Volcano Seismology: Detecting Unrest in Wiggly Lines

Abstract

Seismology is a useful tool to gain a better understanding of volcanic unrest in real time as it unfolds. The generation of seismic signals in a volcanic environment has been linked to a number of different physical processes occurring at depth, including fracturing of the volcanic edifice (producing high frequency seismicity) and movement of magmatic fluids (producing low frequency seismicity). Further classification of seismic signals according to their waveform similarity, in addition to their frequency content, allows greater detail in temporal and spatial changes of seismicity to be detected. At Soufrière Hills volcano, Montserrat, one of the target volcanoes of the VUELCO project, families of similar waveforms provided valuable insight into evaluating the significance of ongoing unrest. In June 1997 over 6000 more events were able to be identified over a 5 day period of interest (22 to 25 June) by using families of seismic events, rather than a standard amplitude-based detection algorithm. In total, 11 families were identified, with the events clustering into a number of swarms, suggesting a repeating and non destructive cyclic source mechanism. Since each family is believed to represent a distinct source location and mechanism, identifying 11 coexisting families R.O. Salvage (&)

Renewed Explosive Phreatomagmatic Activity at Poás Volcano, Costa Rica in April 2017

“…We define a "family" of events as events which show similar waveform shape characteristics, defined by having a cross correlation coefficient >0.7. This is in agreement with Green and Neuberg (2006), Thelen et al (2011), and Salvage and Neuberg (2016), who suggest a cross correlation coefficient threshold of 0.7 in andesitic volcanic environments, since this is significantly above the correlation coefficient that can be produced from random correlations between noise and a waveform (Salvage et al, 2017). Here we first identify families of seismicity using a simple amplitude ratio algorithm, in addition to high waveform similarity on a multiple station network (≥ 0.7 cross correlation coefficient) using REDPy (Repeating Earthquake Detector, Hotovec-Ellis and Jeffries, 2016).…”

“…Waveform similarity in terms of shape and duration can be evaluated by a cross correlation procedure where identical signals will result in a maximum cross correlation coefficient of 1 or -1, dependent upon their relative polarity and signals with no correlation resulting in a cross correlation coefficient of 0. The choice of similarity threshold (above which events are considered similar) is important: if it is too low there is a risk of placing events that are not similar into the same family; if it is too high similar events can be missed (Salvage et al, 2017). We define a "family" of events as events which show similar waveform shape characteristics, defined by having a cross correlation coefficient >0.7.…”

Electrokinetic Contributions to Self‐Potential Signals From Magmatic Stressing

“…Volcanic unrest is often accompanied by anomalous geophysical and geochemical signals that are generally attributed to processes within the subvolcanic plumbing system (Salvage et al., 2017). Precise eruption forecasting remains a key issue in volcanology and depends on the correlation of volcanic precursors to subsurface causative mechanisms (Magee et al., 2018; Sparks, 2003).…”

Volcanic Unrest and Pre-eruptive Processes: A Hazard and Risk Perspective

“…The dilemma for scientists is that magma movement does not create uniquely attributable unrest signals and does not necessarily lead to eruption (Table 1). For example, seismicity and ground uplift, both common indicator of unrest, may be induced by the replenishment of a magma reservoir, the ascent of magma towards the surface or the redistribution of aqueous fluids and fluid phase changes (see Salvage et al 2017;Hickey et al 2017;Mothes et al 2017 for examples from VUELCO volcanoes). Similarly, an increase in the gas and heat flux (Christopher et al 2015) at the surface may be induced by magmatic or hydrothermal processes and even tectonic stress changes have also been shown to trigger such behaviour (e.g., Hill et al 1995).…”

“…Thus interpretations of these drivers rely on the secondary interpretation of observable signals associated with those processes (Salvage et al 2017) or the reproduction of interpreted processes via laboratory experiments (Wadsworth et al 2016). In addition, many volcanic processes are intrinsically non-linear and characterized by a chain-link reaction such that minor variations of some uncertain parameters might have ultimately significant consequences on the eruptive outcome.…”