Sub-Bottom Sediment Classification Employing a Multi-Attribute Temporal Convolutional Network

Abstract

Sub-bottom profile data have the potential to characterize sediment properties but are seldom used for offshore site investigations because of uncertainties in rock-physics models. Deep-learning techniques appear to be poised to play very important roles in our processing flows for the interpretation of geophysical data. In this paper, a novel deep learning-based method for this task is proposed in which a nonlinear mapping between the observed data and sediment types is learned using a multi-attribute temporal convolution network (MATCN). Firstly, empirical mode decomposition (EMD) is employed for the original data, and intrinsic mode functions (IMFs) with multiple time scales are generated. Based on different IMFs, instantaneous frequency (IF) data under different IMFs can be obtained, while instantaneous phase (IP) and instantaneous amplitude (IA) data are obtained based on the original data. IF, IA and IP data are called attribute data, and are highly related to the attenuation, reflection, and interior structure of the sediment. Thus, IA, IF, and IP are used as the inputs, and a 1D convolutional neural network (CNN) and a time convolution network (TCN) are used to extract sequential features. Different feature representations are then fused. Combining cross-entropy loss function and class-edge loss function, the network is encouraged to produce classified results with more continuous sediment distributions compared with the traditional loss function. The real-data experiments demonstrate that the proposed MATCN has achieved good performance with an F measure greater than 70% in all cases, and greater than 80% in most cases.