Abstract
Seismic modelling is necessary to understand elasticwave propagation in the subsurface. Modelling is costeffective and insightful, as long as adequate methods are used. An ideal seismic-modelling strategy is to generate complete synthetic seismograms for realistic earth models, then process them as performed with real seismic data. These complete seismograms are best obtained by full-wavefield (FW) approaches. FW methods are therefore used in extensive benchmarking studies, which may require the joint effort of several institutions owing to high resource costs. Though computer power continues to increase, we are still a long way from applying ideal modelling to all cases where synthetic data is necessary to constrain results. This is especially true in interpretation and sensitivity analysis, where the influence of multiple parameters must be assessed. In many situations, ray-based (RB) methods are suitable alternatives, especially when rays, traveltimes, etc., are useful information for the problem at hand (Gjoystdal et al., 2007). However, standard RB methods do not allow modelling of detailed target structures owing to smoothness requirements. Geoscientists needing seismic modelling of such targets will then often resort to 1D convolution (Lecomte et al., 2015). 1D convolution has been successfully used for decades, and is still the method behind most standard well calibration, seismic inversion, etc. However, its conceptual validity is in reality very limited.
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