Geoid determination by least-squares collocation using GRAVSOFT C.C.Tscherning, University of...

UNICA EU Research Contracts Seminar, Brussels, 29-30 May 2000.05.23

Geoid determination by

least-squares collocation using

GRAVSOFT

C.C.Tscherning, University of Copenhagen, 2005-01-28 1


Guide to gravity field modeling, and especially to geoid determination, using least-squares collocation (LSC).

DATA GRAVITY FIELD MODEL EVERYTHING =

Height anomalies, gravity anomalies, gravity disturbances, deflections of the vertical, gravity

gradients, spherical harmonic coeffients

Purpose:



Important:

the term geoid = the quasi-geoid,

i.e. the height anomaly evaluated at the surface of the Earth.

Quasi-geoid:



The use of the GRAVSOFT package of FORTRAN programs will be explained.

A general description of the GRAVSOFT programs are given in

http://cct.gfy.ku.dk/gravsoft.txt

which is updated regularly when changes to the programs have been made.

Gravsoft



• All programs in FORTRAN77.

• Have been run on many different computers under many different operating systems.

• Available commercially, but free charge if used for scientific purposes.

FORTRAN 77



General methodology


• General methodology for (regional or local) gravity field modelling :

• Transform all data to a global geocentric geodetic datum (ITRF99/GRS80/WGS84), (but NO tides, NO atmosphere) GEOCOL

• “geoid-heights” must be converted to height anomalies N2ZETA

• Use the remove-restore method.


• The effect of a spherical harmonic expansion and of the topography is removed from the data and

• subsequently added to the result. GEOCOL, TC,

• TCGRID

• This is used for all gravity modelling methods including LSC.

• This will produce what we will call residual data. (Files with suffix *.rd).

Remove-restore method



Determine at statistical model (a covariance function) for the residual data in the region in question.

EMPCOV, COVFIT

Covariance



Make a homogeneous selection of the data to be used for geoid determination using rule-of-thumbs for the required data density, SELECT

If many data select those with the smallest error XSelection of points O closest to the middle. 6 points selected


Select

X

X x

xx

x

o o o

o o o


• check for gross-errors (make histograms and contour map of data), GEOCOL

• verify error estimates of data, GEOCOL.

Errors



– Determine using LSC a gravity field approximation, including contingent systematic parameters such as height system bias N0. GEOCOL

– Compute estimates of the height-anomalies and their errors. GEOCOL

– If the error is too large, and more data is available add new data and repeat.

–

Gravity field approximation and datum



• Check model, by comparison with data not used to obtain the model. GEOCOL.

• Restore contribution from Spherical Harmonic model and topography. GEOCOL, TC.

• Convert height anomalies to geoid heights if needed N2ZETA.

• The whole process can be carried through using the GRAVSOFT programs

• Compare with results using other methods !

Restoring and checking.



• GRAVSOFT includes data from New Mexico, USA, which can be used to test the programs and procedures. (Files: nmdtm, nmfa, nmdfv etc.)

• They have here been used to illustrate the use of the programs.

Test Data



• The anomalous gravity potential, T, is equal to the difference between the gravity potential W and the so-called normal potential U,

T = W-U.

• T is a harmonic function, and may as such be expanded in solid spherical harmonics, Ynm

• GM is the product of the gravitational constant and the mass of the Earth and the fuly normalized spherical harmonic coefficients.

Anomalous potential.


T r G M C Y rn m n

n

n m n m( , , ) ( , , )

2


Coordinates used.

GEOCOL accepts geocentric, geodetic and Cartesian (X,Y,Z) coordinates but output only in geodetic.



• where a is the semi-major axis and Pnm the Legendre functions.

• We have orthogonality:

Solid spherical harmonics.


Y P Y r

ar

Pm m n

m n m

nm nm

n

n nm

( ) ( , , )

(s in )co s ,

s in | | ,

1

0

0

14

1

0

2

2

/

/

( , , ) ( , , ) co s( )

, ,

,

Y R Y R d d

n i m j

n i m j

nm ij


Bjerhammar-sphere

The functions Ynm(P) are ortho-

gonal basefunctions in a Hilbert

space with an isotropic inner-

product, harmonic down to a

so-called Bjerhammar-sphere

totally enclosed in the Earth.

