A sequence of shallow earthquakes of magnitudes ≤ 5:1 took place in 2004 on the eastern flank of the Red Sea rift, near the city of Tabuk in northwestern Saudi Arabia. The earthquakes could not be well located due to the sparse distribution of seismic stations in the region, making it difficult to associate the activity with one of the many mapped faults in the area and thus to improve the assessment of seismic hazard in the region. We used Interferometric Synthetic Aperture Radar (InSAR) data from the European Space Agency's Envisat and ERS-2 satellites to improve the location and source parameters of the largest event of the sequence (M w 5.1), which occurred on 22 June 2004. The mainshock caused a small but distinct ∼2:7 cm displacement signal in the InSAR data, which reveals where the earthquake took place and shows that seismic reports mislocated it by 3-16 km. With Bayesian estimation, we modeled the InSAR data using a finite-fault model in a homogeneous elastic halfspace and found the mainshock activated a normal fault, roughly 70 km southeast of the city of Tabuk. The southwest-dipping fault has a strike that is roughly parallel to the Red Sea rift, and we estimate the centroid depth of the earthquake to be ∼3:2 km. Projection of the fault model uncertainties to the surface indicates that one of the westdipping normal faults located in the area and oriented parallel to the Red Sea is a likely source for the mainshock. The results demonstrate how InSAR can be used to improve locations of moderate-size earthquakes and thus to identify currently active faults.

Summary Centroid moment-tensor (CMT) parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, CMTs are inferred as solutions to inverse problems which are generally under-determined and require significant assumptions, including assumptions about data noise. Broadly speaking, we consider noise to include both theory and measurement errors, where theory errors are due to assumptions in the inverse problem and measurement errors are caused by the measurement process. While data errors are routinely included in parameter estimation for full CMTs, less attention has been paid to theory errors related to velocity-model uncertainties and how these affect the resulting moment-tensor (MT) uncertainties. Therefore, rigorous uncertainty quantification for CMTs may require theory-error estimation which becomes a problem of specifying noise models. Various noise models have been proposed, and these rely on several assumptions. All approaches quantify theory errors by estimating the covariance matrix of data residuals. However, this estimation can be based on explicit modelling, empirical estimation, and/or ignore or include covariances. We quantitatively compare several approaches by presenting parameter and uncertainty estimates in non-linear full CMT estimation for several simulated data sets and regional field data of the Ml 4.4, 13 June 2015 Fox Creek, Canada, event. While our main focus is at regional distances, the tested approaches are general and implemented for arbitrary source model choice. These include known or unknown centroid locations, full MTs, deviatoric MTs, and double-couple MTs. We demonstrate that velocity-model uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. However, not all approaches perform equally well. Including theory errors by estimating non-stationary (non-Toeplitz) error covariance matrices via iterative schemes during Monte Carlo sampling performs best and is computationally most efficient. In general, including velocity-model uncertainties is most important in cases where velocity structure is poorly known.

With increasing availability and improving spatial and temporal resolution of geodetic data, more details of earthquake fault geometries and slip can be determined. Detailed estimates of fault model parameters are beneficial for better understanding of the earthquake mechanics at the different fault systems in the world. However, for the same earthquake, notably dissimilar coseismic fault-slip models have been produced by different authors depending on their diverse estimation methods and modeling assumptions, for example, regarding the fault geometry, elastic layering, smoothing parameters, etc. (Lay, 2018;Mai & Thingbaijam, 2014;Razafindrakoto et al., 2015). Bayesian inference of earthquake sources allows for constraining the posterior probability distributions of the different fault model parameters and can also take uncertain knowledge of the elastic layering, elastic parameters, etc. into account through model covariances or a priori constraints (

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