Long-term analysis of microseism during extreme weather events: Medicanes and common storms in the Mediterranean Sea.

In this work, we analyze 12 meteorological events that occurred in the Mediterranean Sea during the period November 2011-November 2021 from a seismic point of view. In particular, we consider 8 Medicanes and 4 more common storms. Each of these events, in spite of the marked differences between them, caused heavy rainfall, strong wind gusts and violent storm surge with significant wave heights usually >3 m. We deal with the relationships between these meteorological events and the features of microseism (the most continuous and widespread seismic signal on Earth) in terms of spectral content, space-time variation of the amplitude and source locations tracked employing two different methods (amplitude decay-based grid search and array techniques). By comparing the positions of the microseism sources with the areas of significant storm surges, we observe that the microseism locations align with the actual locations of the storm surges for 10 out of 12 events analyzed (two Medicanes present very low intensity in terms of meteorological parameters and the microseism amplitude does not show significant variations during these two events). We also perform two analyses that allowed us to obtain both the seismic signature of these events, by using a method that exploits the coherence of continuous seismic noise, and their strength from a seismic point of view, called Microseism Reduced Amplitude. In addition, by integrating the results obtained from these two methods, we are able to "seismically" distinguish Medicanes and common storms. Consequently, we demonstrate the possibility of creating a novel monitoring system for Mediterranean meteorological events by incorporating microseism information alongside with other commonly employed techniques for studying meteorological phenomena. The integration of microseism with the data provided by routinely used techniques in sea state monitoring (e.g., wave buoy and HF radar) has the potential to offer valuable insights into the examination of historical extreme weather events within the context of climate change.

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.

Sensitivity and Uncertainty Analysis of Micro-Flow Imaging for Sub-Visible Particle Measurements Using Artificial Neural Network.

PURPOSE: During biopharmaceutical drug manufacturing, storage, and distribution, proteins in both liquid and solid dosage forms go through various processes that could lead to protein aggregation. The extent of aggregation in the sub-micron range can be measured by analyzing a liquid or post-reconstituted powder sample using Micro-Flow Imaging (MFI) technique. MFI is widely used in biopharmaceutical industries due to its high sensitivity in detecting and analyzing particle size distribution. However, the MFI's sensitivity to various factors makes accurate measurement challenging. Therefore, in light of the inherent variability of the method, this work aims to explore the capabilities of an adopted coupled sensitivity analysis and machine learning algorithm to quantify the influencing factors on the formed sub-visible particles and method variability. METHODS: The proposed algorithm consists of two interconnected components, namely a surrogate model with a neural network and a sensitivity analyzer. A machine learning tool based on artificial neural networks (ANN) is constructed with MFI data. The best fit with an optimized configuration is found. Sensitivity and uncertainty analysis is performed using this network as the surrogate model to understand the impacts of input parameters on MFI data. RESULTS: Results reveal the most impactful reconstitution preparation factors and others that are masked by the instrument variabilities. It is shown that instrument inaccuracy is a function of size category, with higher variabilities associated with larger size ranges. CONCLUSION: Utilizing this tool while assessing the sensitivity of outputs to various parameters, measurement variabilities for analytical characterization tests can be quantified.

Monitoring extreme meteo-marine events in the Mediterranean area using the microseism (Medicane Apollo case study).

Microseism is the continuous background seismic signal caused by the interaction between the atmosphere, the hydrosphere and the solid Earth. Several studies have dealt with the relationship between microseisms and the tropical cyclones, but none focused on the small-scale tropical cyclones that occur in the Mediterranean Sea, called Medicanes. In this work, we analysed the Medicane Apollo which impacted the eastern part of Sicily during the period 25 October-5 November 2021 causing heavy rainfall, strong wind gusts and violent sea waves. We investigated the microseism accompanying this extreme Mediterranean weather event, and its relationship with the sea state retrieved from hindcast maps and wave buoys. The spectral and amplitude analyses showed the space-time variation of the microseism amplitude. In addition, we tracked the position of Apollo during the time using two different methods: (i) a grid search method; (ii) an array analysis. We obtained a good match between the real position of Apollo and the location constraint by both methods. This work shows that it is possible to extract information on Medicanes from microseisms for both research and monitoring purposes.

Shallow conduit dynamics fuel the unexpected paroxysms of Stromboli volcano during the summer 2019.

