Separation and location of microseism sources

Moni, Aishwarya; Craig, David; Bean, Christopher J.

doi:10.1002/grl.50566

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Through the following years, several research papers were published that demonstrated agreement with these conclusions (e.g., Schimmel et al, 2011;Moni et al, 2013;Sergeant et al, 2013). Besides the seasonal variability, seismic noise is also dependent on the station location within continents, close to the coast, or on islands (e.g., Stutzmann et al, 2009).…”

Section: Introductionmentioning

confidence: 76%

Characterization of Microseismic Noise in Cape Verde

Carvalho

Silveira

Schimmel

et al. 2019

Bulletin of the Seismological Society of America

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The interaction of ocean waves with either the seafloor or other ocean waves generates primary (PM) and secondary microseisms (SM) that propagate through the crust and mantle, predominantly as Rayleigh waves. The horseshoe geometry and surrounding bathymetry of the Cape Verde archipelago play a significant role in the ambient-noise generation in this region. We analyze the microseisms recorded in the region using two different temporary seismic networks, and we determine the number of signals polarized as Rayleigh waves and their back azimuth (BAZ) as a function of time and frequency. The relative number of polarized signals between PM and SM varies between the stations. At most of the stations, the SM can be divided into two frequency bands. At lower frequencies (0.1-0.2 Hz), the number of SM signals is stable throughout the year, whereas at higher frequencies (0.2-0.3 Hz) this number varies with the season, with more polarized signals during the northern hemisphere spring and summer. In both frequency ranges and at most stations, the BAZ does not vary significantly over the year and points toward sources within the archipelago and outside. We compute the source site effect and show that the local bathymetry around the Cape Verde Islands strongly amplifies local SM sources. Finally, we compare the measured BAZ with source areas derived from an ocean-wave model, which confirms that Cape Verde stations mostly record local sources.

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Section: Introductionmentioning

confidence: 76%

Characterization of Microseismic Noise in Cape Verde

Carvalho

Silveira

Schimmel

et al. 2019

Bulletin of the Seismological Society of America

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“…While the resolution and pattern recognition capabilities of the human auditory system are generally well known [e.g., Hartmann, 1999;Wang and Brown, 2006a], the seismology community does not entirely appreciate the potential of auditory display as a tool for seismic data analysis. A case in point is the interesting work of Moni et al [2012] and Moni et al [2013],…”

Section: Discussionmentioning

confidence: 99%

On the Perception of Audified Seismograms

Boschi

Delcor

Carrou

et al. 2017

Seismological Research Letters

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“…BSS can be used to separate signals with different slowness from these mixtures. Based on this idea, a BSS method called DUET has been used in different settings to unmix signals from the volcano Mt Etna (Moni et al 2012), and also from secondary microseisms (Moni et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Blind source separation of temporally independent microseisms

Meschede

Stutzmann

Schimmel

2018

Geophysical Journal International

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Secondary microseismic sources emit seismic waves over long time spans. Reoccurring signals with similar slowness and frequency therefore arrive at seismic arrays. Blind source separation techniques can be used to identify and isolate such reoccurring signals from other signals and from diffuse seismic noise. Along these lines, we use non-negative matrix factorization as blind source separation technique to decompose continuous seismic array records. We model the recorded energy as a mixture of a few components with static slowness-frequency and time-dependent amplitudes. Components and amplitudes are fitted to optimally explain the recorded seismic energy over time. These components represent secondary microseismic signals with quasi-static slowness-frequency vector and fluctuating amplitude. Each fitted component reveals the geographical origin (through the slowness-frequency vector) and time evolution of an active secondary microseism with high precision because it is separated from other signals and diffuse seismic noise. Furthermore, relative traveltimes can be automatically extracted for the signals that correspond to a specific component that can potentially be used in tomographic studies. We show two examples of seismic signals that were extracted with this technique, one focusing on P waves from the typhoons Goni and Atsani, and the other showing secondary microseism PKP signals from typhoon Glenda.

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Separation and location of microseism sources

Cited by 7 publications

References 19 publications

Characterization of Microseismic Noise in Cape Verde

Characterization of Microseismic Noise in Cape Verde

On the Perception of Audified Seismograms

Blind source separation of temporally independent microseisms

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