Automated fault model discretization for inversions for coseismic slip distributions

Abstract

[1] Geoscientists increasingly rely on coseismic slip distributions inferred from geodetic observations to drive sophisticated models of the seismic cycle. To date, little work has been done on optimizing the parameterization of these fault models so that they reflect the resolving power of observed surface displacements. The locations of noisy surface displacement observations are often widely scattered far from features we wish to analyze in the subsurface and result in highly variable model resolution with depth. The few attempts to produce variably discretized fault planes are arduously constructed by hand and may not correctly reflect the ability of the data to resolve slip features. Motivated by the increasing size of geodetic data pools and the need for distributed slip models that accurately represent features the data can resolve, we present a fully automated algorithm that iteratively adjusts the sizes of dislocations in a fault model. We use the concept of smoothing scales, derived from the model resolution matrix, to resize dislocations so that each dislocation is sized appropriately given the area over which slip in that region of the fault would be smoothed. We present a series of synthetic tests that utilize both sparse and dense data sets, and we compare our variably discretized inversions to traditional regularly discretized inversions. We also use our approach to invert for slip from geodetic observations of the 2004 M w 6.0 Parkfield, California, earthquake and the 1995 M w 8.1 Antofagasta, Chile, earthquake.

The 2016 M7 Kumamoto, Japan, Earthquake Slip Field Derived From a Joint Inversion of Differential Lidar Topography, Optical Correlation, and InSAR Surface Displacements

“…The use of a curving fault is rare (Wang et al, 2013). Because data resolution of slip decreases with depth (Motagh et al, 2006;Simons et al, 2002), we increase the area of the subfaults from less than 1 km 2 near the surface to 15 km 2 at 20km depth using Barnhart and Lohman's (2010) approach. We damp the kinking at depth, so that the fault becomes increasingly planar at depth where local complexities in the fault geometry are poorly constrained (Figure 2b).…”

“…We extend Barnhart and Lohman's (2010) technique and calculate how each data set resolves the slip field. We extend Barnhart and Lohman's (2010) technique and calculate how each data set resolves the slip field.…”

Regularized Inversion of Coseismic Slip Distribution Based on Smoothness-Constrained Model

“…反演中加入了先验光滑约束条件，但由于 GPS 测量数据的分辨率仍然无法保证断层面的滑动分布模型的 合理性 [3]；此时，除了光滑约束外，需要借助于更强的约束；Barnhart 和 Lohman 尝试了固定滑动角，再 利用最小二乘法进行求解 [4]；Pollitz 等将滑动角固定到纯走滑或纯倾滑的情况，只反演滑动量 [5] (1) …”

Modeling crustal deformation near active faults and volcanic centers: a catalog of deformation models and modeling approaches

“…The surface deformation can be inverted onto fault planes that are discretized with evenly sized dislocations; fault tiles in the model may or may not slip (see Jonsson and others, 2002;Barnhart and Lohman, 2010). The observed surface displacements d can be described as a function g(m) of the fault model parameters m:…”