Ali Almomin

On the separation of simultaneous‐source data by inversion

Simultaneous-source data can be adequately separated using an inversion formulation. To recover component shot records, we formulate the data-separation as a simultaneous Radon inversion problem. By minimizing the resulting objective function with a robust hybrid solver, we obtain high-quality estimates of the component shot records. Furthermore, regularization with directional Laplacians improves the data quality. In our approach, we estimate a single model that predicts all recorded data, and we treat all components of the recorded data as signal. Our method can be applied to any number of sources within a single survey and can be easily extended to multiple (time-lapse) surveys. Using 2D sections the SEAM model and simultaneous-source data from a 2D land data set, we show that our method can give results of quality comparable to the reference independent shot records.

Tomographic Full Waveform Inversion (TFWI) by Extending the Velocity Model along the Time-lag Axis

The extension of the velocity model along the subsurface-offset axes enables to define a tomographic full waveform inversion that converges robustly towards high-resolution velocity models. However the resulting numerical algorithms are extremely expensive and their performance is uncertain when transmitted events (e.g. diving waves) are present in the data. We discuss the advantages of extending the velocity model along the time-lag axis instead of the subsurface offsets. This extension leads to a linearization of the wave equation that overcomes the well-known limitation of the first-order Born approximation to model large time shifts in the data.

Correlation-based wave-equation migration velocity analysis

Wave equation migration velocity analysis (WEMVA) is a family of techniques that aims to improve the subsurface velocity model by minimizing the residual in image space. However, since the true image unknown, measuring the residual in image space is a challenge for WEMVA techniques. In his paper, I present a new method of measuring the image perturbation that is based on the cross-correlation of the observed image with a reference image in reflection angle gathers. I derive the gradient of this technique and show that it does not have the problem of cycle skipping and could be easily automated. I then show some synthetic examples and compare its gradient to the optimum WEMVA gradient.