SM2.3 | Challenges and Opportunities for Machine Learning in Solid Earth Geophysics
EDI
Challenges and Opportunities for Machine Learning in Solid Earth Geophysics
Convener: Jannes MünchmeyerECSECS | Co-conveners: Sophie Giffard-Roisin, Fabio Corbi, Chris Marone
Orals
| Tue, 16 Apr, 16:15–17:55 (CEST)
 
Room D3
Posters on site
| Attendance Wed, 17 Apr, 10:45–12:30 (CEST) | Display Wed, 17 Apr, 08:30–12:30
 
Hall X1
Orals |
Tue, 16:15
Posters on site
Wed, 10:45
Within the last decade, machine learning has established itself as an indispensable tool across many disciplines of solid earth geophysics. Remote sensing, seismic data exploration, and laboratory data analysis are only a few fields in which novel machine learning tools have already enabled substantially improved workflows and new discoveries. At the same time, it has become apparent that machine learning is not a silver bullet. While machine learning has illuminated new directions and deeper understanding in several areas, some studies applying machine learning have not achieved improvements over classical workflows, suggesting some problems might not be accessible with current machine learning methods.

In this session, we want to trace out the frontiers of machine learning in geophysics. What is the state of the art and what are the obstacles preventing application of machine learning or further improvements of existing methods? At the same time, we want to discuss how the novel machine learning methods impact scientific progress. Which discoveries have already been enabled by the current state of the art and what would be required to further advance science? To answer these questions, we aim to bring together machine learning experts and practitioners with an interest in machine learning from all disciplines of solid earth geophysics.

Orals: Tue, 16 Apr | Room D3

Chairpersons: Jannes Münchmeyer, Sophie Giffard-Roisin, Fabio Corbi
EGU24-8924
|
solicited
|
On-site presentation
Revealing and interpreting patterns from continuous seismic data with unsupervised learning
Léonard Seydoux et al.
EGU24-15571
|
ECS
|
Virtual presentation
Self-Supervised Learning Strategies for Clustering Continuous Seismic Data
Joachim Rimpot et al.
EGU24-16930
|
ECS
|
On-site presentation
How to Limit the Epistemic Failure of Machine Learning Models?
Alexandra Renouard et al.
EGU24-12438
|
ECS
|
On-site presentation
Exploring the Potential of Deep Learning models in Focal Mechanism Computation: A Case Study from Switzerland
(withdrawn)
Maria Mesimeri et al.
EGU24-12357
|
ECS
|
On-site presentation
Deep learning prediction of measured earthquake waveforms from synthetic data
Alexander Bauer et al.

Posters on site: Wed, 17 Apr, 10:45–12:30 | Hall X1

Display time: Wed, 17 Apr 08:30–Wed, 17 Apr 12:30
Chairpersons: Jannes Münchmeyer, Chris Marone, Sophie Giffard-Roisin
EGU24-5031
|
ECS
|
On-site presentation
SeisPolar: Seismic Wave Polarity Module for the SeisBlue Deep Learning Seismology Platform
I-Hsin Chang et al.
EGU24-8233
|
On-site presentation
Double Acoustic Emission events detection using U-net Neural Network
Petr Kolar et al.
EGU24-8913
|
ECS
|
On-site presentation
Benchmarking seismic phase associators: Insights from synthetic scenarios
Jorge Antonio Puente Huerta et al.
EGU24-14438
|
ECS
|
On-site presentation
A deep learning-based earthquake simulator: from source and geology to surface wavefields
Fanny Lehmann et al.
EGU24-18303
|
On-site presentation
Machine learning based rapid earthquake characterization using PEGS in Alaska
Quentin Bletery et al.