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Infrasonic and Seismic Signals from Earthquake and Explosions in Arequipa, Perú
KTH, School of Engineering Sciences (SCI), Physics.
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2006 (English)In: Western Pacific Geophysics Meeting. 24-27 July 2006, Beijing, China, 2006Conference paper (Refereed)
Place, publisher, year, edition, pages
National Category
Physical Sciences
URN: urn:nbn:se:kth:diva-26047OAI: diva2:369420
QC 20101110Available from: 2010-11-10 Created: 2010-11-10 Last updated: 2010-11-10Bibliographically approved
In thesis
1. Filtering extracting features from infrasound data
Open this publication in new window or tab >>Filtering extracting features from infrasound data
2006 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [en]

The goal of the research presented in this thesis is to extract features, to filter and get fingerprints from signals detected by infrasound, seismic and magnetic sensors. If this can be achieved in a real time system, then signals from various events can be detected and identified in an otherwise torrent data.

Several approaches have been analyzed. Wavelet transform methods are used together with ampligram and time scale spectrum to analyze infrasound, seismic and magnetic data. The energy distribution in the frequency domain may be seen in wavelet scalograms. A scalogram displays the wavelet coefficients as a function of the time scale and of the elapsed time. The ampligram is a useful method of presentation of the physical properties of the time series. The ampligram demonstrate the amplitude and phase of components of the signal corresponding to different spectral densities. The ampligram may be considered as an analogy to signal decomposition into Fourier components. In that case different components correspond to different frequencies. In the present case different components correspond to different wavelet coefficient magnitudes, being equivalent to spectral densities. The time scale spectrum is a forward wavelet transform of each row (wavelet coefficient magnitude) in the ampligram. The time scale spectrum reveals individual signal components and indicates the statistical properties of each component: deterministic or stochastic.

Next step is to distinguish between different sources of infrasound on-line. This will require signal classification after detection is made. The implementation of wavelet – neural network in hardware may be a first choice. In this work the Independent Component Analysis is presented to improve the quality of the infrasonic signals by removing background noise before the hardware classification. The implementation of the discrete wavelet transform in a Field Programmable Gate Array (FPGA) is also included in this thesis using Xilinx System Generator and Simulink software.

A study of using infrasound recordings together with a miniature 3-axis fluxgate magnetometer to find meteorites as soon as possible after hitting the earth is also presented in this work.

Place, publisher, year, edition, pages
Stockholm: Fysik, 2006. vi, 48 p.
Trita-FYS, ISSN 0280-316X ; 2006:32
infrasound, seismic signals, feature extraction, wavelets, fingerprints, mining, magnetometer
National Category
Physical Sciences
urn:nbn:se:kth:diva-3978 (URN)
2006-05-31, Sal FA32, AlbaNova, Roslagstullsbacken 21, Stockholm, 10:00
QC 20101110Available from: 2006-05-19 Created: 2006-05-19 Last updated: 2010-11-10Bibliographically approved

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