Seismic imaging is crucial for subsurface exploration and monitoring, with a focus on deep and complex structures. Seismic wave migration solves the wave equation, and an accurate propagator is essential. Full Wavefield Modeling (FWMod) was developed based on recursive and iterative up/down wavefield propagation, modeling both primaries and multiples. Embedded within Full Wavefield Migration (FWM) it can be used to image data including multiples, resulting in better illumination in case primary illumination is not sufficient. FWM can be efficient and effective, but conventional one-way wave operators, such as Phase Shift Plus Interpolation Migration, have limitations in strongly inhomogeneous media. Local velocity-based one-way operator based on eigen decomposition was proposed and integrated within FWMod and FWM in this study, improving image amplitudes and fidelity and improving converage speed in the least-squares inversion process.

Considering the steadily declining prices in the oil and gas industry, nowadays, the requirement for geophysical information becomes more important in order to get the most out of available reservoirs. Electromagnetic measurements could complement seismic data where it lacks information. The EM response to fluid fill complements the resolving power of seismic data. In the current study, we use a probabilistic method to estimate reservoir parameters individually and jointly through two simplistic, synthetic, 2D reservoir models which can be considered as the geometrical limits of water-oil-contacts in oil and gas fields. We demonstrate a constructive contribution of the measurements with different physical natures in the estimation of reservoir parameters.

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Reflection waveform inversion (RWI) is a method that relies on primary pure reflection data to recover the subsurface background velocity based on the associated evolving seismic images. Background velocity updates estimated by conventional RWI are nonoptimal, which is partly attributed to lowresolution tomographic wavepaths and migration isochrones. Preconditioning RWI sensitivity kernels using Hessian information solves this problem but is not practical for a large number of model parameters. One-way reflection waveform inversion (ORWI) is a reflection waveform tomography technique in which the forward modeling scheme operates in one direction (downward and then upward) via virtual parallel data levels in the medium. The ORWI framework allows us to break down the Hessian matrix into smaller operators, which makes the preconditioning operation more efficient and less computationally expensive. This extended abstract turns conventional ORWI into a high-resolution but computationally feasible ORWI (Gauss-Newton ORWI) to improve the nonoptimal background velocity updates.