3,230 research outputs found
Retrieving shallow shear-wave velocity profiles from 2D seismic-reflection data with severely aliased surface waves
The inversion of surface-wave phase-velocity dispersion curves provides a reliable method to derive near-surface shear-wave velocity profiles. In this work, we invert phase-velocity dispersion curves estimated from 2D seismic-reflection data. These data cannot be used to image the first 50 m with seismic-reflection processing techniques due to the presence of indistinct first breaks and significant NMO-stretching of the shallow reflections. A surface-wave analysis was proposed to derive information about the near surface in order to complement the seismic-reflection stacked sections, which are satisfactory for depths between 50 and 700 m. In order to perform the analysis, we had to overcome some problems, such as the short acquisition time and the large receiver spacing, which resulted in severe spatial aliasing. The analysis consists of spatial partitioning of each line in segments, picking of the phase-velocity dispersion curves for each segment in the f-k domain, and inversion of the picked curves using the neighborhood algorithm. The spatial aliasing is successfully circumvented by continuously tracking the surface-wave modal curves in the f-k domain. This enables us to sample the curves up to a frequency of 40 Hz, even though most components beyond 10 Hz are spatially aliased. The inverted 2D VS sections feature smooth horizontal layers, and a sensitivity analysis yields a penetration depth of 20–25 m. The results suggest that long profiles may be more efficiently surveyed by using a large receiver separation and dealing with the spatial aliasing in the described way, rather than ensuring that no spatially aliased surface waves are acquired.Fil: Onnis, Luciano Emanuel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Oficina de CoordinaciĂłn Administrativa Ciudad Universitaria. Instituto de FĂsica de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de FĂsica de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de FĂsica; ArgentinaFil: Osella, Ana Maria. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Oficina de CoordinaciĂłn Administrativa Ciudad Universitaria. Instituto de FĂsica de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de FĂsica de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de FĂsica; ArgentinaFil: Carcione, Jose M.. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; Itali
Co-detection of acoustic emissions during failure of heterogeneous media: new perspectives for natural hazard early warning
A promising method for real time early warning of gravity driven rupture that
considers both the heterogeneity of natural media and characteristics of
acoustic emissions attenuation is proposed. The method capitalizes on
co-detection of elastic waves emanating from micro-cracks by multiple and
spatially separated sensors. Event co-detection is considered as surrogate for
large event size with more frequent co-detected events marking imminence of
catastrophic failure. Using a spatially explicit fiber bundle numerical model
with spatially correlated mechanical strength and two load redistribution
rules, we constructed a range of mechanical failure scenarios and associated
failure events (mapped into AE) in space and time. Analysis considering
hypothetical arrays of sensors and consideration of signal attenuation
demonstrate the potential of the co-detection principles even for insensitive
sensors to provide early warning for imminent global failure
Models for Identifying Structures in the Data: A Performance Comparison
This paper reports on the unsupervised analysis of seismic signals
recorded in Italy, respectively on the Vesuvius volcano, located in Naples, and
on the Stromboli volcano, located North of Eastern Sicily. The Vesuvius dataset
is composed of earthquakes and false events like thunders, man-made quarry
and undersea explosions. The Stromboli dataset consists of explosion-quakes,
landslides and volcanic microtremor signals. The aim of this paper is to apply
on these datasets three projection methods, the linear Principal Component
Analysis (PCA), the Self-Organizing Map (SOM), and the Curvilinear
Component Analysis (CCA), in order to compare their performance. Since
these algorithms are well known to be able to exploit structures and organize
data providing a clear framework for understanding and interpreting their
relationships, this work examines the category of structural information that
they can provide on our specific sets. Moreover, the paper suggests a
breakthrough in the application area of the SOM, used here for clustering
different seismic signals. The results show that, among the three above
techniques, SOM better visualizes the complex set of high-dimensional data
discovering their intrinsic structure and eventually appropriately clustering the
different signal typologies under examination, discriminating the explosionquakes
from the landslides and microtremor recorded at the Stromboli volcano,
and the earthquakes from natural (thunders) and artificial (quarry blasts and
undersea explosions) events recorded at the Vesuvius volcano
Unclassified information list, 12-16 September 1966
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