T will not necessarily be an

element of such a space, but may be approximated arbitrarily well with such functions. In spherical approximation the ellipsoid is replaced by a sphere with radius 6371 km.



where ψ is the spherical distance between P and Q, Pn the Legendre polynomials and σn are positive constants, the (potential) degree-variances.

Reproducing Kernel


K P Q n a Y P Y Q

arr

P

nn

m n

n

nm nm

nn

n

n

( , ) ( ) ( ) ( )

'(co s )

2

2

2

2 1

2 1

P

Qψr

r’


Inner product, Reproducing property


12 1 2

2

( )( ), ( )

n aY P Y P Y

nnn nn nn

T P T Q K P Q

G M C Y Q i a Y P Y Q

G M C Y P i a Y Q Y Q

G M C Y P T P

n m n

n

nm nmi

ij i

i

ij ij

n m n

n

nm nmi

ij i

i

ij ij

n m n

n

nm nm

( ) ( ( ), ( , ))

( ), ( ) ( ) ( )

( ) ( ) ( ) ( )

( ) ( )

2 2

2

2 2

2

2

2 1

2 1


Closed expression – no summation to

• the degree-variances are selected equal to simple polynomial functions in the degree n multiplied by exponential expressions like qn, where q < 1, then K(P,Q) given by a closed expression. Example:


nB

n

nRR

q

2

2

1

1

K P QRR

Rrr

P

RR rr rr R

n

B

n n

n

BB B

( , )'

(co s )

( ' ) '

0

2

2

1 2 1

24 2 2

12


• Everything like in an n-dimensional vector space.

• COORDINATES: • ANGLES between two • functions, f, g

• PROJECTION f ON g:

• IDENTITY MAPPING:

Hilbert Space with Reproducing Kernel


G Ma

C nn2

co s( )

,

f gg f

f ggg

,

T P T Q K P Q( ) ( ), ( , )


Data and Model

In a (RKHS) approximations T from data for which the associated linear functionals are bounded.

• The relationship between the data and T are expressed through functionals Li,

yi is the i'th data element,

Li the functional, ei the error,

Ai a vector of dimension k and

X a vector of parameters also of dimension k.


XA+ e + (T)L = y Tiiii


Data types

GEOCOL codes:11

12

13

16

17

• Also gravity gradients,

• along-track or area mean values.



Test data



Linear Functionals, spherical approximation


T

)(r

1- =

T

r

1- =

r

2T-

r

T- = g

r

T- = g

cos


Best approximation: projection.

Ti pre-selected base functions:



Collocation

LSC tell which functions to select if we also require that approximation and observations agree and

how to find projection !Suppose data error-free:

We want solution to agree with data

We want smooth variation between data



Approximation to T using error-free data is obtained using that the observations are given by, Li(T) = yi

Projection



• The "optimal" solution is the projection on the n-dimensional sub-space spanned by the so-called representers of the linear functionals, Li(K(P,Q)) = K(Li,Q).

• The projection is the intersection between the subspace and the subspace which consist of func tions which agree exactly with the observations, Li(g)=yi.

LSC - mathematical



Collocation solution in Hilbert Space

Normal Equations

Predictions:

}y{ }L,L{K = }{b jji-1

i


~ ( ) ( , )T Q b K L Qi

n

i i

1

L),LK( b = )TL( ii

n

=1iˆ


Statistical Collocation Solution

We want solution with smallest “error” for all configurations of points which by a rotation around the center of the Earth can be obtained from the original data. And agrees with noise-free data.

