
M. J. Cracknell, A. M. Reading, Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information[J]. Computers & Geosciences, 2014,63(1):22-33.
V. G. Rodriguez, M. C. Sanchez, M. O. Chica, et al. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines[J]. Ore Geology Reviews, 2015,71:804-818.
Song, Xianfeng; Duan, Zheng; Jiang, Xiaoguang (2012). Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image. , 33(10), 3301–3320.
Friedl, M.A., Brodley, C.E., 1997. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61, 399–409.
Rogan, J., Miller, J., Stow, D., Franklin, J., Levien, L., Fischer, C., 2003. Land-cover change monitoring with classification trees using Landsat TM and ancillary data. Photogramm. Eng. Remote Sens. 69, 793–804.
Gislason, P.O., Benediktsson, J.A., Sveinsson, J.R., 2006. Random forests for land cover clas sification. Pattern Recogn. Lett. 27, 294–300.
Yang, X., 2011. Parameterizing support vector machines for land cover classification. Photogramm. Eng. Remote Sens. 77, 27–37
Rodriguez-Galiano, V.F., Chica-Rivas, M., 2012. Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and digital terrain models. Int. J. Digit. Earth 7, 492–509.
A. Beucher, P. Österholm, A. Martinkauppi, et al. Artificial neural network for acid sulfate soil mapping: Application to the Sirppujoki River catchment area, south-western Finland[J]. Journal of Geochemical Exploration, 2013,125(1):46-55.
N. K. C. Twarakavi, D. Misra, S. Bandopadhyay, Prediction of Arsenic in Bedrock Derived Stream Sediments at a Gold Mine Site Under Conditions of Sparse Data[J]. Natural Resources Research, 2006, 15(1):15-26
Y. Chen, L. Lu, X. Li, Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly[J]. Journal of Geochemical Exploration, 2014,140(4):56-63.
A. M. Gonbadi, S. H. Tabatabaei, E. J. M.Carranza, Supervised geochemical anomaly detection by pattern recognition[J]. Journal of Geochemical Exploration, 2015, 157:81-91.
L. Chen, Q. Guan, Y. Xiong, J. Liang, Y. Wang, Y. Xu, A Spatially Constrained Multi-Autoencoder Approach for Multivariate Geochemical Anomaly Recognition. Computers and Geosciences, 2019, 125: 43–54.
L. Chen, Q. Guan, B. Feng, H. Yue, J. Wang, F. Zhang, A Multi-Convolutional Auto-encoder Approach for Multivariate Geochemical Anomaly Recognition, Minerals, 2019, 9(5): 270.
Q. Zhang, X. Y. Jia, Z. Wu, J. R. Wang, S. T. Jiao, W. F. Chen, Big data will lead to a great change in Geological Science Research[A].2015 China Geoscience union annual meeting, Beijing, China, 2015 [C].
M. J. Cracknell, A. M. Reading, Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information[J]. Computers & Geosciences, 2014,63(1):22-33.
K. Regenauerlieb, M. Veveakis, T. Poulet, et al. Multiscale coupling and multiphysics approaches in earth sciences: Applications[J]. Journal of Coupled Systems & Multiscale Dynamics, 2013,1(3):281-323.
M. Hilbert, Big Data for Development: A Review of Promises and Challenges[J]. Development Policy Review, 2016,34(1):135-174
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
