Paper
20 December 2021 Recent advances in machine learning for geological and geophysical case studies
Wenda Zhou
Author Affiliations +
Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 1215519 (2021) https://doi.org/10.1117/12.2626783
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Reference

[1] 

Bolton, M. S., Jensen, B. J., Wallace, K., Praet, N., Fortin, D., Kaufman, D., & De Batist, M. (2020). Machine learning classifiers for attributing tephra to source volcanoes: an evaluation of methods for Alaska tephras. Journal of Quaternary Science, 35(1-2), 81–92. https://doi.org/10.1002/jqs.v35.1-2 Google Scholar

[2] 

Lüdtke, A., Jerosch, K., Herzog, O., & Schlüter, M. (2012). Development of a machine learning technique for automatic analysis of seafloor image data: Case example, Pogonophora coverage at mud volcanoes. Computers & Geosciences, 39, 120–128. https://doi.org/10.1016/j.cageo.2011.06.020 Google Scholar

[3] 

Ouzounis, A. G., & Papakostas, G. A. (2021). Machine Learning in Discriminating Active Volcanoes of the Hellenic Volcanic Arc. Applied Sciences, 11(18), 8318. https://doi.org/10.3390/app11188318 Google Scholar

[4] 

Zhang, L., & Zhan, C. (2017, May). Machine learning in rock facies classification: an application of XGBoost. In International Geophysical Conference, Qingdao, China, 17–20 April 2017 (pp. 1371–1374). Society of Exploration Geophysicists and Chinese Petroleum Society. Google Scholar

[5] 

Ren, Q., Wang, G., Li, M., & Han, S. (2019). Prediction of rock compressive strength using machine learning algorithms based on spectrum analysis of geological hammer. Geotechnical and Geological Engineering, 37(1), 475–489. https://doi.org/10.1007/s10706-018-0624-6 Google Scholar

[6] 

Xie, Z., Haritashya, U. K., Asari, V. K., Young, B. W., Bishop, M. P., & Kargel, J. S. (2020). GlacierNet: a deep-learning approach for debris-covered glacier mapping. IEEE Access, 8, 83495–83510. https://doi.org/10.1109/Access.6287639 Google Scholar

[7] 

Baraka, S., Akera, B., Aryal, B., Sherpa, T., Shresta, F., Ortiz, A., … & Bengio, Y. (2020). Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya. arXiv preprint arXiv:2012.05013. Google Scholar

[8] 

Zhang, J., Jia, L., Menenti, M., & Hu, G. (2019). Glacier facies mapping using a machine-learning algorithm: The Parlung Zangbo Basin case study. Remote Sensing, 11(4), 452. https://doi.org/10.3390/rs11040452 Google Scholar

[9] 

Xie, Y., Ebad Sichani, M., Padgett, J. E., & DesRoches, R. (2020). The promise of implementing machine learning in earthquake engineering: A state-of-the-art review. Earthquake Spectra, 36(4), 1769–1801. https://doi.org/10.1177/8755293020919419 Google Scholar

[10] 

Rouet-Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C. J., & Johnson, P. A. (2017). Machine learning predicts laboratory earthquakes. Geophysical Research Letters, 44(18), 9276–9282. https://doi.org/10.1002/2017GL074677 Google Scholar

[11] 

Xiong, P., Tong, L., Zhang, K., Shen, X., Battiston, R., Ouzounov, D., … & Zhou, H. (2021). Towards advancing the earthquake forecasting by machine learning of satellite data. Science of The Total Environment, 771, 145256. https://doi.org/10.1016/j.scitotenv.2021.145256 Google Scholar

[12] 

Assouline, D., Mohajeri, N., Gudmundsson, A., & Scartezzini, J. L. (2019). A machine learning approach for mapping the very shallow theoretical geothermal potential. Geothermal Energy, 7(1), 1–50. https://doi.org/10.1186/s40517-019-0135-6 Google Scholar

[13] 

Haklidir, F. S. T., & Haklidir, M. (2020). Prediction of reservoir temperatures using hydrogeochemical data, Western Anatolia geothermal systems (Turkey): a machine learning approach. Natural Resources Research, 29(4), 2333–2346. https://doi.org/10.1007/s11053-019-09596-0 Google Scholar

[14] 

Xu, F., Duan, L., Guo, X., Li, L., & Hu, F. (2018, November). Multiple classifiers global dynamic fusion location system based on Wi-Fi and geomagnetism. In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) (pp. 1–5). IEEE. Google Scholar

[15] 

Terzi, M. B., Arıkan, O., Karatay, S., Arıkan, F., & Gulyaeva, T. (2016). Classification of regional ionospheric disturbance based on machine learning techniques. European Space Agency, Special Publication, 740. Google Scholar

[16] 

Qiu, K., Chen, R., & Huang, H. (2021, February). A Practical Indoor and Outdoor Seamless Navigation System Based on Electronic Map and Geomagnetism. In 2021 13th International Conference on Machine Learning and Computing (pp. 588–594). Google Scholar

Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Machine learning

Earthquakes

Analytical research

Data modeling

Geology

Earth observing sensors

Geophysics

Back to Top