We want solution to be linear in the observations


~ ( ) ( )T P y L Ti

n

i ii

n

i i

1 1

)~

()( TLTL ii


Mean-square error - globally


~ ( ) ( ) ( ) ( )T P T P L T T Pi ii

n

1

18 2 2

2

0

2

1

2

/

/

( ( ) ( ))

co s( )

T P L T

d d di

n

i i

P P i


Global Covariances:


C C O V T P L T

T P L T d d d

C T P C O V T P T P

T P d d d

C C O V L T L T

L T L T d d d

P i i

i

ij i j

j i

( ( ), ( ))

( ) ( ) co s

( ( )) ( ( ), ( ))

( ) co s

( ( ), ( ))

( ) ( ) co s

18

18

18

2

0

22

2


Covariance – series development


C O V P Q C O V T P T Q

Rrr

P

G MR

C D E G R E E V A R IA N C E S

ii

i

i

i ijj i

i

( , ) ( ( ), ( )

'(co s ),

,

2

2 1

2


Collocation Solution


~( ) ( ( ), ( ))

(( ( ), ( ))

(( ( ), ( ) ( ( ), ( ))

T P b C O V T P L T

b C O V L T L T y

C O V L T L T C O V T P L T

i

n

i i

i i j i

j i j i

1

1

1


• If the data contain noise, then the elements σij of the variance-covariance matrix of the noise-vector is added to K(Li,Lj) = COV(Li(T),Lj(T)).

• The solution will then both minimalize the square of the norm of T and the noise variance.

• If the noise is zero, the solution will agree exactly with the observations.

• This is the reason for the name collocation.• BUT THE METHOD IS ONLY GIVING THE MINIMUM

LEAST-SQUARES ERROR IN A GLOBAL SENSE.

Noise



Minimalisation of mean-square error

The reproducing kernel must be selected equal to the empirical covariance function, COV(P,Q).

This function is equal to a reproducing kernel with the degree-variances equal to


22

_

2nm

n

nm

Ca

GM


• The covariances are computed using the "law" of covariance propagation, i.e.

• COV(Li,Lj) = Li(Lj(COV(P,Q))),

• where COV(P,Q) is the basic "potential" covariance function.

• COV(P,Q) is an isotropic reproducing kernel harmonic for either P or Q fixed.

Covariance Propagation



Appy the functionals on

K(P,Q)=COV(P,Q)

Covariance of gravity anomalies


ev T T P

g PTr r

ev T

P

P P

( ) ( )

( ) ( )

2

)( rr1-(i

r1-i

Prr

R) =

Prr

R ev - r

- =

Prr

Rev

r -

r-

ev r

- r

- = g(Q))g(P),COV(

i

2 1+i2

i

2i=

i

2 1+i

i

I

2i=P

i

2 1+i

i

2i=Q

P

cos

'cos

cos'

2

2


The quantities COV(L,L), COV(L,Li) and COV(Li,Lj) may all be evaluated by the sequence of subroutines COVAX, COVBX and COVCX

which form a part of the programs GEOCOL and COVFIT.

Evaluation of covariances



If we want to compute height-anomalies from gravity anomalies, we need a global data distribution.

If we work in a local area, the information outside the area may be represented by a spherical harmonic model. If we subtract the contribution from such a model, we have to a certain extend taken data outside the area into account.

(The contribution to the height anomalies must later be restored=added).

Remove-restore (I).



Change of Covariance Function



• minimum mean square error in a very specific sense:

• as the mean over all data-configurations which by a rotation of the Earth's center may be mapped into each other.

• Locally, we must make all areas of the Earth look alike.

• This is done by removing as much as we know, and later adding it. We obtain a field which is statistically more homogeneous.

Homogenizing the data



• 1.We remove the contribution Ts from a known spherical harmonic expansion like the OSU91A field, EGM96 or a GRACE model complete to degree N=360

• 2. We remove the effect of the local topography, TM, using Residual Terrain Modelling (RTM): Earths total mass not changed,

• but center of mass may have changed !!!• We will then be left with a residual field, with a

smoothness in terms of standard deviation of gravity anomalies between 50 % and 25 % less than the original standard deviation.

