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a6_b_eikonal_eq_flat_1DanisoRT.m
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a6_b_eikonal_eq_flat_1DanisoRT.m
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% Read in the eventcs structures and apply eikonal tomography on each event.
% Written by Ge Jin, [email protected]
% 2013.1.16
%
% JBR 3/19/19 : add first derivative smoothing
% JBR 11/19 : Invert all events directly for isotropic velocity and
% 1-D azimuthal anisotropy.
%
clear
%plot native
% setup parameters
setup_parameters
setup_ErrorCode
% JBR
cohere_tol = parameters.cohere_tol;
is_offgc_propagation = parameters.is_offgc_propagation; % Account for off-great-circle propagation using eikonal tomography maps? Otherwise will assume great-circle propagation.
off_azi_tol = parameters.off_azi_tol; % [deg] tolerance for propagation off great-circle
% % Smoothing parameters
% flweight_array = 100*ones(length(parameters.periods)); %parameters.flweight_array
% dterrtol = 2; % largest variance of the inversion error allowed
% inverse_err_tol = 2; %2 % count be number of standard devition
% azi_bin_deg = 20; % [deg] size of azimuthal bins
% min_nbin = 10; % minimum number of measurements in order to include bin
% % Main QC parameters
% fiterr_tol = 1e-2; % wavelet fitting error, throw out measurements greater than this
% maxstadist = 600;
% minstadist = 200;
% cohere_tol = 0.80; % 0.65
% min_stadist_wavelength = 0.33; %0.5; % minimum station separation in wavelengths
% max_stadist_wavelength = 999;
% ref_phv = [3.9936 4.0041 4.0005 3.9999 3.9929 3.9832 3.9813 3.9841 3.9874 3.9996 4.0138 4.0519 4.0930 4.1677 4.2520]; % for calculating wavelength
% APM = 114; % absolute plate motion (GSRM 2.1; NNR) https://www.unavco.org/software/geodetic-utilities/plate-motion-calculator/plate-motion-calculator.html
% FSD = 75;
% Smoothing parameters
flweight_array = 100*ones(length(parameters.periods)); %parameters.flweight_array
dterrtol = 2; % largest variance of the inversion error allowed
inverse_err_tol = 2; %2 % count be number of standard devition
azi_bin_deg = 20; % [deg] size of azimuthal bins
min_nbin = 10; % minimum number of measurements in order to include bin
% Main QC parameters
fiterr_tol = 1e-2; % wavelet fitting error, throw out measurements greater than this
maxstadist = 600;
minstadist = 200;
cohere_tol = 0.80; % 0.65
min_stadist_wavelength = 0.33; %0.5; % minimum station separation in wavelengths
max_stadist_wavelength = 999;
ref_phv = 4*ones(1,length(parameters.periods)); % for calculating wavelength
APM = 114; % absolute plate motion (GSRM 2.1; NNR) https://www.unavco.org/software/geodetic-utilities/plate-motion-calculator/plate-motion-calculator.html
FSD = 75;
% Norm damping for azimuthal anisotropy
damp_azi = [1 1 1e10 1e10]; % [2c 2s 4c 4s] % Damping individual parameters
aziweight = 1; % global weight
% debug setting
isfigure = 1;
isdisp = 1;
is_overwrite = 1;
% % input path
% eventcs_path = './CSmeasure/';
% % output path
% eikonl_ani_output_path = './eikonal/';
workingdir = parameters.workingdir;
% input path
eventcs_path = [workingdir,'CSmeasure/'];
% output path
eikonl_propazi_output_path = [workingdir,'eikonal_propazi/'];
eikonl_ani_output_path = [workingdir];
comp = parameters.component;
lalim=parameters.lalim;
lolim=parameters.lolim;
gridsize=parameters.gridsize;
periods = parameters.periods;
raydensetol=parameters.raydensetol;
smweight_array = parameters.smweight_array;
% flweight_array = parameters.flweight_array; % JBR
Tdumpweight0 = parameters.Tdumpweight;
Rdumpweight0 = parameters.Rdumpweight;
fiterrtol = parameters.fiterrtol;
% dterrtol = parameters.dterrtol;
isRsmooth = parameters.isRsmooth;
% inverse_err_tol = parameters.inverse_err_tol;
min_amp_tol = parameters.min_amp_tol;
% setup useful variables
xnode=lalim(1):gridsize:lalim(2);
ynode=lolim(1):gridsize:lolim(2);
Nx=length(xnode);
Ny=length(ynode);
[xi, yi]=ndgrid(xnode,ynode);
% Setup universal smoothing kernel
disp('initial the smoothing kernel')
tic
% longtitude smoothing
[i,j] = ndgrid(1:Nx,2:(Ny-1));
ind = j(:) + Ny*(i(:)-1);
dy = diff(ynode)*cosd(mean(xnode)); % correct smoothing for latitude
dy1 = dy(j(:)-1);
dy2 = dy(j(:));
Areg = sparse(repmat(ind,1,3),[ind-1,ind,ind+1], ...
[-2./(dy1.*(dy1+dy2)), 2./(dy1.*dy2), -2./(dy2.*(dy1+dy2))],Nx*Ny+4,Nx*Ny+4);
% latitude smoothing
[i,j] = ndgrid(2:(Nx-1),1:Ny);
ind = j(:) + Ny*(i(:)-1);
dx = diff(xnode);
dx1 = dx(i(:)-1);
dx2 = dx(i(:));
Areg = [Areg;sparse(repmat(ind,1,3),[ind-Ny,ind,ind+Ny], ...
