Seismic high-resolution processing is an essential part of seismic processing. Sparsespike deconvolution is a widely used method for improving the resolution of seismic data. However, the stratigraphic reflection coefficients do not fully satisfy the hypothesis of sparse-spike deconvolution, and this method does not make full use of prior information, such as well-logging data. In this paper, we have developed a highresolution processing method based on joint sparse representation using logging and seismic data. This method can extract stratigraphic information from well-logging reflection coefficients and observational seismic data at the same location by joint dictionary learning. Through joint sparse representation, the relationship between observed seismic data and the reflection coefficient is established. Under the framework of joint sparse representation, the deconvolution of seismic data is realized. The synthetic data and field data tests show that our method can reveal thin layers and can invert reflection coefficients from strongly noisy seismic data accurately. Moreover, the deconvolution results of our method match well with the well-logging data. The tests demonstrate that the improvement of accuracy of deconvolution results with our method, compared to sparse-spike deconvolution.

Deconvolution is an essential step in seismic data processing. Sparse-spike deconvolution is often used to enhance the resolution of the seismic image by adding a model driven regularization term. However, this method does not consider the features of the data, nor does it exactly describe the relationship between seismic data and the desired attribute (such as seismic reflectivity). We propose a data and model dual-driven seismic deconvolution method based on error-constrained joint sparse representation using borehole measurement and surface seismic data. The combined features of the borehole reflectivity and the surface seismic data can be obtained through joint dictionary learning. With the help of the joint dictionary, the relationship between seismic waveforms and reflectivity is captured by the sparse coefficients. We construct the regularization term of deconvolution by alternately decomposing the error of the synthesized data via sparse reconstruction and the observed seismic data. Unlike model-driven methods, the constraint term of the new method can be established by the error-constrained sparse representation. Based on this sparse representation, the initial model of reflectivity is obtained to realize the sparse deconvolution of seismic data under the constraint of borehole data features. In general, this method is a data and model dual-driven deconvolution. Synthetic and field data tests demonstrate that this method can effectively improve the resolution and accuracy of deconvolution.