Joining statistics and geophysics for assessment and uncertainty quantification of three-dimensional seismic Earth models.

Seismic inversions produce seismic models, which are 3-dimensional (3D) images of wave velocity of the entire planet retrieved by fitting seismic measurements made on records of past earthquakes or other seismic events. Computing power of the TeraFlop era, along with the dataflow from new, very dense, seismic arrays, has led to a new generation of 3D seismic Earth models with an unprecedented level of resolution. Here we compare two recent models of western United States from the Dynamic North America (DNA) seismic imaging effort. The two models only differ in the wave propagation that was used for their inversion: one is based on ray theory (RT), and the other on finite frequency (FF). We evaluate the two models using an independent numerical method and statistical tests. We show that they differ in how they produce seismic signals from a subset of earthquakes that were used in the original inversion and were recorded on the US array. This is especially true for measurements done in the Yellowstone area which has a large negative seismic anomaly. This result is of importance for seismologists who have been debating on the practical benefit of using FF in ill-posed Earth inversions. Model evaluation, such as the one reported here, represents an opportunity for collaboration between geophysical and statistical communities. More opportunities should arise with the upcoming Exascale era, which will provide enough computational power to explore together several sources of errors in models with thousands of parameters, opening the way of uncertainty quantification of seismic models.