[-2./(dx1.*(dx1+dx2)), 2./(dx1.*dx2), -2./(dx2.*(dx1+dx2))],Nx*Ny+4,Nx*Ny+4)];
F=sparse(Nx*Ny*2*2,Nx*Ny*2+4);
for n=1:size(Areg,1)
ind=find(Areg(n,:)~=0);
F(2*n-1,2*ind-1)=Areg(n,ind);
F(2*n,2*ind)=Areg(n,ind);
end
toc
% JBR - define first derivative "flatness" kernel
F2 = flat_kernel_build_2pt(xnode, ynode, Nx*Ny);
Ftemp = sparse(size(F2,1),size(F2,2)+4);
Ftemp(1:Nx*Ny*2*2,1:Nx*Ny*2) = F2;
F2 = Ftemp;
%JBR - add anisotropic terms to smoothing matrix (add 4 columns and 4 rows)
% damping matrix
Areg_azi = zeros(4,Nx*Ny*2+4);
azipart = eye(4,4) .* diag(damp_azi);
Areg_azi(1:4,Nx*Ny*2+1:Nx*Ny*2+4) = azipart;
F_azi_damp = Areg_azi;
% read in bad station list, if existed
if exist('badsta.lst')
badstnms = textread('badsta.lst','%s');
disp('Found Bad stations:')
disp(badstnms)
end
csmatfiles = dir([eventcs_path,'/*cs_',comp,'.mat']);
% Loop through the periods
clear eventphv_ani;
for ip = 1:length(periods)
clear mat_azi azi rays ddist dt w azi_vec
disp(periods(ip));
smweight0 = smweight_array(ip);
flweight0 = flweight_array(ip); % JBR
raynum = 0;
for ie = 1:length(csmatfiles)
%for ie = 30
% read in data and set up useful variables
temp = load([eventcs_path,csmatfiles(ie).name]);
eventcs = temp.eventcs;
% disp(eventcs.id)
evla = eventcs.evla;
evlo = eventcs.evlo;
matfilename = [eikonl_ani_output_path,'/eikonal_ani_',comp,'.mat'];
if exist(matfilename,'file') && ~is_overwrite
disp(['Exist ',matfilename,', skip']);
continue;
end
if is_offgc_propagation==1
eikonal_in = [eikonl_propazi_output_path,'/',eventcs.id,'_eikonal_',comp,'.mat'];
if ~exist(eikonal_in,'file')
error('No propagation azimuth found. Need to first run a6_a0_eikonal_eq_GetPropAzi.m');
end
temp = load(eikonal_in);
phase_lat = temp.eventphv(ip).GVx; % phase slowness in x-direction
phase_lon = temp.eventphv(ip).GVy; % phase slowness in y-direction
% Use event eikonal tomography results to get propagation azimuth
azimat = 90 - atan2d(phase_lat,phase_lon);
azimat(azimat<0) = azimat(azimat<0) + 360;
[~, azimat_ev] = distance(xi,yi,evla,evlo,referenceEllipsoid('GRS80'));
azimat(isnan(azimat)) = azimat_ev(isnan(azimat));
% Ensure that propagation azimuth is not too far from great-circle
diff_az = angdiff(azimat*pi/180,azimat_ev*pi/180)*180/pi;
if mean(abs(diff_az(:))) > off_azi_tol
% disp('off_azi_tol exceeded... skipping');
continue
end
else
[~, azimat] = distance(xi,yi,evla,evlo,referenceEllipsoid('GRS80'));
end
if exist('badstnms','var')
badstaids = find(ismember(eventcs.stnms,badstnms));
else
badstaids = [];
end
% Build the rotation matrix
razi = azimuth(xi+gridsize/2,yi+gridsize/2,evla,evlo,referenceEllipsoid('GRS80'))+180;
% R = sparse(2*Nx*Ny+4,2*Nx*Ny+4);
% for i=1:Nx
% for j=1:Ny
% n=Ny*(i-1)+j;
% theta = razi(i,j);
% R(2*n-1,2*n-1) = cosd(theta);
% R(2*n-1,2*n) = sind(theta);
% R(2*n,2*n-1) = -sind(theta);
% R(2*n,2*n) = cosd(theta);
% end
% end
% Calculate the relative travel time compare to one reference station
% travel_time = Cal_Relative_dtp(eventcs);
% Build the ray locations
for ics = 1:length(eventcs.CS)
raynum = raynum+1;
if eventcs.CS(ics).cohere(ip)<cohere_tol && eventcs.CS(ics).isgood(ip)>0
eventcs.CS(ics).isgood(ip) = ErrorCode.low_cohere;
end
if (abs(eventcs.CS(ics).ddist) < ref_phv(ip)*periods(ip)*min_stadist_wavelength || ...
abs(eventcs.CS(ics).ddist) > ref_phv(ip)*periods(ip)*max_stadist_wavelength) && eventcs.CS(ics).isgood(ip)>0
eventcs.CS(ics).isgood(ip) = ErrorCode.min_stadist_wavelength;
end
if (abs(eventcs.CS(ics).ddist) > maxstadist || ...
