Superconducting gravimeter: DETERMINATION OF A DRIFT MODEL

Analysis of drift using expfitbm
Source code in ~/tap/p/expfits.f  and  ~/tap/mo/expfitbm.f
  1. expfitbm open-file block and namelist
  2. HOW TO
  3. Make a drift ts-file
  4. Plots
  5. Example


Open-file block

21 ^ ${EXPFIT_INPUT}                 Input, ts-file, reduced for tides, polar motion, atmosphere....
31 < div/rm${EXPFIT_OUT}.ts          Output, prediction
32 < div/rr${EXPFIT_OUT}.ts          Output, residual
33 A ${EXPFIT_TSE}                   Output, TSF EDIT commands for drift synthesis
41 * < div/rvar_${EXPFIT_OUT}.mc     Output, IUMCV=41, variation of parameters by permutation
42 < div/reig_${EXPFIT_OUT}.mc       Output, IUMCE=42, variation of parameters by eigenvectors
51 < div/rt${DT}.pef                 Input, PEF-bank
52 < div/rrf${EXPFIT_OUT}.ts         Output, PR-filtered residual
   q

Namelist &param

Parameter
 Type
 Explanation
 Default
ITER
 int
 Maximum number of iterations
 100
GTOL
 real*8
 Solution error tolerance
 1e-10
P(NMP)
2)
 real*8
 Initial values; how many need to be set depends on size of problem.
Smallest possible: 4
For every exponential and for every step-feature two additional parameters need to be set
p(1), p(2) relate to initial bias and slope
p(3), p(4) to  first exponential.
From there on it depends whether there is another exponential: p(5), p(6)
Then, there is place for five more steps, either from p(5), p(6) etc. or from p(7),p(8) etc.

PVAR-START(NMP) 2)
 real*8
 Initial variation of P() for the iteration of standard deviations, one parameter at a time

EPS
 real*8
 In the iteration of standard deviations (by bisection), the convergence parameter, which is the difference of the penalty function of the previous two iterations
 1e-3
INPUT_MEASURE
 char*16
 Input data unit
 'V'
CAL
 real*8
 Calibration coefficient, nm/s^2
 -776.014
NEND
 int
 Cut input data at NEND
 NMAX1)
DEL_P(NMP) 2)
 real*8
 In the section on eigenvalue solution to error covariance, the variation of a parameter for calculating the Jacobian.
The formula is p' = p × (1 + DEL_P)
 all 1.d-4
IUMCV
 int
 If  IUMCV > 0 and a file unit IUMCV is open, output the time series pertaining to a permutation of varied parameters.
Can only be used when number of parameters is 6 or less.
0
IUMCE
 int
 If  IUMCE > 0 and a file unit IUMCE is open, output the time series pertaining to the eigenvalues / eigenvector-derived variations of P(). Those of the smallest eigenvalue are suitable for an error bound of the predicted drift 0
N_PEF
 int
 Order of a Prediction-Error Filter that whitens the input noise. If N_PEF = -1, no PEF will be used. If N_PEF=0, a trivial PEF is used (e.g. to test the program behaviour). If N_PEF > 0, a file with a PEF bank must be open on unit 51.
CONFID
 real*4
 Confidence level for parameter covariance, Chi^2 distribution
 0.95
SCLDP
 real*4
 Scale CONFID up with this factor
 1.0
IDATE(3), IHMS(4)
 int
 Date and time of first step, Year, Month, Day...Millisecond
IDATE_EXP(3)
IHMS_EXP(4)
 int
 Date and time of first exponential. Year must be > 2000, otherwise feature will be ignored.
IDATE_SECOND_EXP(3)
IHMS_SECOND_EXP(4)
 int
 Date and time of second exponential. Year must be > 2000.
IDATE_x_STEP(3)
IHMS_x_STEP(4)
 int
 where x is SECOND, THIRD, FOURTH, FIFTH, SIXTH; start with SECOND and extend as needed
Dates and times of later steps. Year must be > 2000, otherwise feature will be ignored.




Remarks


1)
 NMAX
 Time series max. length. Defined in ~/tap/p/expfits.fp

2)
 NMP
 Maximum number of parameters. Defined in ~/tap/p/expfits.fp

HOW TO...

 1. Using 1-h data as is (not discussed)

2. With whitening using a prediction error-filter on a decimated time series.

Pre-requisit: An analysis of as much data as possible using  urtap-openend-smpl.ins . smpl stands for 'simple'.
The  smpl  strategy ignores
- Kattegat luni-solar tides in the water level: They will be picked up in the tide coefficients.
- Kattegat response to air pressure: The coefficients for water level and barometric pressure account for the superposition.
- Regional atmosphere effects: The Atmacs series does not cover the entire period. More important to have a consistent signal model.
The  smpl  strategy includes
- Polar motion in-phase and cross-phase.
- Nodal tide.
- A preliminary drift model: It will be added back to the residual and provide the input to expfitbm.

