A nonlinear multiscale inversion approach for ambient noise tomography

Ambient noise tomography (ANT) has been considerably used in the last decade in both academic and industrial research. In this work, we propose an innovative technique for ANT based on nonlinear multiscale inversions. Our method relies on a progressive increase in the model parametrization to reduce the nonlinearity of the inverse problem. The developed method is compared with conventional inversion schemes (linear and nonlinear), using different regularization techniques and two different network configurations. The inversion is tested on 22 different synthetic models including classical checkerboard tests. Furthermore, we performed the inversion using real data from a campaign in 2018 at Cumbre Vieja volcano (Canary Islands). The results obtained on both network configurations show an improvement compared to conventional linear and nonlinear inversion schemes, especially when the ray path density is low. This technique does not require expensive computational resources, making it convenient for small-scale industrial applications, especially in the framework of geothermal exploration.