In 1988 a complex 2D model was created and synthetic seismie data were generated from this model. Model and data were designed specifically by the IFP for the blind test of the Copenhagen workshop. This artiele describes the philosophy bebind the model and the creation of model and data. The Model Philosophy behind the modelBefore the creation of the model several demands were formulated to which the model (and the data) should adhere. First, the model should be geologically plausible, meaning that it preferably should be based on a real situation. Second, the model should be complex, meaning that it must contain many reflectors, steep dips and strong velocity gradients in both vertical and lateral directions. Thus, the data should be so complex that the assumptions on which conventional processing relies should not hold: obtaining a good image from the data should require the use of depth imaging methods like prestack depth migration or inversion.Obviously the creation of such a model requires the cooperation of geologists, geophysicists and computer scientists. Model generationThe Marmousi model is based on a profile through the North Quenguela trough in the Cuanza basin (Angola) (fig. 1) as described in Verrier and Branco (1972).Based on this section a geometrie model containing 160 layers was created using the MIMIC™ module of the SIERRA package (colour figure 1). Then the velocity and density distributions were defined by introducing realistic horizontal and vertical velocity gradients. The velo city distribution in the shallower part of the series (the detrital series) has a high vertical gradient to model the strong compaction. This model was transformed into a 2-D velocity/density grid (colour figures 2 and 3) with dimensions 9200 meters (horizontal distanee) by 3000 meters (depth) with a grid size of 4 meters (both horizontal and vertical). Geological model descriptionThe geological model of the basin consists of:• A deltaic sediment interval, thickening from west to east, deposited upon a saliferous evaporitic series. In the eastern part, which seems to he more affected by the continuous lateral salt creep, normal growth faults are developed.• presaliferous folded carbonate platform deposits, in which a structural hydrocarbon trap is expected. :1;1 1 Q)s:s:.~s:~a .-Cl ) Cf)s:Geological HistoryThe geological history consists of two quite distinct phases:The fust phase corresponds to a continuous platform sedimentation (of marls and carbonates). At the end of this sedimentation these deposits were slightly folded and then eroded (with the erosion surface being flat).The second phase began with the deposition of an isopachous saliferous evaporitic series. On this series, a clayey-marly series rich in organic matter was deposited.These sediments were followed by a thick deposit of shaly-sandy detrital sediments whose facies thickness was govemed by continuous lateral creep of salt, resulting from the overburden pressure. Linked to this salt creep, which may locally cause complete disappearance of t...

A B S T R A C TIn common-reflection-surface imaging the reflection arrival time field is parameterized by operators that are of higher dimension or order than in conventional methods. Using the common-reflection-surface approach locally in the unmigrated prestack data domain opens a potential for trace regularization and interpolation. In most data interpolation methods based on local coherency estimation, a single operator is designed for a target sample and the output amplitude is defined as a weighted average along the operator. This approach may fail in presence of interfering events or strong amplitude and phase variations. In this paper we introduce an alternative scheme in which there is no need for an operator to be defined at the target sample itself. Instead, the amplitude at a target sample is constructed from multiple operators estimated at different positions. In this case one operator may contribute to the construction of several target samples. Vice versa, a target sample might receive contributions from different operators. Operators are determined on a grid which can be sparser than the output grid. This allows to dramatically decrease the computational costs. In addition, the use of multiple operators for a single target sample stabilizes the interpolation results and implicitly allows several contributions in case of interfering events. Due to the considerable computational expense, common-reflection-surface interpolation is limited to work in subsets of the prestack data. We present the general workflow of a common-reflection-surface-based regularization/interpolation for 3D data volumes. This workflow has been applied to an OBC common-receiver volume and binned common-offset subsets of a 3D marine data set. The impact of a common-reflectionsurface regularization is demonstrated by means of a subsequent time migration. In comparison to the time migrations of the original and DMO-interpolated data, the results show particular improvements in view of the continuity of reflections events. This gain is confirmed by an automatic picking of a horizon in the stacked time migrations.

Adaptive subtraction is a key element in predictive multiple-suppression methods. It minimizes misalignments and amplitude differences between modeled and actual multiples, and thus reduces multiple contamination in the dataset after subtraction. Due to the high crosscorrelation between their waveform, the main challenge resides in attenuating multiples without distorting primaries. As they overlap on a wide frequency range, we split this wide-band problem into a set of more tractable narrow-band filter designs, using a 1D complex wavelet frame. This decomposition enables a single-pass adaptive subtraction via complex, single-sample (unary) Wiener filters, consistently estimated on overlapping windows in a complex wavelet transformed domain. Each unary filter compensates amplitude differences within its frequency support, and can correct small and large misalignment errors through phase and integer delay corrections. This approach greatly simplifies the matching filter estimation and, despite its simplicity, narrows the gap between 1D and standard adaptive 2D methods on field data * .

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