Full Waveform Inversion (FWI) delivers high-resolution quantitative images and is a promising technique to obtain macro-scale physical properties model of the subsurface. In most geophysical applications, prior information, as those collected in wells, is available and should be used to increase the image reliability. For this, we propose to introduce three

Full‐waveform inversion is an appealing technique for time‐lapse imaging, especially when prior model information is included into the inversion workflow. Once the baseline reconstruction is achieved, several strategies can be used to assess the physical parameter changes, such as parallel difference (two separate inversions of baseline and monitor data sets), sequential difference (inversion of the monitor data set starting from the recovered baseline model) and double‐difference (inversion of the difference data starting from the recovered baseline model) strategies. Using synthetic Marmousi data sets, we investigate which strategy should be adopted to obtain more robust and more accurate time‐lapse velocity changes in noise‐free and noisy environments. This synthetic application demonstrates that the double‐difference strategy provides the more robust time‐lapse result. In addition, we propose a target‐oriented time‐lapse imaging using regularized full‐waveform inversion including a prior model and model weighting, if the prior information exists on the location of expected variations. This scheme applies strong prior model constraints outside of the expected areas of time‐lapse changes and relatively less prior constraints in the time‐lapse target zones. In application of this process to the Marmousi model data set, the local resolution analysis performed with spike tests shows that the target‐oriented inversion prevents the occurrence of artefacts outside the target areas, which could contaminate and compromise the reconstruction of the effective time‐lapse changes, especially when using the sequential difference strategy. In a strongly noisy case, the target‐oriented prior model weighting ensures the same behaviour for both time‐lapse strategies, the double‐difference and the sequential difference strategies and leads to a more robust reconstruction of the weak time‐lapse changes. The double‐difference strategy can deliver more accurate time‐lapse variation since it can focus to invert the difference data. However, the double‐difference strategy requires a preprocessing step on data sets such as time‐lapse binning to have a similar source/receiver location between two surveys, while the sequential difference needs less this requirement. If we have prior information about the area of changes, the target‐oriented sequential difference strategy can be an alternative and can provide the same robust result as the double‐difference strategy.

We compare various forms of single-arrival Kirchhoff prestack depth migration to a full-waveform, finitedifference migration image, using synthetic seismic data generated from the structurally complex 2-D Marmousi velocity model. First-arrival-traveltime Kirchhoff migration produces severe artifacts and image contamination in regions of the depth model where significant reflection energy propagates as late or multiple arrivals in the total reflection wavefield. Kirchhoff migrations using maximum-energy-arrival traveltime trajectories significantly improve the image in the complex zone of the Marmousi model, but are not as coherent as the finite-difference migration image. By carefully incorporating continuous phase estimates with the associated maximum-energy arrival traveltimes, we obtain single-arrival Kirchhoff images that are similar in quality to the finite-difference migration image. Furthermore, maximum-energy Green's function traveltime and phase values calculated within the seismic frequency band give a Kirchhoff image that is (1) far superior to a first-arrival-based image, (2) much better than the analogous high-frequency paraxial-ray Green's function image, and (3) closely matched in quality to the full-waveform finite-difference migration image.

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