SiameseFWI: A Deep Learning Network for Enhanced Full Waveform Inversion
Omar M. Saad, Randy Harsuko, Tariq Alkhalifah
Abstract
Abstract The performance of full‐wave inversion (FWI) depends highly on how we compare the simulated data to observed ones. The simplified assumptions used to generate the simulated data make such comparison even harder. To address this challenge, we introduce SiameseFWI, a novel approach to FWI that plays a critical role in the comparative analysis of simulated and observed seismic data. Employing a Siamese network, this methodology transforms the data into a shared latent space, enabling a robust and effective comparison of data representations. SiameseFWI leverages two identical Convolutional Neural Networks with shared weights trained in a self‐supervised framework, eliminating the necessity for labeled data. In each FWI iteration, the Siamese network and the velocity model are updated to minimize Euclidean distance loss between the latent representations of the data. Empirical evaluation conducted on the Marmousi2 and Overthrust models affirms the robust inversion performance of SiameseFWI compared to traditional FWI methodologies. Furthermore, its application to field data from Western Australia demonstrates its strength and efficacy in inversion. Notably, SiameseFWI exhibits robust inversion performance even in the presence of noise or when employing a linear initial model.