Analysis of the primary and secondary microseisms in the wavefield of the ambient noise recorded in northern Poland

Lepore, Stefano; Grad, M.

doi:10.1007/s11600-018-0194-2

Cited by 11 publications

(17 citation statements)

References 65 publications

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“…Hence, the classical location algorithms, used in earthquake seismology and based on the picking of the different seismic phases, cannot be applied to locate microseism sources. Array processing techniques can overcome the above-mentioned difficulties and provide information on the microseism source areas that generally coincide with coastal regions and/or oceanic storm systems (e.g., Chevrot et al, 2007;Juretzek and Hadziioannou, 2017;Pratt et al, 2017;Lepore and Grad, 2018).…”

Section: Introductionmentioning

confidence: 99%

Insights Into Microseism Sources by Array and Machine Learning Techniques: Ionian and Tyrrhenian Sea Case of Study

et al. 2020

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In this work, we investigated the microseism recorded by a network of broadband seismic stations along the coastline of Eastern Sicily. Microseism is the most continuous and ubiquitous seismic signal on Earth and is mostly generated by the ocean-solid earth interaction. On the basis of spectral content, it is possible to distinguish three types of microseism: primary, secondary, and short-period secondary microseism (SPSM). We showed how most of the microseism energy recorded in Eastern Sicily is contained in the secondary and SPSM bands. This energy exhibits strong seasonal patterns, with maxima during the winters. By applying array techniques, we observed how the SPSM sources are located in areas of extended shallow water depth: the Catania Gulf and a part of the Northern Sicily coastlines. Finally, by using the significant wave height data recorded by two buoys installed in the Ionian and Tyrrhenian Seas, we developed an innovative method, selected among up-to-date machine learning techniques (MLTs), able to reconstruct the time series of sea wave parameters from microseism recorded in the three microseism period bands by distinct seismic stations. In particular, the developed model, based on random forest regression, allowed estimating the significant wave height with a low average error (∼0.14-0.18 m). The regression analysis suggests that the closer the seismic station to the sea, the more information concerning the sea state are contained in the recorded microseism. This is particularly important for the future development of an experimental monitoring system of the sea state conditions based on microseism recordings.

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Section: Introductionmentioning

confidence: 99%

Insights Into Microseism Sources by Array and Machine Learning Techniques: Ionian and Tyrrhenian Sea Case of Study

et al. 2020

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“…It is well known that secondary microseisms are dominated by Rayleigh waves (Lee 1935;Lacoss et al 1969;Tanimoto et al 2006); however, recent studies have shown that Love waves (Nishida et al 2008;Juretzek and Hadziioannou 2016;Gal et al 2017) can be detected in the secondary microseism frequency band. As reported in the literature (Bromirski et al 2005;Koper and Burlacu 2015;Lepore and Grad 2018;Xiao et al 2018), the secondary microseism sometimes splits into two peaks, involving the simultaneous activation of two oceanic source regions. The first peak corresponds to the long-period double-frequency (LPDF) microseism (0.1-0.25 Hz), while the second one, to the short-period double-frequency (SPDF) microseism (0.25-0.8 Hz).…”

Section: Introductionmentioning

confidence: 61%

“…The description of spatial and temporal variations of the ambient noise wavefield is fundamental for many aspects of seismology. Ambient noise can be used not only to infer the characteristics of ocean storms (Ebeling 2012), but also to evaluate the performance of seismic arrays (Wilson et al 2002) and to investigate the Earth's structure (Shapiro and Campillo 2004;Sabra et al 2005;Lepore and Grad, 2018). Temporal changes of the noise wavefield during strong ocean storms are consistent with the variations in the wave height and wavewave interaction (Friedrich et al 1998;Nishida et al 2008;Ardhuin et al 2011Ardhuin et al , 2015Obrebski et al 2012;Davy et al 2015;Möllhoff and Bean 2016).…”

Section: Introductionmentioning

confidence: 93%

“…In the 0.1-1-Hz range, the noise, known as the secondary (or double-frequency) microseism, is generated by the interaction of ocean waves travelling with similar frequencies in opposite directions. Below 0.1 Hz, the noise, identified as the primary microseism, is produced by ocean waves interacting with the seafloor near the coastlines (swells) (Longuet-Higgins 1950;Hasselmann 1963;Bromirski and Duennebier 2002;Koper et al 2009; Kurrle and Widmer-Schnidrig 2010; Ardhuin et al 2011;Stutzmann et al 2012;Bromirski et al 2013;Sergeant et al 2013;Gualtieri et al 2015;Lepore and Grad 2018). Despite this dissimilarity, sources of primary and secondary microseisms are both well correlated with ocean wave activity (Stehly et al 2006;Schimmel et al 2011;Xiao et al 2018;Stopa et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Relation between ocean wave activity and wavefield of the ambient noise recorded in northern Poland

