Data

Associated files for a geophysical study using spatial uncertainty as inversion constraints

Commonwealth Scientific and Industrial Research Organisation
Lindsay, Mark ; Ogarko, Vitaliy ; Giraud, Jeremie ; Khademi, Mosayeb
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/hjxq-a442&rft.title=Associated files for a geophysical study using spatial uncertainty as inversion constraints&rft.identifier=https://doi.org/10.25919/hjxq-a442&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=The repository contains:* gravity measurements as supplied by Geoscience Australia (Geoscience Australia, 2020);* grids calculated using INLA;* inversion parameter files for Tomofast-x;* batch files to run inversion;* model mesh files in Tomofast-x format;* readme.txt describing these contents and background information.File formats are: gravity measurements – csv; grids - ERMapper.ers format; parameter files - .txt; batch files - .sh; spatial uncertainty values – .csv format with x,y,z,property; model mesh files - .txt and .csv, readme - .txtThis repository is a companion to paper published in Geophysical Journal International (see 'Related Links').Lineage: There are three data forms.1) Measurements of gravitational acceleration used in the study and obtained from Geoscience Australia (Geophysical Acquisition & Processing Section 2020. National Gravity Compilation 2019 (CSCBA). https://pid.geoscience.gov.au/dataset/ga/144786.)2) Grids produced by the R library INLA (Rue H, Martino S, Chopin N. (2009) Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations. J R Stat Soc Ser B Stat Methodol 2009;71:319–92. https://doi.org/10.1111/J.1467-9868.2008.00700.X) and https://www.r-inla.org/Inversion conducted using Tomofast-x (https://github.com/TOMOFAST/Tomofast-x).&rft.creator=Lindsay, Mark &rft.creator=Ogarko, Vitaliy &rft.creator=Giraud, Jeremie &rft.creator=Khademi, Mosayeb &rft.date=2026&rft.edition=v2&rft.relation=https://doi.org/10.1093/gji/ggag133&rft.coverage=westlimit=127.6; southlimit=-17.9; eastlimit=128.33333333333334; northlimit=-16.75; projection=WGS84&rft_rights=Creative Commons Attribution-Noncommercial 4.0 Licence https://creativecommons.org/licenses/by-nc/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2026.&rft_subject=spatial uncertainty&rft_subject=geophysics&rft_subject=uncertainty&rft_subject=gravity&rft_subject=modelling&rft_subject=Applied geophysics&rft_subject=Geophysics&rft_subject=EARTH SCIENCES&rft_subject=Geospatial information systems and geospatial data modelling&rft_subject=Geomatic engineering&rft_subject=ENGINEERING&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution-Noncommercial 4.0 Licence
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Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2026.

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The repository contains:
* gravity measurements as supplied by Geoscience Australia (Geoscience Australia, 2020);
* grids calculated using INLA;
* inversion parameter files for Tomofast-x;
* batch files to run inversion;
* model mesh files in Tomofast-x format;
* readme.txt describing these contents and background information.

File formats are: gravity measurements – csv; grids - ERMapper.ers format; parameter files - .txt; batch files - .sh; spatial uncertainty values – .csv format with x,y,z,property; model mesh files - .txt and .csv, readme - .txt

This repository is a companion to paper published in Geophysical Journal International (see 'Related Links').
Lineage: There are three data forms.
1) Measurements of gravitational acceleration used in the study and obtained from Geoscience Australia (Geophysical Acquisition & Processing Section 2020. National Gravity Compilation 2019 (CSCBA). https://pid.geoscience.gov.au/dataset/ga/144786.)

2) Grids produced by the R library INLA (Rue H, Martino S, Chopin N. (2009) Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations. J R Stat Soc Ser B Stat Methodol 2009;71:319–92. https://doi.org/10.1111/J.1467-9868.2008.00700.X)

and https://www.r-inla.org/

Inversion conducted using Tomofast-x (https://github.com/TOMOFAST/Tomofast-x).

Available: 2026-04-11

This dataset is part of a larger collection

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Subjects
Applied Geophysics | Earth Sciences | Engineering | Geomatic Engineering | Geophysics | Geospatial Information Systems and Geospatial Data Modelling | geophysics | gravity | modelling | spatial uncertainty | uncertainty |

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Identifiers
ACN 633 798 857