Open conduit basaltic volcanoes can be potentially hazardous as the eruptive activity may turn suddenly from a steady state to highly explosive. Unexpected changes in explosion intensity are recurrent at Stromboli volcano, where major explosions and large-scale paroxysms sometimes break off the ordinary, Strombolian activity with little or no warning. Two powerful paroxysmal eruptions took place at Stromboli volcano during the summer 2019, causing widespread fires, consistent damages across the island, injuries and one fatality. Prediction of similar events is really challenging for the modern volcanology, though models propaedeutic to early-warning monitoring systems are not properly assessed yet in many volcanoes worldwide. Here, we present a multi-parametric study that combines petrological and geophysical data to investigate processes generating the two paroxysms. The time information derived by Li enrichments in plagioclase crystals correlates with tilt time series derived by seismometers installed on the island, highlighting the dominant role of shallow conduit processes in triggering the 2019 paroxysmal activity. Our dataset conceives a mechanism of gas slug formation and fast upward migration that finally triggered the eruptions in very limited times. The proposed model questions our capability to forecast such kind of paroxysms in times that are rapid enough to allow mitigation of the associated risk.

Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures.

Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. Although the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This quiet period provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of human activities.

Exploring the link between microseism and sea ice in Antarctica by using machine learning.

The most continuous and ubiquitous seismic signal on Earth is the microseism, closely related to ocean wave energy coupling with the solid Earth. A peculiar feature of microseism recorded in Antarctica is the link with the sea ice, making the temporal pattern of microseism amplitudes different with respect to the microseism recorded in low-middle latitude regions. Indeed, during austral winters, in Antarctica the oceanic waves cannot efficiently excite seismic energy because of the sea ice in the Southern Ocean. Here, we quantitatively investigate the relationship between microseism, recorded along the Antarctic coasts, and sea ice concentration. In particular, we show a decrease in sea ice sensitivity of microseism, due to the increasing distance from the station recording the seismic signal. The influence seems to strongly reduce for distances above 1,000 km. Finally, we present an algorithm, based on machine learning techniques, allowing to spatially and temporally reconstruct the sea ice distribution around Antarctica based on the microseism amplitudes. This technique will allow reconstructing the sea ice concentration in both Arctic and Antarctica in periods when the satellite images, routinely used for sea ice monitoring, are not available, with wide applications in many fields, first of all climate studies.

On a Possible Unified Scaling Law for Volcanic Eruption Durations.

Volcanoes constitute dissipative systems with many degrees of freedom. Their eruptions are the result of complex processes that involve interacting chemical-physical systems. At present, due to the complexity of involved phenomena and to the lack of precise measurements, both analytical and numerical models are unable to simultaneously include the main processes involved in eruptions thus making forecasts of volcanic dynamics rather unreliable. On the other hand, accurate forecasts of some eruption parameters, such as the duration, could be a key factor in natural hazard estimation and mitigation. Analyzing a large database with most of all the known volcanic eruptions, we have determined that the duration of eruptions seems to be described by a universal distribution which characterizes eruption duration dynamics. In particular, this paper presents a plausible global power-law distribution of durations of volcanic eruptions that holds worldwide for different volcanic environments. We also introduce a new, simple and realistic pipe model that can follow the same found empirical distribution. Since the proposed model belongs to the family of the self-organized systems it may support the hypothesis that simple mechanisms can lead naturally to the emergent complexity in volcanic behaviour.

Real Time Tracking of Magmatic Intrusions by means of Ground Deformation Modeling during Volcanic Crises.

Volcano observatories provide near real-time information and, ultimately, forecasts about volcano activity. For this reason, multiple physical and chemical parameters are continuously monitored. Here, we present a new method to efficiently estimate the location and evolution of magmatic sources based on a stream of real-time surface deformation data, such as High-Rate GPS, and a free-geometry magmatic source model. The tool allows tracking inflation and deflation sources in time, providing estimates of where a volcano might erupt, which is important in understanding an on-going crisis. We show a successful simulated application to the pre-eruptive period of May 2008, at Mount Etna (Italy). The proposed methodology is able to track the fast dynamics of the magma migration by inverting the real-time data within seconds. This general method is suitable for integration in any volcano observatory. The method provides first order unsupervised and realistic estimates of the locations of magmatic sources and of potential eruption sites, information that is especially important for civil protection purposes.