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The acoustical impedance distribution of the substratum, or equivalently, the reflection coefficient sequence, is determined from VSP data. This nonlinear inverse problem is solved by a least‐squares method. As the wavelet is unknown, the impedance distribution and the Neumann boundary condition (which characterizes the excitation of the medium) are simultaneously identified. The inversion method is applied to synthetic and field VSP's; the result is satisfactory, even when strong noise corrupts the data, provided that a suitable constraint on the impedance distribution is introduced in order to ensure the stability of the inverse problem. The reliability of the inversion result in the case of field VSP, is confirmed. Some ways in which this result may be used are illustrated (calibration of the seismic surface data, multiple identification, prediction ahead of the bit).

Acquisition of the full elastic response (compressional and shear) of the subsurface is an important technology in the seismic industry because of its potential to improve the quality of seismic data and to infer accurate information about rock properties (fluid type and rock lithology). In the framework of 3-D propagation in 1-D media, we propose a computational tool to analyze the information about elastic parameters contained in the amplitudes of reflected waves with offset. The approach is based on singular value decomposition (SVD) analysis of the linearized elastic inversion problem and can be applied to any particular seismic data. We applied this tool to examine the type of information in the model space that can be retrieved from sea‐bottom multicomponent measurements. The results are compared with those obtained from conventional streamer acquisition techniques. We also present multiparameter linearized inversion results obtained from synthetic data that illustrate the resolution of elastic parameters. This approach allows us to investigate the reliability of the elastic parameters estimated for different offset ranges, wave modes, data types, and noise levels involved in data space.