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

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. Plain Language Summary Seismic recordings contain information on atmospheric and anthropogenic phenomena which can occur over much larger portions of the daily records than tectonic events. We characterize signals with earthquake- and tremor-like waveforms that are generated as wind gusts interact with objects on the surface and modulate the ambient environmental noise. The wind interaction may produce microfailures in the shallow crust generating high-frequency energy that contributes to the local seismic noise. The classification of nontectonic signals is becoming increasingly important as earthquake detection algorithms employ machine learning techniques that utilize the data to build a detection model. Properly identifying different classes of signals will provide better detection models as the algorithms continue to improve.