Dust Devil Heartbeat Detection on Infrasound Sensors

Particle-loaded convective vortices, or dust devils, are coherent, columnar shaped, fast rotating, dust-laden vortices at least one meter high and lasting for at least 10 seconds. These vortices are created from heating of near-surface air by insolation and so are subject to boundary layer processes. Dust devils consist of reduced pressure at their centers and are observed moving across pressure sensors as a pressure dip of varying duration and amplitude, a function of dust devil radius and magnitude. These result in large, prominent signals on low frequency sound, or infrasound, stations. However, on raw infrasound data, this signal appears as a distinctly heartbeat shape due to convolution of the dust devil pressure dip and the instrument response. We create a series of heartbeat templates containing a variety of expected dust devil signals and apply template-matching cross-correlation to identify and characterize dust devils within recorded infrasound time-series data. We statistically quantify the limitations and potential applications of this method to synthetic infrasound data and dust devils. In addition, we develop an empirical background noise model for infrasound sensors deployed in the Mojave desert in southern Nevada and deploy wavelet transform-based methods to differentiate dust devil signatures from background atmospheric variability. Following these results, we apply both template-matching and wavelet transform-based methods to infrasound data recorded across multiple sensors in the Mojave desert. Our findings demonstrate that infrasound networks (including the globe-spanning International Monitoring System) can be used to detect and characterize convective vortex activity. Not only is this of scientific interest in characterization of the planetary boundary layer, but it also has implications for the risk to life and property posed by these vortices. Furthermore, automatic dust devil detection may improve understanding of these signatures against instrument noise, and applicable to understanding the occurrence and variability of dust devils on Mars, which is answerable to several high-priority goals set forth by the Mars Exploration Program Analysis Group (MEPAG). SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.