دورية أكاديمية

Noise source localization using deep learning.

التفاصيل البيبلوغرافية
العنوان: Noise source localization using deep learning.
المؤلفون: Zhou, Jie, Mi, Binbin, Xia, Jianghai, Zhang, Hao, Liu, Ya, Chen, Xinhua, Guan, Bo, Hong, Yu, Ma, Yulong
المصدر: Geophysical Journal International; Jul2024, Vol. 238 Issue 1, p513-536, 24p
مصطلحات موضوعية: DEEP learning, LOCALIZATION (Mathematics), MICROSEISMS, NOISE, SPARSE matrices, CARBON dioxide, INFORMATION resources
مستخلص: Ambient noise source localization is of great significance for estimating seismic noise source distribution, understanding source mechanisms and imaging subsurface structures. The commonly used methods for source localization, such as the matched field processing and the full-waveform inversion, are time-consuming and not applicable for time-lapse monitoring of the noise source distribution. We propose an efficient alternative of using deep learning for noise source localization. In the neural network, the input data are noise cross-correlation functions and the output are matrices containing the information of noise source distribution. It is assumed that the subsurface structure is a horizontally layered earth model and the model parameters are known. A wavefield superposition method is used to efficiently simulate ambient noise data with quantities of local noise sources labelled as training data sets. We use a weighted binary cross-entropy loss function to address the prediction inaccuracy caused by a sparse label matrix during training. The proposed deep learning framework is validated by synthetic tests and two field data examples. The successful applications to locate an anthropogenic noise source and a carbon dioxide degassing area demonstrate the accuracy and efficiency of the proposed deep learning method for noise source localization, which has great potential for monitoring the changes of the noise source distribution in a survey area. [ABSTRACT FROM AUTHOR]
Copyright of Geophysical Journal International is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
قاعدة البيانات: Complementary Index
الوصف
تدمد:0956540X
DOI:10.1093/gji/ggae171