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1 Modeling of the Geomagnetic Field at the Core Surface Bryan Grob Institute for Geophysics, ETH Zurich

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Page 1: 1 Modeling of the Geomagnetic Field at the Core Surface Bryan Grob Institute for Geophysics, ETH Zurich

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Modeling of the Geomagnetic Field at the Core Surface

Bryan Grob

Institute for Geophysics, ETH Zurich

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Outline

1. Introduction and Aims2. Data - the U.S. Maury Collection3. Methodology 4. Results and Conclusions

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1. Introduction and aims

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1.1 Introduction

Modeling of the geomagnetic field at the CMB from historical data • expansion of magnetic potential in spherical

harmonics• Solve resulting non-linear inversion problem

New data set used in geomagnetic modeling• U.S. Maury Collection• Analysis of new data set

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1.2 Aims

Deduction of field morphology at CMB in form of maps of

Detailed analysis of the U.S. Maury Collection

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2. Data - U.S. Maury Collection

Woodruff et al., 2005

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2. Data - U.S. Maury Collection2.1 Biographical sketch

Wikipedia, 2009

Matthew Fontaine Maury (1806-1873) Birth in Virginia, death in Lexington Became midshipman at 19 => started studies of the sea

and navigation At the age of 33 => stagecoach accident

=> severe damage of knee and hip=> from now on unable to undertake further sea voyages

1842: officer-in-charge of ‘Depot of Charts and Instruments‘=> intensive studies of old logbooks and charts=> Brillant idea!!

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2.1 Biographical sketch

Wikipedia, 2009

1843: publication of first wind and current charts(Wind and Current Chart of the North Atlantic, Sailing

Directions and Physical Geography of the Seas and Its Meteorology)

1853: first International Hydrographic Conference in Brussels

‚Pathfinder of the Seas‘

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2.2 The brilliant idea/data provenance His idea:

having a great fleet of volunteers, collecting data on their voyages

His strategy:distribute new logbooks for free against hand in of completely filled out ‘abstract log‘ sheet or in Maury‘s own words: ‘You are expected in conformity with the agreement as per the foregoing receipt to send to the Observatory the abstract [logs] of every voyage you may make until the charts are completed. Vessels that fail to return

abstract [logs] will forfeit their claims to the charts‘

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2.2 The brilliant idea/data provenance His method:

ABSTRACT LOG

Definition of ‘abstract log‘:added extra log sheet prior to actual log pages

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2.2 The brilliant idea

Woodruff et al., 2005

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2.2 The brilliant idea/data provenance

Abstract logs contains information about:- date, hour- latitude, longitude- currents, pressure- temperature (air & water)- form/directoion of clouds- duration of fog/rain/hail/snow - magnetic variation (= declination (D))

oceanographic parameters

=> TOTAL: 78,409D observations!

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2.3 Data analysis

Organisation:

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2.3 Data analysis

Spatial distribution of complete Maury Collection (MC):

outliers (total: 42 of 78,409) => 0.5 ‰

return route

Inbound route

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2.3 Data analysis

Temporal distribution of complete Maury Collection (MC):

Onset American Civil

War (1861)

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2.3 Data analysis

Declination reported in two different units:• Degrees & minutes (convential)

Accuracy: 1/60 = 0.02°

• Points- 32 point compass rose- Accuracy:

1/10 point = 1. 13°

32 points = 360°1 point = 11.25°

Wheeler, 2005

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2.3 Data analysis

Data testing against gufm1 (Jackson et al, 2000):

• normalized residuals: best estimate (gufm1)

prediction error

obersvation

• assigned prediction errors :

Errors are assumed gaussian

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2.3 Data analysis

Test for accuracy of ‘point data‘ against gufm1 (Jackson et al, 2000): D [°] D [points]

##

‘point data‘ are retained!

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2.3 Data analysis

Test (complete MC) against gufm1 (Jackson et al, 2000)

#

(Jackson and Walker, 2000)

What about Central Limit Theorem??

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2.3 Data analysis

Verification of Laplace distribution:

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2.3 Data analysis

Spatial residual distribution (MC):

Residuals < 10 sigma (# obs 77,065)

Residuals < 3 sigma (# obs: 65,662)

Residuals > 20 sigma (# obs: 598)

Residuals > 50 sigma (# obs: 50)

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2.3 Data analysis

Temporal residual distribution (MC):

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2.4 Final data sets

1820.mod 1855.m

od

2 criteria:1)good data

coverage2)as far appart as

possible

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2.4 Final data sets

Distribution of 1820 final data set

Distribution of 1855 final data set

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2.5 Reduction to epoch

Data are reduced to discrete epochs 1820 and 1855

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3. Methodology

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3.1 Boundary conditions/prelimnary assumptions Magnetic vaccum outside Earth‘s surface Mantle => insulator Gaussian assumed errors

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3.2 Core field modeling

Magnetic potential expanded in spherical harmonics:

Spherical harmonic expansion truncated at L=14

Downward continuation of observations=> spherical harmonics with large l are amplified

Non-linear inverse problem

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3.2 Non-linear inverse problem

Solved by damped least-squares parameter estimation

Goal: smoothest model for given fit to data

Strategy: find model vector m that minimizes both misfit to data and spatial norm

Non-uniqueness and instabibility resolved by regularization (norm, which measures model complexity)

Associated forward function:

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3.3 Non-linear inverse problem

Dissipation norm (Gubbins, 1975):

Norm in terms of least-squares:

=> regularization matrix

Errors:

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3.3 Non-linear inverse problem

Combination (=> objective function):

Iterative solution:

data kernel matrix =

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4. Results and Conclusions

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4. Results and Conclusions

Effectiveness criterion:

Data subset statistics:

Jackson et al, 2000: 1.16

Jackson et al, 2003: 1.97

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4. Results and Conclusions

1820 model

red: flux out of core, blue: flux into core; color interval: 100 T

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4. Results and Conclusions

1855 model

red: flux out of core, blue: flux into core; color interval: 100 T

High intensity, high latitude flux patchesReversed flux patchesActive AtlanticQuiet Pacific

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4. Results and Conclusions

Tracing of drifting patches:

D1D1

D3 D3

1820 1855

D2

D2

Feature Drift rate----------------------------D1 0.46°/aD2 0.50°/aD3 0.31°/a

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4. Results and Conclusions

Decay of axial dipole:

Decay: 1.4% in 35a

Slope: 13nT/a (ref. value: 15.46nT/a)

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4. Results and Conclusions

Prospects for model improvement:• Data:

1) increase coverage2) Correct for altitude

• Solution finding process: o Damping parameter o Minimization of L2 rather than L1 norm

• Model type: 1) Usage of time-dependent model