Abstract
In this chapter, after presenting a list of the DL applications with brief descriptions, various advanced applications of deep learning in the fields of seismology and volcanology are presented. The examples are arranged in two separate parts, one for seismological and the other for volcanological aspects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aki K, Richards PG (2002) Quantitative seismology, 2nd edn. University Science Books, Sausalito. http://www.worldcat.org/isbn/0935702962
Anantrasirichai N, Biggs J, Albino F, Bull D (2019) A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets. Remote Sens Environ. https://doi.org/10.1016/j.rse.2019.04.032
Anantrasirichai N, Biggs J, Albino F, Hill P, Bull D (2018) Application of machine learning to classification of volcanic deformation in routinely generated InSAR data. J Geophys Res Solid Earth. https://doi.org/10.1029/2018JB015911
Banna MdH, Taher KA, Kaiser MS, Mahmud M, Rahman MdS, Hosen ASMS, Cho GH (2020) Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges. IEEE Access 8:192880–192923. https://doi.org/10.1109/ACCESS.2020.3029859
Bonheur S, Štern D, Payer C, Pienn M, Olschewski H, Urschler M (2019) Matwo-CapsNet: a multi-label semantic segmentation capsules network, pp 664–672. https://doi.org/10.1007/978-3-030-32254-0_74
Bueno A, Benitez C, de Angelis S, Diaz Moreno A, Ibanez JM (2020) Volcano-Seismic Transfer Learning and Uncertainty Quantification with Bayesian Neural Networks. IEEE Trans Geosci Remote Sens 58(2):892–902. https://doi.org/10.1109/TGRS.2019.2941494
Canário JP, Mello R, Curilem M, Huenupan F, Rios R (2020) In-depth comparison of deep artificial neural network architectures on seismic events classification. J Volcanol Geoth Res. https://doi.org/10.1016/j.jvolgeores.2020.106881
Cheng X, Liu Q, Li P, Liu Y (2019) Inverting Rayleigh surface wave velocities for crustal thickness in eastern Tibet and the western Yangtze craton based on deep learning neural networks. Nonlinear Process Geophys 26(2):61–71. https://doi.org/10.5194/npg-26-61-2019
Crotwell HP, Owens TJ, Ritsema J (1999) The TauP Toolkit: flexible seismic travel-time and ray-path utilities. Seismol Res Lett 70(2):154–160. https://doi.org/10.1785/gssrl.70.2.154
del Rosso MP, Sebastianelli A, Spiller D, Mathieu PP, Ullo SL (2021) On-board volcanic eruption detection through CNNs and satellite multispectral imagery. Remote Sens 13(17). https://doi.org/10.3390/rs13173479
Derakhshani A, Foruzan AH (2019) Predicting the principal strong ground motion parameters: a deep learning approach. Appl Soft Comput J 80:192–201. https://doi.org/10.1016/j.asoc.2019.03.029
Florez MA, Caporale M, Buabthong P, Ross ZE, Asimaki D, Meier M-A (2020, November 17) Data-driven accelerogram synthesis using deep generative models. AGU2020 Fall Meeting. http://arxiv.org/abs/2011.09038
Grijalva F, Ramos W, Perez N, Benitez D, Lara R, Ruiz M (2021) ESeismic-GAN: a generative model for seismic events from Cotopaxi volcano. IEEE J Select Top Appl Earth Observ Remote Sens 14:7111–7120. https://doi.org/10.1109/JSTARS.2021.3095270
Hu J, Qiu H, Zhang H, Ben-Zion Y (2020) Using deep learning to derive shear-wave velocity models from surface-wave dispersion data. Seismol Res Lett 91(3):1738–1751. https://doi.org/10.1785/0220190222
Jozinović D, Lomax A, Štajduhar I, Michelini A (2021) Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network. Geophys J Int 222(2):1379–1389. https://doi.org/10.1093/GJI/GGAA233
Kavianpour P, Kavianpour M, Jahani E, Ramezani A (2021) A CNN-BiLSTM model with attention mechanism for earthquake prediction. http://arxiv.org/abs/2112.13444
Kossobokov VG, Romashkova LL, Panza GF, Peresan A (2002) Stabilizing intermediate-term medium-range earthquake predictions. In: JSEE: Summer and Fall, vol 4, no 3
Kuang W, Yuan C, Zhang J (2021) Real-time determination of earthquake focal mechanism via deep learning. Nat Commun 12(1). https://doi.org/10.1038/s41467-021-21670-x
Le H, Murata T, Iguchi M (2020) Can eruptions be predicted? Short-term prediction of volcanic eruptions via attention-based long short-term memory. Proc AAAI Conf Artif Intell 34(08):13320–13325. https://doi.org/10.1609/aaai.v34i08.7043
Manley GF, Mather TA, Pyle DM, Clifton DA, Rodgers M, Thompson G, Londoño JM (2022) A deep active learning approach to the automatic classification of volcano-seismic events. Front Earth Sci. https://doi.org/10.3389/feart.2022.807926
