View my recently published article on "P and S wave travel time tomography of the SE Asia-Australia

Votemap for all four models produced by the different P-wave datasets (P_A, P_B, P_C, P_D). Positive velocity perturbations (dv/v > 0) were assigned the value 1 for each model. Thus, in places where all the models exhibit a positive perturbation, the votemap has a value of 4, and where all the models exhibit a negative perturbation, the votemap has a value of zero.

The southeast (SE) Asia - Australia collision zone is one of the most tectonically active and seismogenic regions in the world. Here, we present new 3-D P- and S-wave velocity models of the crust and upper mantle. A machine learning algorithm that clusters earthquakes depending on their spatiotemporal density was then applied to significantly improve the consistency of travel-time picks. We performed an iterative non-linear tomographic inversion by applying the software package FMTOMO to retrieve 3-D velocity and interface structures from starting 1-D velocity and Moho models.

https://doi.org/10.1016/j.pepi.2019.05.010

#earthquakes #traveltime #tomography #seasia #machinelearning #DBSCAN