论文标题
与傅立叶神经操作员的速度延续，用于加速不确定性定量
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
论文作者
论文摘要
地震成像是一个不良的逆问题，由于背景平方平方模型中的错误而受到嘈杂数据和建模不准确的挑战。不确定性量化对于确定背景模型中的可变性如何影响地震成像至关重要。由于与前向诞生的建模操作员以及地震图像的高维度相关的成本，因此不确定性的量化在计算上是昂贵的。因此，这项工作的主要贡献是特定于调查的傅立叶神经操作员对速度延续的替代，该速度延续将与一个背景模型相关联的地震图像实际上免费为另一个背景模型。该替代物仅接受200个背景和地震图像对训练，但能够准确预测与新背景模型相关的地震图像，从而加速地震成像不确定性定量。我们通过一个现实的数据示例支持我们的方法，在该示例中，我们使用傅立叶神经操作员替代物来量化地震成像不确定性，这说明了背景模型中的变化如何影响反射器在地震图像中的位置。
Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies -- due to errors in the background squared-slowness model. Uncertainty quantification is essential for determining how variability in the background models affects seismic imaging. Due to the costs associated with the forward Born modeling operator as well as the high dimensionality of seismic images, quantification of uncertainty is computationally expensive. As such, the main contribution of this work is a survey-specific Fourier neural operator surrogate to velocity continuation that maps seismic images associated with one background model to another virtually for free. While being trained with only 200 background and seismic image pairs, this surrogate is able to accurately predict seismic images associated with new background models, thus accelerating seismic imaging uncertainty quantification. We support our method with a realistic data example in which we quantify seismic imaging uncertainties using a Fourier neural operator surrogate, illustrating how variations in background models affect the position of reflectors in a seismic image.
