Earth-printshttps://www.earth-prints.orgThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 17 Aug 2022 15:41:50 GMT2022-08-17T15:41:50Z5021A retrospective comparative forecast test on the 1992 Landers sequencehttp://hdl.handle.net/2122/7294Title: A retrospective comparative forecast test on the 1992 Landers sequence
Authors: Woessner, J.; Hainzl, S.; Marzocchi, W.; Werner, M. J.; Lombardi, A. M.; Catalli, F.; Enescu, B.; Cocco, M.; Gerstenberger, M.; Wiemer, S.
Abstract: We perform a retrospective forecast experiment on the 1992 Landers sequence comparing the predictive power of commonly used model frameworks for short‐term earthquake forecasting. We compare a modified short‐term earthquake probability (STEP) model, six realizations of the epidemic‐type aftershock sequence (ETAS) model, and four models that combine Coulomb stress changes calculations and rate‐and‐state theory to generate seismicity rates (CRS models). We perform the experiment under the premise of a controlled environment with predefined conditions for the testing region and data for all modelers. We evaluate the forecasts with likelihood tests to analyze spatial consistency and the total amount of forecasted events versus observed data. We find that (1) 9 of the 11 models perform superior compared to a simple reference model, (2) ETAS models forecast the spatial evolution of seismicity best and perform best in the entire test suite, (3) the modified STEP model matches best the total number of events, (4) CRS models can only compete with empirical statistical models by introducing stochasticity in these models considering uncertainties in the finite‐fault source model, and (5) resolving Coulomb stress changes on 3‐D optimally oriented planes is more adequate for forecasting purposes than using the specified receiver fault concept. We conclude that statistical models perform generally better than the tested physics‐based models and parameter value updates
using the occurrence of aftershocks generally improve the predictive power in particular for the purely statistical models in space and time.
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/2122/72942011-01-01T00:00:00ZInferring radial models of mantle viscosity from gravity (GRACE) data and an evolutionary algorithmhttp://hdl.handle.net/2122/5884Title: Inferring radial models of mantle viscosity from gravity (GRACE) data and an evolutionary algorithm
Authors: Soldati, G.; Boschi, L.; Deschamps, F.; Giardini, D.
Abstract: Convective flow in the mantle can be thought of (and modeled) as exclusively driven by density hetero-
geneities in the mantle itself, and the resulting lateral variations in the Earth’s gravity field. With this
assumption, and a model of mantle rheology, a theoretical relationship can be found between 3D mantle
structure and flow-related quantities that can be measured on the Earth’s surface, like free-air gravity
anomalies. This relationship can be used to set up an inverse problem, with 1D mantle viscosity as a solu-
tion. In the assumption that seismic velocity anomalies be of purely thermal origin, and related to density
anomalies by a simple scaling factor, we invert the large-scale length component of the above-mentioned
measurements jointly with seismic observations (waveforms and/or travel times) to derive an accurate
5-layer spherically symmetric model of upper- and lower-mantle viscosity. We attempt to account for
non-uniqueness in the inverse problem by exploring the solution space, formed of all possible radial pro-
files of Earth viscosity, by means of a non-deterministic global optimization method: the evolutionary
algorithm (EA). For each sampled point of the solution space, a forward calculation is conducted to deter-
mine a map of gravity anomalies, whose similarity to GRACE (gravity recovery and climate experiment)
is then measured; the procedure is iterated to convergence, according to EA criteria. The robustness of
the inversion is tested by means of synthetic tests, indicating that our gravity data set is able to constrain
less than 6 radial layers, each with uniform viscosity. Independently of the tomographic model or the
scaling factor adopted to convert seismic velocity into density structure, the EA optimization method
finds viscosity profiles characterized by low-viscosity in a depth range corresponding to the transition
zone, and relatively uniform elsewhere.
Tue, 01 Sep 2009 00:00:00 GMThttp://hdl.handle.net/2122/58842009-09-01T00:00:00Z