Modeling of the 1783 Tsunami Event in Scilla Generated by Landslide

In 1783, an event that has gone down in history as the great seismic crisis in Calabria began, during which two major earthquakes occurred, affecting the Calabrian ridge from the Strait to the north. Between 6 and 7 February in Scilla a tsunami occurred that caused the greatest number of victims in Italy: 1500 people. The mechanism that triggered the tsunami was the detachment of a ridge of rock due to a violent earthquake that affected the area; this detachment caused a subaerial landslide which, by sliding, then deposited the rock on the seabed a few kilometers from the coast, immediately generating the tsunami event. The objective of this study is to perform numerical simulations for tsunami events that occurred in history and use models that perform the propagation of a tsunami, using the best possible bathymetric and topographic data and the historical data to compare the validity of the results. In this way, one can obtain the validation of a model that can be used to simulate possible events of this magnitude on the Calabrian coasts and therefore be able to develop a reliable early warning tsunami system; it also has the advantage of perfectly combining computational burdens and the validity of results.

Crisis Communication after Earthquakes in Greece and Japan: Effects on Seismic Disaster Management

The communication of emergency information shortly before or after the manifestation of seismic hazards is a crucial part of disaster management. Crisis communication aims to protect, support and guide the public and emergency services throughout the response and recovery phase. In the case of seismic events, a fundamental query refers to how the information to be released to the public immediately after/before the seismic event affects disaster impacts and management. This paper addresses the uncertainty involved in emergency seismic information, identifies the sources, means, content and mode of emergency communication and points to the effects of different models of crisis communication on public perceptions, on emergency responses and, hence, on disaster management. A review of past experiences of seismic crisis communication strategies in earthquake-prone countries, namely Greece and Japan, reveals successes and failures in managing uncertainty, and in building public trust and improving response capacities. The findings include the importance of crisis communication in seismic disaster management, the levels/layers of uncertainty involved in emergency seismic information and how they impact risk perceptions, the public trust/mistrust effect on scientific and management institutions as well as some recommendations for seismic crisis communication strategies to minimize uncertainty and improve emergency responses.

Use of Local Seismic Network in Analysis of Volcano-Tectonic (VT) Events Preceding the 2017 Agung Volcano Eruption (Bali, Indonesia)

This study provides an attempt to analyze the pre-eruptive seismicity events for volcano eruption forecasting. After more than 50 years of slumber, Agung volcano on Bali Island erupted explosively, starting on November 21, 2017. The eruption was preceded by almost 2 months of significant increase of recorded seismicity, herein defined as “seismic crisis.” Our study provides the first analysis of VT events using data from eight local seismic stations deployed by the Center for Volcanology and Geological Hazard Mitigation of Indonesia (CVGHM) to monitor the Agung Volcano activity. In total, 2,726 Volcano-Tectonic (VT) events, with 13,023 P waves and 11,823 S wave phases, were successfully identified between October 18 and November 30, 2017. We increased the accuracy of the hypocenter locations of these VT events using a double-difference (DD) relative relocation and a new velocity model appropriate to the subsurface geological conditions of Agung volcano. We found two types of seismicity during the recording period that represent the VT events relating to fracture network reactivation due to stress changes (during the seismic crisis) and magma intrusion (after the seismic crisis). The characteristics of each event type are discussed in terms of Vp/Vs values, phase delay times, seismic cluster shapes, and waveform similarity. We interpret that the upward migrating magma reached a barrier (probably a stiff layer) which prohibited further ascent. Consequently, magma pressurized the zone above the magma chamber and beneath the barrier, reactivated the fracture zone between Agung and Batur volcanoes, and caused the seismic crisis since September 2017. In early November 2017, the barrier was finally intruded, and magma and seismicity propagated toward the Agung summit. This reconstruction provides a better depth constraint as to the previous conceptual models and explains the long delay (∼10 weeks) between the onset of the seismic crisis and the eruption. The distinction between the fracture reactivation and magma intrusion VT events observed in this study is significant for eruption forecasting and understanding the subsurface structure of the magmatic system. Based on the results obtained in this study, we emphasize the importance of prompt analysis (location and basic seismic characteristics) of the seismic crisis preceding the Agung eruption.

A possible radio anomaly observed on the occasion of the MW=6.0 earthquake occurred in Dodecanese islands at the end of January 2020

<p>Since 2009, several VLF/LF radio receivers have been installed throughout Europe in order to realize a European radio network for studying the radio precursors of earthquakes, called the INFREP network. The current network has nine VLF/LF receiving stations, two in Romania and<br>Greece, one in Italy, Austria, Portugal, Cyprus, and Serbia. The receivers can measure with 1 min sampling rate the intensity of 10 radio signals in the band VLF (10-50 kHz) and LF (150-300 kHz). The scope of existing transmitters is manifold, e.g. they are used for radio broadcast (LF), for radio-navigation or time signals and mainly for military purposes in the VLF range. At the end of January 2020 an intense seismic crisis occurred in Dodecanese Islands; the main event (Mw= 6.0) occurred on January 30. This seismic activity occurred in the "sensitive" area of the INFREP network. The analysis of the data collected by INFREP receivers has revealed clear anomalies in three VLF signals appearing some days before the main earthquake. The anomalies appear in the trends collected by the Cyprus receiver and the epicenter is inside the 5<sup>th</sup> Fresnel ellipses defined by transmitters-receiver. Here we report the data analysis and we present in detail the anomalies. The possibility that they are precursors of the quoted earthquake seems significant.</p><p>Biagi, P.F., Colella, R., Schiavulli, L., Ermini, A., Boudjada, M., Eichelberger, H., Schwingenschuh, K., Katzis, K., Contadakis, M.E., Skeberis, C., Moldovan, I.A. and Bezzeghoud, M. (2019) The INFREP Network: Present Situation and Recent Results. Open Journal of Earthquake Research,8, 101-115. https://doi.org/10.4236/ojer.2019.82007</p>

