Abufana S A , Dalveren Y , Aghnaiya A , et al. Variational mode decomposition-based threat classification for fiber optic distributed acoustic sensing. IEEE Access, 2020, 8: 100152- 100158. https://doi.org/10.1109/ACCESS.2020.2997941 Cited in this article [1]

Abukrat Y , Sinitsyn P , Reshef M , et al. Applications and limitations of distributed acoustic sensing in shallow seismic surveys and monitoring. Geophysics, 2023, 88 (6): WC1- WC12. https://doi.org/10.1190/geo2022-0574.1 Cited in this article [1]

Atterholt J , Zhan Z W , Shen Z C , et al. A unified wavefield-partitioning approach for distributed acoustic sensing. Geophysical Journal International, 2022, 228 (2): 1410- 1418. https://doi.org/10.1093/gji/ggab407 Cited in this article [3]

Bao F , Zeng X F , Lin R B , et al. Detecting aftershocks of the MS6.9 Menyuan earthquake and mapping blind faults by a distributed acoustic sensing array with an existing fiber-optic cable. Science Bulletin, 2022, 67 (27): 3340- 3347.

Brankovic M , Gildin E , Gibson R L , et al. A machine learning-based seismic data compression and interpretation using a novel shifted-matrix decomposition algorithm. Applied Sciences, 2021, 11 (11): 4874. https://doi.org/10.3390/app11114874 Cited in this article [5]

Bublin M . Event detection for distributed acoustic sensing: Combining knowledge-based, classical machine learning, and deep learning approaches. Sensors, 2021, 21 (22): 7527. https://doi.org/10.3390/s21227527 Cited in this article [1]

Cai H W , Ye Q , Wang Z Y , et al. Distributed, optical fiber acoustic sensing technology based on coherent rayleigh scattering. Laser & Optoelectronics Progress, 2020, 57 (5): 050001. https://doi.org/10.3788/LOP57.050001

Chen J Y , Ning J R , Chen W C , et al. Distributed acoustic sensing coupling noise removal based on sparse optimization. Interpretation, 2019, 7 (2): T373- T382. https://doi.org/10.1190/INT-2018-0080.1 Cited in this article [1]

Chen S Y , Han J , Sui Q , et al. Advanced signal processing in distributed acoustic sensors based on submarine cables for seismology applications. Journal of Lightwave Technology, 2023a, 41 (13): 4164- 4175. https://doi.org/10.1109/JLT.2023.3273268 Cited in this article [1]

Chen Y K , Zu S H , Wang Y F , et al. Deblending of simultaneous source data using a structure-oriented space-varying median filter. Geophysical Journal International, 2020, 222 (3): 1805- 1823. https://doi.org/10.1093/gji/ggaa189 Cited in this article [1]

Chen Y K , Savvaidis A , Fomel S , et al. Denoising of distributed acoustic sensing seismic data using an integrated framework. Seismological Research Letters, 2023b, 94 (1): 457- 472. https://doi.org/10.1785/0220220117 Cited in this article [1]

Chien C C , Jenkins W F , Gerstoft P , et al. Automatic classification with an autoencoder of seismic signals on a distributed acoustic sensing cable. Computers and Geotechnics, 2023, 155: 105223. https://doi.org/10.1016/j.compgeo.2022.105223 Cited in this article [2]

Daley T M , Freifeld B M , Ajo-Franklin J , et al. Field testing of fiber-optic distributed acoustic sensing (DAS) for subsurface seismic monitoring. The Leading Edge, 2013, 32 (6): 699- 706. https://doi.org/10.1190/tle32060699.1 Cited in this article [1]

Dean T , Cuny T , Hartog A H . The effect of gauge length on axially incident P-waves measured using fibre optic distributed vibration sensing. Geophysical Prospecting, 2017, 65 (1): 184- 193. https://doi.org/10.1111/1365-2478.12419 Cited in this article [1]

Dong X R , Feng X , Liu C , et al. Personnel motion trajectory monitoring method based on DAS signal and CNN classification algorithm. Journal of Jilin University (Earth Science Edition), 2022, 52 (3): 1004- 1015. https://doi.org/10.13278/j.cnki.jjuese.20210381 Cited in this article [1]

Dong X T , Li Y , Liu F , et al. New suppression technology for the random noise in the DAS seismic data based on convolutional neural network. Chinese Journal of Geophysics, 2021, 64 (7): 2554- 2565. https://doi.org/10.6038/cjg2021O0274

