This paper presents an algorithmfor estimation of seismic p-wave velocity from multioffset reflection seismograms . The algorithm optimizes a cost function, part of which is the mean-square error between predicted and data seismograms . It also includes a, differential measure of event semblance or coherente, whence its name : differential semlance optimization . Unlike other implemations of the mean-square error criterion, the differential semblance version retains sensitivity over a wide range of velocity models . We present some numerical evidente of this sensitivity, using an example drawn from the Marmousi model [1] . A previous implementation of differential semblance optimization for acoustic p-tau data and layered models has been applied with success to both synthetic and field data sets and has a complete mathematical justification [4] . Therefore a, subsidiary purpose of this paper is to demonstrate an implementation of differential semblance in a non-layered ("complex structure") context . We employ the perturbational (generalized Born, primaries-only) approximation to the 2D constant density acoustic model . The velocity is split into a smooth background velocity v and a rough (oscillatory) reflectivity r = ðv/v, the latter regarded as a perturbation of the former . Denote the seismogram (i .e . suite of shot gathers corresponding to shot positions x,) by S(v)(x,t, x). Note that S depends linearly on R and nonlinearly on v . Let Sdata devote a, data set to be inverted.


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