Hierarchical exploration of single station seismic data with unsupervised learning

<p>Seismic datasets contain an enormous amount of information and a large variety of signals with different origins. We usually observe signatures of earthquakes, volcanic and non-volcanic tremors, rockfalls, road and air traffic, atmospheric perturbations and many other acoustic emissions. More and more seismic sensors are deployed worldwide and record the seismic wavefield in a continuous fashion, generating massive volumes of data that cannot be analyzed manually in decent times. Therefore, identifying classes of signals in seismic data with automatic strategies is a crucial stage towards the understanding of the underlying physics of geological objects. For that reason seismologists have developed different tools to detect and classify certain types of signals. Recently, machine learning gained much attention due to its ability to recognize patterns. While supervised learning is a great tool for detecting and classifying signals within already-known classes, it cannot be used to infer new classes of signals, and can be strongly biased by the labels we impose. We here propose to overcome this limitation with unsupervised learning. In this study, we present a new way to explore single-station continuous seismic data with a dendrogram produced by agglomerative clustering. Our method is motivated by the idea that labels in a seismic data set follow a hierarchical order with different levels of details. For example earthquakes belong to the larger class of stationary signals and can be also divided into subclasses with different focal mechanism or magnitudes. We first use a scattering network (a convolutional neural network that makes use of wavelet filers) in order to extract a multi-scale representation of the continuous seismic waveforms. We then select the most meaningful features by means of independent component analysis, and apply an agglomerative clustering on this representation. We finally explore the dendrogram in a systematic way in order to explore the different signal classes revealed by the strategy. We illustrate our method on seismic data continuously recorded in the vicinity of the North-Anatolian fault, in Turkey. During this time period, a seismic crisis with more than 200 micro-earthquakes occurred, together with many other anthropogenic and meteorological events. By exploring the classes revealed by the dendrogram with <em>a posteriori</em> signal features (occurrence, within-class correlations, etc.) we show that the strategy is capable of retrieving the seismic crisis as well as signals related to anthropogenic and meteorogical activities.</p>

Short- and long-term evolution of the seismicity associated with the New Volcanic Edifice offshore Mayotte island

<p>In May 2018, a seismic crisis started in the Comoros archipelago, East of Mayotte, which was widely felt on the Island. The related discovery of a new, 800-m high, submarine edifice 50 km East of Mayotte showed that the seismicity was caused by the birth of a volcano. The eruption is still on going at the time of writing and has sparked a large interest in the scientific community.</p><p>The seismicity is still active and is being continuously monitored thanks to several seismic stations installed on the island of Mayotte. The oceanographic campaigns that were carried out since the beginning of the crisis deployed a number of ocean bottom seismometers directly above the seismicity, to accurately understand the crisis and particularly its location. A new technique of automatic detection based on Machine Learning enabled to considerably increase the number of earthquakes that can be used to constrain the extent of the seismicity. Furthermore, the development of a new velocity model for the region allowed a precise location of these earthquakes.</p><p>These new developments permitted to reconstruct the seismicity evolution during two years of this seismic crisis and to complete the seismicity map associated with the new seismic activity. These results provide more details on the active structures to study the evolution in time as well as their precise spacial variations, allowing the analysis of the daily-to-yearly timescales of this unprecedented eruption. This is crucial to understand the dynamics of the volcanic and magmatic processes beneath Mayotte island. Linking these spatial and time variations with the real-time data, as well as the deformation and petrology evolutions, will provide crucial details on the dynamics of submarine eruptions.</p>

Remote monitoring of seismic swarms and the August 2016 seismic crisis of Brava, Cabo Verde, using array methods

During the first two days of August 2016 a seismic crisis occurred on Brava, Cabo Verde, which – according to observations based on a local seismic network – was characterized by more than a thousand volcano-seismic signals. Brava is considered an active volcanic island, although it has not experienced any historic eruptions. Seismicity significantly exceeded the usual level during the crisis. We report on results based on data from a temporary seismic-array deployment on the neighbouring island of Fogo at a distance of about 35 km. The array was in operation from October 2015 to December 2016 and recorded a total of 1343 earthquakes in the region of Fogo and Brava; 355 thereof were localized. On 1 and 2 August we observed 54 earthquakes, 25 of which could be located beneath Brava. We further evaluate the observations with regards to possible precursors to the crisis and its continuation. Our analysis shows a significant variation in seismicity around Brava, but no distinct precursory pattern. However, the observations suggest that similar earthquake swarms commonly occur close to Brava. The results further confirm the advantages of seismic arrays as tools for the remote monitoring of regions with limited station coverage or access.