Homogenizing (II)



Residual quantities


XA+e+)T(L-)T(L-(T)L =

)T(L-)T(L-y = yTiiMisii

Misiiir


• Compute residual gravity anomalies and deflections of the vertical using the OSU91A spherical harmonic expansion and the New Mexico DTM, cf. Table 1. The free-air gravity anomalies are shown in http://cct.gfy.ku.dk/geoidschool/nmfa.pdf

• The program GEOCOL may be used to subtract the contribution from OSU91A.

• Job-files: http://cct.gfy.ku.dk/geoidschool/jobosu91.nmfa• http://cct.gfy.ku.dk/geoidschool/jobosu91.nmdfv

Exercise 1.


http://cct.gfy.ku.dk/geoidschool/nmfa.pdf

http://cct.gfy.ku.dk/geoidschool/nmfa.pdf

http://cct.gfy.ku.dk/geoidschool/jobosu91.nmfa

http://cct.gfy.ku.dk/geoidschool/jobosu91.nmdfv


Output from run:http://cct.gfy.ku.dk/geoidschool/appendix2.txtOSU91: http://cct.gfy.ku.dk/geoidschool/osu91a1fDifferences:http://cct.gfy.ku.dk/geoidschool/nmfa.osu91http://cct.gfy.ku.dk/geoidschool/nmdfv.osu91

Difference map:http://cct.gfy.ku.dk/geoidschool/nmfaosu91.pdf

Output-files


http://cct.gfy.ku.dk/geoidschool/appendix2.txt

http://cct.gfy.ku.dk/geoidschool/osu91a1f

http://cct.gfy.ku.dk/geoidschool/nmfa.osu91

http://cct.gfy.ku.dk/geoidschool/nmdfv.osu91


The RTM contribution may be computed and subtracted using the program tc1.– First a reference terrain model must be constructed

using the program TCGRID with the file nmdtm as basis, http://cct.gfy.ku.dk/geoidschool/nmdtm

– A jobfile to run tc1– http://cct.gfy.ku.dk/geoidschool/jobtc.nmfa– The result should be stored in files with names nmfa.rd

and nmdfv.rd, respectively.– The residual gravity anomalies– http://cct.gfy.ku.dk/geoidschool/nmfard.pdf

Residual topography removal



Smoothing or Homogenisation



• The degree-variances will be changed up to the maximal degree, N, sometimes up to a smaller value, if the series is not agreeing well with the local data (i.e. if no data in the area were used when the series were determined).

• The first of N new degree-variances will depend on the error of the coefficients of the series. We will here suppose that the degree-variances at least are proportional to the so-called error-degree-variances,

Consequences for the statistical model.



Error-degree-variances

The scaling factor α must therefore be determined from the data (in the program COVFIT, see later).


CGM/a of deviation- standardthe is where

, =

=

nmnm

2nm

n

-n=m

En

Enn


Covariance function estimation and representation.

The covariance function to be used in LSC is equal to

• where α is the azimuth between P and Q and φ, λ are the coordinates of P.

• This is a global expression, and that it will only dependent on the radial distances r, r' of P and Q and of the spherical distance ψ between the points.

d d d T(Q)T(P) 8

1

= Q)COV(P,2

0

/2

/2-

2

02

cos



Global-local evaluation

• In practice it must be evaluated in a local area by taking a sum of products of the data grouped according to an interval i of spherical distance,

• Δψ is the interval length (also denoted the sampling interval size).


/2+ < /2- ii


Covariance

• In spherical approximation we have already derived

•

• where R is the mean radius of the Earth.


)(Prr

RR

1-n =

g(Q))g(P),COV(

i

2 2+n

n

2

2=n

cos


Exercise 2.

• Compute the empirical gravity anomaly covariance function using the program EMPCOV for the New Mexico area both for the anomalies minus OSU91A and for the anomalies from which also RTM-effects have been subtracted (input files nmfa.osu91 and nmfa.rd).

• A sample input file to EMPCOV is called http://cct.gfy.ku.dk/geoidschool/empcov.nmfa, .

• A sample run is shown in Appendix 3. The estimated covariances are shown in Fig. 5.



Empirical Covariances



Degree-variances

We see here, that if we can find the gravity anomaly degree-variances, we also can find the potential degree variances.

