Frequency-dependent traveltime tomography for near-surface seismic refraction data

Abstract

Traveltime tomography is the main method bywhich the Earth's seismic velocity is determined on all scales, from the near-surface (<100 m) to the core. Usually traveltime tomography uses ray theory, an infinite-frequency approximation of wave propagation. A theory developed in global seismology to account for the finite-frequency nature of seismic data, known as finitefrequency traveltime tomography (FFTT), can theoretically provide a more accurate estimation of velocity.But the FFTT theory is generally not applicable to near-surface data because there is no reference velocity model known in advance that is capable of yielding synthetic waveforms that are close enough to the recorded seismograms to yield a reliable delay time. Also, there is usually no reference model for which the unknown velocity model represents a small (linear) perturbation from the reference model. This paper presents a frequency dependent form of non-linear traveltime tomography specifically designed for near-surface seismic data in which a starting model, iterative approach with recalculated travel paths at each iteration, and the calculation of a frequency-dependent total traveltime, as opposed to a delay time, are used. Frequency-dependent traveltime tomography (FDTT) involves two modifications to conventional traveltime tomography: (1) the calculation of frequency-dependent traveltimes usingwavelength-dependent velocity smoothing(WDVS) and (2) the corresponding sensitivity kernels that arise from using WDVS. Results show that the former modification is essential to achieve significant benefits from FDTT, whereas the latter is optional in that similar results can be achieved using infinite-frequency kernels. The long seismic wavelengths relative to the total path lengths and the size of subsurface heterogeneities of typical near-surface data means the improvements over ray theory tomography are significant. The benefits of FDTT are demonstrated using conventional minimum-structure regularization techniques to address the issue of model non-uniqueness. For synthetic data, the estimated FDTT models are shown to be more accurate than the corresponding infinite-frequency-derived models. Both 2-D and 3-D applications of FDTT to real data from a near-surface study yield estimated models that contain more structure than the corresponding infinite-frequency-derived models. Applications of FDTT without regularization demonstrate the potential of the WDVS-derived sensitivity kernels to provide a natural smoothing of the velocity model and thereby allow the data alone to determine the final model structure.