abs(eventcs.CS(ics).ddist) < minstadist) && eventcs.CS(ics).isgood(ip)>0
eventcs.CS(ics).isgood(ip) = -13;
end
if (eventcs.CS(ics).fiterr(ip) > fiterr_tol)
eventcs.CS(ics).isgood(ip) = -14;
end
if eventcs.CS(ics).cohere(ip)>=cohere_tol && eventcs.CS(ics).isgood(ip)==ErrorCode.low_cohere
eventcs.CS(ics).isgood(ip) = 1;
end
if eventcs.CS(ics).isgood(ip) > 0
dt(raynum,:) = eventcs.CS(ics).dtp(ip);
w(raynum,:) = 1;
% w(raynum,:) = eventcs.CS(ics).fiterr(ip)^(-1/2);
% w(raynum,:) = eventcs.CS(ics).fiterr(ip)^(-1/2) * eventcs.CS(ics).ddist^(1/2);
else
dt(raynum,:) = eventcs.CS(ics).dtp(ip);
w(raynum,:) = 0;
end
if sum(ismember([eventcs.CS(ics).sta1 eventcs.CS(ics).sta2],badstaids)) > 0
w(raynum,:) = 0;
end
evinfo.evid{raynum,:} = eventcs.id;
evinfo.evla(raynum,:) = eventcs.evla;
evinfo.evlo(raynum,:) = eventcs.evlo;
evinfo.evdp(raynum,:) = eventcs.evdp;
evinfo.Mw(raynum,:) = eventcs.Mw;
rays(raynum,1) = eventcs.stlas(eventcs.CS(ics).sta1);
rays(raynum,2) = eventcs.stlos(eventcs.CS(ics).sta1);
rays(raynum,3) = eventcs.stlas(eventcs.CS(ics).sta2);
rays(raynum,4) = eventcs.stlos(eventcs.CS(ics).sta2);
% JBR - Build azimuthal part of data kernel
ddist(raynum,:) = eventcs.CS(ics).ddist;
% dt(ics) = eventcs.dtp(ics);
% phv(ics) = ddist(ics)./dt(ics);
mean_stala = mean([eventcs.stlas(eventcs.CS(ics).sta1), eventcs.stlas(eventcs.CS(ics).sta2)]);
mean_stalo = mean([eventcs.stlos(eventcs.CS(ics).sta1), eventcs.stlos(eventcs.CS(ics).sta2)]);
for i=1:Nx
for j=1:Ny
n=Ny*(i-1)+j;
azi_vec(raynum,n)=azimat(i,j);
end
end
[r, ~] = distance(rays(raynum,1),rays(raynum,2),rays(raynum,3),rays(raynum,4),referenceEllipsoid('GRS80'));
dr = deg2km(mean(diff(xnode)));
Nr = floor(r/dr);
[lat_way,lon_way] = gcwaypts(rays(raynum,1),rays(raynum,2),rays(raynum,3),rays(raynum,4),Nr);
rayazi = interp2(yi,xi,azimat,lon_way,lat_way);
rayazi_mean = angmean(rayazi(:)*pi/180)*180/pi;
if rayazi_mean < 0
rayazi_mean = rayazi_mean + 360;
end
azi(raynum) = rayazi_mean;
mat_azi(raynum,:) = ddist(raynum) * [cosd(2*azi(raynum)), sind(2*azi(raynum)), cosd(4*azi(raynum)), sind(4*azi(raynum)) ];
end
end
azi(azi<0) = azi(azi<0) + 360;
azi_vec(azi_vec<0) = azi_vec(azi_vec<0) + 360;
W = sparse(length(w),length(w));
for id = 1:length(w)
if w(id) > 0
W(id,id) = w(id);
end
end
% Build the kernel
disp('Building up ray path kernel')
tic
% mat_iso=kernel_build(rays,xnode,ynode);
mat_iso=kernel_build_RT(rays,xnode,ynode,azi_vec);
toc
mat = [mat_iso, mat_azi];
% build dumping matrix for ST
% dumpmatT = R(2:2:2*Nx*Ny,:);
% build dumping matrix for SR
% dumpmatR = R(1:2:2*Nx*Ny-1,:);
% Normalize smoothing kernel
NR=norm(F,1);
NA=norm(W*mat,1);
smweight = smweight0*NA/NR;
% JBR - Normalize flatness kernel
NR=norm(F2,1);
NA=norm(W*mat,1);
flweight = flweight0*NA/NR;
% % Normalize dumping matrix for ST
% NR=norm(dumpmatT,1);
% NA=norm(W*mat,1);
% dumpweightT = Tdumpweight0*NA/NR;
%
% % Normalize dumping matrix for SR
% NR=norm(dumpmatR,1);
% NA=norm(W*mat,1);
% dumpweightR = Rdumpweight0*NA/NR;
% Rescale azimuthal anisotropy damping
NR=norm(F_azi_damp,1);
NA=norm(W*mat,1);
aziweight0 = aziweight*NA/NR;
% Set up matrix on both side
% A=[W*mat;smweight*F;flweight*F2;dumpweightT*dumpmatT;dumpweightR*dumpmatR;aziweight0*F_azi_damp];
A=[W*mat;smweight*F;flweight*F2;aziweight0*F_azi_damp];
avgv = eventcs.avgphv(ip);
% rhs=[W*dt;zeros(size(F,1),1);zeros(size(F2,1),1);zeros(size(dumpmatT,1),1);dumpweightR*ones(size(dumpmatR,1),1)./avgv];
