论文标题

通过降低模型阶的速度估计

Velocity estimation via model order reduction

论文作者

Mamonov, Alexander V., Borcea, Liliana, Garnier, Josselin, Zimmerling, Jörn

论文摘要

引入了基于数据驱动的降低订单模型(ROM)的新型方法进行全波形反转(FWI)。用脉冲探测未知培养基,并将时间域压力波形数据记录在主动传感器阵列上。 ROM通过非线性过程从数据构建波动方程运算符的投影,用于有效速度估计。虽然没有低频信息的传统FWI通过非线性最小二乘数据拟合具有挑战性,并且很容易陷入本地最小值(Cycle Skipping),但即使对于初始猜测,ROM MISFIT的最小化效果也更好。对于未知速度的低维参数化,ROM失配函数接近凸。在标准合成测试中,提出的方法始终优于常规FWI。

A novel approach to full waveform inversion (FWI), based on a data driven reduced order model (ROM) of the wave equation operator is introduced. The unknown medium is probed with pulses and the time domain pressure waveform data is recorded on an active array of sensors. The ROM, a projection of the wave equation operator is constructed from the data via a nonlinear process and is used for efficient velocity estimation. While the conventional FWI via nonlinear least-squares data fitting is challenging without low frequency information, and prone to getting stuck in local minima (cycle skipping), minimization of ROM misfit is behaved much better, even for a poor initial guess. For low-dimensional parametrizations of the unknown velocity the ROM misfit function is close to convex. The proposed approach consistently outperforms conventional FWI in standard synthetic tests.

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