Mousavi SM, Beroza GC (2019) Bayesian-deep-learning estimation of earthquake location from single-station observations. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2020.2988770
Mousavi SM, Beroza GC (2020) A machine-learning approach for earthquake magnitude estimation. Geophys Res Lett 47(1). https://doi.org/10.1029/2019GL085976
Mousavi SM, Ellsworth WL, Zhu W, Chuang LY, Beroza GC (2020) Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat Commun 11(1). https://doi.org/10.1038/s41467-020-17591-w
Peng P, He Z, Wang L, Jiang Y (2020) Microseismic records classification using capsule network with limited training samples in underground mining. Sci Rep 10(1). https://doi.org/10.1038/s41598-020-70916-z
Perol T, Gharbi M, Denolle M (2018) Convolutional neural network for earthquake detection and location. Sci Adv 4(2). https://www.science.org
Pu Y, Chen J, Apel DB (2021) Deep and confident prediction for a laboratory earthquake. Neural Comput Appl 33(18):11691–11701. https://doi.org/10.1007/s00521-021-05872-4
Li R, Lu X, Li S, Yang H, Qiu J, Zhang L (2020) DLEP: a deep learning model for earthquake prediction. Int Joint Conf Neural Netw (IJCNN)
Sener O, Savarese S (2017) Active learning for convolutional neural networks: a core-set approach. http://arxiv.org/abs/1708.00489
Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651. https://doi.org/10.1109/TPAMI.2016.2572683
Shoji D, Noguchi R, Otsuki S, Hino H (2018) Classification of volcanic ash particles using a convolutional neural network and probability. Sci Rep 8(1):8111. https://doi.org/10.1038/s41598-018-26200-2
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556
Stepnov A, Chernykh V, Konovalov A (2021) The seismo-performer: a novel machine learning approach for general and efficient seismic phase recognition from local earthquakes in real time. Sensors 21(18). https://doi.org/10.3390/s21186290
Sugiyama D, Tsuboi S, Yukutake Y (2021) Application of deep learning-based neural networks using theoretical seismograms as training data for locating earthquakes in the Hakone volcanic region, Japan. Earth Planets Space 73(1). https://doi.org/10.1186/s40623-021-01461-w
Titos M, Bueno A, García L, Benítez C, Segura JC (2020) Classification of isolated volcano-seismic events based on inductive transfer learning. IEEE Geosci Remote Sens Lett 17(5):869–873. https://doi.org/10.1109/LGRS.2019.2931063
Uchide T (2020) Focal mechanisms of small earthquakes beneath the Japanese islands based on first-motion polarities picked using deep learning. Geophys J Int 223(3):1658–1671. https://doi.org/10.1093/gji/ggaa401
Wang J, Xiao Z, Liu C, Zhao D, Yao Z (2019) Deep learning for picking seismic arrival times. J Geophys Res Solid Earth 124(7):6612–6624. https://doi.org/10.1029/2019JB017536
Yousefzadeh M, Hosseini SA, Farnaghi M (2021) Spatiotemporally explicit earthquake prediction using deep neural network. Soil Dyn Earthq Eng. https://doi.org/10.1016/j.soildyn.2021.106663
Zhao D, Yanada T, Hasegawa A, Umino N, Wei W (2012) Imaging the subducting slabs and mantle upwelling under the Japan Islands. Geophys J Int 190(2):816–828. https://doi.org/10.1111/j.1365-246X.2012.05550.x
Zhu J, Li S, Song J, Wang Y (2021) Magnitude estimation for earthquake early warning using a deep convolutional neural network. Front Earth Sci. https://doi.org/10.3389/feart.2021.653226
Zhu L, Helmberger D (1996) Advancement in source estimation techniques using broadband regional seismograms. Bull Seismol Soc Am 86(5):1634–1641. https://doi.org/10.1785/BSSA0860051634
Zhu L, Rivera LA (2002) A note on the dynamic and static displacements from a point source in multilayered media. Geophys J Int 148(3):619–627. https://doi.org/10.1046/j.1365-246X.2002.01610.x
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hajian, A., Nunnari, G., Kimiaefar, R. (2023). Deep Learning: Applications in Seismology and Volcanology. In: Intelligent Methods with Applications in Volcanology and Seismology. Advances in Volcanology. Springer, Cham. https://doi.org/10.1007/978-3-031-15432-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-15432-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15431-7
Online ISBN: 978-3-031-15432-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)