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Discrimination of nuclear explosions sites by seismic signals using intrinsic mode functions and multi-modal data space
KTH, School of Engineering Sciences (SCI), Physics.
George Mason University.
KTH, School of Engineering Sciences (SCI), Physics.
2008 (English)In: 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings; Boston, MA; 6 July 2008 through 11 July 2008, 2008, II895-II898 p.Conference paper, Published paper (Refereed)
Abstract [en]

Signal processing and feature extraction are investigated using the Empirical Mode Decomposition (EMD). It is believed that this approach is well suited for non-linear and non-stationary data. With EMD any complicated set of data can be decomposed into a finite, and usually small number, of functions called Intrinsic Mode Functions (IMFs). A new discriminating system is presented here that is capable of discriminating between different seismic signals from nuclear testing sites based on the IMFs and the multi-modal data space. The advantage of this space is that multiple metrics of similarity are converted into one single Euclidean space. This space is capable of extracting similarities among several signals through a combination of multiple metrics. This is a new way of associating data. After illustrating the technique with an investigation of an audio data example (piano), we examine the characteristics of seismic signals from nuclear testing (explosions). The results presented in this paper indicate that a relatively simple discriminating system can successfully cluster and classify seismic events.

Place, publisher, year, edition, pages
2008. II895-II898 p.
Series
International Geoscience and Remote Sensing Symposium (IGARSS), Volume 2, Issue 1, 2008
Keyword [en]
Hilbert-Huang transform; Intrinsic mode function; Multi-modal data space; Nuclear testing; Seismic data
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-14103DOI: 10.1109/IGARSS.2008.4779139Scopus ID: 2-s2.0-66549094935OAI: oai:DiVA.org:kth-14103DiVA: diva2:329774
Note
QC 20100713Available from: 2010-07-13 Created: 2010-07-13 Last updated: 2010-07-13Bibliographically approved
In thesis
1. Feature Extraction for Low-Frequency Signal Classification
Open this publication in new window or tab >>Feature Extraction for Low-Frequency Signal Classification
2008 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

The development of the International Monitoring System (IMS) for the verification of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has led to a rapid revival of interest in infrasound. Furthermore, the installation of low-frequency sensors at seismic sites has increased in recent years, providing researchers with large and heterogeneous data-sets in near real-time. New techniques are needed to better process all of this data and to extract meaningful information quickly for various applications. In particular, there is a need to find distinct features in the infrasonic signals that allow one to distinguish low level nuclear tests from seismic events.

In this thesis three methods for feature extraction from infrasound and other types of low frequency signal data are discussed: (1) discrete wavelets transforms (DWTs); (2) time scale spectra (TSSs) using continuous wavelet transforms (CWTs); and (3) empirical mode decomposition (EMD). The dimensionality of the feature space can range from a few to thousands. For processing highdimensional data we use multi-modal data space to find low-dimensional structures. The advantage of this space is that multiple metrics of similarity are converted into one single Euclidean space.

The overall goal of our research is a system for automatic identification and classification of lowfrequency signals in real-time that is easy to implement in hardware. In this thesis we present our design and implementation of the discrete wavelet transform (DWT) on FPGAs for processing a continuous flow of data to obtain optimal extraction of feature information. FPGA simulation and implementation has been realized by using the polyphase structure, the filter reuse method and techniques such as pipelining and basic operations on the QUARTUS II platform. VHDL has been used to describe the functionality of the discrete wavelet transform and ModelSim has been used for the functional verification.

Advancements in electronics provide a vital new option for implementation of low-frequency smart sensors that can perform signal processing close to the sensors and transmit the data wirelessly. These smart sensors can improve the efficiency of an automatic classification system and reduce the cost of actual infrasound microphones. The design of a digital wireless data acquisition system using a QF4512 programmable signal converter from Quickfilter Technologies, a MSP430 microcontroller from Texas Instruments and a F2M03GLA Bluetooth module from Free2move for infrasonic records is also presented in this thesis. The digital wireless data acquisition system has passed extensive laboratory and field tests (e.g. with man-made explosions).

A study of using a miniature 3-axis fluxgate magnetometer to get fingerprints from ferrous objects is also presented in this thesis. In this experiment, distinguishing features of iron samples of four different shapes were determined using wavelet methods. Systematic differences were observed between the signatures of the four shaped iron samples.

Place, publisher, year, edition, pages
Stockholm: KTH, 2008. viii, 61 p.
Series
Trita-FYS, ISSN 0280-316X ; 2008:2
Keyword
infrasound, seismic signals, feature extraction, wavelets, fingerprints, mining, multimodal data space
National Category
Atom and Molecular Physics and Optics
Identifiers
urn:nbn:se:kth:diva-4661 (URN)978-91-7178-856-6 (ISBN)
Public defence
2008-03-31, FA 31, AlbaNova Universitetscentrum, Roslagstullsbacken 21, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20100713Available from: 2008-03-06 Created: 2008-03-06 Last updated: 2010-07-13Bibliographically approved

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