Building accurate velocity models is of major interest for oil and gas companies because it improves the prospect evaluation and reduces the risk of geohazards. Full-waveform inversion (FWI) has become a popular method for finding high-resolution and accurate velocity and has been successfully applied to several offshore case studies. We develop a methodology that allows us to apply diving-wave FWI to a case study from the Colombian Caribbean area. The proposed diving-wave FWI methodology includes the wavelet estimation, velocity updates, and quality control (QC) processes. The QC is performed using the cost function, the crosscorrelation of the observed and synthetic gathers, and the analytical trace information. The first QC tool indicates the difference between observed and synthetic gathers; the other two verify that the observed and synthetic data are not cycle skipped. The velocity model obtained with the proposed methodology is the first successful case study performed in the Colombian Caribbean region. From the obtained model, we conclude that FWI is able to build a velocity model with better-resolved shallow depth areas that assist the subsequent pore pressure prediction and imaging processes.

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A method of Full Waveform Inversion on GPR data for the estimation of subsurface electrical properties such as relative permittivity and conductivity is proposed in this paper. The GPR radar antenna used for subsurface data acquisition is a B-scan acquisition and it operates at a center frequency of 400 MHz. B-scan acquisitions are a challenge in the subsurface parameter estimation process due to lack of illumination. In addition, B-scan acquisitions are more sensitive to the starting point in estimating subsurface parameters in comparison to multiple offset acquisitions. However, despite the challenges, this type of acquisition is used because it allows portability in areas of difficult access and quick data collection, reducing processing times and costs. In this work, Full Waveform Inversion with cost function constraints was evaluated to estimate subsurface relative permittivity and conductivity using B-scan acquisitions. The proposed methods were evaluated using data collected in a study area located in Mogotes, Santander, Colombia. From the results obtained, it can be concluded that the use of regularization in the inversion process gives smoother subsurface models, also preserving discontinuities. In addition, the incoherent noise in the data is reduced by Gaussian regularization, allowing a better interpretation of the study area.

The accurate simulation of seismic surface waves on complex land areas requires elastic models with realistic near-surface parameters. The SEAM Phase II Foothills model, proposed by the SEG Advanced Modeling (SEAM) Corporation, is one of the most comprehensive efforts undertaken by the geophysics community to understand complex seismic wave propagation in foothills areas. However, while this model includes a rough topography, alluvial sediments, and complex geologic structures, synthetic data from the SEAM consortium do not reproduce the qualitative characteristics of the scattering energy that is generally interpreted as the “ground roll energy cone” on shot records of real data. To simulate the scattering, a near-surface elastic model in mountainous areas ideally must include the following three elements: (1) rough topography and bedrock, (2) low-velocity layer, and (3) small-scale heterogeneities (size approximately Rayleigh wavelength). The SEAM Foothills model only includes element (1) and, to a lesser extent, element (2). We represent a heterogeneous near surface as a random medium with two parameters: the average size of the heterogeneities and fractional fluctuation. A parametric analysis shows the influence of each parameter on the synthetic data and how similar it is compared to real data acquired in a foothills area in Colombia. We perform the analysis in the shot gather panel and dispersion image. Our study shows that it is necessary to include the low-velocity layer and small-scale distributed heterogeneities in the shallow part of the SEAM model to get synthetic data with realistic scattered surface-wave energy.

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