Fast Dispersion-Curve Inversion using Automatic Differentiation Gradient-Based Calculation - Irnaka, Theodosius Marwan; Hartantyo, Eddy (Makara Journal of Science, 2025)
Surface wave inversion is a crucial technique in geophysics for subsurface imaging. However, traditional methods can be computationally intensive, especially for complex models. This study introduces automatic differentiation (AD) as an efficient alternative to finite difference (FD) methods for gradient calculation in surface wave inversion. We compare AD and FD methods using three synthetic examples of varying complexity. Our results demonstrate that AD is significantly faster, with speed improvements of 3 to 12 times over FD, depending on model complexity. Moreover, AD requires up to 3 times less memory than FD. In terms of accuracy, AD provides gradient calculations that are exact up to machine precision, while FD is subject to truncation errors. This improved accuracy translates to more reliable inversion results, particularly for complex models. The efficiency and accuracy gains of AD are especially beneficial for gradient-based inversion methods in geophysics, where computational resources often limit the scale and complexity of problems that can be addressed. Our findings suggest that integrating AD into gradient-based inversion methods could significantly enhance subsurface imaging techniques across various geophysical applications.