ABSTRACT Optimized experimental design aims at reducing the cost of a seismic survey by identifying the optimal locations and amounts of sources and receivers. While the acquisition design in the context of seismic imaging applies criteria like fold, offset and spatial sampling, different attributes such as the sensitivity kernels are more relevant for seismic full waveform inversion. An ideal measure to quantify the goodness of an acquisition design relies on the eigenvalue spectrum of the approximate Hessian matrix, but this technique is computationally too expensive for practical use. A more affordable goodness measure has been proposed in the past, but we demonstrate that this measure is inappropriate for target‐oriented optimized experimental design. To address those issues, we derived a sequential receiver‐based procedure using a goodness measure based on the determinant of the approximate Hessian matrix. We show with numerical tests that it efficiently provides an optimized design for target‐oriented as well as for extensive full waveform inversion. This design allows a better reconstruction of the subsurface than an evenly spaced acquisition geometry. Furthermore, the optimization algorithm itself can easily be parallelized, therefore making it attractive for applications to large‐scale three‐dimensional surveys. In addition, our algorithm is able to incorporate variable costs, representing any kind of acquisition‐related costs, for every individual source location. The combined optimization with respect to the information content of sources and to the true cost will allow a more comprehensive and realistic survey planning and has a high potential for further applications.

中文翻译：

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地震全波形反演的优化实验设计——一种计算效率高的方法，包括灵活实现采集成本

摘要 优化的实验设计旨在通过确定震源和接收器的最佳位置和数量来降低地震勘测的成本。虽然地震成像背景下的采集设计应用了折叠、偏移和空间采样等标准，但灵敏度内核等不同属性与地震全波形反演更相关。量化采集设计优劣的理想措施依赖于近似 Hessian 矩阵的特征值谱，但这种技术在实际使用中计算成本太高。过去已经提出了一种更实惠的善良衡量标准，但我们证明这种衡量标准不适用于面向目标的优化实验设计。为了解决这些问题，我们使用基于近似 Hessian 矩阵的行列式的优度度量导出了一个基于顺序接收器的过程。我们通过数值测试表明，它有效地为面向目标和广泛的全波形反演提供了优化设计。这种设计允许比均匀间隔的采集几何结构更好地重建地下。此外，优化算法本身可以很容易地并行化，因此对于大规模三维测量的应用很有吸引力。此外，我们的算法能够为每个单独的源位置合并可变成本，代表任何类型的采购相关成本。