Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium.

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.

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.

A rockslide-generated tsunami in a Greenland fjord rang Earth for 9 days.

Climate change is increasingly predisposing polar regions to large landslides. Tsunamigenic landslides have occurred recently in Greenland (Kalaallit Nunaat), but none have been reported from the eastern fjords. In September 2023, we detected the start of a 9-day-long, global 10.88-millihertz (92-second) monochromatic very-long-period (VLP) seismic signal, originating from East Greenland. In this study, we demonstrate how this event started with a glacial thinning-induced rock-ice avalanche of 25 × 106 cubic meters plunging into Dickson Fjord, triggering a 200-meter-high tsunami. Simulations show that the tsunami stabilized into a 7-meter-high long-duration seiche with a frequency (11.45 millihertz) and slow amplitude decay that were nearly identical to the seismic signal. An oscillating, fjord-transverse single force with a maximum amplitude of 5 × 1011 newtons reproduced the seismic amplitudes and their radiation pattern relative to the fjord, demonstrating how a seiche directly caused the 9-day-long seismic signal. Our findings highlight how climate change is causing cascading, hazardous feedbacks between the cryosphere, hydrosphere, and lithosphere.

Hidden pressurized fluids prior to the 2014 phreatic eruption at Mt Ontake.

A large fraction of volcanic eruptions does not expel magma at the surface. Such an eruption occurred at Mt Ontake in 2014, claiming the life of at least 58 hikers in what became the worst volcanic disaster in Japan in almost a century. Tens of scientific studies attempted to identify a precursor and to unravel the processes at work but overall remain inconclusive. By taking advantage of continuous seismic recordings, we uncover an intriguing sequence of correlated seismic velocity and volumetric strain changes starting 5 months before the eruption; a period previously considered as completely quiescent. We use various novel approaches such as covariance matrix eigenvalues distribution, cutting-edge deep-learning models, and ascribe such velocity pattern as reflecting critically stressed conditions in the upper portions of the volcano. These, in turn, later triggered detectable deformation and earthquakes. Our results shed light onto previously undetected pressurized fluids using stations located above the volcano-hydrothermal system and hold great potential for monitoring.

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.

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.