3-D Seismic Data Recovery via Neural Network-Based Matrix Completion

Abstract

Due to the limitation of the field acquisition environment and economic cost, observed seismic data often contain lots of missing traces. Traditional low-rank matrix completion can recover missing traces by constructing linear models, but it is often insufficient for high-accuracy reconstruction. To better recover seismic data, we propose a novel 3-D seismic data reconstruction approach by constructing a nonlinear model in the frequency domain. Based on the low-rank characteristics of seismic data, we employ the low-dimensional neural network (NN) to construct a nonlinear matrix completion model, which can be optimized by the improved resilient backpropagation algorithm. After optimization, the low-dimensional variables and hidden features learned by the network can be used to update the complete frequency slice. Since the two adjacent frequency slices share highly correlative information during reconstructing data in the frequency domain, we adopt transfer learning to improve the learning efficiency of the network while sequentially reconstructing frequency slices. Compared with the traditional matrix completion, the proposed method demonstrates superior performances in the reconstruction of synthetic and field 3-D seismic data volume.