20251217 13295300 1.0 ISO 19139 Metadata Implementation Specification GML3.2 FALSE 150000000 5000 SWN_AGG_SI.ers 002 2433602.500000 2665327.500000 7378582.500000 7556717.500000 1 Projected GCS_North_American_1983_CSRS Linear Unit: Meter (1.000000) NAD_1983_CSRS_New_Brunswick_Stereographic <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.8'><WKT>PROJCS[&quot;NAD_1983_CSRS_New_Brunswick_Stereographic&quot;,GEOGCS[&quot;GCS_North_American_1983_CSRS&quot;,DATUM[&quot;D_North_American_1983_CSRS&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Double_Stereographic&quot;],PARAMETER[&quot;False_Easting&quot;,2500000.0],PARAMETER[&quot;False_Northing&quot;,7500000.0],PARAMETER[&quot;Central_Meridian&quot;,-66.5],PARAMETER[&quot;Scale_Factor&quot;,0.999912],PARAMETER[&quot;Latitude_Of_Origin&quot;,46.5],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,2953]]</WKT><XOrigin>-28216700</XOrigin><YOrigin>-23260100</YOrigin><XYScale>146555990.15834904</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>2036</WKID><LatestWKID>2953</LatestWKID></ProjectedCoordinateSystem> 32 FALSE None 1 ER Mapper TRUE continuous floating point -999999 20251219 11152900 20251219 11152900 ISO19139 Amirabbas Karbalaeiramezanali GSB amirabbas.karbalaeiramezanali@gnb.ca Serge Allard GSB Serge.Allard@gnb.ca Serge Alard Serge Allard Serge Allard Hernan Ugalde hernan.ugalde@dipgeosciences.com Version 6.2 (Build 9200) ; Esri ArcGIS 10.8.1.14362 SWN_AGG_SI.ers 1 -67.373191 -64.326352 47.010247 45.387866 This dataset represents the Shape Index derived from the gravity tensor of the SWN_NBStereo (Lantern) FTG/AGG survey, gridded at 25 m resolution in NAD83 NB... This dataset represents the Shape Index derived from the gravity tensor of the SWN_NBStereo (Lantern) FTG/AGG survey, gridded at 25 m resolution in NAD83 NB Stereographic projection.The dataset contains the Shape Index calculated from the reconstructed gravity tensor of the SWN_NBStereo survey. The Shape Index quantifies the local curvature and geometry of the gravity field, highlighting shallow structural features such as faults, lithological contacts, and intrusive bodies. This high-resolution dataset is useful for detailed structural interpretation and mineral exploration and is provided as a 25 m grid in NAD83 New Brunswick Stereographic projection.This raster was produced by constructing the gravity tensor from the SWN_NBStereo FTG/AGG profile data processed with a density of 2.8 g/cc, gridding the tensor at 25 m using a tensor-interpolation algorithm, and calculating the Shape Index following the method of Cevallos (2014). The final grid was exported in ER Mapper (.ers) format.Data created Sept 2025. NB Dept of Natural Resources, GSB Section 2015 115.000000 1549 115.000000 2 1 0 2433602.500000 7378582.500000 2433602.500000 7556717.500000 2665327.500000 7556717.500000 2665327.500000 7378582.500000 2549465.000000 7467650.000000 Band_1 0.999541 -0.998280 32 Raster Dataset dataset EPSG 6.2(9.3.0.0) 20251219 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