Annals of Geophysics (INGV, Istituto Nazionale di Geofisica e Vulcanologia)
Not a member yet
3651 research outputs found
Sort by
Geometric scaling of felsic sheet intrusions in the brittle upper crust, eastern Elba Island, Italy, with implications for host-rock strain distribution and strain rates
No full textSheet intrusions (dikes and sills) represent the most viable mode of magma transport and emplacement within the brittle upper crust. The thickness-to-length aspect ratio of such intrusions provides information about magma emplacement mechanisms and provides information on intruded magma volumes. Within the brittle upper crust, the mobility of magma is governed by magma cooling and solidification rates. The cooling rate of sheet intrusions depends on the temperature difference between magma and its host rocks, the thickness of the intrusion and the thermal diffusivity of the surrounding crust. The cooling rate of magma in the upper crust can be expected to be faster than the tectonic strain rate. In eastern Elba Island Italy, well exposed, late Miocene leucogranite sheets that intrude schistose host rocks within the contact aureole of the Porto Azzurro pluton provide a natural laboratory for analysing sheet intrusion geometries (length, thickness, spacing between intrusions). The thickness-to-length ratios of the intrusions define a power law dimensional scaling relationship with exponent ~1. Using this length versus thickness scaling we estimate that the total volume of leucogranite that intruded the Porto Azzurro pluton contact aureole was 4‑15 × 106 m3.Moreover, by analysing the spacing between sheet intrusions along with the size distribution of their thicknesses, we estimate that their emplacement was accommodated by an average bulk strain (volumetric strain) of 13-15% of the Porto Azzurro pluton contact aureole host rocks. Our observations also highlight how host rock brittle structures may control the location and mode of magma emplacement. Well exposed sections of sheet intrusions and their host rocks, as observed on Elba Island, are natural laboratories where geometries of intrusions can be analysed to provide important clues on the mechanisms of magma emplacement within the brittle crust, and interactions between magmatic and deformation processes
Seismic structure at the test site for wind energy research, WINSENT, Southwest Germany
No full textThe subsurface at the Wind Science and Engineering Test Site in Complex Terrain (WINSENT) in SW Germany is studied to derive its underground structure and 3‑D seismic velocity distribution. These parameters are important for further geotechnical studies to better understand the soil‑structure interaction of wind turbines and their underground. This knowledge is needed for the saveconstruction of modern wind turbines on land whose nacelles reach altitudes of more than 150 m above the ground. Another issue are ground motions which are emitted from wind turbines and can be measured up to distances of several kilometers. We describe the fieldwork at the wind energy test site and the seismic inversion models. The seismic velocities are low compared to other studies due to the weathering and karstification of the Jurassic limestone at the site. We derive 3‑D compressional and shear wave velocity models with minor lateral variation which can be used as input for numerical modelling of wave propagation to explore vibrating wind turbines and their emissions
Ground motion amplifications for Bucharest based on 3D geological model and assigned geophysical properties
No full textThe evaluation of seismic hazard at local scale, with the contribution of strong-motion data from a dense seismic network and insightful geological and geophysical data, is one of the key components in seismic risk mitigation. Significant efforts were made to record and predict the highly variable peak spectral amplification values of strong seismic motion in Bucharest, capital city of Romania, especially after the 4 March 1977 Vrancea earthquake with a moment-magnitude of 7.4, which resulted in 1424 victims in the city (90% out of the national casualties). Using a recently compiled geological database, which relies mostly on several hundreds of borehole measurements performed for the subway in Bucharest and a recent DEM for the area, this study establishes the positions of the main seven Quaternary layers beneath the city. In this paper we review studies referring to shear wave velocity () measurements in the area of Bucharest – as key input for seismic site amplification models and microzonation maps, selecting and reprocessing some data in order to obtain a homogenized database. This contains mean weighted values for the uppermost 30 m, 50 m, 70 m and 100 m depth intervals. By mapping and interpreting the newly assembled geological model, as well as the assigned geophysical values (shearwave velocity), we begin to compute the spectral amplification values at surface, using the data recorded at the earthquake from 27.10.2004 at surface and in the depth. The spectral modelling is applied to deeper models than the uppermost 30 m, and considering the 50 m, 70 m and 100 m depth intervals, where we have now an important database for weighted mean shearwave velocities () in the depth. The results attested the importance of this action and we present solid results that the values computed for the deeper models are closer to the computed surface values, especially for the depth intervals of 70 and 100 m depth.
