GITEC: A Generalized Inversion Technique Benchmark

Abstract

Generalized inversion techniques (GITs) have become popular for determining seismological parameters (e.g., source, attenuation, and site response), particularly in low-to-moderate seismicity regions. Indeed, GITs can potentially provide reliable site-response estimates when a minimum number of recordings is available, as well as valuable information about source parameters and regional attenuation characteristics. Significant advances have been made on GITs in which different approaches and hypotheses were investigated, such as the application of “nonparametric” and “parametric” inversion schemes. In this context, several scientific questions have arisen that depend on the final scope of the GITs: What is the optimal inversion strategy for a given dataset configuration? What is the impact of the different choices, assumptions, and implementations on the reliability of the results? Is it possible to quantify the associated epistemic uncertainties? Here, we have considered and compared the different approaches of GITs to improve the understanding of each for use in different applications. A methodological benchmark that includes different GIT methods and dataset configurations is set up to fulfill the objective, using a simple synthetic dataset, a French regional sparse dataset, and an Italian national dense dataset. The benchmark is developed in two phases: (1) phase I: a free phase with no common constraints; and (2) phase II: a constrained phase with unified reference conditions. Despite unifying the reference conditions in the different inversions, the variability was not reduced. Discrepancies are observed between different terms of GITs. Site responses appear to be the most robust estimates, compared to source and attenuation terms. The way that stress drops of earthquakes and quality factors for crustal attenuation are parameterized appears to lead to significant variability between different approaches. Finally, uncertainties are addressed by quantification of the inter-method variability for the different terms and parameters.

On the Limitations of Spectral Source Parameter Estimation for Minor and Microearthquakes

“…These results might also be extended to the spectral fitting carried out on the generalized inversion technique (GIT) source spectra (e.g., Anderson, 1986;Castro et al, 1990) because they are obtained using simplified attenuation models. Furthermore, when the number of stations used in the inversion is also limited (or poorly distributed), it cannot be guaranteed that the estimated average source model is not biased by propagation effects (e.g., Shible et al, 2022).…”

“…Furthermore, using a frequency-dependent Q in the spectral fitting procedure might improve the fitting, but it would generate additional trade-off between the parameters (e.g., Drouet et al, 2008;Shible et al, 2022). This is obviously due to, for example, the fact that Q is included in an exponential term that interferes with the source spectral decay.…”

“…When dealing with real data, collected also by networks with stations in sedimentary basins, a simple 1D approximation for the correction of the recorded spectra may not be sufficient (e.g., Imperatori and Mai, 2012;Pitarka and Mellors, 2021), and a frequency-dependent Q might help in improving the fit to the observation. However, the well-known trade-off between the attenuation parameters might still limit the source parameter estimation, (e.g., Shible et al, 2022). These issues, as well as a comparison of different spectral fitting techniques on synthetic data, are deserving attention for future studies.…”

The SCEC/USGS Community Stress Drop Validation Study Using the 2019 Ridgecrest Earthquake Sequence

“…Various studies have investigated the effects of differences in methods or data selection, including Shearer et al (2019), Goertz-Allmann andEdwards (2013), Abercrombie (2015), Chen and Abercrombie (2020), Pennington et al (2021), andShible et al (2022); Abercrombie (2021) provides a broad review of the difficulties, uncertainties and methods in stress drop estimation and comparison. These studies found that although methodological differences can lead to some system-atic biases, the main differences come from the simplifying assumptions and model parameterization, and the limited quality and quantity of the data.…”