% rhs=[W*dt;zeros(size(F,1),1);zeros(size(F2,1),1);zeros(size(dumpmatT,1),1);dumpweightR*ones(size(dumpmatR,1),1)./avgv;zeros(size(F_azi_damp,1),1)];
rhs=[W*dt;zeros(size(F,1),1);zeros(size(F2,1),1);zeros(size(F_azi_damp,1),1)];
% Least square inversion
phaseg=(A'*A)\(A'*rhs);
% Iteratively down weight the measurement with high error
niter=0;
ind = find(diag(W)==0);
if isdisp
disp(['Before iteration'])
disp(['Good Measurement Number: ', num2str(length(diag(W))-length(ind))]);
disp(['Bad Measurement Number: ', num2str(length(ind))]);
end
niter=1;
while niter < 2
niter=niter+1;
isgood_mat = diag(diag(W)>0);
err = mat*phaseg - dt;
% err = W*err;
err = isgood_mat*err;
% err = W*err;
% stderr=std(err);
stderr=std(err(err~=0));
if stderr > dterrtol
stderr = dterrtol;
end
for i=1:length(err)
if abs(err(i)) > inverse_err_tol*stderr || abs(err(i))==0
W(i,i)=0;
else
W(i,i)=1./stderr;
end
end
ind = find(diag(W)==0);
if isdisp
disp('After:')
disp(['Good Measurement Number: ', num2str(length(diag(W))-length(ind))]);
disp(['Bad Measurement Number: ', num2str(length(ind))]);
end
% Rescale the smooth kernel
NR=norm(F,1);
NA=norm(W*mat,1);
smweight = smweight0*NA/NR;
% JBR - Normalize flatness kernel
NR=norm(F2,1);
NA=norm(W*mat,1);
flweight = flweight0*NA/NR;
% % rescale dumping matrix for St
% NR=norm(dumpmatT,1);
% NA=norm(W*mat,1);
% dumpweightT = Tdumpweight0*NA/NR;
%
% % rescale dumping matrix for SR
% NR=norm(dumpmatR,1);
% NA=norm(W*mat,1);
% dumpweightR = Rdumpweight0*NA/NR;
% Rescale azimuthal anisotropy damping
NR=norm(F_azi_damp,1);
NA=norm(W*mat,1);
aziweight0 = aziweight*NA/NR;
% A=[W*mat;smweight*F;flweight*F2;dumpweightT*dumpmatT;dumpweightR*dumpmatR;aziweight0*F_azi_damp];
A=[W*mat;smweight*F;flweight*F2;aziweight0*F_azi_damp];
% rhs=[W*dt;zeros(size(F,1),1);zeros(size(F2,1),1);zeros(size(dumpmatT,1),1);dumpweightR*ones(size(dumpmatR,1),1)./avgv];
% rhs=[W*dt;zeros(size(F,1),1);zeros(size(F2,1),1);zeros(size(dumpmatT,1),1);dumpweightR*ones(size(dumpmatR,1),1)./avgv;zeros(size(F_azi_damp,1),1)];
rhs=[W*dt;zeros(size(F,1),1);zeros(size(F2,1),1);zeros(size(F_azi_damp,1),1)];
phaseg=(A'*A)\(A'*rhs);
end
% Estimate travel-time residuals
dt_res = dt - mat*phaseg;
% Calculate the kernel density
%sumG=sum(abs(mat),1);
ind=1:Nx*Ny;
rayW = W;
rayW(find(rayW>1))=1;
raymat = rayW*mat;
sumG(ind)=sum((raymat(:,2*ind).^2+raymat(:,2*ind-1).^2).^.5,1);
clear raydense
for i=1:Nx
for j=1:Ny
n=Ny*(i-1)+j;
raydense(i,j)=sumG(n);
end
end
% disp(' Get rid of uncertainty area');
fullphaseg = phaseg;
for i=1:Nx
for j=1:Ny
n=Ny*(i-1)+j;
if raydense(i,j) < raydensetol %&& ~issyntest
phaseg(2*n-1)=NaN;
phaseg(2*n)=NaN;
end
end
end
% Isotropic phase velocity
phv_iso = ddist./(mat_iso*phaseg(1:Nx*Ny*2));
% Change phaseg into phase velocity
for i=1:Nx
for j=1:Ny
n=Ny*(i-1)+j;
GVx(i,j)= phaseg(2*n-1);
GVy(i,j)= phaseg(2*n);
end
end
GV=(GVx.^2+GVy.^2).^-.5;
GV_azi = angle(GVx + GVy.*sqrt(-1));
GV_azi = rad2deg(GV_azi);
GV_av = 1./nanmean(1./GV(:));
slow_av = 1./GV_av;
% get azimuthal anisotropy
Ac2 = -phaseg(Nx*Ny*2+1)./slow_av;
As2 = -phaseg(Nx*Ny*2+2)./slow_av;
Ac4 = -phaseg(Nx*Ny*2+3)./slow_av;
As4 = -phaseg(Nx*Ny*2+4)./slow_av;
A2 = sqrt(Ac2^2+As2^2);
A4 = sqrt(Ac4^2+As4^2);
phi2 = 1/2*atan2d(As2,Ac2);
phi4 = 1/4*atan2d(As4,Ac4);
% save the result in the structure
eventphv_ani(ip).rays = rays;
eventphv_ani(ip).w = diag(W);
eventphv_ani(ip).goodnum = length(find(eventphv_ani(ip).w>0));
eventphv_ani(ip).badnum = length(find(eventphv_ani(ip).w==0));
eventphv_ani(ip).dt = dt;
