On the Comparison of Records from Standard and Engineered Fiber Optic Cables at Etna Volcano (Italy).

Distributed Dynamic Strain Sensing (DDSS), also known as Distributed Acoustic Sensing (DAS), is becoming a popular tool in array seismology. A new generation of engineered fibers is being developed to improve sensitivity and reduce the noise floor in comparison to standard fibers, which are conventionally used in telecommunication networks. Nevertheless, standard fibers already have extensive coverage around the Earth's surface, so it motivates the use of the existing infrastructure in DDSS surveys to avoid costs and logistics. In this study, we compare DDSS data from stack instances of standard multi-fiber cable with DDSS data from a co-located single-fiber engineered cable. Both cables were buried in an area located 2.5 km NE from the craters of Mt. Etna. We analyze how stacking can improve signal quality. Our findings indicate that the stack of DDSS records from five standard fiber instances, each 1.5 km long, can reduce optical noise of up to 20%. We also present an algorithm to correct artifacts in the time series that stem from dynamic range saturation. Although stacking is able to reduce optical noise, it is not sufficient for restoring the strain-rate amplitude from saturated signals in standard fiber DDSS. Nevertheless, the algorithm can restore the strain-rate amplitude from saturated DDSS signals of the engineered fiber, allowing us to exceed the dynamic range of the record. We present measurement strategies to increase the dynamic range and avoid saturation.

Distributed dynamic strain sensing of very long period and long period events on telecom fiber-optic cables at Vulcano, Italy.

Volcano-seismic signals can help for volcanic hazard estimation and eruption forecasting. However, the underlying mechanism for their low frequency components is still a matter of debate. Here, we show signatures of dynamic strain records from Distributed Acoustic Sensing in the low frequencies of volcanic signals at Vulcano Island, Italy. Signs of unrest have been observed since September 2021, with CO2 degassing and occurrence of long period and very long period events. We interrogated a fiber-optic telecommunication cable on-shore and off-shore linking Vulcano Island to Sicily. We explore various approaches to automatically detect seismo-volcanic events both adapting conventional algorithms and using machine learning techniques. During one month of acquisition, we found 1488 events with a great variety of waveforms composed of two main frequency bands (from 0.1 to 0.2 Hz and from 3 to 5 Hz) with various relative amplitudes. On the basis of spectral signature and family classification, we propose a model in which gas accumulates in the hydrothermal system and is released through a series of resonating fractures until the surface. Our findings demonstrate that fiber optic telecom cables in association with cutting-edge machine learning algorithms contribute to a better understanding and monitoring of volcanic hydrothermal systems.

Fibre optic distributed acoustic sensing of volcanic events.

Understanding physical processes prior to and during volcanic eruptions has improved significantly in recent years. However, uncertainties about subsurface structures distorting observed signals and undetected processes within the volcano prevent volcanologists to infer subtle triggering mechanisms of volcanic phenomena. Here, we demonstrate that distributed acoustic sensing (DAS) with optical fibres allows us to identify volcanic events remotely and image hidden near-surface volcanic structural features. We detect and characterize strain signals associated with explosions and locate their origin using a 2D-template matching between picked and theoretical wave arrival times. We find evidence for non-linear grain interactions in a scoria layer of spatially variable thickness. We demonstrate that wavefield separation allows us to incrementally investigate the ground response to various excitation mechanisms. We identify very small volcanic events, which we relate to fluid migration and degassing. Those results provide the basis for improved volcano monitoring and hazard assessment using DAS.

Cyclic magma recharge pulses detected by high-precision strainmeter data: the case of 2017 inter-eruptive activity at Etna volcano.

Unprecedented ultra-small strain changes (~10-8-10-9), preceding and accompanying the 2017 explosive-effusive activity, were revealed by a high precision borehole strainmeter at Etna. No pre- or co-eruptive deformation was detected by the GPS measurements, which often fail to detect ground deformation engendered by short-term small volcanic events due to their limited accuracy (millimetres to few centimetres). Through the analysis and detection of ultra-small strain changes (few tens of nanostrain), revealed by filtering the raw data, a significant time correspondence with the eruptive activity is observed. For the first time, cyclic fast exponential strain changes, preceding the onset of eruptive events, with a timescale of about 2-7 days, were detected. These variations are attributable to the expansion of the shallow magma reservoir, which is replenished with new magma from depth during the inter-eruptive periods. Interpreting the strain changes in terms of pressurization/depressurization of the chamber due to the cyclic influx and withdrawal of magma, allows placing some constraints on the magma recharge volume rate. A Finite Element model has been developed to simulate the temporal evolution of the strain changes generated by the re-pressurization of a spheroidal magma source using a dynamical approach. An average total mass budget of about 1-2 × 109 kg, which is in the range of the erupted mass, is estimated to be accumulated within a shallow vertically elongated magma chamber during the inter-eruptive periods. Such evidence demonstrates that the near-real time analysis of strainmeter records is remarkable for its ability to record small transients and highlight recharging phases preceding eruptive activity, which would go undetected with other current methodologies. Under these conditions, the ability to simulate inter-eruptive periods offers an opportunity to estimate the magma recharge rate with important implications for volcano hazard assessment.

Soil radon measurements as a potential tracer of tectonic and volcanic activity.

In Earth Sciences there is a growing interest in studies concerning soil-radon activity, due to its potential as a tracer of numerous natural phenomena. Our work marks an advance in the comprehension of the interplay between tectonic activity, volcanic eruptions and gas release through faults. Soil-radon measurements, acquired on Mt. Etna volcano in 2009-2011, were analyzed. Our radon probe is sensitive to changes in both volcanic and seismic activity. Radon data were reviewed in light of the meteorological parameters. Soil samples were analyzed to characterize their uranium content. All data have been summarized in a physical model which identifies the radon sources, highlights the mechanism of radon transport and envisages how such a mechanism may change as a consequence of seismicity and volcanic events. In the NE of Etna, radon is released mainly from a depth of <1400 m, with an ascent speed of >50 m/day. Three periods of anomalous gas release were found (February 2010, January and February 2011). The trigger of the first anomaly was tectonic, while the second and third had a volcanic origin. These results mark a significant step towards a better understanding of the endogenous mechanisms that cause changes in soil-radon emission at active volcanoes.

Capturing the fingerprint of Etna volcano activity in gravity and satellite radar data.

Long-term and high temporal resolution gravity and deformation data move us toward a better understanding of the behavior of Mt Etna during the June 1995 - December 2011 period in which the volcano exhibited magma charging phases, flank eruptions and summit crater activity. Monthly repeated gravity measurements were coupled with deformation time series using the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique on two sequences of interferograms from ERS/ENVISAT and COSMO-SkyMed satellites. Combining spatiotemporal gravity and DInSAR observations provides the signature of three underlying processes at Etna: (i) magma accumulation in intermediate storage zones, (ii) magmatic intrusions at shallow depth in the South Rift area, and (iii) the seaward sliding of the volcano's eastern flank. Here we demonstrate the strength of the complementary gravity and DInSAR analysis in discerning among different processes and, thus, in detecting deep magma uprising in months to years before the onset of a new Etna eruption.