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Filtering extracting features from infrasound data
KTH, School of Engineering Sciences (SCI), Physics.
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.
Series
Trita-FYS, ISSN 0280-316X ; 2006:32
Keyword [en]
infrasound, seismic signals, feature extraction, wavelets, fingerprints, mining, magnetometer
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-3978OAI: oai:DiVA.org:kth-3978DiVA: diva2:10244
Presentation
2006-05-31, Sal FA32, AlbaNova, Roslagstullsbacken 21, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20101110Available from: 2006-05-19 Created: 2006-05-19 Last updated: 2010-11-10Bibliographically approved
List of papers
1. Filtering and extracting features from infrasound data
Open this publication in new window or tab >>Filtering and extracting features from infrasound data
2005 (English)In: 2005 14TH IEEE-NPSS Real Time Conference: Stockholm; 4 June 2005 through 10 June 2005, 2005, 451-455 p.Conference paper, Published paper (Refereed)
Abstract [en]

There are many reasons for using infrasound, i.e. low frequency sound, to monitor various events. Inherent features like its long-distance propagation and the use of simple, ground based equipment in very flexible system are some. The disadvantage is that it is a slow system due to the speed of sound. In this papr we try to show that there are several other advantages if one can extract all the features of the signal. In this way it is hoped that we can get a fingerprint of the event that caused the infrasound. Rayleigh waves and sound from epicentre may be obtained for earthquakes, pressure pulses and electro jets from aurora, core radius and funnel shape from tornados, etc. All these possibilities are suggestive for further R&D of the infrasound detection systems.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-14106 (URN)10.1109/RTC.2005.1547494 (DOI)2-s2.0-33751438758 (Scopus ID)978-0-7803-9183-3 (ISBN)
Note
QC 20100713Available from: 2010-07-13 Created: 2010-07-13 Last updated: 2010-11-10Bibliographically approved
2. Infrasonic and Seismic Signals from Earthquake and Explosions in Arequipa, Perú
Open this publication in new window or tab >>Infrasonic and Seismic Signals from Earthquake and Explosions in Arequipa, Perú
Show others...
2006 (English)In: Western Pacific Geophysics Meeting. 24-27 July 2006, Beijing, China, 2006Conference paper, Published paper (Refereed)
National Category
Physical Sciences
Identifiers
urn:nbn:se:kth:diva-26047 (URN)
Note
QC 20101110Available from: 2010-11-10 Created: 2010-11-10 Last updated: 2010-11-10Bibliographically approved
3. Obtaining "images" from iron objects using a 3-axis fluxgate magnetometer
Open this publication in new window or tab >>Obtaining "images" from iron objects using a 3-axis fluxgate magnetometer
Show others...
2007 (English)In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 580, no 2, 1105-1109 p.Article in journal (Refereed) Published
Abstract [en]

Magnetic objects can cause local variations in the Earth's magnetic field that can be measured with a magnetometer. Here we used triaxial magnetometer measurements and an analysis method employing wavelet techniques to determine the "signature" or "fingerprint" of different iron objects. Clear distinctions among the iron samples were observed. The time-dependent changes in the frequency powers were extracted by use of the Morlet wavelet corresponding to frequency bands from 0.1 to 100 Hz. (c) 2007 Elsevier B.V. All rights reserved.

Keyword
magnetometer, feature extraction, wavelets, fingerprints
National Category
Physical Sciences
Identifiers
urn:nbn:se:kth:diva-14107 (URN)10.1016/j.nima.2007.06.070 (DOI)000250128000058 ()2-s2.0-34548476793 (Scopus ID)
Note
QC 20100713Available from: 2010-07-13 Created: 2010-07-13 Last updated: 2017-12-12Bibliographically approved

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