RT Journal Article
T1 Kernel Density Estimation for the Interpretation of Seismic Big Data in Tectonics Using QGIS: The Türkiye–Syria Earthquakes (2023)
A1 Amador Luna, David
A1 Alonso Chaves, Francisco Manuel
A1 Fernández Rodríguez, Carlos
AB Numerous studies have utilized remote sensing techniques to analyze seismic data in activeareas. Point density techniques, widely used in remote sensing, examine the spatial distributionof point clouds related to specific variables. Applying these techniques to complex tectonic settings,such as the East Anatolian Fault Zone, helps identify major active fractures using both surface anddeep information. This study employed kernel density estimation (KDE) to compare two distinctpoint-cloud populations from the seismic event along the Türkiye–Syria border on 6 February 2023,providing insights into the main active orientations supporting the Global Tectonics framework.This study considered two populations of seismic foci point clouds containing over 40,000 events,recorded by the Turkish Disaster and Emergency Management Authority (AFAD) and Kandilli Observatoryand Earthquake Research Institute (KOERI). These populations were divided into twodatasets: crude and relocated-filtered. Kernel density analysis demonstrated that both datasetsyielded similar geological interpretations. The high-density cores of both datasets perfectlymatched, exhibiting identical structures consistent with geological knowledge. Areas with a minimalconcentration of earthquakes at depth were also identified, separating different crustal strengthlevels.
PB MDPI
SN 2072-4292 (electrónico)
YR 2024
FD 2024-10
LK https://hdl.handle.net/10272/24284
UL https://hdl.handle.net/10272/24284
LA eng
NO Amador Luna, D., Alonso-Chaves, F. M., & Fernández, C. (2024). Kernel Density Estimation for the Interpretation of Seismic Big Data in Tectonics Using QGIS: The Türkiye–Syria Earthquakes (2023). In Remote Sensing (Vol. 16, Issue 20, p. 3849). MDPI AG. https://doi.org/10.3390/rs16203849
NO The authors acknowledge the funding provided by the Research and Transfer Policy Strategy (EPIT) of the University of Huelva through the predoctoral contract for the promotion of hiring early-career researchers (EPIT20/00832), which enabled David Amador Luna to carry out his research, the results of which are reflected in this publication.
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026