Published 2025
| Version 1
Dataset
Open
NonLinLoc high-precision earthquake location catalog for Mount Etna (2000-2024) (Etna_NLL_SC_2000-2024)
- 1. ALomax Scientific
- 2. Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Contributors
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Description
High-precision earthquake locations are indispensable for a wide range of seismological studies, including seismic hazard assessment, fault rupture process analysis and subsurface structural imaging.
Here we present a catalog of high-precision relocated volcano-tectonic earthquakes, recorded by the seismic network managed by Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) from 2000 to 2024 and originating on Etna volcano area.
To perform hypocentral location we adopt the NLL-SSST-coherence (NLL-SC; Lomax et al., 2022) probabilistic non-linear location method. NLL-SC is an advanced location method designed to improve precision across multiple scales. This is achieved through the correction of seismic wave travel times and the analysis of waveform similarity.
We produced a 24-year catalog of hypocentral locations for Etna volcano, representing a previously unexplored dataset using high-precision location techniques.
This comprehensive dataset not only will contribute to understanding the seismic behaviour of Etna volcano but also will plays a key role in ongoing monitoring and scientific research related to volcanic activity in the region.
Methods (English)
The NLL-SC method (Lomax & Savvaidis, 2022; Lomax et al, 2024) improves earthquake location by refining hypocenter accuracy. Based on the NonLinLoc (NLL) algorithm, a probabilistic global search technique, it estimates earthquake hypocenters using three-dimensional probability density functions (PDFs). A key component of this method is the application of Source-Specific Station Term (SSST) corrections, which iteratively refine travel-time estimates to minimize biases from velocity model inaccuracies. Additionally, Waveform Coherence Relocation utilizes waveform similarities between nearby events to improve relative locations, enhancing the clustering of seismic events. By integrating these approaches, the NLL-SC method produces a more precise and internally consistent earthquake catalog, providing valuable insights into the seismotectonic processes and structural characteristics of Etna volcano area.Technical info (English)
The dataset, used as input for the new hypocentral locations consists of approximately 22,000 earthquakes recorded in the Mount Etna region from 2000 to 2024, covering a geographical area defined by latitudes 37.5°N to 37.9°N and longitudes 14.7°E to 15.3°E. The seismic network operating during this long time period underwent several technical upgrades. From 2000 to 2024, the network transitioned from a sparse configuration primarily equipped with short-period (1s) analog sensors to a significantly denser network featuring exclusively three-component digital sensors (40s and 120s). This evolution resulted in an enhanced detection capability, enabling a minimum local magnitude (ML) threshold of approximately 0.2 to be achieved in certain areas (Ferrari et al., 2024). The present new catalog reports, for each earthquake, different parameters as the origin time in Coordinated Universal Time (UTC), the hypocentral coordinates (latitude N and longitude E), the depth of the earthquake in kilometres b.s.l., the local magnitude (ML) and the moment magnitude (MW) as derived from Saraò et al., 2023. Additional parameters such as the number of stations used for location (NO), the root mean square of residuals (RMS), and the horizontal and vertical location errors (ERH and ERZ) are also reported together with P and S- wave arrival times. Earthquake location accuracy through the NLL-SC method is directly dependent on the quality of the initial velocity model. For this analysis, we have adopted a smooth, 1D velocity model derived from the 1D model used at INGV-OE for monitoring purposes (see Lomax et al., 2024 and references therein). The configuration of NLL-SC used here follows closely that of Lomax et al. (2024).Files
Etna_Seismicity_2000-2024_INGV-OE_NLL-SC.csv
Files
(4.1 MB)
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