Automatic early arrival traveltime tomography and its applications

Abstract

We have developed a method of macrovelocity inversion that does not require explicit picking of either common-image point gathers or first breaks. The method uses head waves, diving waves, and wide-angle reflections in seismic data (collectively early arrival energies) for accurate estimation of velocity and anisotropy parameters. In this method, seismic data are first decomposed into Gaussian packets. Packets associated with early arrival energies are selected and used as input to a tomography solver. The outputs of the solver are velocity and Thomsen’s anisotropy parameters, or any of their combinations. Using information contained in the packets, we can correctly model the early arrival energies (first breaks and/or other refractions). The workflow is fully automatic and can be used in a batch processing environment with minimum human intervention. We have tested the method on synthetic and field data sets. In one synthetic test, we were able to reduce traveltime residuals of diving waves from 400 to 5 ms and recover anisotropic model parameters that are sensitive to early arrival traveltimes. In another synthetic test, we were able to recover a large shallow low-velocity anomaly with a very simple starting velocity model. The first field data set was for a shallow marine seismic data project. We were able to obtain a better shallow velocity model using our method than when using a legacy approach. In the second field data test, we applied our method on a deepwater data set from a dual-coil acquisition, with full-azimuth and long-offset coverage. Our method can correctly model early arrival energies recorded at long offsets and use them in the iterative inversion such that better estimation of velocities and anisotropy parameters in shallow sediments can be achieved. We have tested different starting models for the inversion. We are able to get very similar results, suggesting that our method is not sensitive to the accuracy of a starting model.