基于CBDNet的被动源面波信号增强
Enhancement of Passive Source Surface Wave Signals Using CBDeNet for Near-Surface Imaging
DOI: 10.12677/ag.2024.1411129, PDF,    科研立项经费支持
作者: 孟佳佳, 郑 晶：陕西省煤田地质集团有限公司(自然资源部煤炭资源勘查与综合利用重点实验室)，陕西 西安；中国矿业大学(北京)地球科学与地质工程学院，北京；谭浩阳, 滕星智：中国矿业大学(北京)地球科学与地质工程学院，北京；孙 远：中矿华安能源科技(北京)有限公司，北京
关键词: 被动源面波信号增强；重构虚拟道集；CBDNet；Passive Source Wave； Signal Enhancement； Reconstruction of Virtual Gather； CBDNet
摘要: 在被动源面波成像中低质量的频谱图会直接导致后续频散曲线的拾取出现较大的不确定性，为了进一步提高被动面波频谱成像图的质量，同时提高频散曲线的拾取的准确性。文章提出了将在原始数据重构得到的虚拟道集上应用CBDNet进行被动源面波信号增强，降低频散成像能量谱中的噪声、增强频散曲线连续性。文中首先通过数值模拟的方式，验证了网络的有效性；然后将其用于北京市广渠门和通州两个地区的实际被动源数据处理中。结果表明，文章提出的网络和被动源面波去噪方法能够很好的解决实际工程中频谱图质量低，频散曲线错位和不连续的问题。
Abstract: In passive-source surface wave imaging, low-quality spectrograms directly lead to significant uncertainties in the subsequent picking of dispersion curves. To further enhance the quality of passive surface wave spectral imaging and improve the accuracy of dispersion curve picking, this study proposes applying CBDNet to virtual shot gathers reconstructed from the raw data. This approach enhances passive-source surface wave signals, reduces noise in the dispersion imaging energy spectrum, and improves the continuity of dispersion curves. First, the effectiveness of the network is validated through numerical simulations; then, it is applied to real passive-source data from two regions in Beijing, Guangqumen and Tongzhou. The results demonstrate that the proposed network and denoising method for passive-source surface waves effectively address the issues of low spectrogram quality, misaligned, and discontinuous dispersion curves in practical engineering applications.
文章引用：孟佳佳, 谭浩阳, 孙远, 滕星智, 郑晶. 基于CBDNet的被动源面波信号增强[J]. 地球科学前沿, 2024, 14(11): 1377-1390. https://doi.org/10.12677/ag.2024.1411129

参考文献

[1] Asten, M.W. and Henstridge, J.D. (2001) Surface Wave Methods for Geotechnical Site Characterization. Journal of Environmental & Engineering Geophysics, 6, 15-26.
[2] Xia, J., Xu, Y. and Peng, F. (2016) Multichannel Analysis of Surface Waves for Near-Surface Site Characterization: A Comprehensive Review. Geophysics, 81, 1-26.
[3] Smith, A.B. (2019) Applications of Surface Wave Methods in Urban Environments. Geophysical Journal International, 219, 491-504.
[4] Park, C.B., Miller, R.D. and Xia, J. (1999) Multichannel Analysis of Surface Waves. Geophysics, 64, 800-808. [Google Scholar] [CrossRef] 
[5] Nazarian, S., Stokoe, K.H. (1984) In Situ Shear Wave Velocities from Spectral Analysis of Surface Waves. Proceedings of the 8th Conference on Earthquake Engineering, Vol. 3, 31-38.
[6] Xia, J., Miller, R.D. and Park, C.B. (1999) Estimation of Near‐Surface Shear‐Wave Velocity by Inversion of Rayleigh Waves. Geophysics, 64, 691-700. [Google Scholar] [CrossRef] 
[7] Louie, J.N. (2001) Faster, Better: Shear-Wave Velocity to 100 Meters Depth from Refraction Microtremor Arrays. Bulletin of the Seismological Society of America, 91, 347-364. [Google Scholar] [CrossRef] 
[8] Socco, L.V., Foti, S. and Boiero, D. (2010) Surface-Wave Analysis for Building Near-Surface Velocity Models—Established Approaches and New Perspectives. Geophysics, 75, 75A83-75A102. [Google Scholar] [CrossRef] 
[9] Foti, S., Lai, C.G., Rix, G.J. and Strobbia, C. (2003) Surface Wave Testing of Soils: Interpretation and Applications. Journal of Geotechnical and Geoenvironmental Engineering, 129, 759-770.
[10] Park, C.B., Miller, R.D. and Xia, J. (1998) Imaging Dispersion Curves of Surface Waves on Multi‐Channel Record. In: SEG Technical Program Expanded Abstracts 1998, Society of Exploration Geophysicists, 1377-1380. [Google Scholar] [CrossRef] 
[11] Park, C.B. and Miller, R.D. (2008) Roadside Passive Multichannel Analysis of Surface Waves (MASW) Journal of Environmental and Engineering Geophysics, 13, 1-11. [Google Scholar] [CrossRef] 
[12] Xia, J., Cheng, F., Xu, Z., Shen, C. and Liu, R. (2017) Advantages of Multi-Channel Analysis of Passive Surface Waves (MAPS). International Conference on Engineering Geophysics, Al Ain, 9-12 October 2017, 94-97. [Google Scholar] [CrossRef] 
[13] Cheng, F., Xia, J., Shen, C., Hu, Y., Xu, Z. and Mi, B. (2018) Imposing Active Sources during High-Frequency Passive Surface-Wave Measurement. Engineering, 4, 685-693. [Google Scholar] [CrossRef] 
[14] Wang, L., Liu, J. and Li, X. (2018) Noise Reduction of Multichannel Surface Wave Data Using Wavelet Transform and Singular Value Decomposition. Journal of Applied Geophysics, 150, 141-150.
[15] Li, S. and Zhang, Z. (2020) A New Method for Ambient Noise Reduction in Surface Wave Exploration. Geophysics, 85, EN97-EN107.
[16] Pang, J., Cheng, F., Shen, C., Dai, T., Ning, L. and Zhang, K. (2019) Automatic Passive Data Selection in Time Domain for Imaging Near-Surface Surface Waves. Journal of Applied Geophysics, 162, 108-117. [Google Scholar] [CrossRef] 
[17] Qiu, H., Niu, F. and Qin, L. (2021) Denoising Surface Waves Extracted from Ambient Noise Recorded by 1-D Linear Array Using Three-Station Interferometry of Direct Waves. Journal of Geophysical Research: Solid Earth, 126, e2021JB021712.
[18] Dong, X., Zhong, T. and Li, Y. (2021) A Deep-Learning-Based Denoising Method for Multiarea Surface Seismic Data. IEEE Geoscience and Remote Sensing Letters, 18, 925-929. [Google Scholar] [CrossRef] 
[19] Zheng, J., Jiang, T., Wu, Z. and Sun, Y. (2021) Application of Residual Learning to Microseismic Random Noise Attenuation. Acta Geophysica, 69, 1151-1161. [Google Scholar] [CrossRef]