In this article, we present a new data collection that combines information about earthquake damage with seismic shaking. Starting from the Da.D.O. database, which provides information on the damage ...of individual buildings subjected to sequences of past earthquakes in Italy, we have generated ShakeMaps for all the events with magnitude greater than 5.0 that have contributed to these sequences. The sequences under examination are those of Irpinia 1980, Umbria Marche 1997, Pollino 1998, Molise 2002, L’Aquila 2009 and Emilia 2012. In this way, we were able to combine, for a total of the 117,695 buildings, the engineering parameters included in Da.D.O., but revised and reprocessed in this application, and the ground shaking data for six different variables (namely, intensity in MCS scale, PGA, PGV, SA at 0.3s, 1.0s and 3.0s). The potential applications of this data collection are innumerable: from recalibrating fragility curves to training machine learning models to quantifying earthquake damage. This data collection will be made available within Da.D.O., a platform of the Italian Department of Civil Protection, developed by EUCENTRE.
In Italy, the Mercalli–Cancani–Sieberg (MCS) is the intensity scale in use to describe the level of earthquake ground shaking, and its subsequent effects on communities and on the built environment. ...This scale differs to some extent from the Mercalli Modified scale in use in other countries and adopted as standard within the USGS-ShakeMap procedure to predict intensities from observed instrumental data. We have assembled a new PGM/MCS-intensity data set from the Italian database of macroseismic information, DBMI04, and the Italian accelerometric database, ITACA. We have determined new regression relations between intensities and PGM parameters (acceleration and velocity). Since both PGM parameters and intensities suffer of consistent uncertainties we have used the orthogonal distance regression technique. The new relations are and Tests designed to assess the robustness of the estimated coefficients have shown that single-line parametrizations for the regression are sufficient to model the data within the model uncertainties. The relations have been inserted in the Italian implementation of the USGS-ShakeMap to determine intensity maps from instrumental data and to determine PGM maps from the sole intensity values. Comparisons carried out for earthquakes where both kinds of data are available have shown the general effectiveness of the relations.
In this paper we describe the performance of the ShakeMap software package and the fully automatic procedure, based on manually revised location and magnitude, during the main event of the Amatrice ...sequence with special emphasis to the M6 main shock, that struck central Italy on the 24th August 2016 at 1:36:32 UTC. Our results show that the procedure we developed in the last years, with real-time data exchange among those institutions acquiring strong motion data, allows to provide a faithful description of the ground motion experienced throughout a large region in and around the epicentral area. The prompt availability of the rupture fault model, within three hours after the earthquake occurrence, provided a better descriptions of the level of strong ground motion throughout the affected area. Progressive addition of station data and manual verification of the data insures improvements in the description of the experienced ground motions. In particular, comparison between the MCS intensity shakemaps and preliminary field macroseismic reports show favourable similarities. Finally the overall spatial pattern of the ground motion of the main shock is consistent with reported rupture directivity toward NW and reduced levels of ground shaking toward SW probably linked to the peculiar source effects of the earthquake.
We study the crustal velocity changes occurred at the restart of produced water injection at a well in the Val d'Agri oil field in January–February 2015 using seismic noise cross-correlation ...analysis. We observe that the relative velocity variations fit well with the hydrometric level of the nearby Agri river, which may be interpreted as a proxy of the total water storage in the shallow aquifers of the Val d'Agri valley. We then remove from the relative velocity trend the contribution of hydrological variations and observe a decrease in relative velocity of ≈ 0.08% starting seven days after the injection restart. In order to investigate if this decreasing could be due to the water injection restart, we compute the medium diffusivity from its delay time and average station-well distance. We found diffusivity values in the range 1–5 m
2
/s, compatible with the observed delay time of the small-magnitude (
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≤ 1.8) induced seismicity occurrences, triggered by the first injection tests in June 2006 and with the hydraulic properties of the hydrocarbon reservoir. Our results show that water storage variations can not be neglected in noise-based monitoring, and they can hide the smaller effects due to produced water injection.
We present here the first application of the fast reacting framework for 3D simulations of seismic wave propagation generated by earthquakes in the Italian region with magnitude Mw 5. The driven ...motivation is to offer a visualization of the natural phenomenon to the general public but also to provide preliminary modeling to expert and civil protection operators. We report here a description of this framework during the emergency of 24 August 2016 Mw 6.0 central Italy Earthquake, a discussion on the accuracy of the simulation for this seismic event and a preliminary critical analysis of the visualization structure and of the reaction of the public.
