Seismicity analysis using space-time density peak clustering method
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Seismicity analysis using space‑time density peak clustering method Rahul Kumar Vijay1 · Satyasai Jagannath Nanda2 Received: 5 May 2019 / Accepted: 7 September 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract This paper presents two-stage clustering approach for accurate analysis of earthquake catalogs where aim is to categorize events in terms of aftershock (AF) clusters or independent backgrounds (BGs). In stage I, the Gaussian kernel-based temporal density estimation is used for grouping of events based on their occurrence time. From the graph, local peak (maxima), local minima and their timing information are utilized to group the events into significant time zones. In stage II, on events of each time zone, coordinate and magnitude information is combined together (weighted mechanism) to determine effective local weighted spatial density ( 𝜌w ). Based on 𝜌w and event distance ( 𝛿 ), a decision graph is drawn to find out the spatial cluster centroids for each time zone. Event’s assignment to the centroid is carried out based on its nearest neighbor of higher density. Outliers (non-clustered) are also detected in stage II which is considered as independent backgrounds. The experimental analysis is carried out on historical seismicity of California, Himalaya, Japan and Sumatra–Andaman region. The results indicate that obtained AFs and total number of events follow a similar cumulative and 𝜆 rate, whereas BGs have linear cumulative and consistent 𝜆 rate. It is also observed that AFs and total events have similar ergodic behavior, quantified from the inverse TM metric plot. The competitive performance of the proposed approach is obtained over state-of-the-art declustering methods. Keywords Space-time density peak clustering · Earthquake catalogs · Inverse TM metric · Coefficient of variation
1 Introduction Clustering is an important subject in data mining due to its extensive applications in geographic information systems [12], wildfires [3, 11], seismology [8, 9] etc. Accurate cluster analysis of such real-life data is essential to take important decisions that can help human/other species survival with the pre- or post-remedial effects of a natural calamity [16]. Conventional clustering algorithms like K-means and its variant have the drawback of selection of true cluster centers, and this is extremely difficult because of simultaneously dealing with heterogeneous features like time, * Rahul Kumar Vijay [email protected]; [email protected]
Satyasai Jagannath Nanda [email protected]; [email protected]
1
Department of Computer Science and Engineering, Banasthali Vidyapith, Tonk, Rajasthan 304022, India
Department of Electronics and Communication Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan 302017, India
2
coordinate (longitude, latitude in degree), magnitude (in Richter scale), depth (in kilometer), temperature, etc. An effective algorithm has to consider these features to extract useful in
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