A New Strategy to Compare Inverted Rupture Models Exploiting the Eigenstructure of the Inverse Problem

Model name; Method | Description | Data processing |
---|---|---|

Gallovic0.01; Gallovič et al. (2015) |
Linear multitime window inversion approach with long duration of slip rate functions (equal to the assumed duration of the rupture process). Constraints: (1) smoothing by means of a prior k^{-2} covariance functions, and (2) positivity of the slip rate function. The smoothing weight is relatively small (perhaps not applicable in real-data application). |
Butterworth bandpass filter in range of 0.05–0.5 Hz (four poles, causal) |

Gallovic0.1; Gallovič et al. (2015) |
Same as Gallovic0.01 but with more severe smoothing (similar to that used in real-data applications). | Same as Gallovic0.01 |

Hoby; Razafindrakoto and Mai (2014) | Parametric (single-time window) inversion assuming triangular slip rate function. Parameters: rupture times, rise times, and peak slip rates. Metropolis algorithm is used to optimize the parameters considering L2 norm. | Butterworth bandpass in range of 0.01–1 Hz |

CedricT3; Twardzik et al. (2012) |
Simplified source model considering two elliptical subfault patches, triangular slip rate function, and constant rupture velocities along the patches. Parameters: location and size of the ellipses, rupture velocities, and onset times of the subfault patches. Neighborhood algorithm is used to find the best fitting parameters considering L2 norm. | Butterworth bandpass filter in range of 0.1–1.0 Hz (four poles, two passes—acausal) |

Asano; Sekiguchi et al. (2000) |
Multitime window linear inversion with spatiotemporal smoothing constraint. Weight of smoothing is determined by minimizing Akaike Bayesian information criterion. | Bandpass filter in range of 0.05–1.0 Hz |

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