Borehole acoustic full-waveform inversion

基于声弹性理论和全波形反演的残余应力反演新方法

A new approach for the inversion of residual stress based on acoustoelasticity theory and full waveform inversion

针对传统超声检测无法获取非均匀残余应力分布的问题，提出基于声弹性理论与全波形反演的残余应力反演新方法，通过伴随法推导目标函数梯度并进行数值模拟验证，为超声应力测试应用奠定理论与模拟基础。

ML-misfit: Learning a robust misfit function for full-waveform inversion using machine learning

Most of the available advanced misfit functions for full waveform inversion (FWI) are hand-crafted, and the performance of those misfit functions is data-dependent. Thus, we propose to learn a misfit function for FWI, entitled ML-misfit, based on machine learning. Inspired by the optimal transport of the matching filter misfit, we design a neural network (NN) architecture for the misfit function in a form similar to comparing the mean and variance for two distributions. To guarantee the resulting learned misfit is a metric, we accommodate the symmetry of the misfit with respect to its input an…

Computationally efficient full-waveform inversion of the brain using frequency-adaptive grids and lossy compression

A tomographic technique called full-waveform inversion has recently shown promise as a fast, affordable, and safe modality to image the brain using ultrasound. However, its high computational cost and memory footprint currently limit its clinical applicability. Here, we address these challenges through a frequency-adaptive discretisation of the imaging domain and lossy compression techniques. Because full-waveform inversion relies on the adjoint-state method, every iteration involves solving the wave equation over a discretised spatiotemporal grid and storing the numerical solution to calculat…