3D forward modelling of time domain electromagnetic data
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 20023D forward modelling of time domain electromagnetic dataAuthors: Eldad HaberUri AscherDouglas W. OldenburgEldad HaberEMI‐Schlumberger, Richmond, CA, Uri AscherDept. of Computing sciences, UBC, Vancouver, Canada, and Douglas W. OldenburgUBC‐Geophysical Inversion Facility, Dept. of Earth&Ocean Sciences, UBC, Vancouver, Canadahttps://doi.org/10.1190/1.1817334 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817334FiguresReferencesRelatedDetailsCited byFast 2.5D and 3D inversion of transient electromagnetic surveys using the octree-based finite-element methodLongying Xiao, Gianluca Fiandaca, Bo Zhang, Esben Auken, and Anders Vest Christiansen20 June 2022 | GEOPHYSICS, Vol. 87, No. 43-D Large-Scale TEM Modeling Using Restarting Polynomial Krylov MethodIEEE Transactions on Geoscience and Remote Sensing, Vol. 603D finite-volume time-domain modeling of geophysical electromagnetic data on unstructured grids using potentialsXushan Lu and Colin G. Farquharson22 October 2020 | GEOPHYSICS, Vol. 85, No. 6Three-dimensional transient electromagnetic inversion with optimal transport3 September 2021 | Journal of Inverse and Ill-posed Problems, Vol. 0, No. 0Rational Krylov method for 3D time-domain airborne EM modelingChangkai Qiu*, Changchun Yin, Yunhe Liu, Bo Zhang, Xiuyan Ren, Cong Wang, and Jing Cai28 September 20193D transient electromagnetic modeling using a shift-and-invert Krylov subspace method26 April 2018 | Journal of Geophysics and Engineering, Vol. 15, No. 4Three-Dimensional Transient Electromagnetic Modeling Based on Fictitious Wave Domain Methods23 March 2017 | Pure and Applied Geophysics, Vol. 174, No. 5Three-dimensional transient electromagnetic modelling using Rational Krylov methods25 July 2015 | Geophysical Journal International, Vol. 202, No. 3Finite-volume modelling of geophysical electromagnetic data using potentials on unstructured staggered gridsHormoz Jahandari* and Colin G. Farquharson19 August 2015A Study on 2D CSAMT Forward Modeling and Inversion with a Dipole Source and Topography and Its Applications31 May 2013 | Chinese Journal of Geophysics, Vol. 53, No. 2Time-domain modeling of electromagnetic diffusion with a frequency-domain codeWim A. Mulder, Marwan Wirianto, and Evert C. Slob13 November 2007 | GEOPHYSICS, Vol. 73, No. 1Time‐domain CSEM modeling with a frequency‐domain codeWim A. Mulder, Marwan Wirianto, and Evert C. Slob14 September 2007Finite-difference simulation of borehole EM measurements in 3D anisotropic media using coupled scalar-vector potentialsJunsheng Hou, Robert K. Mallan, and Carlos Torres-Verdín28 August 2006 | GEOPHYSICS, Vol. 71, No. 5An accelerated time domain finite difference simulation scheme for three-dimensional transient electromagnetic modeling using geometric multigrid concepts24 June 2006 | Radio Science, Vol. 41, No. 3A parallel finite‐difference approach for 3D transient electromagnetic modeling with galvanic sourcesMichael Commer and Gregory Newman27 September 2004 | GEOPHYSICS, Vol. 69, No. 5 SEG Technical Program Expanded Abstracts 2002ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2002 Pages: 2478 publication data© 2002 Copyright © 2002 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 03 Jan 2005 CITATION INFORMATION Eldad Haber, Uri Ascher, and Douglas W. Oldenburg, (2002), "3D forward modelling of time domain electromagnetic data," SEG Technical Program Expanded Abstracts : 641-644. https://doi.org/10.1190/1.1817334 Plain-Language Summary PDF DownloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".