eventphv_ani(ip).dt_res = dt_res; % data residuals
eventphv_ani(ip).GV = GV;
eventphv_ani(ip).GV_azi = GV_azi;
eventphv_ani(ip).GV_av = GV_av;
eventphv_ani(ip).GVx = GVx;
eventphv_ani(ip).GVy = GVy;
eventphv_ani(ip).phv_iso = phv_iso;
eventphv_ani(ip).raydense = raydense;
eventphv_ani(ip).lalim = lalim;
eventphv_ani(ip).lolim = lolim;
eventphv_ani(ip).gridsize = gridsize;
eventphv_ani(ip).azi = azi;
eventphv_ani(ip).rays = rays;
eventphv_ani(ip).ddist = ddist;
eventphv_ani(ip).id = evinfo.evid;
eventphv_ani(ip).evla = evinfo.evla;
eventphv_ani(ip).evlo = evinfo.evlo;
eventphv_ani(ip).evdp = evinfo.evdp;
eventphv_ani(ip).Mw = evinfo.Mw;
eventphv_ani(ip).period = periods(ip);
% eventphv_ani(ip).traveltime = travel_time(ip).tp;
% eventphv_ani(ip).stlas = eventcs.stlas;
% eventphv_ani(ip).stlos = eventcs.stlos;
% eventphv_ani(ip).stnms = eventcs.stnms;
eventphv_ani(ip).isgood = eventphv_ani(ip).w>0;
eventphv_ani(ip).phv = ddist./dt;
eventphv_ani(ip).A2 = A2;
eventphv_ani(ip).A4 = A4;
eventphv_ani(ip).phi2 = phi2;
eventphv_ani(ip).phi4 = phi4;
disp(['Period:',num2str(periods(ip)),', Goodnum:',num2str(eventphv_ani(ip).goodnum),...
'Badnum:',num2str(eventphv_ani(ip).badnum)]);
end % end of periods loop
%% Make event list
fid = fopen([workingdir,'azi_evlist.txt'],'w');
for ip = 3 % 50 s
isgood = eventphv_ani(ip).isgood;
evids_all = eventphv_ani(ip).id(isgood);
[evids,I] = unique(evids_all);
evlas = eventphv_ani(ip).evla(isgood); evlas = evlas(I);
evlos = eventphv_ani(ip).evlo(isgood); evlos = evlos(I);
evdps = eventphv_ani(ip).evdp(isgood); evdps = evdps(I);
evMws = eventphv_ani(ip).Mw(isgood); evMws = evMws(I);
for iev = 1:length(evids)
evid = evids{iev};
evla = evlas(iev);
evlo = evlos(iev);
evdp = evdps(iev);
evMw = evMws(iev);
fprintf(fid,'%13s %12f %12f %5.1f %5.2f\n',evid,evla,evlo,evdp,evMw);
end
% display(length(evids))
end
fclose(fid);
%% Fit anisotropy
fit_azi = [];
for ip = 1:length(periods)
isgood = eventphv_ani(ip).isgood;
phv = eventphv_ani(ip).phv(isgood);
azi = eventphv_ani(ip).azi(isgood);
w = eventphv_ani(ip).w(isgood);
w(w > (median(w)+rms(w)) ) = median(w)+rms(w);
weight = w.*(-1/2);
if length(find(~isnan(phv))) > 3
[para fiterr]=fit_azi_anisotropy(azi,phv,weight);
parastd=confint(para,.95);
fit_azi.periods(ip)=periods(ip);
fit_azi.c_iso(ip)=para.a;
fit_azi.c_iso_95(ip)=parastd(2,1)-para.a;
fit_azi.A2(ip)=para.d;
fit_azi.A2_95(ip)=parastd(2,2)-para.d;
fit_azi.phi2(ip)=para.e;
fit_azi.phi2_95(ip)=parastd(2,3)-para.e;
else
fit_azi.periods(ip)=periods(ip);
fit_azi.c_iso(ip)=nan;
fit_azi.c_iso_95(ip)=nan;
fit_azi.A2(ip)=nan;
fit_azi.A2_95(ip)=nan;
fit_azi.phi2(ip)=nan;
fit_azi.phi2_95(ip)=nan;
end
end
fit_azi_bin = [];
weight = 0;
% Fit binned measurements
for ip = 1:length(periods)
isgood = eventphv_ani(ip).isgood;
phv = eventphv_ani(ip).phv(isgood);
azi = eventphv_ani(ip).azi(isgood);
azi(azi<0) = azi(azi<0)+360;
w = eventphv_ani(ip).w(isgood);
[~,Isort] = sort(azi);
azi_srt = azi(Isort);
phv_srt = phv(Isort);
w_srt = w(Isort); %phv_std(Isort); % weight by std
bins = [0:azi_bin_deg:360];
phv_bin = 0; azi_bin = 0; phv_bin_err = 0; azi_bin_err = 0;
for ibin = 1:length(bins)-1
I_bin = azi_srt>=bins(ibin) & azi_srt<bins(ibin+1);
if sum(I_bin)<min_nbin
I_bin = false(size(I_bin));
end
wbin = w_srt(I_bin);
wbinnorm = wbin/sum(wbin);
% Weighted means and standard deviations
fit_azi_bin.meas(ip).phv(ibin) = nansum(wbin .* phv_srt(I_bin)) / nansum(wbin);
fit_azi_bin.meas(ip).phv_std(ibin) = sqrt( var(phv_srt(I_bin) , wbinnorm) );
