Enhancing Fault Detection through One-Dimensional Multiscale Wavelet Analysis of Potential Field Data | Chapter 3 | Research Advances in Environment, Geography and Earth Science Vol. 2

Identifying faults is pivotal in mineral exploration and volcanic research, presenting a formidable task for geoscientists. Multiscale wavelet analysis has emerged as a potent tool for filtering and denoising geophysical data, outperforming conventional Fourier methods, especially in scenarios with discontinuous signals. This paper introduces a novel approach utilizing one-dimensional multiscale wavelet analysis for fault identification from potential field data. By leveraging the discrete wavelet transform with the Daubachies wavelet, our method exploits breakline and discontinuity detection concepts to discern faults effectively. We validate our approach through synthetic and real potential field data from Dagang, southern China demonstrating its effectiveness.

Author(s) Details:

S. Morris Cooper,
Department of Physics, University of Liberia, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.

Liu Tianyou,
Department of Physics, University of Liberia, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.

Innocent Ndoh Mbue,
Department of Physics, University of Liberia, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.

Please see the link here: https://stm.bookpi.org/RAEGES-V2/article/view/14208

Extraction of Wavelet Features for the Classification of Sleep Stages Using Single Channel EEG | Chapter 15 | Recent Developments in Engineering Research Vol.2

 

Sleep is just as important as diet and exercise. Humans spend about one third of their lives asleep.
Sleep tests involves processing and analysis of many signals combination called as
Polysomnographic signal (PSG). In the large data sets like Sleep Electroencephalogram (Sleep EEG), to do analysis it becomes tedious and time taken. Instead of considering the whole data, considering a few critical features from the signal makes the analysis simpler and the memory requirements are also less, since the analysis could be carried out on digital platform. A feature is a distinguishable sectional property obtained from a portion of signal. Feature extraction depicts the number of feature to be extracted from the signal. Thus the feature extraction plays a pivotal role in the analysis of Sleep EEG. In this work we discussed the decomposition of Sleep EEG signal into required frequency bands and adopted feature extraction techniques of wavelet decomposition method to extract features from Sleep EEG signal by considering single channel EEG.

Author (s) Details

Vijayakumar Gurrala

Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India.

Padmasai Yarlagadda
Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India.

Padmaraju Koppireddi
Department of Electronics and Communication Engineering, JNTU Kakinada, Kakinada, Andhra Pradesh, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/236