Characterization of Microseismic Noise in Cape Verde

Abstract

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.

Seasonality of California Central Coast Microseisms

“…These islands break up and channelize swell to within the Santa Barbara basin. The south-facing coastline combined with the presence of the Channel Islands is likely a coastal geometry which is conducive to the constructive interference of ocean waves in such a fashion as to yield strong secondary microseisms (Kimman et al, 2012;Carvalho et al, 2019).…”

On the use of the microtremor HVSR for tracking velocity changes: a case study in Campo de Dalías basin (SE Spain)

“…Secondary microseisms (SM) acting in the band from 0.1 to 0.4 Hz were proved to have stronger stability in time and to be more influenced by the local climate than by global patterns as the primary microseisms (PM) between 0.03 and 0.1 Hz demonstrably do (Stutztman et al, 2009;Carvalho et al, 2019). The local climatic variables of air temperature, barometric pressure, and wind speed were obtained from the series repository of the Spanish Agency of Meteorology (www.aemet.es, AEMET).…”

Towards a Digital Twin of the Earth System: Geo-Soft-CoRe, a Geoscientific Software & Code Repository

“…In practice, we improve EGF convergence and signal-to-noise ratio using more elaborated processing flows (Bensen et al, 2007;Schimmel et al, 2011b;Moreau et al, 2017;Ventosa et al, 2017;Schimmel et al, 2018) organized in: 1) preprocessing, that may include instrument response correction, anomalous signal rejection, and spectral whitening, usually done with programs like ObsPy (Krischer et al, 2015), SAC (Goldstein and Snoke, 2005), or MSNoise (Lecocq et al, 2014); 2) correlation, a geometrically-normalized, 1-bit (Bensen et al, 2007) or phase (Schimmel, 1999;Schimmel et al, 2011b;) correlation of many short data sequences; and 3) stacking, a sum of correlations that may include weights and denoising (Schimmel and Gallart, 2007;Ventosa et al, 2017). Aside, we include other tools, for instance to robustly measure group velocities of surface waves extracted from noise cross-correlations (Haned et al, 2016;Nuñez et al, 2020), and to characterize elliptically polarized signals within the noise wave field and corresponding sources (Schimmel et al, 2011a;Davy et al, 2015;Carvalho et al, 2019). Ambient noise data are routinely used to extract EGFs for seismic noise monitoring and imaging studies with a wide range of applications, e.g., fault and volcano monitoring (Wegler and Sens-Schönfelder, 2007;Brenguier et al, 2008;D'Hour et al, 2016;Sánchez-Pastor et al, 2019) and imaging structures at different scales (mapping discontinuities, seismic ambient noise tomography, Shapiro and Campillo, 2004;Haned et al, 2016;Romero and Schimmel, 2018;Andrés et al, 2020, among others).…”

“…The approach is based on the degree of polarization, which measures the stability, or robustness of any arbitrary polarization as presented in Schimmel and Gallart (2003), Schimmel and Gallart (2004). This program can be used to identify Rayleigh waves in seismic noise (e.g., Schimmel et al, 2011a;Davy et al, 2015;Carvalho et al, 2019) and to identify Rayleigh waves from seismic events. The data mining of the identified signals and polarization attributes as function of time and frequency, permits the detection of seasonal changes and to find the directions of the noise sources.…”