Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network

Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progres- sively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes

Explorative search of distributed bio-data to answer complex biomedical questions

Background The huge amount of biomedical-molecular data increasingly produced is providing scientists with potentially valuable information. Yet, such data quantity makes difficult to find and extract those data that are most reliable and most related to the biomedical questions to be answered, which are increasingly complex and often involve many different biomedical-molecular aspects. Such questions can be addressed only by comprehensively searching and exploring different types of data, which frequently are ordered and provided by different data sources. Search Computing has been proposed for the management and integration of ranked results from heterogeneous search services. Here, we present its novel application to the explorative search of distributed biomedical-molecular data and the integration of the search results to answer complex biomedical questions. Results A set of available bioinformatics search services has been modelled and registered in the Search Computing framework, and a Bioinformatics Search Computing application (Bio-SeCo) using such services has been created and made publicly available at http://www.bioinformatics.deib.polimi.it/bio-seco/seco/. It offers an integrated environment which eases search, exploration and ranking-aware combination of heterogeneous data provided by the available registered services, and supplies global results that can support answering complex multi-topic biomedical questions. Conclusions By using Bio-SeCo, scientists can explore the very large and very heterogeneous biomedical-molecular data available. They can easily make different explorative search attempts, inspect obtained results, select the most appropriate, expand or refine them and move forward and backward in the construction of a global complex biomedical query on multiple distributed sources that could eventually find the most relevant results. Thus, it provides an extremely useful automated support for exploratory integrated bio search, which is fundamental for Life Science data driven knowledge discovery

Structural Health Monitoring Using Wireless Technologies: An Ambient Vibration Test on the Adolphe Bridge, Luxembourg City

Major threats to bridges primarily consist of the aging of the structural elements, earthquake-induced shaking and standing waves generated by windstorms. The necessity of information on the state of health of structures in real-time, allowing for timely warnings in the case of damaging events, requires structural health monitoring (SHM) systems that allow the risks of these threats to be mitigated. Here we present the results of a short-duration experiment carried out with low-cost wireless instruments for monitoring the vibration characteristics and dynamic properties of a strategic civil infrastructure, the Adolphe Bridge in Luxembourg City. The Adolphe Bridge is a masonry arch construction dating from 1903 and will undergo major renovation works in the upcoming years. Our experiment shows that a network of these wireless sensing units is well suited to monitor the vibration characteristics of such a historical arch bridge and hence represents a low-cost and efficient solution for SHM

Informing Observers: Quality-driven Filtering and Composition of Web 2.0 Sources

Current Web technologies enable an active role of users, who can create and share their contents very easily. This mass of information includes opinions about a variety of key interest topics and represents a new and invaluable source of marketing information. Public and private organizations that aim at understanding and analyzing this unsolicited feedback need adequate platforms that can support the detection and monitoring of key topics. Hence, there is an emerging trend towards automated market intelligence and the crafting of tools that allow monitoring in a mechanized fashion. We therefore present an approach that is based on quality of Web 2.0 sources as the key factor for information filtering and also allows the users to flexibly and easily compose their analysis environments thanks to the adoption of a mashup platform

Ranking-aware integration and explorative search of distributed bio-data

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Detection of Spatial and Temporal Stress Changes During the 2016 Central Italy Seismic Sequence by Monitoring the Evolution of the Energy Index

We consider approximately 23,000 microearthquakes that occurred between 2005 and 2016 in central Italy to investigate the crustal strength before and after the three largest earthquakes of the 2016 seismic sequence (i.e., the Mw 6.2, 24 August 2016 Amatrice, the Mw 6.1, 26 October 2016 Visso, and the Mw 6.5, 30 October 2016 Norcia earthquakes). We monitor the spatiotemporal deviations of observed radiated energy, ES, with respect to theoretical values, ESt, derived from a scaling model between ES and M0 calibrated for background seismicity in central Italy. These deviations, defined here as Energy Index (EI), allow us to identify in the years following the Mw 6.1, 2009 L’Aquila earthquake a progressive evolution of the dynamic properties of microearthquakes and the existence of high EI patches close to the Amatrice earthquake hypocenter. We show the existence of a crustal volume with high EI even before the Mw 6.5 Norcia earthquake. Our results agree with the previously suggested hypothesis that the Norcia earthquake nucleated at the boundary of a large patch, highly stressed by the two previous mainshocks of the sequence. We highlight the mainshocks interaction both in terms of EI and of the mean loading shear stress associated to microearthquakes occurring within the crustal volumes comprising the mainshock hypocenters. Our study shows that the dynamic characteristics of microearthquakes can be exploited as beacons of stress change in the crust and thus be exploited to monitor the seismic hazard of a region and help to intercept the preparation phase of large earthquakes

GFZ Wireless Seismic Array (GFZ-WISE), a Wireless Mesh Network of Seismic Sensors: New Perspectives for Seismic Noise Array Investigations and Site Monitoring

Over the last few years, the analysis of seismic noise recorded by two dimensional arrays has been confirmed to be capable of deriving the subsoil shear-wave velocity structure down to several hundred meters depth. In fact, using just a few minutes of seismic noise recordings and combining this with the well known horizontal-to-vertical method, it has also been shown that it is possible to investigate the average one dimensional velocity structure below an array of stations in urban areas with a sufficient resolution to depths that would be prohibitive with active source array surveys, while in addition reducing the number of boreholes required to be drilled for site-effect analysis. However, the high cost of standard seismological instrumentation limits the number of sensors generally available for two-dimensional array measurements (i.e., of the order of 10), limiting the resolution in the estimated shear-wave velocity profiles. Therefore, new themes in site-effect estimation research by two-dimensional arrays involve the development and application of low-cost instrumentation, which potentially allows the performance of dense-array measurements, and the development of dedicated signal-analysis procedures for rapid and robust estimation of shear-wave velocity profiles. In this work, we present novel low-cost wireless instrumentation for dense two-dimensional ambient seismic noise array measurements that allows the real–time analysis of the surface-wavefield and the rapid estimation of the local shear-wave velocity structure for site response studies. We first introduce the general philosophy of the new system, as well as the hardware and software that forms the novel instrument, which we have tested in laboratory and field studies

Testing the "PRESTo" Early Warning Algorithm in North-Eastern Italy, Austria and Slovenia: update analysis

Since 2002 OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) in Udine (Italy), the Agencija Republike Slovenije za Okolje (ARSO) in Ljubljana (Slovenia) and the Zentralanstalt für Meteorologie und Geodynamik (ZAMG) in Vienna (Austria), are collecting, analyzing, archiving and exchanging seismic data in real time. The data exchange has proved to be effective and very useful in case of seismic events at the borders between Italy, Austria and Slovenia, where the poor coverage of individual national seismic networks precluded a precise earthquake location, while the usage of common data from the integrated networks improves significantly the overall capability of real time event detection and rapid characterization in this area. In order to extend the seismic monitoring in North-eastern Italy, Slovenia and Southern Austria, towards earthquake early warning applications, at the end of 2013 OGS, ARSO and ZAMG teamed with the RISSCLab group (http://www.rissclab.unina.it) of the Department of Physics at the University of Naples Federico II in Italy. The collaboration focuses on massive testing on OGS, ARSO and ZAMG data of the EW platform PRESTo (Probabilistic and Evolutionary early warning SysTem) developed by RISSC-Lab (http://www.prestoews.org)