b'Data trends Data trendsTim Keeping Associate Editor for geophysicaldata management and analysis technical-standards@aseg.org.auRegularising and mixing points with gridsI present results of my short ArcGIS Python code for regularising points using gravity stations from Andamooka, South Australia (Figure 1 - top)\x08 I hopeFigure 1.Gravity stations in the Andamooka region. Toporiginal locations. Bottom - regularised by the it is easy to use and implementation isPython code to 200 m spacing.explained effectively\x08 The demonstration area is 13\x082 km x 9 km and contains 10 gravity surveys ranging in acquisition date from a 1970 survey carried out by WMC survey to the 2009 Northern Olympic Domain (NOD) survey with company infill\x08 The most common station spacing is 200 m, but incursions from several 1970 and early 1980s surveys with spacing varying from 100 m to 600 m cause overlap, effective duplication at some locations and some clear disagreements in levelling\x08A 50 m cell-sized grid of the original located data shows high frequency responses and multiple linear features are prominent characteristics (Figure2top)\x08 The grey scale colour stretch shows artefacts due to the near or overlapping survey stations (Figure3top)\x08The regularised grid began with points 3200 m apart being assigned the nearest real value within a radius\x08 This method was used to generate a grid at quarter spacing (800 m cell size)\x08 The second iteration created points with half the spacing (1600 m) and these were assigned the nearest real values\x08 Any points still empty were assignedFigure 2.Colour stretched 50 m cell size grids of gravity stations shown in Figure 1 using ArcGIS spline the nearest interpolated value from thefunction. Toporiginal locations. Bottomregularised to 200 mAPRIL 2021 PREVIEW 34'