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12 pages, 22446 KB

Open AccessArticle

Detection of Seismic and Acoustic Sources Using Distributed Acoustic Sensing Technology in the Gulf of Catania

by Abdelghani Idrissi, Danilo Bonanno, Letizia S. Di Mauro, Dídac Diego-Tortosa, Clara Gómez-García, Stephan Ker, Florian Le Pape, Shane Murphy, Sara Pulvirenti, Giorgio Riccobene, Simone Sanfilippo and Salvatore Viola

J. Mar. Sci. Eng. 2025, 13(4), 658; https://doi.org/10.3390/jmse13040658 - 25 Mar 2025

Cited by 2 | Viewed by 2533

Abstract

Distributed Acoustic Sensing (DAS) technology presents an innovative method for marine monitoring by adapting existing underwater optical fiber networks. This paper examines the use of DAS with the Istituto Nazionale di Fisica Nucleare–Laboratori Nazionali del Sud (INFN-LNS) optical fiber infrastructure in the Gulf of Catania, Eastern Sicily, Italy. This region in the Western Ionian Sea provides a unique natural laboratory due to its tectonic and volcanic activity, proximity to Mount Etna, diverse marine ecosystems and significant human influence through maritime traffic. By connecting a 28 km long optical cable to an Alcatel Submarine Network OptoDAS interrogator, DAS successfully detected a range of natural and human–made signals, including a magnitude 3.5 ML earthquake recorded on 14 November 2023, and acoustic signatures from vessel noise. The earthquake–induced Power Spectral Density (PSD) increased to up to 30 dB above background levels in the 1–15 Hz frequency range, while vessel noise exhibited PSD peaks between 30 and 60 Hz with increases of up to 5 dB. These observations offered a detailed spatial and temporal resolution for monitoring seismic wave propagation and vessel acoustic noise. The results underscore DAS’s capability as a robust tool for the continuous monitoring of the rich underwater environments in the Gulf of Catania. Full article

(This article belongs to the Section Marine Environmental Science)

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24 pages, 14131 KB

Open AccessArticle

SEISMONOISY: A Quasi-Real-Time Seismic Noise Network Monitoring System

by Giuseppe Ruzza, Rocco Cogliano, Ciriaco D’Ambrosio, Luigi Falco, Vincenzo Cardinale, Felice Minichiello, Antonino Memmolo, Angelo Castagnozzi, Giovanni De Luca and Annamaria Vicari

Sensors 2024, 24(11), 3474; https://doi.org/10.3390/s24113474 - 28 May 2024

Cited by 2 | Viewed by 2169

Abstract

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time. Full article

(This article belongs to the Special Issue Sensors and Sensing Technologies for Seismic Detection and Monitoring)

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15 pages, 32688 KB

Open AccessArticle

Structure-Preserving Random Noise Attenuation Method for Seismic Data Based on a Flexible Attention CNN

by Wenda Li, Tianqi Wu and Hong Liu

Remote Sens. 2022, 14(20), 5240; https://doi.org/10.3390/rs14205240 - 20 Oct 2022

Cited by 6 | Viewed by 3195

Abstract

The noise attenuation of seismic data is an indispensable part of seismic data processing, directly impacting the following inversion and imaging. This paper focuses on two bottlenecks in the AI-based denoising method of seismic data: the destruction of structural information of seismic data and the inferior generalizability. We propose a flexible attention-CNN (FACNN) and realized the denoising work of seismic data. This paper’s main work and advantages were concentrated on the following three aspects: (i) We propose attention gates (AGs), which progressively suppressed features in irrelevant background parts and improved the denoising performance. (ii) We added a noise level map M as an additional channel, making a single CNN model expected to inherit the flexibility of handling noise models with different parameters, even spatially variant noises. (iii) We propose a mixed loss function based on MS_SSIM to improve the performance of FACNN further. Adding the noise level map can improve the network’s generalization ability, and adding the attention structure with the mixed loss function can better protect the structural information of the seismic data. The numerical tests showed that our method has better generalization and can better protect the details of seismic events. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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24 pages, 10148 KB

Open AccessArticle

Passive Seismic Surveys for Beach Thickness Evaluation at Different England (UK) Sites

by David Morgan, David Gunn, Andres Payo and Michael Raines

J. Mar. Sci. Eng. 2022, 10(5), 667; https://doi.org/10.3390/jmse10050667 - 13 May 2022

Cited by 5 | Viewed by 2902

Abstract

In an era of environmental change leading to rising sea levels and increased storminess, there is a need to quantify the volume of beach sediment on the coast of Britain in order to assess the vulnerability to erosion using cheap, easy-to-deploy and non-invasive methods. Horizontal-to-vertical spectral ratio (HVSR) is a technique that uses the natural background seismic ‘noise’ in order to determine the depth of underlying geological interfaces that have contrasting physical properties. In this study, the HVSR technique was deployed at a number of settings on the coast of England that represented a range of different compositions, geomorphology, and underlying bedrock. We verified the results by comparison to other survey techniques, such as ground-penetrating RADAR, multichannel analysis of surface waves (MASW), and cone penetration tests. At locations where there was sufficient contrast in physical properties of the beach material compared to the underlying bedrock, the beach thickness (and therefore the volume of erodible material) was successfully determined, showing that HVSR is a useful tool to use in these settings. Full article

(This article belongs to the Special Issue Innovative Representation of the Coastal Topo-Bathymetry and Subsurface for Flooding and Erosion Risk Reduction)

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14 pages, 2988 KB

Open AccessArticle

Volcanic Tremor of Mt. Etna (Italy) Recorded by NEMO-SN1 Seafloor Observatory: A New Perspective on Volcanic Eruptions Monitoring

by Tiziana Sgroi, Giuseppe Di Grazia and Paolo Favali

Geosciences 2019, 9(3), 115; https://doi.org/10.3390/geosciences9030115 - 5 Mar 2019

Cited by 5 | Viewed by 5379

Abstract

The NEMO-SN1 seafloor observatory, located 2100 m below sea level and about 40 km from Mt. Etna volcano, normally records a background seismic signal called oceanographic noise. This signal is characterized by high amplitude increases, lasting up to a few days, and by two typical 0.1 and 0.3 Hz frequencies in its spectrum. Particle motion analysis shows a strong E-W directivity, coinciding with the direction of sea waves; gravity waves induced by local winds are considered the main source of oceanographic noise. During the deployment of NEMO-SN1, the vigorous 2002–2003 Mt. Etna eruption occurred. High-amplitude background signals were recorded during the explosive episodes accompanying the eruption. The spectral content of this signal ranges from 0.1 to 4 Hz, with the most powerful signal in the 0.5–2 Hz band, typical of an Etna volcanic tremor. The tremor recorded by NEMO-SN1 shows a strong NW-SE directivity towards the volcano. Since the receiver is underwater, we inferred the presence of a circulation of magmatic fluids extended under the seafloor. This process is able to generate a signal strong enough to be recorded by the NEMO-SN1 seafloor observatory that hides frequencies linked to the oceanographic noise, permitting the offshore monitoring of the volcanic activity of Mt. Etna. Full article

(This article belongs to the Special Issue Submarine Volcanic Hazards: Ancient and Modern Perspectives)

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