However, we also see that we need to determine infinitely many quantities in order to find the covariance function



Model-degree-variances

• Use a degree-variance model, i.e. a functional dependence between the degree and the degree-variances.

• In COVFIT, three different models (1, 2 and 3) may be used. The main difference is re lated to whether the (potential) degree-variances go to zero like n-2, n-3 or n-4. The best model is of the type 2,

•

• where RB is the radius of the Bjerhammar-sphere, A is a constant in units of (m/s)2, B an integer.


RR

B)-2)(n-1)(n-(n

A = B

2

n


COVFIT

• The actual modelling of the empirically determined values is done using the program COVFIT. The factors a, A and RB need to be de termined (the first index N+1 must be fixed).

•The program makes an iterative non-linear adjust ment for the Bjerhammar-sphere radius, and linear for the two other quantities



Divergence ?

Unfortunately the iteration may diverge (e.g. result in a Bjerhammar-sphere radius larger than R).

• This will normally occur, if the data has a very inhomogeneous statistical character.

• Therefore simple histograms are always produced together with the covariances (in EMPCOV) in order to check that the data distribution is reasonably sym metric, if not normal.



Exercise 3.

Compute using COVFIT an analytic representation for the covariance function.

An example of an input file is found in http://cct.gfy.ku.dk/geoudschool/covfit.nmfa, . An example of a run is shown in Appendix 3. Gravity error-degree-variances for the OSU91A coefficients are found in the file edgv.osu91.

• The estimated and the fitted covariance values are shown above.



Table of model-covariances



LSC geoid determination from residual data.

• We now have all the tools available for using LSC: residual data and a covariance model.

• 1.establish the normal equations,• 2.solve the equations, and• 3. compute predictions and error estimates.• This may be done using GEOCOL.



Equations

• However, as realized from eq. (8) we have to solve a system of equations as large as the number of observations. GEOCOL has been used to handle 50000 observations simultaneously.

• This is one of the key problems with using the LSC method. The problem may be re duced by using means values of data in the border area.

• Globally gridded data can be used (sphgric)



Necessary data density (d)• Function of correlation length of the covariance function.

• We want to determine geoid height differences with an error of 10 cm over 100 km. This corresponds to an error in deflections of the vertical of 0.2".

• This is equivalent to that we must be able to interpolate gravity anomalies with

a mean error of 1.2

mgal. The

rule-of-thumb is


distance ncorrelatio the

variance,gravity the C

, st.dev.the is e where

)/ 0.3 (dC e

1

0

d

1

20

2d


Exercise 5. Data density.

• Use the residual gravity variance C0, and the correlation distance determined in exercise 3 for the deter mination of the needed data spacing.

• Then use the program SELECT for the selection of points as close a possible to the nodes of a grid having the required data spacing, and which covers the area of interest.



Exercise 5. Data selection.

• The area covered should be larger than the area in which the geoid is to be computed. Data in a distance at least equal to the distance for which gravity and geoid becomes less than 10 % correlated, cf. the result of exercise 3.

• Denote this file nmfa.rd1.• When data have been selected (as described in

exercise 5) it is recommended to prepare a contour plot of the data. This will show whether the data should contain any gross-errors. LSC may also be used for the detection of gross-errors.



Exercise 5.GEOCOL INPUT.

An input file for the program GEOCOL must then be prepared, or the program may be run interactively.

In order to compute height-anomalies at terrain altitude, a file with points consisting of number, latitude, longitude and altitude must be prepared. This may be prepared using the program GEOIP, and input from a digital terrain model (nmdtm).



Exercise 6.

• Prepare a file named nm.h covering the area bounded by 33.0o and 34.0o in latitude and -107.0o and -106.0o in longitude consisting of sequence number, latitude, longitude and height given in a grid with 0.1 degree spacing.

• Use the program GEOIP with input from nmdtm. This will produce a grid-file. This must be converted to a standard point data file (named nmh2) using the program GLIST.



GEOCOL INPUT/SPECIFICATIONS.

