Attention and Hybrid Loss Guided 2-D Network for Seismic Impedance Inversion

Abstract

Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), achieve state-of-the-art performance on seismic impedance inversion. Most of the methods are based on one-dimensional (1D) convolution, tending to yield lateral discontinuities of impedance on field data applications. To alleviate this problem, we design a network equipped with two-dimensional (2D) convolutions and a coordinate attention (CA) block. The former can take the relationship between adjacent traces into consideration. The latter can capture the positional relationship of the geological structure, both horizontally and vertically. At the same time, we use a hybrid loss combined with edge operator and Mean Square Error (MSE) to further improve the stability of the designed network. Comparison experiments on synthetic SEAM model and field seismic data demonstrate the effectiveness of the adopted components, 2D convolution, CA and hybrid loss function, in improving the lateral continuity of inverted impedance. For field seismic data, the impedance predicted by proposed method shows improved lateral continuity and high resolution compared with the 1D network and Constrained Sparse Spike Inversion (CSSI) method using commercial software (InverTrace Plus module in Jason).