High-Resolution Subsurface Characterization Using Seismic Inversion—Methodology and Examples

Subsurface characterization for lithological and fluid properties is important for all aspects of geophysical exploration where estimating a high-resolution elastic property through seismic inversion is vital. Starting with an initial subsurface model, computing synthetic or predicted seismic data, and matching these data with observed seismic data, seismic inversion uses an optimization process to iteratively modify the initial model until the prediction reasonably matches the observation. Routine applications of seismic inversion for subsurface reservoir characterization are currently restricted to amplitude-variation-with-angle inversion, which uses convolution as the basis for forward modeling to compute synthetic seismic data. Although computationally efficient, the inherent convolutional assumption ignores complex wave propagation effects and often fails to estimate subsurface models with sufficient accuracy. Here, we review the current state of the art for seismic inversion, and we discuss a method that uses an analytical wave equation solver for forward modeling and a global method for optimization that can overcome the current limitations of amplitude-variation-with-angle inversion. Using real seismic data, we demonstrate the accuracy of this method. Because this waveform-based method is computationally demanding, we also discuss the current advances of computational technology, including artificial intelligence that can improve its computational efficiency.