Timeseries Signal Processing for Enhancing Mobile Surveys: Learning from Field Studies
Monday, 14 December 2015
Poster Hall (Moscone South)
Vehicle-based surveys using laser and other analyzers are now commonplace in research and industry. In many cases when these studies target biologically-relevant gases like methane and carbon dioxide, the minimum detection limits are often coarse (ppm) relative to the analyzer’s capabilities (ppb), because of the inherent variability in the ambient background concentrations across the landscape that creates noise and uncertainty. This variation arises from localized biological sinks and sources, but also atmospheric turbulence, air pooling, and other factors. Computational processing routines are widely used in many fields to increase resolution of a target signal in temporally dense data, and offer promise for enhancing mobile surveying techniques. Signal processing routines can both help identify anomalies at very low levels, or can be used inversely to remove localized industrially-emitted anomalies from ecological data. This presentation integrates learnings from various studies in which simple signal processing routines were used successfully to isolate different temporally-varying components of 1 Hz timeseries measured with laser- and UV fluorescence-based analyzers. As illustrative datasets, we present results from industrial fugitive emission studies from across Canada’s western provinces and other locations, and also an ecological study that aimed to model near-surface concentration variability across different biomes within eastern Canada. In these cases, signal processing algorithms contributed significantly to the clarity of both industrial, and ecological processes. In some instances, signal processing was too computationally intensive for real-time in-vehicle processing, but we identified workarounds for analyzer-embedded software that contributed to an improvement in real-time resolution of small anomalies. Signal processing is a natural accompaniment to these datasets, and many avenues are open to researchers who wish to enhance existing, and future datasets.