AI Full Waveform Inversion (FWI)

 Full Waveform Inversion (FWI) is an advanced seismic imaging technique that creates highly detailed subsurface models by iteratively refining a velocity model to match the observed seismic data. FWI leverages the full seismic waveform, including both amplitude and phase information, to achieve a precise representation of subsurface properties, such as velocity, density, and even anisotropy.

FWI works by comparing the recorded seismic data with synthetic data generated from an initial subsurface model. The differences, or residuals, between these datasets are used to update the model iteratively. Each iteration aims to reduce the mismatch between the observed and synthetic waveforms, resulting in a progressively more accurate subsurface image.

This technique is particularly valuable in complex geological settings, where traditional seismic methods may struggle to resolve fine details or handle complicated structures. FWI is used in oil and gas exploration, geotechnical studies, and earthquake seismology to provide high-resolution images of the subsurface. Its ability to produce detailed and accurate models makes FWI a critical tool for reducing exploration risk and enhancing the understanding of subsurface geology.