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

Least-squares reverse time migration is well-known for its capability togenerate artifact-free true-amplitude subsurface images through fittingobserved data in the least-squares sense. However, when applied to realisticimaging problems, this approach is faced with issues related to overfitting andexcessive computational costs induced by many wave-equation solves. The factthat the source function is unknown complicates this situation even further.Motivated by recent results in stochastic optimization and transform-domainsparsity-promotion, we demonstrate that the computational costs of inversioncan be reduced significantly while avoiding imaging artifacts and restoringamplitudes. While powerful, these new approaches do require accurateinformation on the source-time function, which is often lacking. Without thisinformation, the imaging quality deteriorates rapidly. We address this issue bypresenting an approach where the source-time function is estimated on the flythrough a technique known as variable projection. Aside from introducingnegligible computational overhead, the proposed method is shown to perform wellon imaging problems with noisy data and problems that involve complex settingssuch as salt. In either case, the presented method produces high resolutionhigh-amplitude fidelity images including an estimates for the source-timefunction. In addition, due to its use of stochastic optimization, we arrive atthese images at roughly one to two times the cost of conventional reverse timemigration involving all data.

Time-domain sparsity promoting least-squares reverse time migration with source estimation