Empirical Wavelet Transform-based Detection of Anomalies in ULF Geomagnetic Signals Associated to Seismic Events with a Fuzzy Logic-based System for Automatic Diagnosis

Abstract

Owing to the relevance and severity of damages caused by earthquakes EQs , the development and application of new methods for seismic activity detection that offer an efficient and reliable diagnosis in terms of processing and performance are still demanding tasks. In this work, the application of the Empirical Wavelet Transform EWT for seismic detection in ultra-low-frequency ULF geomagnetic signals is presented. For this, several ULF signals associated to seismic activities and random calm periods are analysed. These signals have been obtained through a tri-axial fluxgate magnetometer at the Juriquilla station localized in Queretaro, Mexico, longitude -. ° N and latitude . °E. In order to show the advantages of the proposal, a comparison with the discrete wavelet transform DWT is presented. The results shown a better detection capability of seismic signals before, during, and after the main shock than the ones obtained by the DWT, which makes the proposal a more suitable and reliable tool for this task. Finally, a fuzzy logic FL -based system for automatic diagnosis using the variance of the EWT outputs for the tri-axial fluxgate magnetometer signals is also proposed.

Using Wavelets for Gait and Arm Swing Analysis

“…Conventional processing techniques and methods allow to filter signals in a frequency range, extract relevant characteristics such as maximum and minimum peaks, fill data by interpolation, and transform and decompose signals in other domains such as frequency and time. Among these processing techniques, wavelet has shown to have a broad application panorama; the literature documents wavelet uses in different and varied fields such as detection of anomalies associated with seismic events in ultralow-frequency geomagnetic signals [32]; it is also possible to use wavelet techniques for image compression, as detailed in [33], who decompose into singular values and use a discrete wavelet transform to improve the maximum ratio of signal-to-noise ratio compared to techniques such as JPEG2000.…”

Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

“…The EWT algorithm has been used as a signal filtering stage in different applications, such as in the processing of voice and movement signals for the classification of Parkinson's disease severity [42], detection of mechanical failures from vibration signals [43] and in the analysis of geomagnetic signals for the detection of seismic activity [44]. However, this technique demands a high computational cost, since the model can decompose the signal into many components according to the criteria and then chooses the optimal component, and is therefore a non-viable method for a real-time system.…”

“…However, this technique demands a high computational cost, since the model can decompose the signal into many components according to the criteria and then chooses the optimal component, and is therefore a non-viable method for a real-time system. Therefore, many researchers have sought ways to apply improvements to the method in order to obtain the desired components in a more effective way, as described in [42][43][44].…”

Recent Advancements in Empirical Wavelet Transform and Its Applications

“…Alegria et al (2015) [24] presented a novel application of the EWT for seismic detection in ultra-low-frequency (ULF) geomagnetic signals, which comprises the ULF geomagnetic signals analysis via EWT, a statistical parameter based on variance and an automatic diagnosis through a FL system (Figure 4). The results show a better detection capability of seismic signals before, during, and after the main shock, which makes the proposal a more suitable and reliable tool.…”