Search for: All records

When and why earthquakes trigger volcano and geyser eruptions remains unclear. In September 2022, Steamboat Geyser in Yellowstone, USA erupted 8.25 hours after a local M3.9 earthquake—an improbable coincidence based on the geyser’s eruption intervals. We leverage monitoring data from the surrounding geyser basin to determine if the earthquake triggered this eruption. We calculate a peak ground velocity of 1.2 cm s−1, which is the largest ground motion in the area since Steamboat reactivated in March 2018 and exceeds a threshold associated with past earthquake-triggered geyser eruptions in Yellowstone. Despite no changes in other surface hydrothermal activity, we found abrupt, short-lived shifts in ambient seismic noise amplitude and relative seismic velocity in narrow frequency bands related to the subsurface hydrothermal system. Our analysis indicates that Steamboat’s eruption was likely earthquake-triggered. The hours-long delay suggests that dynamic strains from seismic waves altered subsurface permeability and flow which enabled eruption. 

Systems driven far from equilibrium often retain structural memories of their processing history. This memory has, in some cases, been shown to dramatically alter the material response. For example, work hardening in crystalline metals can alter the hardness, yield strength, and tensile strength to prevent catastrophic failure. Whether memory of processing history can be similarly exploited in flowing systems, where significantly larger changes in structure should be possible, remains poorly understood. Here, we demonstrate a promising route to embedding such useful memories. We build on work showing that exposing a sheared dense suspension to acoustic perturbations of different power allows for dramatically tuning the sheared suspension viscosity and underlying structure. We find that, for sufficiently dense suspensions, upon removing the acoustic perturbations, the suspension shear jams with shear stress contributions from the maximum compressive and maximum extensive axes that reflect or “remember” the acoustic training. Because the contributions from these two orthogonal axes to the total shear stress are antagonistic, it is possible to tune the resulting suspension response in surprising ways. For example, we show that differently trained sheared suspensions exhibit (1) different susceptibility to the same acoustic perturbation, (2) orders of magnitude changes in their instantaneous viscosities upon shear reversal, and (3) even a shear stress that increases in magnitude upon shear cessation. We work through these examples to explain the underlying mechanisms governing each behavior. Then, to illustrate the power of this approach for controlling suspension properties, we demonstrate that flowing states well below the shear jamming threshold can be shear jammed via acoustic training. Collectively, our work paves the way for using acoustically induced memory in dense suspensions to generate rapidly and widely tunable materials. Published by the American Physical Society2024 

Combining visual and sonic representations of data can make science more accessible and help reveal subtle details. The recent decade-long eruption of Hawaii’s Kīlauea Volcano offers a prime example. 

Abstract Sonification of time series data in natural science has gained increasing attention as an observational and educational tool. Sound is a direct representation for oscillatory data, but for most phenomena, less direct representational methods are necessary. Coupled with animated visual representations of the same data, the visual and auditory systems can work together to identify complex patterns quickly. We developed a multivariate data sonification and visualization approach to explore and convey patterns in a complex dynamic system, Lone Star Geyser in Yellowstone National Park. This geyser has erupted regularly for at least 100 years, with remarkable consistency in the interval between eruptions (three hours) but with significant variations in smaller scale patterns between each eruptive cycle. From a scientific standpoint, the ability to hear structures evolving over time in multiparameter data permits the rapid identification of relationships that might otherwise be overlooked or require significant processing to find. The human auditory system is adept at physical interpretation of call-and-response or causality in polyphonic sounds. Methods developed here for oscillatory and nonstationary data have great potential as scientific observational and educational tools, for data-driven composition with scientific and artistic intent, and towards the development of machine learning tools for pattern identification in complex data. 

Steamboat Geyser in Yellowstone National Park’s Norris Geyser Basin began a prolific sequence of eruptions in March 2018 after 34 y of sporadic activity. We analyze a wide range of datasets to explore triggering mechanisms for Steamboat’s reactivation and controls on eruption intervals and height. Prior to Steamboat’s renewed activity, Norris Geyser Basin experienced uplift, a slight increase in radiant temperature, and increased regional seismicity, which may indicate that magmatic processes promoted reactivation. However, because the geothermal reservoir temperature did not change, no other dormant geysers became active, and previous periods with greater seismic moment release did not reawaken Steamboat, the reason for reactivation remains ambiguous. Eruption intervals since 2018 (3.16 to 35.45 d) modulate seasonally, with shorter intervals in the summer. Abnormally long intervals coincide with weakening of a shallow seismic source in the geyser basin’s hydrothermal system. We find no relation between interval and erupted volume, implying unsteady heat and mass discharge. Finally, using data from geysers worldwide, we find a correlation between eruption height and inferred depth to the shallow reservoir supplying water to eruptions. Steamboat is taller because water is stored deeper there than at other geysers, and, hence, more energy is available to power the eruptions. 

Abstract Doublet Pool is an active hydrothermal feature in Yellowstone National Park, USA. Approximately every half hour, it thumps for about 10 min due to bubbles collapsing at the base of the pool. To understand its thermodynamics and sensitivity to external factors, we performed a recurring multiple‐year passive seismic experiment. By linking recorded hydrothermal tremor with active thumping, we determine the onset and end of thumping, and the duration of silence between each thumping cycle. The silence interval decreased from around 30 min before November 2016 to around 13 min in September 2018. This change followed unusual thermal activity on the surrounding Geyser Hill. On a shorter time scale, wind‐driven evaporative cooling can lengthen the pre‐thumping silence interval. Based on energy conservation, we determine the heating rate and heat needed to initiate thumping to be 3–7 MW and ∼6 GJ, respectively.