The plan for the next drift solution (prospectively mid 2017: Include -1h-files
- ECCO2 non-tidal ocean loading ( ~/TD/jpb-oce/HOW.TO )
- ERAin hydrological loading ( ~/TD/jpb-hyd/HOW.TO )
- Atmacs does cover the entire period:
   Local+Regional in  lm/os2009_lm2_12km_13deg.grav , 
   global in  ./orig/gm/*.grav 
   eventually append more recent series.

The script part of the section below is included in a preliminary "superscript":
~/Ttide/SCG/drift-analysis-superscript
(primarily meant to suggest what to do when; variants may consider different subdirectories for iterations of the process, parameters, record time scope,....)

We assume that the power spectrum of the SCG noise is more flat at periods longer than 5 days. That it can be whitened with a short PEF.
Make a short, dt = 5 days series
cd ~/Ttide/SCG
set startd=090615
set begdat=2009,10,19
set begdat=2009,06,15
set enddat=2013,10,23
set enddat=2015,11,19
set begd=090615
set begdm=-$begd

setenv URTAPFD 090615-OPNEND
setenv URTAPDT 1h
If you want to start 2009-06-15 you have to get Ringhals from that date. Append
/home/hgs/SMHI/o/tgg-rin-100301.ts
cd ~/SMHI
tslist-app -I -o /home/hgs/SMHI/o/tgg-rin-090615-1h.ts \
           + TGG/Ringhals-090615-151123-1h.ts /home/hgs/SMHI/o/tgg-rin-100301.ts

Append Polar Motion series:
cd ~/Ttide/SCG
cp d/PMG090615-OPNEND-1h.ts d/PMG090615-OPNEND-1h-old.ts
tslist-app -I -o d/PMG090615-OPNEND-1h.ts + d/PMG090615-OPNEND-1h-old.ts d/PMG100202-OPNEND-1h.ts

Append the input data
source urtap-openend-smpl.ins
tslist-app -I -O:GRAV_________VAL tmp/tmp.mc ++ -LGRAV_________VAL + d/g${URTAPFD}-${URTAPDT}.mc d/g100202-OPNEND-${URTAPDT}.mc
tslist-app -I -O:BARO_________VAL tmp/tmp.mc ++ -LBARO_________VAL + d/g${URTAPFD}-${URTAPDT}.mc d/g100202-OPNEND-${URTAPDT}.mc
mv tmp/tmp.mc d/g${URTAPFD}-${URTAPDT}.mc

Append (re-calc) the nodal tide series:
cd ~/TD
 mv d/gnt${URTAPFD}-${URTAPDT}.ts d/gnt${URTAPFD}-${URTAPDT}.ts-bup
 run_urtip -t t/nodal-only.trs -BM d/gnt${URTAPFD}-${URTAPDT}.ts d/g${URTAPFD}-${URTAPDT}.mc

Run urtapt:
cd ~/Ttide/SCG
urtapt @ urtap-openend-smpl.ins :U >! urtap-openend-smpl.log 

Composing the input series to expfitbm: Add drift back into residual. If the preliminary drift model given to urtap was not precise, esp. concerning the decay time parameter, expfitbm will improve it (that's its purpose).
setenv FNSUB o/g$startd-OPNEND-1h-smpl.ph03.ts
setenv FACSUB 1.0
tslist o/g$startd-OPNEND-1h-smpl.ra.ts -Esubts.tse,ADD -Elpf.tse,LP5DR -I -o tmp/tmp4drift-5d.ts

At the start in June 2009, there is a short linear drift term; too short to spend much effort on determination. lpf.tse,SLOPE  provides a hird-wired correction:
tslist o/g$startd-OPNEND-1h-smpl.ra.ts -Esubts.tse,ADD -Elpf.tse,LP1DR -Elpf.tse,SLOPE -I -o tmp/tmp4drift-1d.ts
Extract the interesting date range:
tslist tmp/tmp4drift-1d.ts -Bc$begdat -U$enddat -I -o ~/TD/div/ra+ph03$begdm-1d.ts

Run expfitbm:
setenv EXPFIT_INPUT div/ra+ph03$begdm-1d.ts
setenv EXPFIT_OUT -pef-ra+ph03$begdm-1d
setenv EXPFIT_TSE div/expfit-pef$begdm-1d.tse

cd ~/TD
setenv DT -1d
 expfitbm @ expfitm.ins :2XPEF >! expfit-2xpef-1d.log
tslq div/rrf-pef-ra+ph03$begdm-1d.ts
Make a PEF from the residual (Example is for 1-d data):
setenv DT -1d
tslist div/rr-pef-ra+ph03-090615-1d.ts -I -E~/Ttide/SCG/lpf.tse,ACV \
       -o div/rr-pef-ra+ph03-090615-1d.acv
and re-run  expfitbm