In this paper we describe the results of an experimental implementation of the recent guidelines issued by the Italian regulatory body for monitoring hydrocarbon production activities. In particular, ...we report about the pilot study on seismic, deformation, and pore pressure monitoring of the Mirandola hydrocarbon cultivation facility in Northern Italy. This site hosts the Cavone oil field that was speculated of possibly influencing the 2012
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5.8 Mirandola earthquake source. According to the guidelines, the monitoring center should analyse geophysical measurements related to seismicity, crustal deformation and pore pressure in quasi real-time (within 24–48 h). A traffic light system would then be used to regulate underground operations in case of detecting significant earthquakes (i.e., events with size and location included in critical ranges). For these 2-year period of guidelines experimentation, we analysed all different kinds of available data, and we tested the existence of possible relationship between their temporal trends. Despite the short time window and the scarce quantity of data collected, we performed the required analysis and extracted as much meaningful and statistically reliable information from the data. We discuss here the most important observations drawn from the monitoring results, and highlight the lessons learned by describing practical issues and limitations that we have encountered in carrying out the tasks as defined in the guidelines. Our main goal is to contribute to the discussion about how to better monitor the geophysical impact of this kind of anthropogenic activity. We point out the importance of a wider seismic network but, mostly, of borehole sensors to improve microseismic detection capabilities. Moreover, the lack of an assessment of background seismicity in an unperturbed situation -due to long life extraction activities- makes it difficult to get a proper picture of natural background seismic activity, which would be instead an essential reference information for a tectonically-active regions, such as Northern Italy.
SUMMARY
We derived new, reversible relationships between macroseismic intensity (I), expressed in either the European Macroseismic (EMS-98) or the Mercalli–Cancani–Sieberg (MCS) scales and peak ...ground acceleration (PGA), peak ground velocity (PGV) and the spectral acceleration (SA) at 0.3, 1.0 and 3.0 s SA(0.3), SA(1.0) and SA(3.0) for Italy. We adopted the orthogonal distance regression technique to fit a quadratic function. This research aims to improve ground motion and intensity estimates for earthquake hazard applications, and for the calculation of shakemaps in Italy. To this end, the recently published INGe data set was used (https://doi.org/10.13127/inge.2). The new relations are:
$$\begin{equation*}
I = 3.01 \pm 0.12 + 0.86 \pm 0.04 \log ^2 \mathrm{ PGA},~\sigma = 0.30,~~\sigma _{\mathrm{ PGA}} = 0.25,~~\sigma _{I} = 0.16
\end{equation*}$$$\begin{equation*}
I = 4.31 \pm 0.15 + 1.99 \pm 0.18 \log \mathrm{ PGV} + 0.58 \pm 0.18 \log ^2 \mathrm{ PGV},~\sigma = 0.34,~~\sigma _{\mathrm{ PGV}} \\
= 0.31,~~\sigma _{I} = 0.15
\end{equation*}$$$\begin{equation*}
I = 2.77 \pm 0.15 + 0.68 \pm 0.03 \log ^2 \mathrm{ SA}(0.3),~\sigma = 0.31,~~\sigma _{\mathrm{ SA}(0.3)} = 0.28,~~\sigma _{I} = 0.14
\end{equation*}$$$\begin{equation*}
I = 3.00 \pm 0.28 + 0.91 \pm 0.55 \log \mathrm{ SA}(1.0) + 0.51 \pm 0.20 \log ^2 \mathrm{ SA}(1.0),~\sigma = 0.40,~~\sigma _{\mathrm{ SA}(1.0)} \\
= 0.38,~~\sigma _{I} = 0.14
\end{equation*}$$$\begin{equation*}
I = 4.04 \pm 0.20 + 1.63 \pm 0.19 \log \mathrm{ SA}(3.0) + 0.66 \pm 0.20 \log ^2 \mathrm{ SA}(3.0),~\sigma = 0.38,~~\sigma _{\mathrm{ SA}(3.0)} \\
= 0.35,~~\sigma _{I} = 0.14
\end{equation*}$$where PGA and SAs are expressed in cm s−2 and PGV is expressed in cm s−1. Tests performed to assess the robustness and the accuracy of the results demonstrate that adoption of quadratic relationships for this regression problem is a suitable choice within the range of values of the available data set. Comparison with similar published regressions for Italy evidences that the proposed relations provide statistically significant improved fits to the data. The new relations are also tested by inserting them in the ShakeMap system of the Italian configuration evidencing a significant improvement when compared to those implemented.
