Frequency-dependent traveltime tomography using fat rays: application to near-surface seismic imaging

Frequency-dependent traveltime tomography does not rely on the high frequency assumption made in classical ray-based tomography. By incorporating the effects of velocity structures in the first Fresnel volume around the central ray, it offers a more realistic and accurate representation of the actual physics of seismic wave propagation and thus, enhanced imaging of near-surface structures is expected. The objective of this work was to apply frequency-dependent first arrival traveltime tomography to surface seismic data that were acquired for exploration scale and near-surface seismic imaging. We adapted a fat ray tomography algorithm from global-earth seismology that calculates the Fresnel volumes based on source and receiver (adjoint source) traveltime fields. The fat ray tomography algorithm was tested on synthetic model data that mimics the dimensions of two field data sets. The field data sets are presented as two case studies where fat ray tomography was applied for near-surface seismic imaging. The data set of the first case study was recorded for high-resolution near-surface imaging of a Quaternary valley (profile length < 1 km); the second data set was acquired for hydrocarbon search (profile length > 10 km). All results of fat ray tomography are compared against the results of dassical ray-based tomography. We show that fat ray tomography can provide enhanced tomograms and that it is possible to recover more information on the subsurface when compared to ray tomography. However, model assessment based on the column sum of the Jacobian matrix revealed that especially the deep parts of the structure in the fat ray tomograms might not be adequately covered by fat rays. Furthermore, the performance of the fat ray tomography depends on the chosen input frequency in relation to the scale of the seismic survey. Synthetic data testing revealed that the best results were obtained when the frequency was chosen to correspond to an approximate wavelength-to-target depth ratio of 0.1. (C) 2016 Elsevier B.V. All rights reserved.