LAUSR

Near-surface solutions often play a significant role in imaging the subsurface structures for land or shallow marine environments. Unfortunately, the standard approach to first-arrival traveltime tomography may involve the inversion of a large number of traveltime picks and require a considerable computational effort. We propose to improve the efficiency of traveltime tomography by adopting a method inspired by the field of stochastic optimization. First, we verify that traveltime tomography is solvable by two methods in the field of stochastic optimization. These methods are named Sample Average Approximation (SAA) and Stochastic Approximation (SA). In the SAA, random subsets of the whole data are inverted via non-linear optimization. The final result is the average of the all inverted models. SA is similar to the SAA method. However, in the SA method new random data subsets are used in each iteration of the non-linear iterative inversion. The final result is also the average of multiple inversions. We f...