Dong X T , Li Y , Zhong T , et al. Random and coherent noise suppression in DAS-VSP data by using a supervised deep learning method. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 8001605. https://doi.org/10.1109/LGRS.2020.3023706

Fang G S , Xu T W , Feng S W , et al. Phase-sensitive optical time domain reflectometer based on phase-generated carrier algorithm. Journal of Lightwave Technology, 2015, 33 (13): 2811- 2816. https://doi.org/10.1109/JLT.2015.2414416 Cited in this article [1]

Feng Q K , Li Y , Wang H Z . Intelligent random noise modeling by the improved variational autoencoding method and its application to data augmentation. Geophysics, 2021, 86 (1): T19- T31. https://doi.org/10.1190/geo2019-0815.1 Cited in this article [1]

Feng Q K , Li Y . Denoising deep learning network based on singular spectrum analysis—DAS seismic data denoising with multichannel SVDDCNN. IEEE Transactions on Geoscience and Remote Sensing, 2022a, 60: 5902911. https://doi.org/10.1109/TGRS.2021.3071189 Cited in this article [1]

Feng Q K , Li Y . Transform learning in the synchrosqueezing frequency domain—A novel denoising strategy for optical fiber seismic records. Journal of Applied Geophysics, 2022b, 201: 104648. https://doi.org/10.1016/j.jappgeo.2022.104648 Cited in this article [1]

Guo J K , Chen S H . Research on location accuracy and sensing distance of distributed optical fiber under mine. Journal of Jilin University (Information Science Edition), 2018, 36 (5): 382- 387. https://doi.org/10.19292/j.cnki.jdxxp.2018.05.005

Hou Q L, Liu D W, Wang X K, et al. 2021. Adaptive DAS coupling noise suppression based on local MCA. //SEG Technical Program Expanded Abstracts. SEG, 2979-2983, doi: 10.1190/segam2021-3594559.1. Cited in this article [1]

Huang B , Zhang H B , Wang Q , et al. Contrastive study of generalized S transform with short-time fourier transform in seismic time-frequency analysis. Coal Geology of China, 2017, 29 (1): 59- 63. 59-63, 78

Isken M P , Vasyura-Bathke H , Dahm T , et al. De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter. Geophysical Journal International, 2022, 231 (2): 944- 949. https://doi.org/10.1093/gji/ggac229 Cited in this article [3]

Jia R S , Zhao T B , Sun H M , et al. Micro-seismic signal denoising method based on empirical mode decomposition and independent component analysis. Chinese Journal of Geophysics, 2015, 58 (3): 1013- 1023. https://doi.org/10.6038/cjg20150326

Jousset P , Currenti G , Schwarz B , et al. Fibre optic distributed acoustic sensing of volcanic events. Nature Communications, 2022, 13: 1753. https://doi.org/10.1038/s41467-022-29184-w Cited in this article [1]

Lapins S , Butcher A , Kendall J M , et al. DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data. Geophysical Journal International, 2024, 236 (2): 1026- 1041. https://doi.org/10.1093/gji/ggad460 Cited in this article [3]

Lecoulant J , Ma Y Y , Dettmer J , et al. Strain-based forward modeling and inversion of seismic moment tensors using distributed acoustic sensing (DAS) observations. Frontiers in Earth Science, 2023, 11: 1176921. https://doi.org/10.3389/feart.2023.1176921 Cited in this article [1]

Lellouch A , Schultz R , Lindsey N J , et al. Low-magnitude seismicity with a downhole distributed acoustic sensing array - Examples from the FORGE geothermal experiment. Journal of Geophysical Research: Solid Earth, 2021, 126 (1): e2020JB020462. https://doi.org/10.1029/2020JB020462 Cited in this article [2]

Li D , Huang L J , Zheng Y C , et al. Feasibility of source-free DAS logging for next-generation borehole imaging. Scientific Reports, 2022, 12: 11910. https://doi.org/10.1038/s41598-022-16027-3 Cited in this article [1]

Li Y , Yang B J , Lin H B , et al. Suppression of strong random noise in seismic data by using time-frequency peak filtering. Science China Earth Sciences, 2013, 56 (7): 1200- 1208.

Li Z C , Liu k , Jiang J F , et al. A high-accuracy event discrimination method in optical fiber perimeter security system. Infrared and Laser Engineering, 2018, 47 (9): 167- 172.

