Seismic reflectivity inversion is a deconvolution process for quantitatively extracting the reflectivity series and depicting the layered subsurface structure. The conventional method is a single channel inversion and cannot clearly characterise stratified structures, especially from seismic data with low signal-to-noise ratio. Because it is implemented on a trace-by-trace basis, the continuity along reflections in the original seismic data is deteriorated in the inversion results. We propose here multichannel inversion algorithms that apply the information of adjacent traces during seismic reflectivity inversion. Explicitly, we incorporate a spatial prediction filter into the conventional Cauchy-constrained inversion method. We verify the validity and feasibility of the method using field data experiments and find an improved lateral continuity and clearer structures achieved by the multichannel algorithms. Finally, we compare the performance of three multichannel algorithms and merit the effectiveness based on the lateral coherency and structure characterisation of the inverted reflectivity profiles, and the residual energy of the seismic data at the same time.