The spectral acceleration values as well as the PGA computed at surface are based first on new database of geological model and assigned geological emerging in the last years, as well as on an extended measuring of the strong motion values of an earthquake at surface and also in the depth down to 100 m depth
Unusual vertical oscillations in sodium density and the formation of sporadic sodium layer over the Zhongshan station
No full textUnusual vertical oscillations with some wave structures were observed in the sodium (Na) density layers (SLs) in Antarctica on 1 September 2019, by means of the lidar located at Zhongshan (69°S, 76°E). There was a clear vertical convection of the wavy sodium density layers which populated the region at an altitude of between ~90 and ~102 km producing the Sporadic/Sudden Sodium Layers (SSLs).
The vertical oscillations had an average wavelength, average speed and period of ∼3.0‑4.5 km, ∼7.8 m/s and ∼7.3‑8.5 min, respectively. The possible cause of these vertical oscillations, as well as the mechanisms that could be behind the generation of these oscillations and wavy SSLs, were investigated. The Global Positioning System (GPS) satellite receiver located at Davis (68.6°S, 77.9°E), 116 km away from Zhongshan, was used to derive the Total Electron Content (TEC) perturbations in the region surrounding Zhongshan. SuperDARN HF radar at Zhongshan also showed some waves in the first 10 range gates (180‑800 km away), suggesting that the Traveling Ionospheric Disturbances (TIDs) were propagating in the ‑region. The cross‑correlation between GPS and lidar wave structures was computed. A good to strong correlation of –0.6‑–0.9 was found between waves observed by GPS and lidar. Additionally, a moderate correlation was found between the SuperDARN radar and lidar wave structures. The lidar neutral temperature showed upward Atmospheric Gravity Waves (AGWs), while SuperDARN and GPS showed the downward TIDs. Based on the polar cap (PC) index, TIDs could have been generated by Joule heating due to geomagnetic storm effects in the region. The estimated Richardson number values between 80 and 105 km at 16:00‑24:00 UT suggest that convective and dynamic instabilities could have generated the observed SSLs and AGWs.
Vertical oscillation of the sodium density layers could have taken place because of waves breaking and interference from the downward TIDs and upward AGWs
Visualizing wavefields with AdriaArray
No full textLarge seismic networks like AdriaArray consisting of more than 1000 stations enable new techniques for the visualization and analysis of seismic wave fields. With inter-station distances of less than 40 km, entire wavefields of regional and teleseismic events can be imaged and depicted as animations. In this study we use common data processing techniques applied to data of AdriaArray and neighbouring networks to showcase the propagation of body and surface waves in space and time. A normalization approach based on a low pass filtered envelope assures that both body waves of smaller amplitudes as well as surface waves with large amplitudes are equally distinguishable in the animations. Examples are given for the 7.5 Magnitude 2024 Noto and the 7.8 Magnitude 2023 Turkey earthquakes. Regional effects of wave front deflections, reflections and returning body and surface waves are easily identifiable when comparing with theoretical arrival times. The effect of the station density on the measurement of wavefields is discussed. We show that these animations are well suited to improve the understanding of seismic waves among both seismologists and the general public
Preface
No full textThis Special Issue aims to compile a collection of scientific and technical papers related to applications and research in the field of geophysical site characterization for geotechnical studies. Several geophysical techniques are indeed nowadays available and diffusely adopted to aid geotechnical parameters estimation and help in imaging their spatial distribution at the site. These techniques are currently adopted providing specific parameters directly involved in geotechnical evaluations (e.g. shear wave velocity for earthquake engineering studies). However, in most cases, combined interpretation of geophysical and geotechnical test requires specific analyses and integration strategies. The idea for this Special Issue was launched during the 7th International Conference on Geotechnical andGeophysical Site Characterization – ISC’7 “Ground models, from big data to engineering judgement” held in Barcelona, Spain, in June 2024. On that occasion, a large number of papers regarding the current practices and future developments of geophysical site characterization for geotechnical studies were submitted to the conference by researchers from many countries, and Professor Marcos Arroyo, the Chair of the conference, together with Paola Montone, the Editor in Chief of Annal of Geophysics, supported the initiative to develop this Special Issue. The main idea was to collect in the Special Issue the best selected papers presented at the Conference in order to contribute to the definition of current practices and future developments of geophysical methods for site characterization and geotechnical studies. Contributions of this special issue were then collected both by inviting researchers who participated at ISC’7 and whose works were considered of high relevance and by opening a call for paper submission related to: the development of ground models for large and small projects through specific integration and combined interpretationof geophysical and geotechnical tests; the direct application of in situ geophysical and geotechnical tests for earthquake engineering and seismic microzonation studies; the development of new testing apparatus andprocedures involving a combination of geophysical and geotechnical testing methodologies; the integration of geophysical and geotechnical monitoring techniques for time-lapse evaluations; the role of big data and machine learning in site characterization; and to the use of numerical simulation techniques as an aid to geotechnical and geophysical testing
Istituto Nazionale di Geofisica
No full textIn occasione della X Assemblea Generale dell\u27Unione Geodesia e Geofisica Internazionale che si tiene a Roma, l\u27Istituto Nazionale di Geofisica vuole presentare questa breve pubblicazione, con lo scopo di offrire un panorama delle attività scientifiche e organizzative da esso svolte.
