Due to the bandwidth limitation of seismic data, the seismic reflection axis cannot accurately depict the top and bottom of overlimited reservoirs. Thick reservoirs often exhibit the illusion of multi axis reflection on seismic profiles, which seriously affects the fine study of reservoirs. The fundamental reason for this phenomenon is the lack of low-frequency information in seismic data. In order to restore the seismic response characteristics of the "thick" layer in the upper Ming section, accurately identify the top and bottom interfaces of the reservoir, and guide the deployment of well positions, this study conducted a prediction of the “thick” layer based on compressive sensing and inversion theory. According to the theory of compressed sensing, if a signal has sparsity, even if some elements in the signal are missing or affected by noise interference, the original signal can be accurately restored through appropriate measurement matrices and sparse algorithms. After using compressive sensing theory to obtain the reflection coefficient sequence, it is transformed into the frequency domain, and then the reconstruction algorithm is used to compensate the low-frequency components of the reflection coefficient sequence into seismic data, achieving low-frequency compensation of seismic data. Wave impedance inversion was performed on seismic data after low-frequency compensation, and the inversion data volume significantly improved the response of thick layers, which is consistent with the actual drilling thickness, proving that this method has significant advantages in improving well seismic matching in thick reservoirs.