论文标题
全波形反演，随机选择使用自适应梯度下降
Full waveform inversion with random shot selection using adaptive gradient descent
论文作者
论文摘要
完整的波形反转（FWI）是一种功能强大但昂贵的技术，可以以高分辨率产生地下模型。随机选择的镜头（“迷你批次”）可用于近似FWI的不合适和梯度，从而降低其计算成本。在这里，我们提出了一种使用ADAM算法（基于随机梯度下降的自适应优化方案）执行微型FWI的方法。它通过在迭代中平滑梯度来提供稳定的模型更新，还可以解释优化景观的曲率。我们描述了选择ADAM算法的超参数和最佳迷你批量大小的经验标准。在Marmousi模型的合成数据上说明了概述方案的性能。
Full waveform inversion (FWI) is a powerful yet computationally expensive technique that can yield subsurface models at high resolution. Randomly selected shots ("mini-batches") can be used to approximate the misfit and the gradient of FWI, thereby reducing its computational cost. Here, we present a methodology to perform mini-batch FWI using the Adam algorithm, an adaptive optimization scheme based on stochastic gradient descent. It provides for stable model updates by smoothing the gradient across iterations and can also account for the curvature of the optimization landscape. We describe empirical criteria to choose the hyperparameters of the Adam algorithm and the optimal mini-batch size. The performance of the outlined scheme is illustrated on synthetic data from the Marmousi model.
