the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Apr 2026
Self-Supervised Contrastive Learning in the Context of Volcano-Seismic Datasets
Abstract. Volcano-seismic datasets are expensive to label due to the requirement for expertise to understand the signals and the time-intensive nature of extracting and labeling different events that are occurring. This work evaluates whether self supervised methods can enable volcanologists to gain knowledge about the content of volcanic datasets without the use of labels, or reduce the amount of labels required. The aim of this work is to compare several common techniques and illustrate their usefulness for the volcanic community, where labeled data is an even more precious commodity than the wider seismic community. Experiments have been performed on three real-world datasets containing isolated volcano-seismic datasets from Llaima volcano, Colima volcano, and Mount Etna. Time-Series Representation Learning via Temporal and Contextual Contrasting (TS-TCC) shows particularly high performance in this task for finding structures in an self-supervised fashion. This indicates the untapped potential of self-supervised training to aid in different data analysis tasks within the volcano-seismology community.
- Preprint
(2292 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (extended)
- RC1: 'Comment on egusphere-2026-1201', Anonymous Referee #1, 10 Jul 2026 reply
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 281 | 205 | 23 | 509 | 20 | 24 |
- HTML: 281
- PDF: 205
- XML: 23
- Total: 509
- BibTeX: 20
- EndNote: 24
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The manuscript presents an interesting and potentially high-impact study on the application of Self-Supervised Contrastive Learning to volcano-seismic datasets. The work addresses a relevant issue—the cost and time-intensive nature of labeling volcanic datasets—and I believe it is valid and merits publication in Natural Hazards and Earth System Sciences (NHESS).
However, before the manuscript can be accepted, it requires some revisions to improve its overall completeness, the fluency of the writing, and the factual contextualization of the work. Please address the following main points:
1. Introduction, Problem Framing, and the "Lack of Labels" Claim
The introduction currently feels a bit sparse, especially regarding the description of the seismic signals and the presentation of the methods. Furthermore, the authors state that there is a lack of available labels for training supervised machine learning models on volcano-seismic tasks. Is this absolute statement entirely accurate? While creating specialized ML datasets is indeed time-consuming, volcanic observatories worldwide have maintained extensive, labeled seismic catalogs for decades. The authors should nuance this statement, perhaps clarifying the distinction between the availability of raw observatory catalogs and curated ML-ready datasets. Additionally, it lacks clarity regarding the "practical monitoring objectives" this specific method aims to achieve. The authors should better clarify how their approach stands out from the state of the art and how it practically aids monitoring compared to existing methods.
2. Dataset Justification and Ground Truth (Mt. Etna)
Regarding the Mount Etna dataset, the authors claim that there is no ground truth public catalog available and that the dataset was analyzed in an exploratory manner for this reason. This assertion is highly questionable. Mount Etna is one of the most heavily instrumented and closely monitored volcanoes in the world, managed by INGV, which undoubtedly maintains detailed and robust operational catalogs. Is it truly possible that no certain labels exist for Etna? If the authors mean that a specific, pre-formatted public machine-learning dataset was not readily accessible for their study period, they must state this explicitly. Claiming a general "lack of ground truth" for Etna undermines the credibility of the premise and overlooks the extensive monitoring efforts present at this volcano.
3. Fluency and Completeness of the Methods
The methodology section requires more thorough explanations and a more fluid narrative. Currently, the presentation of the techniques (such as SimCLR and TS-TCC) feels unbalanced. I recommend expanding this section to provide a broader, more balanced overview, properly justifying the choice of all the adopted strategies (rather than focusing heavily on TS-TCC at the expense of SimCLR) and ensuring the text flows more naturally for the reader.
4. Discussion and Conclusions
The Discussion and Conclusion sections need to be better argued and characterized. Currently, they appear slightly weak compared to the amount of work that was conducted. It is crucial that the conclusions refer more specifically and explicitly to the obtained results and the figures presented in the text, explaining in detail how the metrics and visible clusters support the final claims regarding the utility of SSL for the volcanic observatory community.
5. Improvement of Figures (Waveforms)
The images presented in the manuscript need to be revised and replaced. In particular, the figures displaying the waveforms (e.g., the time and frequency representations of the clusters) require significant graphical improvement to make them clearer and more readable. These figures are fundamental for visually supporting the effectiveness of the cluster separation proposed in the text.
I am confident that by addressing these points, the manuscript will reach the level of clarity and maturity required for publication in NHESS.
I have provided a few suggested references below. However, I highly recommend expanding the reference list further, particularly in the introduction, to better frame the study:
1. Abed, W., Zali, Z., Sciotto, M., et al. (2026). Hidden patterns in volcanic seismicity: deep learning insights from Mt. Etna’s 2020–2021 activity. *Scientific Reports*, 16, 6155. [https://doi.org/10.1038/s41598-026-36677-x](https://doi.org/10.1038/s41598-026-36677-x)
2. D’Auria, L., Koulakov, I., Prudencio, J., et al. (2022). Rapid magma ascent beneath La Palma revealed by seismic tomography. *Scientific Reports*, 12, 17654. [https://doi.org/10.1038/s41598-022-21818-9](https://doi.org/10.1038/s41598-022-21818-9)
3. Gammaldi, S., Cabrera-Pérez, I., Koulakov, I., D’Auria, L., Barberi, G., García-Hernández, R., et al. (2025). Seismic tomography of a newborn volcano. *Geophysical Research Letters*, 52, e2025GL114932. [https://doi.org/10.1029/2025GL114932](https://doi.org/10.1029/2025GL114932)
4. Gammaldi, S., Donne, D. D., Cantiello, P., et al. (2025). A near real-time framework for monitoring very-long-period signals at volcanoes. *Scientific Reports*, 15, 41626. [https://doi.org/10.1038/s41598-025-25636-7](https://doi.org/10.1038/s41598-025-25636-7)
5. Grimaldi, A., Amoroso, O., Scarpetta, S., et al. (2026). Single-station analysis of Campi Flegrei (Italy) seismic signals using multiscale entropy and unsupervised learning. *Scientific Reports*, 16, 7669. [https://doi.org/10.1038/s41598-026-38257-5](https://doi.org/10.1038/s41598-026-38257-5)
6. Tan, X., et al. (2025). A clearer view of the current phase of unrest at Campi Flegrei caldera. *Science*, 390, 70-75. [https://doi.org/10.1126/science.adw9038](https://www.google.com/search?q=https://doi.org/10.1126/science.adw9038)