Lepore

Grad

2020

J Seismol

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The temporal and spatial variations of the wavefield of ambient noise recorded at ‘13 BB star’ array located in northern Poland were related to the activity of high, long-period ocean waves generated by strong storms in the Northern Indian Ocean, the Atlantic Ocean, and the Northern Pacific Ocean between 2013 and 2016. Once pre-processed, the raw noise records in time- and frequency-domains, and spectral analysis and high-resolution three-component beamforming techniques were applied to the broadband noise data. The power spectral density was analysed to quantify the noise wavefield, observing the primary (0.04–0.1 Hz) microseism peak and the splitting of the secondary microseism into long-period (0.2–0.3 Hz) and short-period (0.3–0.8 Hz) peaks. The beam-power analysis allowed to determine the changes in the azimuth of noise sources and the velocity of surface waves. The significant wave height, obtained by combining observed data and forecast model results for wave height and period, was analysed to characterise ocean wave activity during strong storms. The comparison of wave activity and beam-power led to distinguish the sources of Rayleigh and Love waves associated to long-period microseisms, of short-period microseisms, and of primary microseisms. High, long-period ocean waves hitting the coastline were found to be the main source of noise wavefield. The source of long-period microseisms was correlated to such waves in the open sea able to reach the shore, whereas the source of primary microseisms was tied to waves interacting with the seafloor very close to the coastlines. The source of short-period microseisms was attributed to strong storms constituted of short-period waves not reaching the coast.

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“…We observe that the secondary microseism noise level is minimum during local summer, with higher levels occurring during local winter when the Southern Ocean experiences violent winter storms. The relatively higher secondary microseisms in austral winter could also be explained by instances of icebreakups and near-coastal interactions related to iceberg reflections (Grob et al 2011;Davy et al 2016;Pratt et al 2017;Lepore & Grad 2018). For the primary microseism band, noise levels are reduced in the austral winter.…”

Section: Seasonal Variations Of Microseismic Signalmentioning

confidence: 99%

A Feasibility Study on Monitoring Crustal Structure Variations by Direct Comparison of Surface Wave Dispersion Curves from Ambient Seismic Noise

Muhumuza

2020

International Journal of Geophysics

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This work assesses the feasibility of the direct use of surface-wave dispersion curves from seismic ambient noise to gain insight into the crustal structure of Bransfield Strait and detect seasonal seismic velocity changes. We cross-correlated four years of vertical component ambient noise data recorded by a seismic array in West Antarctica. To estimate fundamental mode Rayleigh wave Green's functions, the correlations are computed in 4-hr segments, stacked over 1-year time windows and moving windows of 3 months. Rayleigh wave group dispersion curves are then measured on two spectral bands-primary (10-30 s) and secondary (5-10 s) microseisms-using frequency-time analysis. We analyze the temporal evolution of seismic velocity by comparing dispersion curves for the successive annual and 3-month correlation stacks. Our main assumption was that the Green's functions from the cross-correlations, and thus the dispersion curves, remain invariant if the crustal structure remains unchanged. Maximum amplitudes of secondary microseisms were observed during local winter when the Southern Ocean experiences winter storms. The Rayleigh wave group velocity ranges between 2.1 and 3.7 km/s, considering our period range studied. Inter-annual velocity variations are not much evident. We observe a slight velocity decrease in summer and increase in winter, which could be attributed to the pressure melting of ice and an increase in ice mass, respectively. The velocity anomalies observed within the crust and upper mantle structure correlate with the major crustal and upper mantle features known from previous studies in the area. Our results demonstrate that the direct comparison of surface wave dispersion curves extracted from ambient noise might be a useful tool in monitoring crustal structure variations.In this study, we used vertical component continuous seismic data from 4 stations in the Argentine Antarctica region from ASAIN (Antarctic Seismographic Argentine-Italian Network) in West Antarctica (Fig. 1). The stations Carlini-formerly Jubany (JUBA)-station, Esperanza (ESPZ), Orcada (ORCD) and San Martin (SMAI) are part of the 5 seismological stations that have been set under ASAIN collaborative agreement.The seismologic network provide essential data to study the internal structure of the Antarctic continent, monitor seismicity, and to monitor large-scale phenomena, such as melting of the Antarctic ice sheet, among others. We analyzed 4 years (2008-2011) of seismic data recorded recorded by the 4 stations with a sampling rate of 1 sample per second. The initial data preparation involves unpacking the raw data from its standard SEED format to obtain noise data in SAC format. We have to ensure that the two amplitudes of each record are completely consistent by running a time normalization so that the records share the same absolute time and the same amplitude information is retained. It is important to note that if the instruments of two stations are inconsistent, it is necessary to remove instrument responses from the raw data.Here ...

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Analysis of the primary and secondary microseisms in the wavefield of the ambient noise recorded in northern Poland

Cited by 11 publications

References 65 publications

Insights Into Microseism Sources by Array and Machine Learning Techniques: Ionian and Tyrrhenian Sea Case of Study

Insights Into Microseism Sources by Array and Machine Learning Techniques: Ionian and Tyrrhenian Sea Case of Study

Relation between ocean wave activity and wavefield of the ambient noise recorded in northern Poland

A Feasibility Study on Monitoring Crustal Structure Variations by Direct Comparison of Surface Wave Dispersion Curves from Ambient Seismic Noise

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