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Arbejdsgruppemøde - SkyTEM processering Esben Auken and Nikolaj Foged HydroGeophysics Group Department of Earth Sciences, University of Aarhus, Denmark.

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Presentation on theme: "Arbejdsgruppemøde - SkyTEM processering Esben Auken and Nikolaj Foged HydroGeophysics Group Department of Earth Sciences, University of Aarhus, Denmark."— Presentation transcript:

1 Arbejdsgruppemøde - SkyTEM processering Esben Auken and Nikolaj Foged HydroGeophysics Group Department of Earth Sciences, University of Aarhus, Denmark

2 Dagsorden Intro til processering Import: stack division, fortegn og linie fil, +30 sekunder Processeringsvindue: højdeprocessering, midlingsfiltre, genkørsel Manuel klipning af data: rådata versus average data Workflow: Koblinger fjerne fra raw data Støjbeskæring i average Fast inversion Evaluering og justering af processering Nye data - tæt sampling til tidlige tider - ny software - nyt møde

3 Processing, Inversion and Management of SkyTEM Data Esben Auken and Kurt Sørensen HydroGeophysics Group Department of Earth Sciences, University of Aarhus, Denmark

4 Outline What is in the database? Pitch and roll correction Data filtering Implementation in the Aarhus Workench

5 Ensuring Best Possible Data Quality Obtain maximum information about the Earth resistivity distribution 5% data reproducibility of SkyTEM data on national test site 5% repeatability at each take off and landing Accurate data processing and inversion

6 Data Basis Data types measured by the system GPS – 2 per sec. Tilt of transmitter frame – 1.5 per sec. in in- and off-flight directions Laser altitude – 20 per sec. from two devices Transmitter current – 1 per dataset Single decays from 2 or more receiver channels – Mb per hour

7 Data Processing Voltage data and altitudes corrected with respect to the tilt of the frame To some degree manual intervention is necessary for Altitudes Culling of distorted data caused by coupling to wires and fences Data are not leveled

8 Altitude and Tilt Correction ”Normal” situation

9 Altitude and Tilt Correction ”Normal” situation

10 Altitude and Tilt Correction ”Normal” situation – modelled in the inversion

11 Altitude and Tilt Correction ”Field” situation

12 Altitude and Tilt Correction ”Field” situation

13 Altitude and Tilt Correction ”Field” situation

14 Altitude and Tilt Correction ”Normal” situation – modelled in the inversion

15 Tilt data 5 min  3.5 line km Tilt of +/-10°  correction of voltage data y-tilt +/- 4° x-tilt +/- 8° Filtered

16 Altitude Processing Raw Laser 1 Raw Laser 2 Removed by filtering Final – tilt corrected and relocated Removal of laser reflection from tree tops Correction of altitudes for tilt – non perpendicular reflections from the ground Relocation of altitudes to center of Tx and Rx coils Laser reflections from tree tops Difference due to high y-tilt 5 min  3.5 line km

17 Box average – Narrow/Wide SkyTEM data example (Airborne TEM) 30s ~ 400m SkyTEM data, 2 min ~ 1.5 Km 5s ~ 65 m gate 1 gate 2 gate gate n

18 Box average – Narrow/Wide Raw Average 30 s 5 s 2 min ~ 1.5 Km

19 Trapezoid shaped average combines the best from the narrow and the wide box average used for SkyTEM data Trapezoid shaped average Box shaped average

20 Average Trapezoid shaped average Average

21 Trapezoid average - Sounding view Trapezoid average Box average, medium width

22 Trapezoid average - Sounding view Trapezoid average Box average, medium width 3-4 gates culled Only 2 gates culled High lateral resolution

23 Summary Shallow part of model section/early time gates maximum lateral resolution is obtain with a narrow data average large data stack is not needed because of a high signal to noise ratio Deep part of model section/late time gates Wide data average suppress the background noise and increase penetration depth. Wide data average does not reduce the lateral resolution Trapezoid shaped average maximum lateral resolution maximum penetration depth Could be applied to any continuous TEM-method

24 Summary Frame tilt is used for correction of altitudes and voltage data Coupled data are culled before inversion Data average using trapezoid shaped filters and pruning


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