• the coordinate system used (GRS80),• the spherical harmonic expansion subtracted (and

later to be added),• the constants defining the covariance model and

contingently its tabulation• the input data files (nmfa.rd or nmfa.rd1 if a

selected subset is used)• the files containing the points in which the pre

dictions should be made (nm.h2).



GEOCOL OPTIONS

• Several options must be selected such as whether error-estimates should be computed or whether we want statistics to be output.

• produce a so-called restart file. This file is an ASCII-file which contains input to GEOCOL which enables the calculation of predictions only. But it has the advantage that it may be used on different computers.



Exercise 7.

• Run the program GEOCOL (geocol16) with the selected gravity data for the prediction of geoid heights and their errors in the points given by nm.h2.

• Output to a file named nm.geoid. Predict also residual deflections of the vertical (nmdfv.rd) and compare with the observed quantities.

• A model input file is found in jobnmlsc • An example of a run where all data in a sub-area

are used is found in http://cct.gfy.ku.dk/geoidschool/appendix5.txt .


http://cct.gfy.ku.dk/geoidschool/appendix5.txt


Exercise 7. RESTORE.

When the LSC-solution has been made, the RTM contribution to the geoid must be determined.

Use tc1 with the file nm.h defining the points of computation.

The LSC de termined residual geoid heights and the associated error-estimates are shown in

http://cct.gfy.ku.dk/geoidschool/nmgeoid.pdf

http://cct.gfy.ku.dk/geoidschool/nmgeoidh.pdf .


http://cct.gfy.ku.dk/geoidschool/nmgeoid.pdf

http://cct.gfy.ku.dk/geoidschool/nmgeoidh.pdf


Exercise 8.

• Compute the RTM contribution to the geoid using tc1 and add the contribution to the output file from exercise 7, nm.geoid.

• If mean gravity anomalies, deflections or GPS/levelling determined geoid-heights were to be used, they could easily have been added to the data.

• It would not be necessary to recalculate the full set of normal-equations.

• Only the columns related to the new data need to be added. Likewise, an obtained solution may be used to calculate such quantities and their error-estimates.



Exercise 9.

• Compute a new solution with the same observations as in exercise 7, but add as observation one of the predicted residual geoid heights. Define the error to be 0.01 m.

• Recalculate the geoid heights and the error-estimates.• Use the possibility for re-using the Cholesky-reduced

normal-equations generated in exercise 7.• Verify that the error-estimates, which now are equivalent

to error-estimates of geoid height differences, have a magnitude smaller than the one specified in exercise 5. (Error-estimates corresponding to one observed geoid height are shown in http://www.gfy.ku.dk/~cct/geoidschool/nmgeoidf.pdf ).


http://www.gfy.ku.dk/~cct/geoidschool/nmgeoidf.pdf

http://www.gfy.ku.dk/~cct/geoidschool/nmgeoidf.pdf


Exercise 9..

• The use of deflections and geoid heights (e.g. from satellite altimetry) may require that parameters such as datum shifts and bias/tilts are determined. These possibilities are also included in GEOCOL

• See next lecture.



Conclusion (I)

• We have now went through all the steps from data to predicted geoid heights.

• The exercises describes the use of gravity data only, but observed mean gravity anomalies,

• GPS/levelling derived height anomalies as well as deflections could have been used as well.



Conclusion (II)

• The difficult steps in the application of LSC is the estimation of the covariance function and subsequent selection of an analytic representation.

• The flexibility of the method is very useful in many circumstances, and is one of the reasons why the method has been applied in many countries.

• If the reference spherical harmonic expansion is of good quality, only a limited amount of data outside the area of interest are needed in order to obtain a good solution.

•



Conclusion (III)

• But if this is not the case, data from a large border-area must be used so that a vast computational effort is needed to obtain a solution.

• This may make it impossible to apply the method.• A way out is then to use the method only for the

determination of gridded values, which then may be used with Fourier transform techniques or Fast Collocation.


Geoid determination by least-squares collocation using GRAVSOFT C.C.Tscherning, University of...

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