English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Full Waveform Inversion (FWI) is an advanced geophysical inversion technique.In fields such as oil exploration and geology, FWI is used for providing imagesof subsurface structures with higher resolution. The conventional algorithmminimises the misfit error by calculating the least squares of the wavefieldsolutions between observed data and simulated data, followed by gradientdirection and model update increment. Since the gradient is calculated byforward and backward wavefields, the high-accuracy model update relies onaccurate forward and backward wavefield modelling. However, the quality ofwavefield solutions obtained in practical situations could be poor and does notmeet the requirements of high-resolution FWI. Specifically, the low-frequencywavefield is easily affected by noise and downsampling, which influences dataquality, while the high-frequency wavefield is susceptible to spatial aliasingeffects that produce imaging artefacts. Therefore, we propose using analgorithm called Sparse Relaxation Regularised Regression to optimise thewavefield solution in frequency domain FWI, which is the forward and backwardwavefield obtained from the Helmholtz equation, and thus improve the accuracyof the FWI. The sparse relaxed regularised regression algorithm combinessparsity and regularisation, allowing the broadband FWI to reduce the effectsof noise and outliers, which can provide data supplementation in thelow-frequency band and anti-aliasing in the high-frequency band. Our numericalexamples demonstrate the wavefield optimization effect of the sparse relaxedregularised regression-based algorithm in various cases. The accuracy andstability of the improved algorithm are verified in comparison to the Tikhonovregularisation algorithm.

Improving full-waveform inversion based on sparse regularisation for geophysical data