Mark Simons | Searcharxiv

arXiv2025

Exploring the Interior Structure and Mode of Tidal Heating in Enceladus

Enceladus is among the most intriguing bodies in the solar system due to its astrobiological potential. Determining the extent and duration of habitability (i.e., sustained habitability) requires characterizing the interior properties and the level and distribution of tidal heating in Enceladus. Inferring the intensity of geophysical activity in the core has direct implications for the potential hydrothermal activity and supply of chemical species important for habitability to the ocean. We build a statistical framework to constrain the interior using estimates of libration, shape, heat flux, gravity, and total mass. We use this framework to examine the extent that geodetic measurements can improve our understanding of the interior structure, with an emphasis on partitioning of dissipation between the shell and the core. We quantify plausible ranges of gravitational (k2) and displacement (h2, l2) tidal Love numbers consistent with existing observations. We demonstrate that measuring k2 alone can only constrain the total tidally dissipated energy, but not its radial distribution. However, measuring the amplitude and phase of h2 or l2 facilitates determining the extent of tidal dissipation in the shell and the core. We provide the precisions required for measuring k2, h2, and l2 that enable distinguishing between the main tidal heating scenarios, i.e., in the shell versus the core. We also explore the effect of the structural heterogeneities of the shell on the tidal response. Lastly, we evaluate the efficacy of future geodetic measurements to constrain key interior properties essential to understand the present-day (instantaneous) and long-term (sustained) habitability at Enceladus.

astro-ph.EP↗

arXiv2021

Deep Learning-based Damage Mapping with InSAR Coherence Time Series

Satellite remote sensing is playing an increasing role in the rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth's surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth's surface. In this study, we propose a novel approach to damage mapping, combining deep learning with the full time history of SAR observations of an impacted region in order to detect anomalous variations in the Earth's surface properties due to a natural disaster. We quantify Earth surface change using time series of Interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of the confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local behavior of coherence through time before the event. We apply this method to calculate estimates of damage for three earthquakes using multi-year time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with observed damage and quantitative improvement compared to using pre- to co-event coherence loss as a damage proxy.

physics.geo-ph↗