Posters

SM4.6

The past few years have seen an increase in the application of machine learning methods for seismic data analysis. This is due to the increased adoption and visibility of freely available and easy-to-use machine learning toolkits, faster computation, reduced cost of data storage, and the very large sets of continuous geophysical and laboratory experimental data. The combination of these factors means that now is the time to consider machine learning as one of the key strategies modeling tools in both improving routine data processing and for better understanding the underlying geophysical processes.

Already, significant progress has been made in seismic waveform detection and classification of seismic waves for automatic onset picking. Such advances are allowing us to vastly speed up and improve the accuracy of previously laborious processing flows. In other notable recent applications, waveforms and ground motions, from both laboratory and natural datasets, are being used to understand the precursory physics of sudden- and slow-slip and to predict aftershock locations within supervised learning frameworks.

In this session, we will see machine learning focussed presentations covering topics such as seismic waveform processing, earthquake cataloging, earthquake classification, and earthquake cycle behavior from numerical and laboratory experiments.

In particular we would like to highlight invited talks from
Beroza et al.: Earthquake Monitoring with Deep Learning
Hulbert et al.: Probing Fault Physics Applying Machine Learning
De Hoop et al.: Unsupervised learning for identification of seismic signals
and
Kriegerowski et al.: Deep learning for localizing and detecting earthquake swarm activity based on full waveforms: Chances, challenges and questions

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Convener: Leonard Seydoux | Co-conveners: Jonathan Bedford, Fabio Corbi, Jens Dittrich, Piero Poli
Orals
| Mon, 08 Apr, 14:00–15:45
 
Room -2.91
Posters
| Attendance Mon, 08 Apr, 10:45–12:30
 
Hall X2

Attendance time: Monday, 8 April 2019, 10:45–12:30 | Hall X2

X2.361 |
EGU2019-5037
Flavio Cannavo', Salvatore Moschella, Andrea Cannata, Stefano Gresta, and Laura Spina
X2.362 |
EGU2019-4486
Payman Janbakhsh, Russell Pysklywec, and Hosein Shahnas
X2.363 |
EGU2019-6483
Chun-Ming Huang, Hao Kuo‐Chen, Pei-Yu Jhong, and Zhuo-Kang Guan
X2.364 |
EGU2019-7141
Yousef Rajaeitabrizi, Robabeh Salehiozoumchelouei, Luca D'Auria, and José Luis Sánchez de la Rosa
X2.365 |
EGU2019-9115
Luis Fernandez-Prieto and Antonio Villaseñor
X2.366 |
EGU2019-9722
Jean Soubestre, Léonard Seydoux, Luca D'Auria, José Barrancos, German D. Padilla, Nikolai M. Shapiro, and Nemesio M. Perez
X2.367 |
EGU2019-10807
Fabio Corbi, Laura Sandri, Jonathan Bedford, Francesca Funiciello, Silvia Brizzi, Matthias Rosenau, and Serge Lallenand
X2.368 |
EGU2019-18984
Do seismic waveforms exhibit features of earthquake imminence? Machine Learning investigation of the Northern Chile broadband seismic data
(withdrawn)
Joris Nix, Jens Dittrich, and Jonathan Bedford
X2.369 |
EGU2019-11271
Sensibility analysis of of the InSight seismic data to the Martian structure: Application to the MSS blind test data
(withdrawn)
Salma Barkaoui, Philippe Lognonné, Mélanie Drilleau, Taichi Kawamura, Maria Saadé, Balthazar Kenda, Naomi Murdoch, and Martin van Driel
X2.370 |
EGU2019-11926
Cheng Nan Liu, Ting Chung Huang, and Yih Min Wu
X2.371 |
EGU2019-14454
Carlo Giunchi and Rasoul Sadeghian
X2.372 |
EGU2019-16377
Data augmentation for improved classification of geophysical signals with deep learning
(withdrawn)
Glenn Cougoulat, Piero Poli, Leonard Seydoux, and Michel Campillo
X2.373 |
EGU2019-19067
Deniz Ertuncay, Andrea De Lorenzo, Giovanni Costa, and Eric Medvet