An Adaptable Random Forest Model for the Declustering of Earthquake Catalogs

Aden-Antoniow, Florent; Frank, W.; Seydoux, Léonard

doi:10.1029/2021jb023254

Cited by 22 publications

(11 citation statements)

References 83 publications

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“…Steinmann et al (2022) proposes a hierarchical clustering strategy to classify noise and seismicity based on the deep scattering spectrum of the seismic data. Aden-Antoniow et al (2022) compresses the seismic information into a low-dimensional latent space using an autoencoder before these latent vectors are clustered to identify seismicity from different sources.…”

Section: Earthquake Data Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.

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Section: Earthquake Data Applicationsmentioning

confidence: 99%

“…Aden‐Antoniow et al. (2022) compresses the seismic information into a low‐dimensional latent space using an autoencoder before these latent vectors are clustered to identify seismicity from different sources.…”

Section: Highlightsmentioning

confidence: 99%

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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“…The parent can be identified as the nearest neighbor using the proximity function of the spatio-temporal metric based on the Gutenberg-Richter law [7], which relates the magnitude and frequency of aftershocks, and modified Omori's law [13,18,19], which relates the time after the mainshock and occurrence rate of aftershocks. This metric follows a bimodal distribution related to the background seismicity and aftershocks, and methods to obtain two separate distributions are being actively studied [1]. Furthermore, the correlation metric is promising because it resembles the epidemic-type aftershock sequence model.…”

Section: Link-based Declusteringmentioning

confidence: 99%

A New Centrality Measure and Visualization Technique Using Multiple-Parent Nodes of Earthquakes Based on Correlation-Metric

Yamagishi

Saito

Hirahara

2022

Preprint

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In this paper, we address the problem of earthquake declus-tering, and propose a k-nearest neighbors approach based on the selection of multiple-parent nodes with respect to each of the given earthquakes , which can be regarded as a natural extension of the conventional correlation-metric method based on the selection of a single-parent node. Based on this approach, we develop a new centrality measure that exploits link weight assigned by a logarithmic-distance scheme and a new technique of individually visualizing each set of child nodes with respect to given target earthquakes. For experimental evaluation, we used an earthquake catalog covering Japan and selected 24 earthquakes that caused considerable damage or casualties. We first show that our proposed centrality measure using a logarithmic-distance scheme can rank these 24 major earthquakes higher than four link-weighting schemes (i.e., uniform, magnitude, inverse-distance, and normalized-inverse-distance weighting) and conventional single-parent selection. We then show that unlike the conventional approach to simultaneously visualizing all the events in the catalog, our proposed technique can produce a naturally interpretable classification result for these 24 major earthquakes, by individually visualizing each set of the first to k-th child nodes with different colored markers plotted in the directly interpretable spatio and temporal metrics. As a consequence, we confirm that our approach based on multiple-parent selection is vital and promising.

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“…We may cite for instance those based on the Epidemic Type Aftershock Sequence (ETAS) model (Iacoletti et al, 2022;Zhang & Huang, 2022;Mizrahi et al, 2022;Field et al, 2021Field et al, , 2022Hainzl, 2022), on nearest-neighbour distances (Zaliapin et al, 2008;Zhuang et al, 2002) or on supervised machine learning (Aden-Antoniow et al, 2022;Pavez O & Estay H, 2021;Seydoux et al, 2020). The reason why there are so many methods is that each of them takes into account new or additional statistical and/or physical features that are assumed to better describe the behaviour of earthquakes in the specific seismotectonic context for which they are applied (Zaliapin & Ben-Zion, 2021).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised probabilistic machine learning applied to seismicity declustering: a new approach to represent earthquake catalogues with fewer assumptions

Septier¹,

Renouard

Déverchère

et al. 2023

Preprint

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Many applications in seismology require to isolate earthquake clusters from a background activity. Relative declustering methods essentially find a 2D representation of an earthquake catalogue that distinguishes between two classes of events: crisis and non-crisis events. However, the number of statistical and/or physical parameters to be used is often limited due to the difficulty of concatenating the information onto a physically meaningful 2D grid. In this study, we propose to alleviate the declustering task by using the ability of unsupervised artificial intelligence to model complex spatio-temporal relationships directly from data. Through a data-driven approach, we define an easily transferable declustering model that provides declustering results with fewer assumptions and no prior selection of thresholds. We first obtain this model by training a self-organising neural network (SOM) that learns to cluster data points according to their feature similarity on a 2D map. We then assign each SOM cluster a label (crisis or non-crisis class) using an agglomerative clustering procedure. We quantify the classification uncertainty by developing a probabilistic function based on the projection learned by SOM. Our method is applied to a synthetic dataset and to real catalogues from the Gulf of Corinth, Central Italy and Taiwan. We discuss the validity of the method by estimating its classification accuracy. For real data, we qualitatively compare our results to previous declustering attempts. We show that our approach is easy to handle, provides a fairly new representation of earthquake catalogues and has the potential to reduce classification ambiguities between nearby events.

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An Adaptable Random Forest Model for the Declustering of Earthquake Catalogs

Cited by 22 publications

References 83 publications

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

A New Centrality Measure and Visualization Technique Using Multiple-Parent Nodes of Earthquakes Based on Correlation-Metric

Unsupervised probabilistic machine learning applied to seismicity declustering: a new approach to represent earthquake catalogues with fewer assumptions

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