摘要
Abstract
Although full waveform inversion technique has been successfully applied,the amount of calculation of least-squares nonconvex optimization problem is still a challenge.Random sampling technology reduces the number of shot and frequency,and save the full waveform inversion calculation greatly,but it brings curse of dimensionality and departure from Moore's Law.In this paper with the successful improvement of full-waveform inversion,the current trend of incessantly pushing for higher quality models in increasingly complicated regions of the Earth reveals fundamental shortcomings in our ability to handle increasing problem size numerically.Two main culprits can be identified.First,there is the so-called curse of dimensionality exemplified by Nyquist's sampling criterion,which puts disproportionate strain on current acquisition and processing systems as the size and desired resolution increases.Secondly,there is the recent departure from Moore's law that forces us to lower our expectations to compute ourselves out of this.In this paper,we address this situation by randomized dimensionality reduction,which we adapt from the field of compressive sensing.In this approach,we combine deliberate randomized suhsampling with structure-exploiting transform-domain sparsity promotion.Our approach is successful because it reduces the size of seismic data volumes without loss of information.With this reduction,we compute Newton-like updates at the cost of roughly one gradient update for the fully-sampled wavefield.Sparsity constrain is employed in the model update in inversion without changing the target function of waveform inversion and suppressing the virtual image noise raised by sub-sampling.The North Sea model testing result proves the feasibility and validity of the method.关键词
压缩感知/波形反演/曲波变换/稀疏促进Key words
compressive sensing/full-waveform inversion/curvelet transform/sparsity promoting分类
天文与地球科学
