Synopsis
		sfangmigM < in.rsf > out.rsf semb=semb.rsf mask=mask.rsf vin=vin.rsf adj=n weighting=y na= amax= ng=na gmax=amax nxi=nx xi0=x0 dxi=dx ntau=nt tau0=t0 dtau=dt nh= h0= dh= ng=na gmax=amax nx=nxi x0=xi0 dx=dxi nt=ntau t0=tau0 dt=dtau eps=0.00001 l2=y

Parameters
		bool adj=n [y/n] if y modeling, if n, migration   float amax= maximum dip angle   float dh= offset increment   float dt=dtau time increment   float dtau=dt output vertical sampling   float dx=dxi data domain spatial increment   float dxi=dx output sampling   float eps=0.00001 epsilon for division in semblance calc   float gmax=amax maximum reflection angle   float h0= initial offset   bool l2=y [y/n] if y use l2 norm for semb, if n, use l1 norm   string mask= input file contining image mask locations, 0 = skip (auxiliary input file name)   int na= number of dip angles   int ng=na number of reflection angles   int nh= number of offsets   int nt=ntau number time samples   int ntau=nt output vertical samples   int nx=nxi data domain spatial samples   int nxi=nx output samples   string semb= output file containing Semblance (auxiliary output file name)   float t0=tau0 time orgin   float tau0=t0 output vertical orgin   string vin= input velocity file (auxiliary input file name)   bool weighting=y [y/n] kirchhoff weighting?   float x0=xi0 data domain spatial orgin   float xi0=x0 output orgin