Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment

Abstract

Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts' seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and the K-means clustering technique has become the most famous one. However, K-means can be affected by noise events (large location error events) and initial cluster centers. In this paper, a data field-based K-means clustering methodology is proposed for seismicity analysis. The application of synthetic data and real seismic data have shown its effectiveness in removing noise events as well as finding good initial cluster centers. Furthermore, we introduced the time parameter into the K-means clustering process and applied it to seismic events obtained from the Chinese Yongshaba mine. The results show that the time-event location distance and data field-based K-means clustering can divide seismic events by both space and time, which provides a new insight for seismicity analysis compared with event location distance and data field-based K-means clustering. The Krzanowski-Lai (KL) index obtains a maximum value when the number of clusters is five: the energy index (EI) shows that clusters C1, C3 and C5 have very critical periods. In conclusion, the time-event location distance, and the data field-based K-means clustering can provide an effective methodology for seismicity analysis and hazard assessment. In addition, further study can be done by considering time-event location-magnitude distances.

Spatio – Temporal Characteristics of the Aftershocks Sequences that followed the Strong Earthquakes in Ionian Islands, Greece

“…This observation agrees with the study of [50] as well as the work of [45], [47]. Also, from the study of focal mechanisms [52], [53] indicates that west of Lefkas, the activation of a strike-slip fault occurred. East of Zande up to the western part of the Peloponnese predominate horizontal sliding mechanisms while in the bay of Zande, they respond mainly reverse burglary mechanisms [54].…”

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Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model

“…SG clusters soil spectral data using K-means clustering to predict the SOM content. K-means clustering is a commonly used algorithm that classifies data by dividing the samples into a predetermined number of clusters so that each sample is associated with the nearest cluster center [37]. We divided the soil spectral data into six classes (Clusters 1-6) and determined the optimal number of clusters based on the minimum Euclidean distance and maximum separability.…”

Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar

“…To visualize the evaluation results and distinguish plots with different quality indexes, each plot in an R-D map is classified into one of the four quality levels according to its plot quality index. In this article, the K-Means clustering algorithm [32,33] is applied to the obtained plot quality indexes to produce four plot quality levels, named as Level 1 (high quality)-Level 4 (low quality). Higher level means better quality.…”