Some thoughts on the use of InSAR data to constrain models of surface deformation: Noise structure and data downsampling

By Rowena Lohman, Mark Simons. 2005

451 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Geophysics

View Paper: https://www.semanticscholar.org/paper/f045219b034c3e253348f0da3d1a7cc1123116d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. A. Hooper
2. Kang Wang
3. Marco Bagnardi
4. Y. Fialko
5. Yunjun Zhang
6. Heresh Fattahi
7. Falk Amelung
8. G. Peltzer

In Geophysics

1. A. Hooper
2. Marco Bagnardi
3. Y. Fialko
4. Kang Wang
5. Yunjun Zhang
6. Heresh Fattahi
7. Falk Amelung
8. Marie-Pierre Doin

Citations in the corpus

• Introduction of uncertainty of Green's function into waveform inversion for seismic source processes (2011, background): “D underground structure model (e.g. Liu & Archuleta 2004) or by using observed waveforms for small earthquakes (e.g. Fukuyama & Irikura 1986; Mori & Harzel 1990).”
• Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models (2009, methodology): “…type of approach is to characterize the statistical properties of phase delay patterns (Goldstein, 1995;Williams et al., 1998; Emardson et al., 2003; Lohman and Simons, 2005), which are used to construct covariance matrices of observables and to separate stochastic noise from ground motion signal.”
• Geodetic model of the 2016 Central Italy earthquake sequence inferred from InSAR and GPS data (2017, methodology): “We performed the geodetic modeling using rectangular dislocations in an elastic, homogeneous, and isotropic half-space [Okada, 1985].”
• Accounting for prediction uncertainty when inferring subsurface fault slip (2014, methodology): “However, for observations like InSAR or seismic data, off-diagonal components should be included in Cd to allow correlation of measurement errors between neighbouring data samples (e.g. Lohman & Simons 2005; Fukahata & Wright 2008; Duputel et al. 2012a).”
• Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction (2019, methodology): “To highlight differences, we also show the difference in mean RMSE with respect to inverse-variance weighting (Fig.”
• Slip model of the 2015 Mw 7.8 Gorkha (Nepal) earthquake from inversions of ALOS‐2 and GPS data (2015, methodology): “To avoid oversampling in areas with large phase gradients due to noise (atmospheric delays, decorrelation, unwrapping errors, etc.), our downsampling of the InSAR data was implemented iteratively using model predictions [e.g., Lohman and Simons, 2005].”
• Inversion of Surface Deformation Data for Rapid Estimates of Source Parameters and Uncertainties: A Bayesian Approach (2018, methodology): “Three main types of InSAR data downsampling methods can be applied: uniform sampling (e.g., Pritchard et al., 2002), resolution-based sampling (e.g., Lohman & Simons, 2005; Wang et al., 2014), and gradient-based sampling (e.g., Jonsson, 2002; Simons, 2002).”