A Siamese Network-based Full Wave Inversion: Application on Real Data

Full waveform inversion (FWI) hinges on accurately comparing the observed and simulated seismic data, a task complicated by the sinusoidal nature of the wavefield and the simplified assumptions (like acoustic) used to simulate data. To overcome this challenge, we introduce SiameseFWI, a framework that incorporates a Siamese network to transform data into a shared latent representation, enhancing comparative analysis. The Siamese network employs two identical Convolutional Neural Networks (CNNs) with shared weights to ensure consistent feature extraction from observed and simulated data. The primary goal of SiameseFWI is to minimize Euclidean distance loss between the latent representations of observed and simulated data. The parameters of the Siamese network are optimized in an unsupervised manner during the FWI process. This integration leads to improved FWI performance without imposing a significant computational cost. Empirical assessments, including the Overthrust model, consistently demonstrate SiameseFWI's superiority over traditional FWI methods. Furthermore, practical validation with real field data from Western Australia emphasizes the robust inversion performance of the SiameseFWI.