Multi-wavelet analysis of three-component seismic arrays: Application to measure effective anisotropy at Piñon Flats, California

Bear, Lorie K.; Pavlis, Gary L.; Bokelmann, Götz

doi:10.1785/bssa0890030693

Cited by 30 publications

(4 citation statements)

References 21 publications

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“…Moreover, records from all three components (3C) of the seismic stations are essential for estimating polarization properties of the waves. Such 3C seismic data from 2-D arrays were successfully used for the analysis of complex wavefields in many previous studies (Jurkevics 1988;Dainty & Toksöz 1990;Wagner & Owens 1993;Kuwahara et al 1997;Bear et al 1999;Cornou et al 2003a,b;Poggi & Fäh 2010;Hobiger et al 2012;Maranò et al 2012Maranò et al , 2017.…”

Section: Seismic Wavefield Analysismentioning

confidence: 99%

Diffracted wavefield decomposition and multidimensional site effects in the Argostoli valley, Greece

Imtiaz

Cornou

Bard

et al. 2020

Geophysical Journal International

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Summary Effects of seismic ground motion induced by surface geology and geometry are known to be associated with the generation of a substantial proportion of surface waves. As a consequence, surface waves significantly contribute to ground-motion variability and site amplification. There is a growing body of literature recognizing that an understanding of physical patterns of the wavefield crossing a site is the key aspect to characterize and quantify them. However, this task remains technically challenging due to the complexity of such effects as well as the limitations of geophysical investigations, especially in case of small sedimentary valleys. The present study attempts to investigate the waves propagating across two two-dimensional dense seismic arrays from a number of earthquakes, and explore the extent to which they are contributing to the multi-dimensional site effects. The arrays were deployed in the small-size, shallow alluvial valley of Koutavos-Argostoli, located in Cephalonia Island, Greece, and consisted of three-component velocimeters with interstation distances ranging from 5 to 160 meters. A set of 46 earthquakes, with magnitudes between 2 and 5 and epicentral distances up to 200 km, was analyzed by using an advanced seismic array processing technique, MUSIQUE. The phase velocity, backazimuth and energy of the dominant waves crossing the array were extracted, and their identification as Love or prograde/retrograde Rayleigh waves was obtained. The results clearly indicate a predominance of scattered surface waves (up to 60 per cent of total energy), mainly from the closest valley edges, above the fundamental frequency (∼1.5 Hz) of the valley. Love waves dominate the low-frequency wavefield (< 3 Hz) while Rayleigh waves dominate some high-frequency bands. An excellent consistency is observed, in a given frequency range, among the dominance of the type of diffracted surface waves, group velocities estimated from the ground velocity structure, and site amplification. The outcomes of this research provide a better understanding of the contribution of edge-diffracted surface waves and the 2D/3D site amplification at small and shallow alluvium valleys like Argostoli. The method applied here can be used to calibrate and validate 3D models for simulating seismic ground motion.

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Section: Seismic Wavefield Analysismentioning

confidence: 99%

Diffracted wavefield decomposition and multidimensional site effects in the Argostoli valley, Greece

Imtiaz

Cornou

Bard

et al. 2020

Geophysical Journal International

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“…However recent works have proposed several more sophisticated schemes to “clean” seismic traces. These include methods based on the independent component analysis (Cabras et al., 2010; Comon, 1994; Moni et al., 2012), beamforming methods (Boué et al., 2013; Brooks et al., 2009; Gibbons et al., 2008), and MUltiple SIgnal Classification (Bear et al., 1999; Schmidt, 1986). All of these methods, however, can fall short when the noise shares frequencies with the earthquake generated signal.…”

Section: Introductionmentioning

confidence: 99%

Cold Diffusion Model for Seismic Denoising

Trappolini,

Laurenti,

Poggiali

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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Seismic waves contain information about the earthquake source, the geologic structure they traverse, and many forms of noise. Separating the noise from the earthquake is a difficult task because optimal parameters for filtering noise typically vary with time and, if chosen inappropriately, may strongly alter the original seismic waveform. Diffusion models based on Deep Learning have demonstrated remarkable capabilities in restoring images and audio signals. However, those models assume a Gaussian distribution of noise, which is not the case for typical seismic noise. Motivated by the effectiveness of “cold” diffusion models in speech enhancement, medical anomaly detection, and image restoration, we present a cold variant for seismic data restoration. We describe the first Cold Diffusion Model for Seismic Denoising (CDiffSD), including key design aspects, model architecture, and noise handling. Using metrics to quantify the performance of CDiffSD models compared to previous works, we demonstrate that it provides a new standard in performance. CDiffSD significantly improved the Signal to Noise Ratio by about 18% compared to previous models. It also enhanced Cross‐correlation by 6%, showing a better match between denoised and original signals. Moreover, testing revealed a 50% increase in the recall of P‐wave picks for seismic picking. Our work show that CDiffSD outperforms existing benchmarks, further underscoring its effectiveness in seismic data denoising and analysis. Additionally, the versatility of this model suggests its potential applicability across a range of tasks and domains, such as GNSS, Lab Acoustic Emission, and Distributed Acoustic Sensing data, offering promising avenues for further utilization.

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“…Other methods of source separation, such as independent‐vector analysis (Hiroe, 2006; Kim et al., 2006) and MUltiple SIgnal Classification [ MUSIC; Schmidt, 1986; which later was extended to three‐component seismic data by Bear et al. (1999)], were also proposed in the field of signal processing.…”

Section: Introductionmentioning

confidence: 99%

“…Boué et al (2013) used Double Beamforming Processing to separate low-amplitude body waves from high-amplitude dispersive surface waves. Other methods of source separation, such as independent-vector analysis (Hiroe, 2006;Kim et al, 2006) and MUltiple SIgnal Classification [MUSIC; Schmidt, 1986; which later was extended to three-component seismic data by Bear et al (1999)], were also proposed in the field of signal processing.In the multi-receiver setting, those methods work well. For instance, when more than one seismic station is available, source separation is widely employed.…”

mentioning

confidence: 99%

SEDENOSS: SEparating and DENOising Seismic Signals With Dual‐Path Recurrent Neural Network Architecture

2022

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Seismologists have to deal with overlapping and noisy signals. Techniques such as source separation can be used to solve this problem. Over the past few decades, signal processing techniques used for source separation have advanced significantly for multi‐station settings. But not so many options are available when it comes to single‐station data. Using Machine Learning, we demonstrate the possibility of separating signals for single‐station, one‐component seismic recordings. The technique that we use for seismic signal separation is based on a dual‐path recurrent neural network which is applied directly to the time domain data. Such source separation may find applications in most tasks of seismology, including earthquake analysis, aftershocks, nuclear verification, seismo‐acoustics, and ambient‐noise tomography. We train the network on seismic data from STanford EArthquake Dataset and demonstrate that our approach is (a) capable of denoising seismic data and (b) capable of separating two earthquake signals from one another. In this work, we show that Machine Learning is useful for earthquake‐induced source separation. We provide a reproducible research repository with the algorithms here: https://github.com/crimeacs/source-separation.

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Multi-wavelet analysis of three-component seismic arrays: Application to measure effective anisotropy at Piñon Flats, California

Cited by 30 publications

References 21 publications

Diffracted wavefield decomposition and multidimensional site effects in the Argostoli valley, Greece

Diffracted wavefield decomposition and multidimensional site effects in the Argostoli valley, Greece

Cold Diffusion Model for Seismic Denoising

SEDENOSS: SEparating and DENOising Seismic Signals With Dual‐Path Recurrent Neural Network Architecture

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