Implicit Full Waveform Inversion Imaging

Clicks: 1
ID: 320751
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #193 of 216 articles by views in geophysical journal international

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 216 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Summary To obtain subsurface images/reflectivity, a sequential workflow of full waveform inversion (FWI) and least-squares reverse time migration (LSRTM) is often used. With the vector reflectivity-based acoustic wave equation, we can simultaneously obtain a high-resolution velocity model and the corresponding impedance-derived reflectivity image. However, the inversion process is quite non-linear and the field data is often blurred by noise, which makes the inversion process challenging. In this work, we propose an implicit FWI imaging (IFWIM) workflow, where the velocity and impedance models are implicitly represented by the weights of a neural network and can be resampled from the neural network at any desired resolution or even on irregular grids. The reflectivity components are then computed from the inverted impedance. The synthetic and field data examples show that the proposed method can recover high-resolution velocity models and reflectivity images as an effective way to perform joint imaging and velocity inversion.
Reference Key
openalex_W7168086261 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shaowen Wang, Tariq Alkhalifah
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag277
URL
https://doi.org/10.1093/gji/ggag277
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.