fit_azi_bin.meas(ip).azi(ibin) = (bins(ibin)+bins(ibin+1))/2;
weight(ibin) = sum(I_bin);
end
% [para fiterr]=fit_azi_anisotropy(fit_azi_bin.meas(ip).azi, fit_azi_bin.meas(ip).phv, fit_azi_bin.meas(ip).phv_std.^(1/2) .* sqrt(weight));
% [para fiterr]=fit_azi_anisotropy(fit_azi_bin.meas(ip).azi, fit_azi_bin.meas(ip).phv, sqrt(weight));
if length(find(~isnan(fit_azi_bin.meas(ip).phv))) > 3
[para fiterr]=fit_azi_anisotropy(fit_azi_bin.meas(ip).azi, fit_azi_bin.meas(ip).phv);
parastd=confint(para,.95);
fit_azi_bin.periods(ip)=periods(ip);
fit_azi_bin.c_iso(ip)=para.a;
fit_azi_bin.c_iso_95(ip)=parastd(2,1)-para.a;
fit_azi_bin.A2(ip)=para.d;
fit_azi_bin.A2_95(ip)=parastd(2,2)-para.d;
fit_azi_bin.phi2(ip)=para.e;
fit_azi_bin.phi2_95(ip)=parastd(2,3)-para.e;
else
fit_azi_bin.periods(ip)=periods(ip);
fit_azi_bin.c_iso(ip)=nan;
fit_azi_bin.c_iso_95(ip)=nan;
fit_azi_bin.A2(ip)=nan;
fit_azi_bin.A2_95(ip)=nan;
fit_azi_bin.phi2(ip)=nan;
fit_azi_bin.phi2_95(ip)=nan;
end
end
fit_azi_bin_res = [];
weight = 0;
% Fit binned measurements
for ip = 1:length(periods)
isgood = eventphv_ani(ip).isgood;
phv = eventphv_ani(ip).phv(isgood);
phv_iso = eventphv_ani(ip).phv_iso(isgood);
dphv = (phv-phv_iso)./phv_iso;
azi = eventphv_ani(ip).azi(isgood);
azi(azi<0) = azi(azi<0)+360;
w = eventphv_ani(ip).w(isgood);
[~,Isort] = sort(azi);
azi_srt = azi(Isort);
dphv_srt = dphv(Isort);
w_srt = w(Isort); %phv_std(Isort); % weight by std
bins = [0:azi_bin_deg:360];
phv_bin = 0; azi_bin = 0; phv_bin_err = 0; azi_bin_err = 0;
for ibin = 1:length(bins)-1
I_bin = azi_srt>=bins(ibin) & azi_srt<bins(ibin+1);
if sum(I_bin)<min_nbin
I_bin = false(size(I_bin));
end
wbin = w_srt(I_bin);
wbinnorm = wbin/sum(wbin);
% Weighted means and standard deviations
fit_azi_bin_res.meas(ip).dphv(ibin) = nansum(wbin .* dphv_srt(I_bin)) / nansum(wbin);
fit_azi_bin_res.meas(ip).dphv_std(ibin) = sqrt( var(dphv_srt(I_bin) , wbinnorm) );
fit_azi_bin_res.meas(ip).azi(ibin) = (bins(ibin)+bins(ibin+1))/2;
weight(ibin) = sum(I_bin);
end
% [para fiterr]=fit_azi_anisotropy(fit_azi_bin.meas(ip).azi, fit_azi_bin.meas(ip).phv, fit_azi_bin.meas(ip).phv_std.^(1/2) .* sqrt(weight));
% [para fiterr]=fit_azi_anisotropy(fit_azi_bin.meas(ip).azi, fit_azi_bin.meas(ip).phv, sqrt(weight));
if length(find(~isnan(fit_azi_bin_res.meas(ip).dphv))) > 3
[para fiterr]=fit_azi_anisotropy2theta_resid(fit_azi_bin_res.meas(ip).azi, fit_azi_bin_res.meas(ip).dphv);
parastd=confint(para,.95);
fit_azi_bin_res.periods(ip)=periods(ip);
fit_azi_bin_res.A2(ip)=para.d;
fit_azi_bin_res.A2_95(ip)=parastd(2,1)-para.d;
fit_azi_bin_res.phi2(ip)=para.e;
fit_azi_bin_res.phi2_95(ip)=parastd(2,2)-para.e;
else
fit_azi_bin_res.periods(ip)=periods(ip);
fit_azi_bin_res.A2(ip)=nan;
fit_azi_bin_res.A2_95(ip)=nan;
fit_azi_bin_res.phi2(ip)=nan;
fit_azi_bin_res.phi2_95(ip)=nan;
end
end
matfilename = [eikonl_ani_output_path,'/eikonal_ani1D_',comp,'.mat'];
save(matfilename,'eventphv_ani','fit_azi','fit_azi_bin','fit_azi_bin_res');
disp(['Save the result to: ',matfilename])
%%
if isfigure
N=3; M = floor(length(periods)/N) +1;
figure(88)
clf
for ip = 1:length(periods)
subplot(M,N,ip)
ax = worldmap(lalim, lolim);
set(ax, 'Visible', 'off')
h1=surfacem(xi,yi,eventphv_ani(ip).GV);
% set(h1,'facecolor','interp');
% load pngcoastline
% geoshow([S.Lat], [S.Lon], 'Color', 'black','linewidth',2)
title(['Periods: ',num2str(periods(ip))],'fontsize',15)
avgv = nanmean(eventphv_ani(ip).GV(:));
if isnan(avgv)
