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South Australia gravity gets better

An innovative methodology for producing regional gravity maps from ground-station data creates a superior quality Bouguer gravity image of South Australia.

The ‘supervised variable density method’, developed by the Geological Survey of South Australia, delivers a seamless interpolation that supersedes and significantly improves on previous versions of the South Australian gravity grid.

Figure 1 Final Bouguer gravity grid, 100 m cells. The result of eight iterations of supervised variable density gridding. The final grid (light background image) has been clipped to a region outside of the state, but within the envelope of gravity stations (black dots). This eliminates potential edge extrapolation effects.
Figure 1 Final Bouguer gravity grid, 100 m cells. The result of eight iterations of supervised variable density gridding. The final grid (light background image) has been clipped to a region outside of the state, but within the envelope of gravity stations (black dots). This eliminates potential edge extrapolation effects.

Approximately 530,000 gravity stations of variable spacing were gridded using a methodology that combines GIS geoprocessing methods and minimum curvature interpolation to optimise and control an iterative interpolation process. The resulting statewide grid (Fig. 1), with a cell size of 100 m, is free of gridding and survey boundary artefacts found in previous versions. The supervised method provides a systematic approach to optimally gridding regional ground gravity data of variable density and distribution, providing seamless regional or continental-scale gravity products that support geological interpretation, modelling and quantitative analysis (Katona 2017).

Minimum curvature has been the interpolator of choice for gridding potential field data for decades (Briggs 1974), nevertheless, interpolation continues to be problematic when applied to regional or continental data with large variation in data point spacing. When the ratio of target cell size to data point spacing exceeds a nominal limit of 1:5, spurious results are produced, confounding geological interpretation and analysis. These issues can be overcome to some degree by:

  • preconditioning the interpolated grids at the expense of loss of information
  • increasing the target cell size at the expense of resolution and loss of information
  • applying algorithms that repeatedly grid the data at decreasing cell sizes and then combine the results to optimise for the variation in station spacing, at the expense of introducing gridding artefacts into the result.
Figure 2 Comparison of first vertical derivatives of Bouguer gravity of South Australia, supervised (top) and unsupervised (bottom) variable density gridding. The images illustrate some of the improvements using the supervised approach: station locations in the bottom image are clearly visible and sometimes render as point anomalies; and gridding artefacts are clearly evident in areas where the optimal station spacing to grid cell size has been violated. In the top image the region at the left illustrates the improvement in image clarity and smoothness gained by gridding lines of gravity stations at their optimal resolution; there is also a smoother gradation between variations in station spacing.
Figure 2 Comparison of first vertical derivatives of Bouguer gravity of South Australia, supervised (top) and unsupervised (bottom) variable density gridding. The images illustrate some of the improvements using the supervised approach: station locations in the bottom image are clearly visible and sometimes render as point anomalies; and gridding artefacts are clearly evident in areas where the optimal station spacing to grid cell size has been violated. In the top image the region at the left illustrates the improvement in image clarity and smoothness gained by gridding lines of gravity stations at their optimal resolution; there is also a smoother gradation between variations in station spacing.

Gridding artefacts become particularly evident in filtered data such as vertical derivatives, residuals or inversions and limit the utility of these regional datasets. The supervised method overcomes these limitations by rigorously controlling the data points included in an iterative gridding approach and applying the concept of pseudo-stations to overcome scale issues associated with gridding variable density data. The data is progressively gridded at multiple resolutions from the coarsest to the finest, feeding filtered subsets of data points into successive iterations of gridding while using masking to subset and add selected grid cell values (pseudo-stations) into each iteration, ultimately producing a final grid of 100 m cell size. The ensuing visual and quantitative results are robust when compared with alternative methods (Fig. 2).

The effort required to assign an optimal resolution value to the gravity stations provides a number of worthwhile benefits, including the ability to validate results at a number of scales, produce a reliability map and perform the supervised method on very large regions using data of multiple densities. The supervised approach has produced a surface that lends itself to greater confidence of geological interpretations as the interpreter does not need to deal with interpolation artefacts that may easily be mistaken for geology and, with the use of the reliability map, can better qualify interpretations.

Future generations of statewide gravity grids can now be routinely generated annually, with the first update to be released in the final quarter of 2017, incorporating new data captured since the current version was released in December 2016.

References

Briggs IC 1974. Machine contouring using minimum curvature. Geophysics 39(1):39–48

Katona LF 2017. Gridding of South Australian ground gravity data, using the supervised variable density method, Report Book 2017/00012. Department of the Premier and Cabinet, South Australia, Adelaide.

– Laz Katona

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