20251217
13295800
1.0
ISO 19139 Metadata Implementation Specification GML3.2
FALSE
150000000
5000
SWN_AGG_Txx.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["NAD_1983_CSRS_New_Brunswick_Stereographic",GEOGCS["GCS_North_American_1983_CSRS",DATUM["D_North_American_1983_CSRS",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Double_Stereographic"],PARAMETER["False_Easting",2500000.0],PARAMETER["False_Northing",7500000.0],PARAMETER["Central_Meridian",-66.5],PARAMETER["Scale_Factor",0.999912],PARAMETER["Latitude_Of_Origin",46.5],UNIT["Meter",1.0],AUTHORITY["EPSG",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
11163600
20251219
11163600
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_Txx.ers
1
-67.373191
-64.326352
47.010247
45.387866
This dataset represents the Txx component of the gravity tensor derived from the SWN_NBStereo (Lantern) FTG/AGG survey, gridded at 25 m resolution in NAD83 NB...
This dataset represents the Txx component of the gravity tensor derived from the SWN_NBStereo (Lantern) FTG/AGG survey, gridded at 25 m resolution in NAD83 NB Stereographic projection.The dataset contains the Txx tensor component extracted from the reconstructed gravity tensor of the SWN_NBStereo survey. Txx represents the second derivative of the gravity field along the X-axis, capturing lateral density variations in the subsurface. This component is useful for mapping faults, lithological contacts, intrusive bodies, and other structural features, supporting high-resolution structural interpretation and mineral exploration. The dataset is provided as a 25 m grid in NAD83 New Brunswick Stereographic projection.This raster was produced by processing the SWN_NBStereo FTG/AGG profile data using a density of 2.8 g/cc, reconstructing the full gravity tensor from the measured components, interpolating the tensor to a 25 m grid using a tensor-interpolation algorithm, and extracting the Txx component. 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
46.151093
-90.172508
32
Raster Dataset
dataset
EPSG
6.2(9.3.0.0)
20251219
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