  Enhancing the frequency bandwidth of the seismic data is always the pursuance
at the geophysical community. High resolution of seismic data provides the key
resource to extract detailed stratigraphic knowledge. Here, a novel approach,
based on deep learning model, is introduced by extracting reflections from well
log data to broaden spectrum bandwidth of seismic data through boosting low and
high frequencies. The corresponding improvement is observed from the
enhancement of resolution of seismic data as well as elimination of sidelobe
artifacts from seismic wavelets. During the training stage of deep learning
model, geo-spatial information by taking consideration of multiple wells
simultaneously is fully guaranteed, which assures that laterally and vertically
geological information are constrained by and accurate away from the well
controls during the inversion procedure. Extensive experiments prove that the
enhanced seismic data is consistent with well log information, and honors rock
property relationships defined from the wells at given locations. Uncertainty
analysis could also be quantitatively assessed to determine the possibilities
of a range of seismic responses by leveraging the outputs from the proposed
approach.