Make a ts-file from the drift parameters

1. In output file  $EXPFIT_TSE select the parameters, blocks EXP and EXPM (the two last blocks), paste them into a tse-file, e.g.  div/expfit-sel.tse
  You can use the following awk:
awk '/TSF EDIT EXP/&& \!/TSF EDIT EXPM/{p="";qp=1;qq=0} \
     /TSF EDIT EXPM/{q="";qq=1;qp=0} \
     (qq){q=q"\n"$0} (qp){p=p"\n"$0} /END/{qp=0;qq=0} \
     END{print p; print q}' ${EXPFIT_TSE} >! div/expfit-sel.tse
2. Use the new relaxation parameters in a rerun of urtapt. Set the environment parameters with
setenv ADECAY `awk '/MAPPED/&&/EXP1 DELAY/{print $5}'  expfit-2xpef-1d.log`
setenv EDECAY `awk '/MAPPED/&&/EXP2 DELAY/{print $5}'  expfit-2xpef-1d.log`
  Re-iterate the whole monty from "Run urtapt" to this point.
 
3. Use tslist to create a DC-free 1-h series 
cd ~/TD
set bdate=`tslp2d -, F ~/Ttide/SCG/o/g090615-OPNEND-1h-smpl.pa.ts`
set ndata=`tslqn ~/Ttide/SCG/o/g090615-OPNEND-1h-smpl.pa.ts`
cp div/synth-drift-sel.ts div/synth-drift-sel-old.ts
tslist _$ndata -BH$bdate -r1. -I -Ediv/expfit-sel.tse,EXPM -D -o div/synth-drift-sel.ts
Use it e.g. in AG super-campaign:
cd ~/TD/a/Allcamps/d ; ln -s ~/TD/div/synth-drift-sel.ts syn-drift.ts

Up to this point, the super-script  ~/Ttide/SCG/ts4expf-drift  may guide you through the process.
(symlink in ~/TD )

Better however, to make a copy
cd ~/TD/a/Allcamps/d
cp ~/TD/div/synth-drift-sel.ts expfit-synth-drift.ts
ln -s expfit-synth-drift.ts syn-drift.ts

Plots

Make a plot of the results (with the environment parameters $EXPFIT_...):
plot-expfit-results
Result in http://barre.oso.chalmers.se/hgs/4me/ag-superc/expfit${EXPFIT_OUT}.png

Make a plot of the 95%-confidence deviations (only plotting the smallest EV):
plot-varying-expfit reig EIGEN_P12 EIGEN_M12
Result in http://barre.oso.chalmers.se/hgs/4me/ag-superc/expfit_reig${EXPFIT_OUT}.png

Make a plot of the drift and the confidence tunnel
plot-expfit+dev
Result in http://barre.oso.chalmers.se/hgs/4me/ag-superc/expfit+dev${EXPFIT_OUT}.ps

EXAMPLE

Command

expfitbm @ expfitm.ins :2XPEF > ! expfit-2xpef.log
expfitm.ins:
Two exponentials, four extra steps.
The values for pvar_start can be critical. The program should modify them if the initial variation does not
cause a sign change in the iteration for the one-parameter confidence intervals.
Anyway, the eigenvalue-based confidence intervals are the preferred ones. However, the program does not
reach the eigensolution stage if the one-parameter stage fails. 
2XPEF>
21 ^ ${EXPFIT_INPUT}
31 < div/rm${EXPFIT_OUT}.ts
32 < div/rr${EXPFIT_OUT}.ts
33 A ${EXPFIT_TSE}
41 * < div/rvar_${EXPFIT_OUT}.mc
42 < div/reig_${EXPFIT_OUT}.mc
51 < div/rt${DT}.pef
52 < div/rrf${EXPFIT_OUT}.ts
   q
for expfitbm (bisection) (scldp was 24.)
 &param
 n_pef=3
 gtol=1.d-6
 cal=-774.421

 p= 6.131548D+02 -1.355156D+02 -7.238925D+01 -1.034555D+03,
    9.754596D+01 -1.070419D+03 -8.153017D+01  1.053482D+02,
    3.030403D-02  4.216255D+00  1.842617D+00  2.577364D+01

 pvar_start = -100d0, -10.d0, 100.d0, 20.d0, 100.d0, -1.0, -2.0, -1.,
              -1000.0, -10.0, -100.0, -10.0, -100.0, -10.0
 eps=1.d-6
 idate_exp=2009,06,15,         ihms_exp=0,0,0,0
 idate_second_exp=2011,02,24,  ihms_second_exp=0,0,0,0
 idate_second_step=2011,02,24, ihms_second_step=0,0,0,0
 idate_third_step=2012,02,06,  ihms_third_step=0,0,0,0
 idate_fourth_step=2013,10,24, ihms_fourth_step=0,0,0,0
 nend=99999
 iter=1000, qitermon=.true.
 input_measure='nm/s2'
 del_p=0.01d0
 varf=1.1d0, sig_d=-1.0d0, exp_sc=1.001d0
 iumcv=41, iumce=42
 &end


This is the status of 2016-01-08

.bye