Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

Ren, Christopher X.; Peltier, Aline; Ferrazzini, Valérie; Rouet‐Leduc, Bertrand; Johnson, P. A.; Brenguier, Florent

doi:10.1029/2019gl085523

Cited by 45 publications

(37 citation statements)

References 34 publications

(69 reference statements)

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“…Therefore, a more complete characterization of the wave field of the tremors may overcome this issue, especially in limited monitoring systems. A suitable choice of few, information-rich parameters could also be the starting point for the definition of efficient feature vectors for Self-Organized Maps (Carniel et al 2013) or other Machine Learning algorithms (Ren et al 2020).…”

Section: Introductionmentioning

confidence: 99%

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

Melchor

Almendros

Carniel

et al. 2020

Earth Planets Space

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Improving the ability to detect and characterize long-duration volcanic tremor is crucial to understand the long-term dynamics and unrest of volcanic systems. We have applied data reduction methods (permutation entropy and polarization degree, among others) to characterize the seismic wave field near Copahue volcano (Southern Andes) between June 2012 and January 2013, when phreatomagmatic episodes occurred. During the selected period, a total of 52 long-duration events with energy above the background occurred. Among them, 32 were classified as volcanic tremors and the remaining as noise bursts. Characterizing each event by averaging its reduced parameters, allowed us to study the range of variability of the different events types. We found that, compared to noise burst, tremors have lower permutation entropies and higher dominant polarization degrees. This characterization is a suitable tool for detecting long-duration volcanic tremors in the ambient seismic wave field, even if the SNR is low.

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Section: Introductionmentioning

confidence: 99%

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

Melchor

Almendros

Carniel

et al. 2020

Earth Planets Space

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“…It is currently agreed that this technique leads to the best performance compared to other modelling algorithms [29]. For example, XGBoost was used to determine the dominant frequency of an eruptive tremor of the volcano Piton de la Fournaise [30]. XGBoost stands for eXtreme Gradient Boosting.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Modelling and Feature Engineering in Seismology Experiment

Брыков

Petryshynets

Pruncu

et al. 2020

Sensors

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This article aims to discusses machine learning modelling using a dataset provided by the LANL (Los Alamos National Laboratory) earthquake prediction competition hosted by Kaggle. The data were obtained from a laboratory stick-slip friction experiment that mimics real earthquakes. Digitized acoustic signals were recorded against time to failure of a granular layer compressed between steel plates. In this work, machine learning was employed to develop models that could predict earthquakes. The aim is to highlight the importance and potential applicability of machine learning in seismology The XGBoost algorithm was used for modelling combined with 6-fold cross-validation and the mean absolute error (MAE) metric for model quality estimation. The backward feature elimination technique was used followed by the forward feature construction approach to find the best combination of features. The advantage of this feature engineering method is that it enables the best subset to be found from a relatively large set of features in a relatively short time. It was confirmed that the proper combination of statistical characteristics describing acoustic data can be used for effective prediction of time to failure. Additionally, statistical features based on the autocorrelation of acoustic data can also be used for further improvement of model quality. A total of 48 statistical features were considered. The best subset was determined as having 10 features. Its corresponding MAE was 1.913 s, which was stable to the third decimal point. The presented results can be used to develop artificial intelligence algorithms devoted to earthquake prediction.

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“…Extracted feature vectors can become inputs to several different techniques of machine learning. We can cite among others Cluster Analysis (CA) [60], Self-Organizing Maps (SOM) [61][62][63] [72].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…CA is probably the most used class of unsupervised techniques and the applications to volcano seismology follow this general rule. Spectral clustering was applied e.g., to seismic data of Piton de la Fournaise [60]. The fact that e.g., LP seismic signals can be clustered into families indicates that the family members are very similar to each other.…”

Section: Applications To Seismo-volcanic Datamentioning

confidence: 99%

“…At Piton de la Fournaise a system based on RF was implemented [115]. At the same volcano, a multi-station approach was used to classify tremor measurements and identify fundamental frequencies of the tremor associated to different eruptive behavior [60]. A scalable multi-station, multi-channel classifier, using also the empirical mode decomposition (EMD) first proposed by [31] was applied to Ubinas volcano (Peru).…”

Section: Applications To Seismo-volcanic Datamentioning

confidence: 99%

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Machine Learning in Volcanology: A Review

Carniel¹,

Guzmán²

2021

Updates in Volcanology - Transdisciplinary Nature of Volcano Science

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A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches.

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Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

Cited by 45 publications

References 34 publications

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

Machine Learning Modelling and Feature Engineering in Seismology Experiment

Machine Learning in Volcanology: A Review

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