Adaptive traveltime tomography of densely sampled seismic data.

We present a new 2-D traveltime tomography method for the inversion of densely sampled seismic streamer data. This method was especially designed for the efficient inversion of long-offset multichannel data. A layer–interface model is used to fit ray-traced traveltime data to observed seismic data. The solution of the forward problem is based on initial-value ray tracing in a triangulated grid with a linear interpolation of the squared slowness. We implement an adaptive model parametrization based on ray density, which allows for smaller velocity cells with subsequent iteration steps. We solve the inverse problem using an iterative linearized joint inversion of reflection and refraction data for interface and velocity structures. Adaptive smoothing regularization is implemented in the form of a priori model covariances. As the cell sizes decrease with increasing iteration numbers, the model covariance ranges are reduced, allowing for more detail to emerge in the model. We demonstrate the algorithm's ability to invert successfully a realistic crustal velocity structure in a synthetic model. Several adaptive and non-adaptive model parametrizations are tested. The joint interface and velocity inversion of real long-offset reflection and refraction traveltime data is presented as a second example. We demonstrate that our results are in good agreement with independently derived velocity models