Magma reservoir failure and the onset of caldera collapse at Kilauea Volcano in 2018.

Caldera-forming eruptions are among Earth's most hazardous natural phenomena, yet the architecture of subcaldera magma reservoirs and the conditions that trigger collapse are poorly understood. Observations from the formation of a 0.8-cubic kilometer basaltic caldera at Kilauea Volcano in 2018 included the draining of an active lava lake, which provided a window into pressure decrease in the reservoir. We show that failure began after <4% of magma was withdrawn from a shallow reservoir beneath the volcano's summit, reducing its internal pressure by ~17 megapascals. Several cubic kilometers of magma were stored in the reservoir, and only a fraction was withdrawn before the end of the eruption. Thus, caldera formation may begin after withdrawal of only small amounts of magma and may end before source reservoirs are completely evacuated.

Seismic detection of increased degassing before Kilauea's 2008 summit explosion.

The 2008 explosion that started a new eruption at the summit of Kilauea Volcano, Hawai'i, was not preceded by a dramatic increase in earthquakes nor inflation, but was associated with increases in SO2 emissions and seismic tremor. Here we perform shear wave splitting analysis on local earthquakes spanning the onset of the eruption. Shear wave splitting measures seismic anisotropy and is traditionally used to infer changes in crustal stress over time. We show that shear wave splitting may also vary due to changes in volcanic degassing. The orientation of fast shear waves at Kilauea is usually controlled by structure, but in 2008 showed changes with increased SO2 emissions preceding the start of the summit eruption. This interpretation for changing anisotropy is supported by corresponding decreases in Vp/Vs ratio. Our result demonstrates a novel method for detecting changes in gas flux using seismic observations and provides a new tool for monitoring under-instrumented volcanoes.

Pure random search for ambient sensor distribution optimisation in a smart home environment.

Smart homes are living spaces facilitated with technology to allow individuals to remain in their own homes for longer, rather than be institutionalised. Sensors are the fundamental physical layer with any smart home, as the data they generate is used to inform decision support systems, facilitating appropriate actuator actions. Positioning of sensors is therefore a fundamental characteristic of a smart home. Contemporary smart home sensor distribution is aligned to either a) a total coverage approach; b) a human assessment approach. These methods for sensor arrangement are not data driven strategies, are unempirical and frequently irrational. This Study hypothesised that sensor deployment directed by an optimisation method that utilises inhabitants' spatial frequency data as the search space, would produce more optimal sensor distributions vs. the current method of sensor deployment by engineers. Seven human engineers were tasked to create sensor distributions based on perceived utility for 9 deployment scenarios. A Pure Random Search (PRS) algorithm was then tasked to create matched sensor distributions. The PRS method produced superior distributions in 98.4% of test cases (n=64) against human engineer instructed deployments when the engineers had no access to the spatial frequency data, and in 92.0% of test cases (n=64) when engineers had full access to these data. These results thus confirmed the hypothesis.

Development of a smart home simulator for use as a heuristic tool for management of sensor distribution.

Smart Homes offer potential solutions for various forms of independent living for the elderly. The assistive and protective environment afforded by smart homes offer a safe, relatively inexpensive, dependable and viable alternative to vulnerable inhabitants. Nevertheless, the success of a smart home rests upon the quality of information its decision support system receives and this in turn places great importance on the issue of correct sensor deployment. In this article we present a software tool that has been developed to address the elusive issue of sensor distribution within smart homes. Details of the tool will be presented and it will be shown how it can be used to emulate any real world environment whereby virtual sensor distributions can be rapidly implemented and assessed without the requirement for physical deployment for evaluation. As such, this approach offers the potential of tailoring sensor distributions to the specific needs of a patient in a non-evasive manner. The heuristics based tool presented here has been developed as the first part of a three stage project.