Brief description
Purpose
This package comprises a set of 86 thematic grids (rasters) derived from national coverages of gravity and magnetic survey data. These datasets provide valuable information about the distribution of geological features, physical property variations, and the composition of the Earth's crust. All grids have been resampled to the same cell size, map extent, and projection to allow them to be integrated into predictive mapping and modelling workflows using machine learning. Users can download individual grids or the whole grid package.
Input Data
The following Australian national datasets were used:
1. 2019 Australian National Gravity Grids: Free Air Anomaly, Complete Bouguer Anomaly, De-trended Global Isostatic Residual, 400 m cell size (Lane et al., 2020).
2. Total Magnetic Intensity (TMI) Grid of Australia 2019 - seventh edition Enhanced Products Package (Morse, 2020).
Processing
All processing of the national grids were undertaken using Intrepid software. The following was performed on the input data:
1. The grids were reprojected from GDA94 geodetic to Australian Albers (EPSG 3577).
2. The grids were aligned to the same grid cell registration point and interpolated to fit within an 80 m cell size using a cubic spline method to ensure that the cell locations for all images are common.
3. Various Fast Fourier Transforms (FFT) were applied to each grid (see ‘Grids_for_Machine_Learning_dataset_notes.pdf’).
Metadata (all grids)
· Datum: GDA94
· Projection: Australian Albers (EPSG 3577)
· Cell size: 80 m
· File format: GeoTiff (.tif)
Data is available on request from clientservices@ga.gov.au - Quote eCat# 149130
Lineage
Maintenance and Update Frequency: asNeeded
Statement:
This dataset publication was created by the Geophysical Acquisition and Processing (GAP) and Integrated Geological Mapping (IGM) sections at Geoscience Australia in 2024. Please refer to the release notes (Grids_for_Machine_Learning_dataset_notes.pdf) for a description of how the data were processed.
Notes
Purpose
Grids created for input into data analysis and machine learning projects. See 'Grids_for_Machine_Learning_dataset_notes.pdf' for a list of all grids.