Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration

Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration

ICM-CRM Meeting 2023: New Bridges between Marine Sciences and Mathematics, 2-10 November 2023

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Main Authors: Villaseñor, Antonio, Fernández-Prieto, Luis, Perea, Héctor, Martínez-Loriente, S., Canari Bordoy, Ariadna, Ercilla, Gemma, Estrada, Ferran, Lo Iacono, Claudio
Format: comunicación de congreso biblioteca
Language:English
Published: CSIC - Instituto de Ciencias del Mar (ICM) 2023-11
Online Access:http://hdl.handle.net/10261/338594
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spelling dig-icm-es-10261-3385942023-11-08T10:13:36Z Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration Villaseñor, Antonio Fernández-Prieto, Luis Perea, Héctor Martínez-Loriente, S. Canari Bordoy, Ariadna Ercilla, Gemma Estrada, Ferran Lo Iacono, Claudio ICM-CRM Meeting 2023: New Bridges between Marine Sciences and Mathematics, 2-10 November 2023 The field of geophysics has undergone a profound transformation with the emergence of Artificial Intelligence (AI). Deep Learning algorithms (DL), in particular, have demonstrated exceptional capabilities in addressing long-standing challenges and expanding the boundaries of traditional geophysical and geomorphological data analysis. We present two compelling examples that highlight the potential of DL techniques in enhancing earthquake detection and the promising future of DL in geomorphological analysis. In the first example, we delve into the remarkable progress achieved in earthquake detection through the application of DL algorithms. Due to their inherent complexity and unpredictable nature, earthquakes pose substantial challenges for accurate detection and prompt response. However, DL-based approaches have exhibited remarkable efficacy in overcoming these challenges. We have analyzed recent available DL pickers, comparing the results against data picked by a human operator and against non-DL programs. We found that DL algorithms have exhibited proficiency in precisely identifying and distinguishing the arrival times of P and S phases, even in the presence of low signalto- noise ratios and intricate triggering mechanisms. Their ability to effectively filter out transient noise and their efficiency in recognizing S waves, tasks often challenging even for experienced human analysts, positions DL algorithms as powerful tools in earthquake monitoring. Furthermore, their minimal parameter tuning requirements ensure accessibility to geophysicists with varying degrees of expertise in neural networks. The second example explores the potential of AI in geomorphological analysis, specifically by leveraging DL techniques to overcome limitations in traditional methods. Digital topographic and bathymetric models are extensively used in geosciences to describe landscape features. However, these methods often suffer from subjective parameter selection and qualitative assessments, which hinder interpretation accuracy and uncertainty estimation. Integrating mathematical models and DL techniques can enhance interpretations and improve uncertainty estimation. DL algorithms can be trained to identify and quantify specific landscape features like fault scarps or landslides, enabling a comprehensive characterization of the terrain. The integration of DL and mathematical expertise offers a data-driven and objective approach for robust interpretations and uncertainty quantification, leading to a deeper understanding of the landscape and improved resource management. Ongoing research in this area shows promising potential for advancing geomorphological analysis through AI Peer reviewed 2023-11-08T10:13:14Z 2023-11-08T10:13:14Z 2023-11 comunicación de congreso ICM-CRM Meeting 2023: New Bridges between Marine Sciences and Mathematics (2023) http://hdl.handle.net/10261/338594 en Sí none CSIC - Instituto de Ciencias del Mar (ICM) Centre de Recerca Matemàtica
institution ICM ES
collection DSpace
country España
countrycode ES
component Bibliográfico
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databasecode dig-icm-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del ICM España
language English
description ICM-CRM Meeting 2023: New Bridges between Marine Sciences and Mathematics, 2-10 November 2023
format comunicación de congreso
author Villaseñor, Antonio
Fernández-Prieto, Luis
Perea, Héctor
Martínez-Loriente, S.
Canari Bordoy, Ariadna
Ercilla, Gemma
Estrada, Ferran
Lo Iacono, Claudio
spellingShingle Villaseñor, Antonio
Fernández-Prieto, Luis
Perea, Héctor
Martínez-Loriente, S.
Canari Bordoy, Ariadna
Ercilla, Gemma
Estrada, Ferran
Lo Iacono, Claudio
Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration
author_facet Villaseñor, Antonio
Fernández-Prieto, Luis
Perea, Héctor
Martínez-Loriente, S.
Canari Bordoy, Ariadna
Ercilla, Gemma
Estrada, Ferran
Lo Iacono, Claudio
author_sort Villaseñor, Antonio
title Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration
title_short Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration
title_full Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration
title_fullStr Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration
title_full_unstemmed Enhancing Earthquake Detection and Geomorphological Analysis through AI Integration
title_sort enhancing earthquake detection and geomorphological analysis through ai integration
publisher CSIC - Instituto de Ciencias del Mar (ICM)
publishDate 2023-11
url http://hdl.handle.net/10261/338594
work_keys_str_mv AT villasenorantonio enhancingearthquakedetectionandgeomorphologicalanalysisthroughaiintegration
AT fernandezprietoluis enhancingearthquakedetectionandgeomorphologicalanalysisthroughaiintegration
AT pereahector enhancingearthquakedetectionandgeomorphologicalanalysisthroughaiintegration
AT martinezlorientes enhancingearthquakedetectionandgeomorphologicalanalysisthroughaiintegration
AT canaribordoyariadna enhancingearthquakedetectionandgeomorphologicalanalysisthroughaiintegration
AT ercillagemma enhancingearthquakedetectionandgeomorphologicalanalysisthroughaiintegration
AT estradaferran enhancingearthquakedetectionandgeomorphologicalanalysisthroughaiintegration
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