The Seismic Footprint of Traffic Nima Riahi University of California, San Diego (UCSD) Scripps Institution of Oceanography The vibrations caused by moving wheeled vehicles can reveal much about vehicle location, velocity and size. As a data-source they are furthermore anonymous and weather-proof. So it is perhaps surprising that little empirical knowledge exists about the seismic footprint of traffic. We had a serendipitous opportunity to look into this "terra incognita" in 2014: an oil company decided to share with us their seismic imaging data consisting of 5200+ sensors spaced about 90m apart and blanketing Long Beach (CA). Such exceptional data may actually become less exceptional in the future: changes in sensor costs, weight, and power-usage are already causing a trend towards more dense and distributed sensor networks in seismic studies. I’ll start this talk with a quick excursion on the seismic remote-sensing paradigm. I’ll then describe some existing work on seismic effects from vehicles and the use of seismic sensors as in-roadway traffic counters. Of course we’ll also peak into the treasure trove of the Long Beach data to see how various analytical tools such as spatio-temporal filtering, mixture models, network theory, and sparse matched-field processing can be used to reveal traffic information.