continue;
end
r = 0.1;
caxis([avgv*(1-r) avgv*(1+r)])
colorbar
load seiscmap
colormap(seiscmap)
% % Plot ray paths
% jj=0;
% for ii = 1:length(eventphv_ani(ip).w)
% if ~eventphv_ani(ip).isgood(ii) %|| raytomo(ip).sta_dep(ixsp) > -4500
% continue
% end
% jj = jj + 1;
% lat1(jj,1) = eventphv_ani(ip).rays(ii,1);
% lon1(jj,1) = eventphv_ani(ip).rays(ii,2);
% lat2(jj,1) = eventphv_ani(ip).rays(ii,3);
% lon2(jj,1) = eventphv_ani(ip).rays(ii,4);
% end
% hold on;
% h = plotm([lat1 lat2]',[lon1 lon2]','-k','linewidth',0.5); hold on;
end
drawnow;
end
%%
figure(89);
set(gcf,'position',[351 677 560 668]);
clf
clear avgv avgv_std aniso_str aniso_str_std aniso_azi aniso_azi_std
for ip = 1:length(periods)
avgv(ip) = eventphv_ani(ip).GV_av;
avgv_std(ip) = 0; %nanmean(avgphv_aniso(ip).isophv_std(:));
aniso_str(ip) = eventphv_ani(ip).A2;
aniso_str_std(ip) = 0; %nanmean(avgphv_aniso(ip).aniso_strength_std(:));
aniso_azi(ip) = eventphv_ani(ip).phi2;
aniso_azi_std(ip) = 0; %nanmean(avgphv_aniso(ip).aniso_azi_std(:));
c_iso(ip) = fit_azi.c_iso(ip);
c_iso_95(ip) = fit_azi.c_iso_95(ip);
A2(ip) = fit_azi.A2(ip);
A2_95(ip) = fit_azi.A2_95(ip);
phi2(ip) = fit_azi.phi2(ip);
phi2_95(ip) = fit_azi.phi2_95(ip);
c_iso_bin(ip) = fit_azi_bin.c_iso(ip);
c_iso_95_bin(ip) = fit_azi_bin.c_iso_95(ip);
A2_bin(ip) = fit_azi_bin.A2(ip);
A2_95_bin(ip) = fit_azi_bin.A2_95(ip);
phi2_bin(ip) = fit_azi_bin.phi2(ip);
phi2_95_bin(ip) = fit_azi_bin.phi2_95(ip);
A2_bin_res(ip) = fit_azi_bin_res.A2(ip);
A2_95_bin_res(ip) = fit_azi_bin_res.A2_95(ip);
phi2_bin_res(ip) = fit_azi_bin_res.phi2(ip);
phi2_95_bin_res(ip) = fit_azi_bin_res.phi2_95(ip);
end
aniso_azi(aniso_azi<0) = aniso_azi(aniso_azi<0)+180;
%plot native
subplot(3,1,1); hold on;
errorbar(periods,avgv,avgv_std*2,'-or');
errorbar(periods,c_iso,c_iso_95,'-ob');
errorbar(periods,c_iso_bin,c_iso_95_bin,'-ok');
ylim([3.85 4.4]);
xlim([20 150]);
ylabel('c (km/s)');
%plot native
subplot(3,1,2); hold on;
errorbar(periods,aniso_str*100*2,aniso_str_std*100*2,'-or');
errorbar(periods,A2*100*2,A2_95*100,'-ob');
errorbar(periods,A2_bin*100*2,A2_95_bin*100,'-ok');
errorbar(periods,A2_bin_res*100*2,A2_95_bin_res*100,'-om');
ylim([0 5]);
xlim([20 150]);
ylabel('2A');
%plot native
subplot(3,1,3);
plot([periods(1),periods(end)],FSD*[1 1],'--k','linewidth',1.5); hold on;
plot([periods(1),periods(end)],APM*[1 1],'--','color',[0.5 0.5 0.5],'linewidth',1.5);
errorbar(periods,aniso_azi,aniso_azi_std*2,'-or');
errorbar(periods,aniso_azi+180,aniso_azi_std*2,'-or');
errorbar(periods,aniso_azi-180,aniso_azi_std*2,'-or');
errorbar(periods,phi2,phi2_95,'-ob');
errorbar(periods,phi2+180,phi2_95,'-ob');
errorbar(periods,phi2-180,phi2_95,'-ob');
errorbar(periods,phi2_bin,phi2_95_bin,'-ok');
errorbar(periods,phi2_bin+180,phi2_95_bin,'-ok');
errorbar(periods,phi2_bin-180,phi2_95_bin,'-ok');
errorbar(periods,phi2_bin_res,phi2_95_bin_res,'-om');
errorbar(periods,phi2_bin_res+180,phi2_95_bin_res,'-om');
errorbar(periods,phi2_bin_res-180,phi2_95_bin_res,'-om');
ylim([50 180]);
xlim([20 150]);
ylabel('\phi');
xlabel('Periods (s)');
%%
figure(90); clf;
for ip = 1:length(periods)
M=4;
N=4;
isgood = eventphv_ani(ip).isgood;
dt = eventphv_ani(ip).dt(isgood);
azi = eventphv_ani(ip).azi(isgood);
azi(azi<0) = azi(azi<0)+360;
phv = eventphv_ani(ip).phv(isgood);
avgv = eventphv_ani(ip).GV_av;
phv_iso = eventphv_ani(ip).phv_iso(isgood);
azi_bin = fit_azi_bin.meas(ip).azi;
phv_bin = fit_azi_bin.meas(ip).phv;
phv_iso_bin = fit_azi_bin.c_iso(ip);
