On the Analysis of Wind-Induced Noise in Seismological Recordings

Lott, Friederike; Ritter, Joachim; Al-Qaryouti, Mahmoud; Corsmeier, U.

doi:10.1007/s00024-017-1477-2

Cited by 24 publications

(14 citation statements)

References 36 publications

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“…None of the vertical ground velocities are greater than the horizontal. Aggregating the PGV for the average and peak wind velocity intervals using all nodes between DOY 50 and 60 further confirms a log linear relationship between PGV and wind gust velocity (Lott et al, ), with horizontal ground motions exceeding the vertical (Figure ). The interval containing 90% of the PGV values for each wind velocity interval shows a change point where the PGV increases above 1–2 m/s.…”

Section: Wind‐generated Ground Motionssupporting

confidence: 55%

“…Wind‐related sources of ground motion are an important class of signals observed in continuous waveforms that can reduce the observation potential of seismic stations located in many environments and obscure tectonic events (De Angelis & Bodin, ; Lott et al, ; Naderyan et al, ; Withers et al, ). Daily and seasonal changes in atmosphere pressure and temperature produce ground motion at various frequency bands (Hillers & Ben‐Zion, ; Sorrells, ; Sorrells et al, ; Sorrells & Goforth, ; Tanimoto & Wang, ) with increased noise amplitudes that correlate with the wind velocity produced by these atmospheric variations.…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

et al. 2019

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Analysis of continuous seismic waveforms from a temporary deployment at Sage Brush Flats on the San Jacinto fault reveals earthquake‐ and tremor‐like signals generated by the interaction of wind with obstacles above the surface. Tremor‐like waveforms are present at the site during wind velocities above 2 m/s, which occur for 70% of the deployment duration. The response to the wind has significant spatial variability with highest ground motions near large surface objects. The wind‐related signals show ground velocities that exceed the average ground motions of M1.0–1.5 earthquakes for 6–31% of the day. Waveform spectra indicate a modulation of amplitude that correlates with wind velocity and distance from local structures. Earthquake‐like signals are found to originate from local structures and vegetation, and are modified on length scales of tens of meters. Transient signals originating beyond the study area are also observed with amplitudes greater than some microseismic events. The wind‐related ground motions contribute to local high‐frequency seismic noise. Some of these signals may be associated with small failures of the subsurface material. During elevated wind conditions a borehole seismometer at a depth of 148 m shows increased energy in the 1–8‐Hz band that is commonly used for earthquake and tremor detection. The wind‐related earthquake‐ and tremor‐like signals should be accounted for in earthquake detection algorithms due to the similar features in both time and frequency domains. Proper recognition of wind‐related ground motions can contribute to understanding the composition of continuous seismic waveforms and characterize mechanical properties of the shallow crust.

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Section: Wind‐generated Ground Motionssupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

et al. 2019

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“…Previous studies identified road traffic noise in the frequency band 3–25 Hz while a large variety of industrial activities affect the 2–10 Hz frequency band 28 . Since other sources of non-anthropic noise, such as wind, can be observed over a broad frequency band with large differences between sites, we decided to cut out the lower frequencies (F < 5 Hz) to limit the effect of non-anthropic fluctuation on data 29 .…”

Section: Methodsmentioning

confidence: 99%

COVID-19 lockdown and its latency in Northern Italy: seismic evidence and socio-economic interpretation

Piccinini

Giunchi

Olivieri

et al. 2020

Sci Rep

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The Italian Government has decreed a series of progressive restrictions to delay the COVID-19 pandemic diffusion in Italy since March 10, 2020, including limitation in individual mobility and the closure of social, cultural, economic and industrial activities. Here we show the lockdown effect in Northern Italy, the COVID-19 most affected area, as revealed by noise variation at seismic stations. The reaction to lockdown was slow and not homogeneous with spots of negligible noise reduction, especially in the first week. A fresh interpretation of seismic noise variations in terms of socio-economic indicators sheds new light on the lockdown efficacy pointing to the causes of such delay: the noise reduction is significant where non strategic activities prevails, while it is small or negligible where dense population and strategic activities are present. These results are crucial for the a posteriori interpretation of the pandemic diffusion and the efficacy of differently targeted political actions.

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“…The initial purpose of the data analyses is to examine whether the variance in the seismic data is enhanced under different WT operating conditions and the degree to which vibrations from individual WT components are differentiable within the seismic data. Spectral analysis has been widely applied to seismic data sets to characterize ambient noise, wind‐seismic interaction, and for WT CM (eg, identifying amplitude of characteristic frequencies for gears and bearings, and cracking in blades). Accordingly, time series of the three seismic channels are transformed into the frequency domain to calculate the seismic power spectral density (PSD) using the Welch method .…”

Section: Methodsmentioning

confidence: 99%

A new seismic‐based monitoring approach for wind turbines

Barthelmie

Letson

et al. 2018

Wind Energy

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Wind turbine performance and condition monitoring play vital roles in detecting and diagnosing suboptimal performance and guiding operations and maintenance. Here, a new seismic‐based approach to monitoring the health of individual wind turbine components is presented. Transfer functions are developed linking key condition monitoring properties (drivetrain and tower acceleration) to unique, robust, and repeatable seismic signatures. Predictive models for extreme (greater than 99th percentile) drivetrain and tower acceleration based on independent seismic data exhibit higher skill than reference models based on hub‐height wind speed. The seismic models detect extreme drivetrain and tower acceleration with proportions correct of 96% and 93%, hit rates of 91% and 82%, and low false alarm rates of 4% and 6%, respectively. Although new wind turbines incorporate many diagnostic sensors, seismic‐based condition/performance monitoring may be particularly useful in extending the productive lifetime of previous generation wind turbines.

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On the Analysis of Wind-Induced Noise in Seismological Recordings

Cited by 24 publications

References 36 publications

Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

COVID-19 lockdown and its latency in Northern Italy: seismic evidence and socio-economic interpretation

A new seismic‐based monitoring approach for wind turbines

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