Articles | Volume 17, issue 3
https://doi.org/10.5194/se-17-465-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Constraining the wavefield of volcano-seismic events on Mt. Etna, Italy through a rotational sensor and seismic array observations
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- Final revised paper (published on 16 Mar 2026)
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RC1: 'Comment on egusphere-2025-4412', Anonymous Referee #1, 26 Oct 2025
- AC1: 'Reply on RC1', Nele. I. K. Vesely, 23 Dec 2025
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RC2: 'Comment on egusphere-2025-4412', Anonymous Referee #2, 06 Nov 2025
- AC2: 'Reply on RC2', Nele. I. K. Vesely, 23 Dec 2025
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EC1: 'Comment on egusphere-2025-4412', Antonella Longo, 08 Nov 2025
- AC3: 'Reply on EC1', Nele. I. K. Vesely, 23 Dec 2025
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AR by Nele. I. K. Vesely on behalf of the Authors (23 Dec 2025)
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ED: Publish as is (27 Dec 2025) by Antonella Longo
ED: Publish as is (18 Jan 2026) by Andrea Di Muro (Executive editor)
AR by Nele. I. K. Vesely on behalf of the Authors (20 Jan 2026)
Author's response
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This manuscript presents a detailed analysis of the wavefield excited by tremor and long-period (LP) events associated with the eruptions of Mount Etna, Italy, during August–September 2019. In particular, the authors use an array composed of a rotational sensor and six seismic stations to estimate the back azimuth and phase velocity of seismic waves generated by these events. By comparing the array-derived back azimuths with the source locations of tremor and LP events estimated from INGV-OE routine processing, they demonstrate a high level of agreement during periods of intense tremor activity, whereas discrepancies are observed in the rotational sensor data. The authors attribute these inconsistencies to local structural heterogeneities. Furthermore, combined analysis of the rotational sensor and seismometers reveals that SH waves are dominant in the wavefield of both tremor and LP events. The integration of rotational sensor data with seismic array observations is highly innovative and provides valuable insights for future observations using rotational sensors. Below are several comments that may help improve the manuscript.
Length of the Time Window for Tremor Analysis
In this study, a relatively long time window of 30 minutes was used for tremor analysis. However, the waveform characteristics may vary during such a long period. In cases of non-stationary seismic activity or changes in propagation paths, averaging over this interval may obscure temporal variations. Including supplementary analyses to evaluate the stability of the waveforms within each window would enhance the reliability of the results.
Significance of Back-Azimuth Variations
During phases 0–1, the back azimuth is reported to change from 210° to 190°, but the estimation uncertainty is ±10–20 degrees. Considering this margin of error, a change of about 20 degrees may not be statistically significant, and the interpretation based on this variation should be made with caution.
In addition, the authors note that the back-azimuth estimates derived from different methods (array, 6C method, and INGV network) show different directions across phases 0–2. However, if all values fall within their uncertainty ranges, emphasizing inter-method differences may not be meaningful. Please clarify whether these differences are statistically significant, or at least interpret the results with due consideration of the uncertainties.
Figure 2
In Figure 2, the plotted colors for phases P2 and P4 are quite similar, making them difficult to distinguish, especially in grayscale printing or for readers with color vision deficiencies. Consider using more distinct hues (e.g., blue and red) to improve visual clarity.
Figure 4
In Figures 4c (array-derived slowness) and 4d (6C-derived phase velocity), uncertainties or confidence intervals of the estimated values are not indicated, making it difficult to assess the reliability of the results. When comparing outcomes obtained from different methods, it is essential to evaluate and display these uncertainties. If possible, please include error bars.
Figure 5
In Figure 5d, high correlation coefficients are observed not only during LP-event periods but also at other times. It is unclear whether these correlations correspond to real events or to noise signals. Please provide a clear explanation in the text regarding the possible causes of these high correlations.
Figure 6
Figure 6 contains a large amount of diverse information (temporal changes, spatial distribution, directional deviations, histograms, etc.) within a single figure, making it difficult for readers to follow. The following reorganization is suggested:
Arrange panels (a), (c), and (d) vertically to align their time axes and clarify temporal consistency.
Combine panels (f), (g), and (h), which contain statistical information on back-azimuth deviations, into a separate figure.
Enlarge and reposition the maps (b) and (e) for improved readability.
Figure 7
In Figures 7b and 7c, it is not specified which instruments (array or rotational sensor) and which components (e.g., HHZ, HJZ) the running spectrograms are based on. Please indicate this information clearly in the figure captions or in the main text.