Open AccessUnsupervised deep domain adaptation framework in remote acoustic time parametric imaging
Author(s) -
Wu Yuezhou,
Tao Jun,
Fu Qiang,
Liu Changjiang
Publication year - 2022
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12071
Subject(s) - computer science , artificial intelligence , parametric statistics , time domain , pattern recognition (psychology) , waveform , feature extraction , signal (programming language) , deep learning , adaptation (eye) , noise (video) , convolution (computer science) , artificial neural network , decoding methods , speech recognition , computer vision , algorithm , image (mathematics) , telecommunications , radar , statistics , physics , mathematics , optics , programming language
Intelligent waveform analysis surveying geological structures is a challenging task in remote acoustic measurement for formation detection, which has two problems: (1) time parametric imaging is disturbed by noisy environments and (2) manually annotated data for machine learning are unattainable. These restrict the deployment of advanced parameter extraction methods in imaging instruments. As a potential theory, domain adaptation makes the intelligent prediction implementable in the above situations. Hence, to counter high precision travel time extraction for acoustic imaging, a deep adaptation‐picking network (DAPN) framework is proposed, which consists of three modules: signal‐to‐signal generator, domain adaptation encoder, and feature recognition decoder. The translation module preprocesses the different datasets to improve the training accuracy and confuses the distribution between source and target domains based on the adversarial network. The core convolution neural network achieves travel time measurement, where the backbone extracts content features with modified maximum mean discrepancy embedding. Meanwhile, the decoding structure preserves the signal features in the training process. The effectiveness and feasibility of DAPN are verified by experimental results, suggesting that DAPN accurately extracts time parameters. Compared with traditional parametric imaging schemes, the proposed method has the advantages of high accuracy and anti‐noise ability.