Showing papers by "Mark Simons published in 2021"

A Stochastic View of the 2020 Elazığ M w 6.8 Earthquake (Turkey)

Abstract: • We infer a stochastic model for the distribution of subsurface fault slip associated with the 2020 Elazig earthquake • We account for uncertainties in both the depth-dependence of the assumed elastic structure and the location and geometry of the fault • Our models are characterized by two primary patches of fault slip where distribution appears to be controlled by geometrical complexities

An EPIC Tikhonov Regularization: Application to Quasi-Static Fault Slip Inversion

Abstract: Fault slip during all the stages of the seismic cycle can produce measurable deformation at the surface of the Earth. We are interested in the inverse problem of inferring subsurface fault slip given surface observations. For the quasi-static problem in a linear elastic media, the relationship between slip m on a discretized fault and crustal surface displacements d can be written as d = Gm, where the Green's functions G represents the response of the medium due to unitary slip on each element of a discretized fault surface (e.g., Segall & Harris, 1987).

Nisar Requirements and Validation Approach for Solid Earth Science

Abstract: The joint NASA/ISRO SAR (NISAR) satellite mission is anticipated to provide routine L-band coverage of most of the Earth's land surface every 12-days for both ascending and descending orbits. In terms of impact on solid earth science (SES), the primary measurement will be Interferometric SAR (InSAR) observations of ground deformation in two satellite line-of-sight (LOS) directions. Key observation characteristics include acquisitions with small interferometric baselines to maximize interferometric coherence and decrease sensitivity to topography, wide bandwidth allowing for split-band processing to model out the impacts of the ionosphere, and joint L- and S-band observations in selected regions. We describe here the key measurement requirements for solid earth science, as well as our approach to validating these requirements once the mission is underway.

Imaging Complex Fault Slip of Large Earthquakes with Sentinel-1 and ALOS-2 SAR Analysis and Other Geodetic and Seismic Data*

Abstract: We study the distribution of slip on faults during earthquakes with integrated analysis of geodetic imaging and seismic data to learn about parameters that control how faults slip and potentially how damaging future earthquakes may be. We mapped complex fault ruptures for a number of large earthquakes in 2015–2020 using analysis of synthetic aperture radar (SAR) data from the Copernicus Sentinel-1A and Sentinel-1B satellites operated by the European Space Agency and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by the Japan Aerospace Exploration Agency (JAXA). We used regular SAR interferometry, along-track or multiple-aperture interferometry, and pixel offset tracking to measure surface displacements and combined this with other geodetic and seismic data to infer slip on faults at depth.

Deep Learning-based Damage Mapping with InSAR Coherence Time Series.

Abstract: 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.