Alan

LindeHe/His

Identifying and characterizing the dynamics of explosive activity is impelling to build tools for hazard assessment at open-conduit volcanoes: machine learning techniques are now a feasible choice. During the summer of 2019, Stromboli experienced two paroxysmal eruptions that occurred in two different volcanic phases, which gave us the possibility to conceive and test an early-warning algorithm on a real use case: the paroxysm on July, 3 was clearly preceded by smaller and less perceptible changes in the volcano dynamics, while the second paroxysm, on August 28 concluded the eruptive phase. Among the changes observed in the weeks preceding the July paroxysm one of the most significant is represented by the shape variation of the ordinary minor explosions, filtered in the very long period (VLP 2-50 s) band, recorded by the Sacks-Evertson strainmeter installed near the village of Stromboli. Starting from these observations, the usage of two independent methods (an unsupervised machine learning strategy and a cross-correlation algorithm) to classify strain transients falling in the ultra long period (ULP 50-200 s) frequency band, allowed us to validate the robustness of the approach. This classification leads us to establish a link between VLP and ULP shape variation forms and volcanic activity, especially related to the unforeseen 3 July 2019 paroxysm. Previous warning times used to precede paroxysms at Stromboli are of a few minutes only. For paroxysmal events occurring outside any long-lasting eruption, the initial success of our approach, although applied only to the few available examples, could permit us to anticipate this time to several days by detecting medium-term strain anomalies: this could be crucial for risk mitigation by prohibiting access to the summit. Our innovative analysis of dynamic strain may be used to provide an early-warning system also on other open conduit active volcanoes.

Key Points

For detecting below surface sources of deformation, strain measurements offer a very large advantage (orders of magnitude) in sensitivity over displacement measurements. On active volcanoes an intriguing open challenge is to measure the strain variations caused by the different types of eruptive activity with the highest possible precision in order to obtain advantages on the clear detecting of phenomena, their modeling and understanding. We present the updated main results obtained from the high precision strain recorded by the borehole dilatometer network on Mt. Etna volcano. The instruments, installed from the end of 2011, detected significant changes during different types of eruptive activity: several lava fountains during 2011-2014; two explosive sequences in 2015 and 2016; moderate effusive activity in 2017 and a dike intrusion in 2018. The strain changes provided powerful diagnostic information on the different ongoing processes, and allowed us to add key information on the different eruptive styles and sources. We also highlight how the recorded signals, with the associated modeling and interpretation, provide a powerful contribution to surveillance requirements on an active volcano. This report demonstrates that the borehole dilatometer network represents a useful tool both for the understanding of the volcano processes and for surveillance needs.

We report the first evidence for the detection of a slow slip event in the Longitudinal Valley, in eastern Taiwan. The slow event, which lasted about 3.5days, has been detected by borehole strainmeters. It occurred at shallow depths (about 2 to 4km), either on the Longitudinal Valley Fault or on the Central Range Fault. Here we investigate whether the event occurrence was influenced by transient and periodic stress perturbations, in particular by the June 2013 M-w 6.2 Nantou earthquake, which occurred about 60km away and 6days prior to the event. Modeled changes in Coulomb stress in the direction parallel to the geologic slip vector on the fault planes show negative static stress changes (approximately -1.5 to -1kPa), while maximum dynamic stress changes generated by the surface waves are ranging from 5.5 to 14.5kPa. We also observe that the slow event initiated during a maximum of Earth and ocean tidal Coulomb stress changes (about 0.8 to 1.5kPa). Dynamic and static stress perturbations represent a few percents to tens of percents of the stress buildup through the slow rupture cycle. However, the absence of recurrent events during the 12years of strain monitoring (2006 to 2018) prevents to estimate the recurrence interval of the slow event, which limits our ability to further interpret the link between the rupture and the perturbations. Finally, there is no large and unique load transient at the time of the initiation, therefore this single event may have likely occurred spontaneously.

We propose an approach for calibrating the horizontal tidal shear components [(differential extension () and engineering shear ()] of two Sacks-Evertson (in Pap Meteorol Geophys 22:195-208, 1971) SES-3 borehole strainmeters installed in the Longitudinal Valley in eastern Taiwan. The method is based on the waveform reconstruction of the Earth and ocean tidal shear signals through linear regressions on strain gauge signals, with variable sensor azimuth. This method allows us to derive the orientation of the sensor without any initial constraints and to calibrate the shear strain components and against tidal constituent. The results illustrate the potential of tensor strainmeters for recording horizontal tidal shear strain.