Seismic amplitude data are part of the input in a geophysical interpretation pipeline. As seismic sensors evolve, resolution and occupied storage space grows. In this context, tasks as seismic deconvolution and inversion become more expensive, in processing time or in – main or secondary – memory. Assuming that, approximately, seismic amplitude traces result from a fusion between an oscillatory content – a pulse generated by a kind of explosion, in the case of marine acquisition – and the sparse presence of impedance constrasts and rock density variation, this work presents contributions to the way of doing two geophysical interpretation activities: deconvolution and inversion, both targeting sparse-spike refletivity extraction. Inspired by works in 3D and 4D volumetric compression, sparse modeling, optimization applied to geophysics, image segmentation and scientific visualization, this thesis presents a set of methods that try to fetch fundamental features that generate amplitude data: (i) an approach for seismic traces segmentation and selection, electing them as representatives of the whole data, (ii) an enhancement of an approach for separation of amplitudes into wavelet and sparse-spike reflectivities via deconvolution, and (iii) a way to generate a linear operator – a dictionary – partially and approximately capable of representing variations on the wavelet shape, emulating some effects of the subsoil, from which is possible to accomplish a reflectivity inversion. By the end, it is presented a set of results that demonstrate the viability of such approaches, the possible gain when they are applied – including compression – and some opportunities for future works mixing geophysics and computer science.