Classification, Characterization, and Automatic Detection of Volcanic Explosion Complexity using Infrasound

Abstract:

Infrasound signals from volcanoes represent the acceleration of the atmosphere during an eruption and have traditionally been classified into two end members: 1) “explosions” consisting primarily of a high amplitude bi-polar pressure pulse that lasts a few to tens of seconds, and 2) “tremor” or “jetting” consisting of sustained, broadband infrasound lasting for minutes to hours. However, as our knowledge and recordings of volcanic eruptions have increased, significant infrasound signal diversity has been found. Here we focus on identifying and characterizing trends in volcano infrasound data to help better understand eruption processes. We explore infrasound signal metrics that may be used to quantitatively compare, classify, and identify explosive eruptive styles by systematic analysis of the data.

We analyze infrasound data from short-to-medium duration explosive events recorded during recent infrasound deployments at Sakurajima Volcano, Japan; Karymsky Volcano, Kamchatka; and Tungurahua Volcano, Ecuador. Preliminary results demonstrate that a great variety of explosion styles and flow behaviors from these volcanoes can produce relatively similar bulk acoustic waveform properties, such as peak pressure and event duration, indicating that accurate classification of physical eruptive styles requires more advanced field studies, waveform analyses, and modeling. Next we evaluate the spectral and temporal properties of longer-duration tremor and jetting signals from large eruptions at Tungurahua Volcano; Redoubt Volcano, Alaska; Augustine Volcano, Alaska; and Nabro Volcano, Eritrea, in an effort to identify distinguishing infrasound features relatable to eruption features. We find that unique transient signals (such as repeated shocks) within sustained infrasound signals can provide critical information on the volcanic jet flow and exhibit a distinct acoustic signature to facilitate automatic detection. Automated detection and characterization of infrasound associated with a diverse spectrum of explosive activity could provide great benefits to volcano monitoring and will be presented here.