How many azimuths do you need?

We describe a Bayesian methodology for designing seismic experiments that optimally maximize model-parameter resolution for imaging purposes. The proposed optimal experiment design algorithm finds the measurements that are likely to optimally reduce the expected uncertainty on the model parameters. This Bayesian [Formula: see text]-optimality-based algorithm minimizes the volume of the expected confidence ellipsoid and leads to the maximization of the expected resolution of the model parameters. Computational efficiency is achieved by a greedy algorithm in which the design is sequentially improved. In contrast to minimizing the uncertainty volume over the entire subsurface simultaneously, a refinement of the algorithm minimizes the marginal uncertainties in a region of interest. Minimizing marginal uncertainties simultaneously accounts for quantitative prior model uncertainties while honoring a qualitative focus on particular regions of interest. The benefits of the proposed method over traditional non-Bayesian ones are demonstrated with several geophysical examples. These include reducing large seismic data volumes for real-time imaging and solving the problem of designing seismic surveys that account for source bandwidth, signal-to-noise ratio, and attenuation.

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How many azimuths do you need?

Abstract: Examples abound of marine seismic quality uplifts due to multi- and wide-azimuth acquisition geometries (Figure 1 ), reflecting the fact that these techniques are rapidly becoming routine and widespread.

Cited by 2 publications

References 8 publications

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Bayesian survey design to optimize resolution in waveform inversion

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