ShakeMaps during the Emilia sequence Valentino Lauciani; Licia Faenza; Alberto Michelini
Annals of geophysics,
01/2012, Letnik:
55, Številka:
4
Journal Article
Recenzirano
Odprti dostop
ShakeMap is a software package that can be used to generate maps of ground shaking for various peak ground motion (PGM) parameters, including peak ground acceleration (PGA), peak ground velocity, and ...spectral acceleration response at 0.3 s, 1.0 s and 3.0 s, and instrumentally derived intensities. ShakeMap has been implemented in Italy at the Istituto Nazionale di Geofisica e Vulcanologia (INGV; National Institute of Geophysics and Volcanology) since 2006 (http://shakemap.rm.ingv.it), with the primary aim being to help the Dipartimento della Protezione Civile (DPC; Civil Protection Department) civil defense agency in the definition of rapid and accurate information on where earthquake damage is located, to correctly direct rescue teams and to organize emergency responses. Based on the ShakeMap software package Wald et al. 1999, Worden et al. 2010, which was developed by the U.S. Geological Survey (USGS), the INGV is constructing shake maps for Ml ≥3.0, with the adoption of a fully automatic procedure based on manually revised locations and magnitudes Michelini et al. 2008. The focus of this study is the description of the progressive generation of these shake maps for the sequence that struck the Emilia-Romagna Region in May 2012. …
SUMMARY
We present the results of the regression analyses between Mercalli‐Cancani‐Sieberg (MCS) intensity and the spectral acceleration (SA) at 0.3, 1.0 and 2.0 s (SA03, SA10 and SA20). In Italy, ...the MCS scale is used to describe the level of ground shaking suffered by manufactures or perceived by the people, and it differs to some extent from the Mercalli Modified scale in use in other countries. We have assembled a new SA/MCS‐intensity data set from the DBMI04 intensity database and the ITACA accelerometric data bank. The SA peak values are calculated in two ways—using the maximum among the two horizontal components, and using the geometrical mean among the two horizontal components. The regression analysis has been performed separately for the two kinds of data sets and for the three target periods. Since both peak ground parameters and intensities suffer of appreciable uncertainties, we have used the orthogonal distance regression technique. Also, tests designed to assess the robustness of the estimated coefficients have shown that single‐line parametrizations for the regressions are sufficient to model the data within the model uncertainties.
For the maximum horizontal component, SAxxhm, the new relations are
For the geometrical mean SA, SAxxgm, the new relations are
Adoption of the geometric mean of the horizontal components, rather than their maximum value, results in a minor shift towards larger values of intensity for the same level of ground motion; this difference, however, is contained within the regression standard errors of the former. Comparisons carried out in various manners for earthquakes where both kinds of data (macroseismic and instrumental data) are available have shown the general effectiveness of the relations.
This study presents the strategies adopted to modify the Proportional Hazard Model to fit the requirements for forecasting testing within the Collaboratory Study for Earthquake Predictability (CSEP) ...experiment. The model was originally proposed to study the spatiotemporal distribution of M5.5+ seismicity in Italy, through two spatial models: a regular grid, and a seismotectonic zonation. A prospective 10-year-forecast test has already been ongoing since 2005, and the results are available on the internet (http://www.bo.ingv.it/earthquake/ ITALY/forecasting/M5.5+/). For that test, we have reported the probability maps of M5.5+ earthquakes for the next 10 years for the two spatial models. As the original model is time-dependent, it is updated every year, and also immediately after the occurrence of a target event, e.g., Mw5.5. Although this prospective test is continuing and the model updates probabilities that are different from those of the CSEP experiments, we argue that a full evaluation of the model can only be achieved through this CSEP testing, where the performances of different models are compared using the same rules and tests. The major modification we have introduced into our model is the simulation of the expected numbers of events in the exposure time Dx. This is performed considering the probability that an event occurs in Dx, and evaluating the change this will cause in the expected number of events. This procedure is also implemented for the first and second generation of aftershocks.
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