Earthquake clusters identification through a Markovian Arrival Process (MAP): Application in Corinth Gulf (Greece)

Bountzis, Polyzois; Papadimitriou, Eleftheria; Tsaklidis, George

doi:10.1016/j.physa.2019.123655

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…Our results are consistent with what was found in these studies (e.g. (Michas et al, 2021), (Bountzis et al, 2020), (Papadimitriou et al, 2022): each step observed in our cumulative curves are indeed identified after the start of each crisis (Figure 10). We also compare our results to seismic clusters described in (Mesimeri et al, 2019) which contain a total of 1560 crisis events.…”

Section: Cumulative Curvessupporting

confidence: 94%

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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Section: Cumulative Curvessupporting

confidence: 94%

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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show abstract

“…Our main assumption is that each state corresponds to a distinct evolution phase of a seismic sequence, independently of its underlying mechanism. In this way, the model has the ability to approximate the temporal evolution of earthquake catalogs that incorporate both aftershock sequences and earthquake swarms [31], as well as datasets with non-stationary characteristics [34].…”

Section: Map As a Tool For The Detection Of Seismicity Rate Changesmentioning

confidence: 99%

“…The temporal distribution of the events is approximated essentially by a non-homogeneous Poisson process, N t , with a piece-wise constant intensity rate determined by the underlying Markov process, J t . Simulation studies and applications on real datasets showed that the model efficiently identifies the changes in the earthquake occurrence rates [31]. Recent works by Lu [32] and Benali et al [33] are based on non-stationary Poisson models whose rate is modulated by a hidden Markov process to determine a set of change-points for seismicity rate.…”

Section: Introductionmentioning

confidence: 99%

Identification and Temporal Characteristics of Earthquake Clusters in Selected Areas in Greece

2022

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The efficiency of earthquake clustering investigation is improved as we gain access to larger datasets due to the increase of earthquake detectability. We aim to demonstrate the robustness of a new clustering method, MAP-DBSCAN, and to present a comprehensive analysis of the clustering properties in three major seismic zones of Greece during 2012–2019. A time-dependent stochastic point model, the Markovian Arrival Process (MAP), is implemented for the detection of change-points in the seismicity rate and subsequently, a density-based clustering algorithm, DBSCAN, is used for grouping the events into spatiotemporal clusters. The two-step clustering procedure, MAP-DBSCAN, is compared with other existing methods (Gardner-Knopoff, Reasenberg, Nearest-Neighbor) on a simulated earthquake catalog and is proven highly competitive as in most cases outperforms the tested algorithms. Next, the earthquake clusters in the three areas are detected and the regional variability of their productivity rates is investigated based on the generic estimates of the Epidemic Type Aftershock Sequence (ETAS) model. The seismicity in the seismic zone of Corinth Gulf is characterized by low aftershock productivity and high background rates, indicating the dominance of swarm activity, whereas in Central Ionian Islands seismic zone where main shock-aftershock sequences dominate, the aftershock productivity rates are higher. The productivity in the seismic zone of North Aegean Sea vary significantly among clusters probably due to the co-existence of swarm activity and aftershock sequences. We believe that incorporating regional variations of the productivity into forecasting models, such as the ETAS model, it might improve operational earthquake forecasting.

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Markovian Arrival Processes for Earthquake Clustering Analysis

Bountzis

Papadimitriou

Tsaklidis

2021

Statistical Methods and Modeling of Seismogenesis

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Earthquake clusters identification through a Markovian Arrival Process (MAP): Application in Corinth Gulf (Greece)

Cited by 7 publications

References 38 publications

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

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

Identification and Temporal Characteristics of Earthquake Clusters in Selected Areas in Greece

Markovian Arrival Processes for Earthquake Clustering Analysis

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