Abstract
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.
Highlights
Earthquake clustering is an essential property of seismicity and is manifested as the concentration of earthquakes in space and time
The stochastic declustering method of Zhuang et al [13] is based on the modeling of earthquake occurrences by the Epidemic Type Aftershock Sequence (ETAS) model [14,15], where events are separated into background and clustered ones according to the estimated probabilities
We present the efficiency of our clustering method, Markovian Arrival Process (MAP)-DBSCAN, on a simulated earthquake catalog where the structure of the clusters is known a priori and its competitiveness against well-known clustering algorithms, as in most cases, shows better results
Summary
Earthquake clustering is an essential property of seismicity and is manifested as the concentration of earthquakes in space and time. Due to the improvement of seismic monitoring worldwide and the development of new powerful algorithms for earthquake detectability [1] additional information is available, which is crucial for reliable regional estimates of aftershock forecasting probabilities [2,3] and the determination of faulting geometry [4,5], among others. The stochastic declustering method of Zhuang et al [13] is based on the modeling of earthquake occurrences by the ETAS model [14,15], where events are separated into background and clustered ones according to the estimated probabilities. Important clustering features can be inferred using the stochastic algorithm [16,17], whereas it can be used for declustering to optimize the background seismicity rate estimates [9,18]. The modified ETAS model is used efficiently to reveal both main shock-aftershocks and earthquake swarms [20,21]
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