A 3D acoustic temperature field reconstruction algorithm using adaptive regularization parameter

Acoustic temperature field reconstruction is an illposed problem. This means especially that their solution is unstable under data perturbations. Numerical methods that can cope with this problem are the so-called regularization methods. The choice of a regularization parameter has an important influence on reconstruction accuracy. In this paper, a 3D acoustic temperature field reconstruction algorithm is proposed. It uses a new criterion for choosing the regularization parameter, which makes a good compromise between de-noise and detail reconstruction of the temperature field. Reconstructions of a single-peak temperature field by using simulated travel-times corrupted with different level noises demonstrate that the new algorithm has high reconstruction accuracy and good anti-noise ability.