Seismic wave characterization using complex trace analysis in the stationary wavelet packet domain

Abstract

One of the most important tasks in seismology and applied geophysics is the identification of the different kinds of waves that form a seismic record by means of polarization analysis. In particular, this involves the extraction of body waves (linear polarization) or surface waves (mostly elliptical polarization) from a set of seismic data and which forms a key point in several studies.In this work, a new method of time-frequency polarization analysis based on the stationary wavelet packet transform is developed. The proposed approach identifies and extracts automatically the different waves included in the signal, dependent upon the reciprocal ellipticity. Moreover, the algorithm provides enough information to the user to allow them to also manually select the reciprocal ellipticity intervals, and then extract the corresponding waves of interest contained in the signals.The proposed polarization estimation method and the automatic features extraction algorithm have been evaluated first using synthetic signals, and then applied to real seismic records. Based on the results obtained from both synthetic and real signals, we can conclude that the proposed method correctly identifies and extracts automatically the linearly and ellipticaly polarized waves from the signal, discerning clearly both types of polarization. Moreover, the proposed method is able to identify and extract signals with different kinds of elliptical polarization, allowing us to understand better the characteristics of Rayleigh waves.Index Terms -Polarization analysis, wave identification, seismic signal processing, stationary wavelet packet transform (SWPT).3

Identification and Extraction of Surface Waves from Three‐Component Seismograms Based on the Normalized Inner Product

“…The timefrequency parameters of the polarization ellipse, which describe the contribution of the fth frequency to the total signal are defined in Pinnegar (2006) in terms of the Stockwell transform. In particular, Pinnegar (2006) and Galiana-Merino et al (2011) have used the ratio of the semiminor to the semimajor axis of the polarization ellipse to identify Rayleigh waves contained in a seismic signal. This ratio is called instantaneous reciprocal ellipticity (IRE) in Galiana-Merino et al (2011) and is defined as…”

“…In particular, Pinnegar (2006) and Galiana-Merino et al (2011) have used the ratio of the semiminor to the semimajor axis of the polarization ellipse to identify Rayleigh waves contained in a seismic signal. This ratio is called instantaneous reciprocal ellipticity (IRE) in Galiana-Merino et al (2011) and is defined as…”

“…The IRE can then be used to discern the different waves contained in a seismic signal. To identify Rayleigh waves, Galiana-Merino et al (2011) and Pinnegar (2006) have considered values of φω; t greater than 0.5. Using this criterion, filters to extract the desired wave from the signal can be readily constructed.…”

“…It is now well recognized that many signal processing problems can be advantageously phrased in a time-frequency language. Pinnegar (2006) and Galiana-Merino et al (2011) have constructed filters to exclude or extract Rayleigh waves using their elliptical polarization as filtering criterion. However, filtering based on the elliptical polarization attribute alone does not work well if the time history contains Rayleigh waves with both retrograde and prograde motion.…”

Assessment of CO2 dynamics in subsurface atmospheres using the wavelet approach: from cavity–atmosphere exchange to anthropogenic impacts in Rull cave (Vall d′Ebo, Spain)

“…To develop an effective data analysis, some tools, which have been applied to other research fields, can also be applied to microclimatic data series as a novel application. For instance, segmentation methods have been successfully applied to tunnel boring (Denis and Cremoux 2002) and in detecting the mechanical behaviour of different geological formations (Frantziskonis and Denis 2003); spectral techniques have been used in analyses of precipitation (Li et al 2014); wavelet analysis has been employed, for instance, for studies of environmental variables such as soil variations (Milne and Lark 2009), seismic wave characterization (Galiana-Merino et al 2011), the 4 identification of karst spring systems (Chinarro et al 2012), long-term aerosol concentration changes (Pal and Devara 2012) and atmospheric boundary layer dynamics and the impact on trace gas variability (Pal et al 2014(Pal et al , 2015. Wavelet analysis is applied to reveal information on the periodicities present in data series.…”