1,721,228 research outputs found
Removing tidal and atmospheric effects from Earth deformation measurements
No full textA new numerical procedure has been developed for removing tidal and atmospheric e¡ects from Earth deformation measurements. It was developed in order to manage semi-automatically sequences that are usually di¤cult to analyse such as very long records, slow earthquakes and volcanic eruptions. Evenly and unevenly spaced time sequences can be analysed for tidal deformation as well as for long-term drift and atmospherically induced terms, even if there are large gaps in the data set. Its performance has been tested by analysing two experimental sequences, one including slow earthquakes and the other an eruption. The results have been compared with those obtained by using the two most widely used codes
Comment on "The 28 December 1908 Messina Straits Earthquake (M-w 7.1): A Great Earthquake throughout a Century of Seismology" by NA Pino, A. Piatanesi, G. Valensise, and E. Boschi
No full textComputer Algebra software for least squares and total least norm inversion of geophysical models
No full textWe consider the model inversion problem that arises in geophysical sciences. Whether it is formulated in a deterministic or stochastic framework, it can be solved by minimizing an appropriate loss function with respect to unknown parameters. In such an optimization the efficiency of local minimization is crucial but the complexity of involved models often restricts the applicability of derivative based techniques. We consider the application of modern computer algebra programs to automatically compute needed derivatives and we propose a computer shell developed under the MATLAB^(R) environment that can be used to efficiently solve these problems. The software can be used by users that do not have specific skills in numerical and symbolic computation techniques. In order to show the general applicability of the proposed procedure and the software tool, we report the application to two different, but simple, ground deformation models and to two solution techniques: the classical nonlinear least squares, that is the most used approach, and the L"1 structured total least norm approach, which has proved to be very effective in dealing with data characterized by large outliers. Experimental results from synthetic and real data are shown
Biological glues and collagen fleece for hemostasis during laparoscopic partial nephrectomy: Technique and results of prospective study
No full textVery long period volcanic tremor at Stromboli volcano, Italy
No full textWe analyze a long time–space series of Stromboli volcanic tremors. A
very-low-frequency content in the range of 0.02–0.5 Hz has been found by using
spectral analysis and independent component analysis. Independent component analysis
is an entropy-based technique. We observe the occurrence of a component having
a period of 30 sec. Polarization analysis shows that the wave field comes mainly
from the crater area, well evidenced by seismometers located around the summit
ring, whereas the radiation becomes increasingly scattered at stations located around
the base of this volcano. Based on its apparent velocity, the 30-sec component appears
to be a slow wave, related to inhomogeneities of the source and/or gas-pressure
fluctuations inside the shallow plumbing system.
Introduction
Stromboli is one of the most active volcanoes in the
world, characterized by persistent explosive activity superimposed
on a background volcanic tremor.
During recent years, Strombolian events, particularly at
high frequencies (0.5 Hz), have been studied by different
authors (Del Pezzo et al., 1992; Chouet et al., 1997; Saccorotti
and Del Pezzo, 2000; Acernese et al., 2003). Many
results have been extracted concerning spectral features, polarization
analysis, location, and modeling of the source,
leading to the general agreement that tremor and explosions
seem to be generated by the same dynamic source process;
namely, the source of tremor and explosions at high frequency
seems to be located at a shallow depth beneath the
active craters.
A preliminary study carried out by Neuberg et al. (1994)
on broadband observations showed that Strombolian eruptions
can produce signals with periods of 10 sec or longer.
Moreover, very-long-period events have been revealed on
other volcanoes in the world (Rowe et al. 1998; Ohminato
et al., 1998). Since then, much attention has been devoted
to the study of the broadband nature of Strombolian events
and, recently, explosion quakes at low frequency (0.02–0.5
Hz) have been analyzed, constraining the geometry and the
dynamics of the source (Chouet et al., 1999, 2003); namely,
the source associated with explosions (whose time length
ranges from 5 to 15 sec) is localized below the same crater
area previously recognized for the high-frequency seismograms.
On the contrary, the characteristics of tremor at low
frequency are still unknown, so the aim of our work is to
study a long series of tremors in the range of 0.02–0.5 Hz.
This could give new insight into the fluid-dynamic mechanisms
involved i
Slow rupture of an aseismic fault in a seismogenic region of Central Italy.
No full textSlow earthquakes and afterslips prove that the Earth does not have just two response time scales, i.e. that of tectonic loading and that of regular earthquakes. A swarm of slow earthquakes, with time constants of the order of hundreds of seconds, has been detected by a laser interferometer below the Gran Sasso massif (Italy). We analyse and model these observations to identify a very plausible source in a local fault, with no historic seismic behavior. While slow earthquakes occurring in subduction
zones, and at the transition between locked and stably sliding segments of the San Andreas fault, are often associated with seismic events, in the case of the Apennines there is no correlation between local seismicity and slow earthquakes. Slow earthquakes, therefore, may also represent a specific failure behavior for a seismically locked fault, adding further complexity to the interpretation of geologic data for seismic hazard estimates
An application of automatic event detection based on neural network at St Gallen (Switzerland) deep geothermal field
No full textIn seismology, when dealing with low signal-to-noise recordings, traditional event detection methods are often unable to recognise all the weak events hidden within the seismic noise. We are interested in investigating how machine learning techniques can be a useful tool to improve automatic event detection by recognising the similarity between events. We are interested in studying areas where anthropogenic activity, related to the exploitation of subsoil resources, can generate induced seismicity. Therefore, it is essential to increase the detection of weak events to improve knowledge about the seismicity of the area and its related consequences.The SOM (Self-Organizing Map) is an unsupervised machine learning approach that is widely used for clustering, visualization and data-exploration tasks in various applications. The SOM carries out a nonlinear mapping of data onto a two-dimensional map, preserving the most important topological and metric relationships of the data. One of the reasons for using SOM for clustering indeed is to benefit from its topological structure when interpreting the data clusters. In the preprocessing stage, features extraction is done by using both the linear prediction coding (LPC) technique for coding the spectrograms, and a waveform parameterization for characterizing amplitude characteristics in the time domain, for each of the three components.The SOM was trained on dataset, recorded at the St Gallen geothermal site, composed of 388 records of seismic noise and 347 earthquakes with magnitude (MLcorr) between -1.2 and 3.5 collected by the Swiss Seismological Service in 2013 while realizing well control measures after drilling and acidizing the GT-1 well.We obtained promising first results as SOM strategy correctly discriminates all known earthquakes events, clustering them into different nodes, distant from the group of nodes where noise falls. We also jointly tested synthetic traces in which we have hidden events traces within seismic noise or noise artificially generated. We studied the signals of each cluster individually, assessing the similarities of the waveform and spectral characteristics for the three components. In addition, the results are also evaluated in terms of events location, hypocentral distance, magnitude, and origin time.This work has been supported by PRIN-2017 MATISSE project, No 20177EPPN2, funded by the Italian Ministry of Education and Research
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