Attribute-Guided Target Data Separation Network for DAS VSP Data

Abstract

Distributed acoustic sensing (DAS) technology is a rapidly evolving fiber-optic sensing technology that has been gradually applied to vertical seismic profile (VSP) data. The DAS VSP data are often contaminated by multiple interference waves, such as random noise, coupled noise, horizontal noise, and the existing methods can only map it from noisy data to effective reflections. However, interference waves are also a relative concept, and many interference waves still have certain application values. Therefore, this article proposes an innovative algorithm called attribute-guided target data separation network (Att-TDSN), which can not only complete the conventional signal-noise separation task but also achieve noise-noise separation task. Specifically, we first propose a flexible and efficient training set, namely, multidimensional weak label training set (Mul-WLTS), which introduces attribute features as the weak labels and specifies the dimension of weak labels according to the number of target data types (effective reflections and several common interference waves). Then, we use the weak labels to guide our network to map training data to specified data types, assisting the network to focus its attention on each kind of target data. Finally, the network parameters are trained by a training mode called “one-way matching and two-way constraint.” “One-way matching” makes data separation result unique, and “two-way constraint” can improve the algorithm’s amplitude preserving ability. Experiments on synthetic and field DAS VSP data show that our network can accurately separate target data. Moreover, for the traditional denoising task, Att-TDSN also has a better denoising performance than existing methods.