Onset Of Unrest Research Articles

Identification of Seismo‐Volcanic Regimes at Whakaari/White Island (New Zealand) Via Systematic Tuning of an Unsupervised Classifier

AbstractWe present an algorithm based on Self‐Organizing Maps (SOM) and k‐means clustering to recognize patterns in a continuous 12.5‐year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano‐seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre‐eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes.

Developing a new, passive diffusion sampler suite to detect helium anomalies associated with volcanic unrest

Helium (He) concentration and 3He/4He anomalies in soil gas and spring water are potentially powerful tools for investigating hydrothermal circulation associated with volcanism and could perhaps serve as part of a hazards warning system. However, in operational practice, He and other gases are often sampled only after volcanic unrest is detected by other means. A new passive diffusion sampler suite, intended to be collected after the onset of unrest, has been developed and tested as a relatively low-cost method of determining He-isotope composition pre- and post-unrest. The samplers, each with a distinct equilibration time, passively record He concentration and isotope ratio in springs and soil gas. Once collected and analyzed, the He concentrations in the samplers are used to deconvolve the time history of the He concentration and the 3He/4He ratio at the collection site. The current suite consisting of three samplers is sufficient to deconvolve both the magnitude and the timing of a step change in in situ concentration if the suite is collected within 100 h of the change. The effects of temperature and prolonged deployment on the suite’s capability of recording He anomalies have also been evaluated. The suite has captured a significant 3He/4He soil gas anomaly at Horseshoe Lake near Mammoth Lakes, California. The passive diffusion sampler suite appears to be an accurate and affordable alternative for determining He anomalies associated with volcanic unrest.