ALBERT

Description: The causes for the formation of large igneous provinces and hotspot trails are still a matter of considerable dispute. Seismic tomography and other studies suggest that hot mantle material rising from the core-mantle boundary (CMB) might play a significant role in the formation of such hotspot trails. An important area to verify this concept is the South Atlantic region, with hotspot trails that spatially coincide with one of the largest low-velocity regions at the CMB, the African large low shear-wave velocity province. The Walvis Ridge started to form during the separation of the South American and African continents at ca. 130 Ma as a consequence of Gondwana breakup. Here, we present the first deep-seismic sounding images of the crustal structure from the landfall area of the Walvis Ridge at the Namibian coast to constrain processes of plume-lithosphere interaction and the formation of continental flood basalts (Paraná and Etendeka continental flood basalts) and associated intrusive rocks. Our study identified a narrow region (〈100 km) of high-seismic-velocity anomalies in the middle and lower crust, which we interpret as a massive mafic intrusion into the northern Namibian continental crust. Seismic crustal reflection imaging shows a flat Moho as well as reflectors connecting the high-velocity body with shallow crustal structures that we speculate to mark potential feeder channels of the Etendeka continental flood basalt. We suggest that the observed massive but localized mafic intrusion into the lower crust results from similar-sized variations in the lithosphere (i.e., lithosphere thickness or preexisting structures).

Description: 〈span〉〈div〉SUMMARY〈/div〉We present a Bayesian approach to solve the problem of simultaneous inversion for optimal hypocentre parameters and 1-D velocity models as well as station corrections for a given set of local earthquakes utilizing a hierarchical, transdimensional Markov chain Monte Carlo (McMC) algorithm. The simultaneous inversion is necessary because of the velocity–hypocentre coupling inherent to the problem.Tests with synthetic arrival time data indicate an excellent performance of the approach, at the same time benefiting from all the advantages related to the McMC algorithm. These advantages are that only minimum prior knowledge is used (i.e. regarding starting focal coordinates, initial velocity model, which are set to random initial values), no regularization parameters (e.g. damping) have to be selected, and the parametrization of the velocity model (i.e. model nodes/layers) is automatically set and adjusted according to the quality of the data, that is noise level. By minimizing the amount of pre-inversion assumptions, which are regularly not available at the required precision or often only available after very careful and time-consuming assessment, the inversion results are therefore almost exclusively data-driven. On output, we obtain a suite of well fitting models which can statistically be analysed and provide direct estimates of the posterior uncertainties of the models.Tests with real arrival time data from a temporary local network deployed in South-Central Chile in 2004 and 2005 show a very good agreement with the results obtained with a conventional inversion method.〈/span〉

Description: 〈span〉〈div〉Summary〈/div〉We present a Bayesian approach to solve the problem of simultaneous inversion for optimal hypocenter parameters and 1-D velocity models as well as station corrections for a given set of local earthquakes utilizing a hierarchical, transdimensional McMC algorithm. The simultaneous inversion is necessary because of the velocity-hypocenter coupling inherent to the problem.Tests with synthetic arrival time data indicate an excellent performance of the approach, at the same time benefiting from all the advantages related to the McMC algorithm. These advantages are that only minimum prior knowledge is used (i.e. regarding starting focal coordinates, initial velocity model, which are set to random initial values), no regularization parameters (e.g. damping) have to be selected, and the parameterization of the velocity model (i.e. model nodes/layers) is automatically set and adjusted according to the quality of the data, i.e. noise level. By minimizing the amount of pre-inversion assumptions, which are regularly not available at the required precision or often only available after very careful and time-consuming assessment, the inversion results are therefore almost exclusively data-driven. On output, we obtain a suite of well fitting models which can statistically be analyzed and provide direct estimates of the posterior uncertainties of the models.Tests with real arrival time data from a temporary local network deployed in South-Central Chile in 2004 and 2005 show a very good agreement with the results obtained with a conventional inversion method.〈/span〉