基于联合加速近端梯度和 对数加权核范数最小化的地震数据重建

Due to surface obstacles or economic constraints, seismic data recorded is often incomplete. Consequently, seismic data reconstruction is an important topic in seismic research. This study presents a seismic data reconstruction approach based on joint accelerated proximal gradient and log-weighted nuclear norm minimization. The process begins by subjecting the original seismic data to low-rank preprocessing through texture-patch operators. Subsequently, the accelerated proximal gradient algorithm is employed for an initial reconstruction of the low-rank seismic data. Finally, an algorithm based on the log-weighted nuclear norm is presented to tackle the optimization problem and reconstruct the missing data. For synthetic seismic data and real seismic data, the reconstruction results of the joint accelerated proximal gradient and log-weighted nuclear norm method have improved both in quantitative and qualitative analysis: The signal-to-noisc ratio of the synthetic data set with a 40% missing rate is 26.135 7 dB and the reconstruction error is 6.789 4; The signal-to-noisc ratio of the Mobil Avo Viking Graben Line 12 data set with a 30% missing rate is 17.247 8 dB and the reconstruction error is 4.762 5; The signal-to-noisc ratio of the Netherlands F3 data set with a 60% missing rate is 26.058 1 dB and the reconstruction error is 7.464 1.