Automated detection and characterization of harmonic tremor in continuous seismic data

Harmonic tremor is a common feature of volcanic, hydrothermal, and ice sheet seismicity and is thus an important proxy for monitoring changes in these systems. However, no automated methods for detecting harmonic tremor currently exist. Because harmonic tremor shares characteristics with speech and music, digital signal processing techniques for analyzing these signals can be adapted. I develop a novel pitch‐detection‐based algorithm to automatically identify occurrences of harmonic tremor and characterize their frequency content. The algorithm is applied to seismic data from Popocatepetl Volcano, Mexico, and benchmarked against a monthlong manually detected catalog of harmonic tremor events. During a period of heightened eruptive activity from December 2014 to May 2015, the algorithm detects 1465 min of harmonic tremor, which generally precede periods of heightened explosive activity. These results demonstrate the algorithm's ability to accurately characterize harmonic tremor while highlighting the need for additional work to understand its causes and implications at restless volcanoes. An algorithm for automated detection and characterization of harmonic tremor in continuous seismic data is presented Benchmarking of the results from the algorithm against a manually detected catalog is used to demonstrate the algorithm's accuracy The rate of automatically detected harmonic tremor at Popocatepetl Volcano, Mexico, increases prior to major explosions in 2014–2015