Rawlinson, Z.J.; Berhe, D.T.; Pedersen, J.B.; Asif, M.R. 2025 Aqua Intel Aotearoa Phase 3: Gisborne SkyTEM processing and resistivity modelling. GNS Science consultancy report 2025/88; Aqua Intel Aotearoa report 2025/03. 92 p. https://doi.org/10.21420/VMD2-1667
Abstract
In February 2024, 3624 km of SkyTEM 306 data were collected in Gisborne by SkyTEM Australia Pty Ltd. This report describes the processing and resistivity modelling of these data. The data were processed and modelled in four areas: Hicks Bay and Te Araroa (Hicks Bay), Waiapu River Valley (Waiapu), Tolaga Bay (Tolaga Bay) and Poverty Bay Flats (Poverty Bay). A recently developed neural network was applied by the Aarhus University HydroGeophysics Group (HGG) to automatically remove electromagnetic noise from the SkyTEM low-moment (LM) and high-moment (HM) data. Manual data processing and post-processing were then undertaken by GNS to check for any remaining data artefacts, which were subsequently removed and a quality check undertaken. Using the retained data, spatially constrained inversions were performed, creating both smooth and a sharp resistivity models, and then a final quality check was undertaken by HGG. Noise features in Hicks Bay and Tolaga Bay were relatively minor and Waiapu noise features were dominated by electrical powerlines, while Poverty Bay contained complex and widespread noise features primarily from electrical powerlines and galvanic couplings from vineyards and orchards. The combination of the SkyTEM 306 instrument features, the smoothing effect of the conductive environment and the widespread galvanic features meant that the neural network automated approach was not particularly effective for these survey areas, as the algorithm only considers whether a 1D inversion can be solved. Brackish conditions encountered at fairly shallow depths through most areas limited depth penetration. Steep terrain through some areas, combined with the complications of brackish locations and offshore flight lines, also required the specification of prior parameter values during the inversions. For each resistivity model, a depth of investigation (DOI) was estimated – resistivity structures below the DOI standard value are very weakly resolved by the data and should normally be disregarded. The resistivity models provide detailed 3D resistivity pictures of the subsurface, and the specifics for each area are as follows (standard DOIs were calculated from the smooth resistivity models): • Hicks Bay: Layer thickness of 1 m in the near-surface, increasing to 66.6 m at 550 m depth; the standard DOI varies from 33 m to 507 m, with a mean of 200 m.; • Waiapu: Layer thickness of 2 m in the near-surface, increasing to 41.3 m at 450 m depth; the standard DOI varies from 33 m to 431 m, with a mean of 227 m.; • Tolaga Bay: Layer thickness of 1 m in the near-surface, increasing to 59 m at 500 m depth; the standard DOI varies from 15 m to 394 m, with a mean of 163 m.; • Poverty Bay: Layer thickness of 2 m in the near-surface, increasing to 54.1 m at 550 m depth; the standard DOI varies from 15 m to 411 m, with a mean of 191 m. Images of the smooth resistivity models are provided in this report, and digital datasets; are provided additional to this report. Interpolated resistivity maps were also created for each layer in the smooth resistivity models (where each layer corresponds to a depth interval in metres below ground level). Those with sufficient spatial coverage were added to ESRI ArcGIS geodatabases, and a layer file was created for immediate use in GIS as raster images. The interpolated raster files extend to 194.1 m depth in Hicks Bay, 205.2 m depth in Waiapu, 122.8 m depth in Tolaga Bay and 170.5 m depth in Poverty Bay. Hydrogeological interpretation of the resistivity results is expected to be undertaken in the future but is not as part of this report. (auths)
