Investigating icequakes on enceladus using an antarctic analog: application of seismic and machine-learning techniques to characterize tidally induced seismicity along icy rifts

semanticscholar(2021)

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摘要
Introduction: The icy shell of Enceladus is riven with fractures, including the four large Tiger Stripe fractures which lie at the moon’s south pole (Figure 1c). Jets of water vapor and ice particles emanating from the Tiger stripes [1] provide strong evidence that these fractures connect to subsurface reservoirs of liquid water, making them a key target for study of ice-shell structure, habitability, and more. The Tiger Stripe fractures exhibit offset features along their ~100 km lengths which show that significant slip has occurred in these locations, driven by continuous cycles of tidal stresses [2]. Like faults on Earth’s surface, the Tiger Stripe Fractures are likely caused by, and the cause of, ongoing seismic activity. Observation and analysis of Enceladean seismicity by a future landed mission will present a valuable opportunity to study energy release within an icy shell and to advance understanding of icycrust deformation as well as interrogate interior structure [3]. In advance of seismometer deployment on Enceladus or another ice-ocean world, a thorough understanding of the characteristics of ice-rift seismicity is needed. Floating ice shelves at the margins of Earth’s polar ice sheets offer the closest terrestrial analog environment in which to investigate tidally driven icerift seismicity. We focus our study on two major fractures, or rifts, within the interior of the largest ice shelf in the world, the Ross Ice Shelf, Antarctica (Figure 1a). The rifts of interest are in the central portion of the Ross Ice Shelf and are in hydrostatic equilibrium with the ocean, presenting a similar environment to a planetary icy shell. The two rifts we study, WR4 and WR6 (Figure 1a), have similar lengths and widths to the Tiger Stripe Fractures, as well as similar inter-rift spacing. As the ocean tide traverses the sub-ice cavity during the daily tidal cycle, the two fractures experience across-rift tension (at falling tide) and compression (at rising tide). We analyze two years of broadband seismic data recorded by two on-ice seismographs, one located adjacent to each rift (red triangles in Figure 1a). We detect 13,000 icequakes at rift WR4 and 4,000 icequakes at WR6 using an automated short-term average/long-term average detection algorithm, paired with manual event review. We attribute the difference in number of icequakes detected at the two rifts to the fact that seismic station DR15 was located four times as far from rift WR6 (~8 km) as seismic station DR14 was from rift WR4 (~2 km; Figure 1a). Icequake Activity: Seismic activity at both iceshelf rifts is strongly tidally modulated, with the highest levels of seismicity at each rift occurring at falling tide [4,5]. Specifically, the highest levels of seismic activity occur when both tensile stress across the rift and extension rate are high. At each rift we explore the distribution of icequake magnitudes to investigate the characteristics of tidally stressed, icy-fault seismicity and to characterize the dimensions of slip patches active in individual icequakes. Icequake magnitudes are small, ranging from moment-magnitudes, Mw, of -2 to 0. We find that icequakes at each rift follow Gutenberg-Richter relationships, with slopes, known as b-values, close to 1.5 (Figure 2). This is higher than typical b-values (~1) observed in large populations of Figure 1. a) Map of study area on Ross Ice Shelf with (black outline) rifts WR4 and WR6, and (red triangles) seismometers DR14 and DR15. Background is MODIS 2014 satellite image. b) Geometry of (grey) full Ross Ice Shelf with black rectangle outlining area shown in a), (blue) ocean, and (white) grounded ice. c) Enceladus’ South Polar Terrain with (black outline) the four Tiger Stripe fractures. Background is Enceladus Polar Map PIA14940, Cassini Imaging Team. Alandria Bhdad Cairo am acus
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icequakes,antarctic analog,icy rifts,induced seismicity,machine-learning
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