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IXPUG Annual Conference 2025 technical talk "Wavenumber Recovery by 2D Frequency-Domain FWI from Slowness- and Source-Frequency-Limited Elastic Seismic Data" presented by Sasmita Mohapatra, University of Texas at Dallas on April 15, 2025. Co-Author: George McMechan, University of Texas at Dallas. Abstract: Full-Waveform Inversion (FWI) is a powerful technique for constructing high-resolution subsurface images, provided that data with broad frequency bands and wide-angle (in 2D) or azimuth (in 3D) apertures are available. Advancements in inversion techniques have significantly enhanced the efficiency of elastic full-waveform inversion (EFWI), achieving an order-of-magnitude performance gain. A multistep-length gradient approach is employed to optimally weight each parameter gradient, stabilizing nonlinear solutions and accelerating convergence to just a few tens of iterations rather than the hundreds typically required. Wavefield extrapolations are performed using parallelized, high-precision finite-element (FE) modeling in the time domain, while inversion is conducted in the frequency domain using discrete Fourier transforms at each time step. Key strategies such as frequency selection, time windowing, and source wavelet estimation mitigate cycle skipping and remove artifacts caused by variations in source spatial patterns. The inversion was validated on a two-dimensional elastic model with a circular anomaly and an elastic Marmousi-2 model using synthetic data generated by a finite-difference modeling scheme, demonstrating robust performance even under a constant-density assumption. Mohapatra and MacMechan (2021) established a numerical relationship between the wavenumbers in the illuminating wavefield and those that can be reconstructed in a target through migration or inversion. Synthetic elastic examples for models containing finite bandwidths illustrate how the ability to recover wavenumbers is limited by the wavenumber information that is contained in the illuminating wavefield, and by the sampling of the data in both time and space. The computation for all 60 iterations was parallelized across sources using MPI on 56 Intel Xeon X5650 CPU cores, completing in approximately 90 hours (Mohapatra & McMechan, 2021).
Event Name
IXPUG Annual Conference 2025
Keywords
IXPUG Annual Conference 2025,Full Waveform Inversion (FWI),Elastic Full Waveform Inversion (EFWI),Marmousi-2 Model,FFT,Cycle Skipping,Wavenumber,Inversion,Frequency Domain