These python routines accompany the paper titled "Optimized Workflows for Efficient and Stable Empirical Green's Function Computation in Ambient-Noise Tomography" by Caio Ciardelli, Yoweri Nseko, Albert Kabanda, and Suzan van der Lee.
$ python correlate_noise_swarm.py
$ ./plot_correlation_swarm.bashCiardelli, C., Nseko, Y., Kabanda, A., & Van der Lee, S. (2025). Optimized Workflows for Efficient and Stable Empirical Green's Function Computation in Ambient-Noise Tomography.
This notebook provides a practical implementation of the methods described in the paper, including step-by-step guidance and code examples for applying the optimized workflows in the computation of Empirical Green’s Functions (EGFs) for ambient-noise tomography.
Please refer to the paper for a detailed explanation of the methodology and theoretical background.