La pubblicazione un carattere puramente dimostrativo; essa contiene alcuni cenni sulle caratteristiche costitutive dell\u27Istituto, una presentazione delle attrezzature scientifiche e una visione dei vari Osservatori costituenti la rete geofisica nazionale.
Riteniamo che questa pubblicazione possa anche servire a richiamare l\u27attenzione di molti su questa scienza che ha preso oggi tanto sviluppo
Application of the relocation-error distribution on geomagnetic databases. Analyses on the «Historical Italian Geomagnetic Data Catalogue»
Get PDFThe reliability of the Historical Italian Geomagnetic Data Catalogue, comprising 536 directions and 393 intensities,
has been assessed by comparing the historical geomagnetic measurements with the GUFM1 model predictions.
Such measurements were assessed at three selected relocation centres. For all the data contained in the
Catalogue it has been calculated the discrepancy between the relocated data and the GUFM1-model prediction
at the relocation centres. There is a correlation between relocation distance and the mean discrepancy. The upper
limit of discrepancy assumable as relocation error has been selected using error distributions previously calculated
using geomagnetic field models. Angular and intensity threshold lines have been slightly shifted upwards
to account for the estimated error of GUFM1 model itself at the considered region, mainly due to the crustal
field. The Italian database proved to contain reliable data, as only a very low percentage of data (namely 14 directions
and 20 intensities) can be considered anomalous. Possible explanations for such questionable data are
suggested. All the remaining data of this catalogue could thus be added to the databases used to produce regional
or global geomagnetic models
AI-Powered Mapping of Sundhnúkur’s Lava Flows: Sentinel-2 Imagery and Random Forest Modeling for the 2023-2024 Eruption
Get PDFVolcanic thermal anomalies are commonly monitored using advanced optical satellite sensors, enhancing the detection of renewed volcanic activity. Traditionally, fixed-threshold hotspot detection algorithms have been widely applied to identify these anomalies, effectively minimizing false alarms. However, the mapping of lava flows and monitoring of volcanic activity, which is essential for hazard mitigation and understanding the behavior of active volcanoes, has been further improved through the use of Machine Learning techniques. These methods allow for the rapid processing of large datasets, making them especially valuable for volcanic studies. Here, a Machine Learning approach based on a Random Forest algorithm, designed and implemented on Google Earth Engine, using data from the Sentinel-2 multispectral sensor (S2-MSI), is applied to detect and accurately map lava flows from the 2023-2024 eruption in Sundhnúkur, Iceland. Despite gaps in satellite coverage due to technical issues or adverse weather, the flow maps generated by the algorithm closely align with the actual lava flow fields. The results demonstrate that the Random Forest model, despite not being trained on this study area, exhibits strong generalization capabilities and high sensitivity to subtle volcanic thermal anomalies
Integrated machine learning approach for volcanic cloud tracking: A Case Study of Etna’s Lava Fountains (2020‑2022)
Get PDFBetween December 2020 and February 2022, Mt. Etna produced extraordinary lava fountains which developed into eruptive columns rising several kilometers above the vent. It is crucial to monitor the volcanic clouds produced during these eruptions to assess their impact on the environment, human health, and aviation. Geostationary satellite missions provide high‑frequency thermal infrared data, which are crucial for monitoring volcanic clouds during intense explosive eruptions. However, the large volume of satellite data necessitates automatic and accurate processing algorithms, especially when dealing with global‑scale observations every 5 minutes. In this work, a robust machine learning approach is developed to identify and track volcanic clouds using images from the EUMETSAT MSG SEVIRI (Meteosat Second Generation – Spinning Enhanced Visible and InfraRed Imager).This approach combines two distinct machine learning models: a deep learning (DL) model for volcanic cloud detection and a supervised machine learning (ML) model for identifying its primary components. The DL model segments volcanic clouds in SEVIRI images by analyzing both the spatial and spectral intensity data. The supervised ML model is able to distinguish the main components of a volcanic cloud by classifying the pixels as ash‑rich, SO2‑rich, or characterized by mixed components. Once an accurate mask of the volcanic cloud is obtained, the volcanic plume height is retrieved from satellite observations for further characterization. This integrated ML approach was applied to characterize the volcanic clouds produced during some of the lava fountains occurred at Etna volcano (Italy) between 2020 and 2022