dv_std_bin = fit_azi_bin.meas(ip).phv_std./phv_iso_bin*100;
dv_bin = (phv_bin-phv_iso_bin)./phv_iso_bin*100;
azi_bin_res = fit_azi_bin_res.meas(ip).azi;
dv_bin_res = fit_azi_bin_res.meas(ip).dphv*100;
dv_std_bin_res = fit_azi_bin_res.meas(ip).dphv_std*100;
dv = (phv-avgv)./avgv*100;
dv2 = (phv-phv_iso)./phv_iso*100;
dv2_not = (eventphv_ani(ip).ddist(~isgood)./eventphv_ani(ip).dt(~isgood)-eventphv_ani(ip).phv_iso(~isgood))./eventphv_ani(ip).phv_iso(~isgood)*100;
dv3 = (phv-nanmean(phv_iso))./nanmean(phv_iso)*100;
A2 = eventphv_ani(ip).A2;
phi2 = eventphv_ani(ip).phi2;
theta = [0:1:360];
subplot(M,N,ip); hold on;
% plot(azi,dv,'.'); hold on;
% plot(eventphv_ani(ip).azi(~isgood),dv2_not,'.','color',[0.8 0.8 0.8]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % WARNING! THIS PLOTS ALL DATA AT ONCE AND MAY REQUIRE A LOT OF MEMORY!
% plot(azi,dv2,'.','color',[0.8 0.8 0.8]); hold on;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
errorbar(azi_bin,dv_bin,dv_std_bin,'ok');
errorbar(azi_bin_res,dv_bin_res,dv_std_bin_res,'om');
% plot(azi,dv3,'.r');
plot(theta,A2*cosd(2*(theta-phi2))*100,'-r');
plot(theta,fit_azi.A2(ip)*cosd(2*(theta-fit_azi.phi2(ip)))*100,'-b');
plot(theta,fit_azi_bin.A2(ip)*cosd(2*(theta-fit_azi_bin.phi2(ip)))*100,'-k');
plot(theta,fit_azi_bin_res.A2(ip)*cosd(2*(theta-fit_azi_bin_res.phi2(ip)))*100,'-m');
title([num2str(periods(ip)),' s']);
ylim([-5 5]);
xlim([0 360]);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % WARNING! THIS PLOTS ALL DATA AT ONCE AND MAY REQUIRE A LOT OF MEMORY!
% figure(91); clf;
% for ip = 1:length(periods)
% isgood = eventphv_ani(ip).isgood;
% dt = eventphv_ani(ip).dt(isgood);
% azi = eventphv_ani(ip).azi(isgood);
% % azi(azi<0) = azi(azi<0)+360;
% phv = eventphv_ani(ip).ddist(isgood)./dt;
% avgv = eventphv_ani(ip).GV_av;
% phv_iso = eventphv_ani(ip).phv_iso(isgood);
% dv = (phv-avgv)./avgv*100;
% dv2 = (phv-phv_iso)./phv_iso*100;
%
% A2 = eventphv_ani(ip).A2;
% phi2 = eventphv_ani(ip).phi2;
% subplot(M,N,ip);
% plot(azi,dv-A2*cosd(2*(azi-phi2))*100,'.'); hold on;
% plot(azi,dv2-A2*cosd(2*(azi-phi2))*100,'.g'); hold on;
% % ylim([-10 10]);
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Plot residuals
clear residuals
for ip = 1:length(eventphv_ani)
isgood = eventphv_ani(ip).isgood;
dt_res = eventphv_ani(ip).dt_res(isgood);
residuals(ip).rms_dt_res = rms(dt_res(:));
residuals(ip).mean_dt_res = mean(dt_res(:));
residuals(ip).dt_res = dt_res(:);
end
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% % WARNING! THIS PLOTS ALL DATA AT ONCE AND MAY REQUIRE A LOT OF MEMORY!
% figure(87); clf; set(gcf,'color','w','position',[1035 155 560 781]);
% for ip = 1:length(periods)
% subplot(2,1,1);
% plot(periods(ip),residuals(ip).mean_dt_res,'o','color',[0.7 0.7 0.7]); hold on;
% plot(periods(ip),nanmean(residuals(ip).mean_dt_res),'rs','linewidth',2,'markersize',10);
% ylabel('mean (dt_{obs}-dt_{pre})')
% set(gca,'linewidth',1.5,'fontsize',15);
% subplot(2,1,2);
% plot(periods(ip),residuals(ip).rms_dt_res,'o','color',[0.7 0.7 0.7]); hold on;
% plot(periods(ip),nanmean(residuals(ip).rms_dt_res),'rs','linewidth',2,'markersize',10);
% xlabel('Period (s)');
% ylabel('RMS (dt_{obs}-dt_{pre})')
% set(gca,'linewidth',1.5,'fontsize',15);
% end
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figure(86); clf;
for ip = 1:length(periods)
subplot(M,N,ip)
histogram(residuals(ip).dt_res);
title([num2str(periods(ip)),' s'],'fontsize',15)
xlabel('Residual (s)');
end