Automated detection of dust-devil-induced pressure signatures

Abstract

Dust devils are common on Mars and in Earth’s arid regions. On Mars, studying the dust devil population is important for understanding dust loading of the Martian climate and has important consequences for robotic and future human exploration. Studying dust devils on Mars is difficult due to insufficient spatio-temporal coverage. Our team is analyzing an infrasound dataset encompassing 7 years of data recorded at the Nevada Nuclear Security Site in the Mojave desert, to identify and characterize terrestrial dust devils as analogues for Martian dust devils. However, the size of this long-duration dataset mandates the use of automated techniques for the detection of dust devils.The automated detection scheme that we have developed follows a two-step process. The first step comprises of significance testing in the time-frequency domain using wavelet transforms and an empirical background red noise model. The second step is a correlation-based, template-matching detector, which utilizes the “heartbeat” pressure signal produced by the dust devil convolved with the microbarometer’s impulse response. In this presentation, we will discuss our automated dust devil detection algorithm, error characterization in the identification of signatures using Monte Carlo analysis and the application of the automated detector to the long-term monitoring dataset.