1 citations · 1 across the 3 of their papers we have counts for
4 papers
Theory-guided Pseudo-spectral Full Waveform Inversion via Deep Neural Networks
Christopher Zerafa, Pauline Galea, Cristiana Sebu
Full-Waveform Inversion seeks to achieve a high-resolution model of the subsurface through the application of multi-variate optimization to the seismic inverse problem. Although no…
Data-Driven Pseudo-spectral Full Waveform Inversion via Deep Neural Networks
Christopher Zerafa, Pauline Galea, Cristiana Sebu
FWI seeks to achieve a high-resolution model of the subsurface through the application of multi-variate optimization to the seismic inverse problem. Although now a mature technolog…
Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging
Christopher Zerafa, Pauline Galea, Cristiana Sebu
This review explores the integration of deep learning (DL) with full-waveform inversion (FWI) for enhanced seismic imaging and subsurface characterization. It covers FWI and DL fun…
Learning to Invert Pseudo-Spectral Data for Seismic Waveform Inversion
Christopher Zerafa, Pauline Galea, Cristiana Sebu
Full-waveform inversion (FWI) is a widely used technique in seismic processing to produce high resolution Earth models that fully explain the recorded seismic data. FWI is a local…