Lin R B , Bao F , Xie J , et al. The influence of cable installment on DAS active and passive source records. Chinese Journal of Geophysics, 2022, 65 (10): 4087- 4098. https://doi.org/10.6038/cjg2022P0444

Lindsey N J , Dawe T C , Ajo-Franklin J B . Illuminating seafloor faults and ocean dynamics with dark fiber distributed acoustic sensing. Science, 2019, 366 (6469): 1103- 1107. https://doi.org/10.1126/science.aay5881 Cited in this article [1]

Lindsey N J , Martin E R . Fiber-optic seismology. Annual Review of Earth and Planetary Sciences, 2021, 49 (1): 309- 336. https://doi.org/10.1146/annurev-earth-072420-065213 Cited in this article [1]

Liu H Y , Ma J H , Yan W F , et al. Traffic flow detection using distributed fiber optic acoustic sensing. IEEE Access, 2018, 6: 68968- 68980. https://doi.org/10.1109/ACCESS.2018.2868418 Cited in this article [1]

Liu W , Liu Y , Li S X , et al. A review of variational mode decomposition in seismic data analysis. Surveys in Geophysics, 2023, 44 (2): 323- 355. https://doi.org/10.1007/s1072-022-09742-z Cited in this article [1]

Lu T R , Duan M Q , Li Z Y , et al. Overview of progress in research on artificial intelligence for earthquake classification. Progress in Geophysics, 2025, 40 (1): 25- 47. https://doi.org/10.6038/pg2025HH0559

Masoudi A , Newson T P . High spatial resolution distributed optical fiber dynamic strain sensor with enhanced frequency and strain resolution. Optics Letters, 2017, 42 (2): 290- 293. https://doi.org/10.1364/OL.42.000290 Cited in this article [1]

Mateeva A , Lopez J , Potters H , et al. Distributed acoustic sensing for reservoir monitoring with vertical seismic profiling. Geophysical Prospecting, 2014, 62 (4): 679- 692. https://doi.org/10.1111/1365-2478.12116 Cited in this article [1]

Milne D , Masoudi A , Ferro E , et al. An analysis of railway track behaviour based on distributed optical fibre acoustic sensing. Mechanical Systems and Signal Processing, 2020, 142: 106769. https://doi.org/10.1016/j.ymssp.2020.106769 Cited in this article [1]

Mousavi S M , Langston C A . Hybrid seismic denoising using higher-order statistics and improved wavelet block thresholding. Bulletin of the Seismological Society of America, 2016, 106 (4): 1380- 1393. https://doi.org/10.1785/0120150345 Cited in this article [1]

Mousavi S M , Langston C A , Horton S P . Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform. Geophysics, 2016, 81 (4): V341- V355. https://doi.org/10.1190/geo2015-0598.1 Cited in this article [1]

Naeem K , Kim B H , Yoon D J , et al. Enhancing detection performance of the phase-sensitive OTDR based distributed vibration sensor using weighted singular value decomposition. Applied Sciences, 2021, 11 (4): 1928. https://doi.org/10.3390/app11041928 Cited in this article [1]

Nishimura T , Emoto K , Nakahara H , et al. Source location of volcanic earthquakes and subsurface characterization using fiber-optic cable and distributed acoustic sensing system. Scientific Reports, 2021, 11: 6319. https://doi.org/10.1038/s41598-021-85621-8 Cited in this article [1]

Nziengui-Ba D , Coutant O , Moreau L , et al. Measuring the thickness and Young's modulus of the ice pack with DAS, a test case on a frozen mountain lake. Geophysical Journal International, 2023, 233 (2): 1166- 1177. https://doi.org/10.1093/gji/ggac504 Cited in this article [1]

Pan Z Q, Liang K Z, Ye Q, et al. 2011. Phase-sensitive OTDR system based on digital coherent detection. //Proceedings Volume 8311, Optical Sensors and Biophotonics Ⅲ. Shanghai, China: SPIE, 168-173, doi: 10.1117/12.905657. Cited in this article [1]

Pastor-Graells J , Martins H F , Garcia-Ruiz A , et al. Single-shot distributed temperature and strain tracking using direct detection phase-sensitive OTDR with chirped pulses. Optics Express, 2016, 24 (12): 13121- 13133. https://doi.org/10.1364/OE.24.013121 Cited in this article [1]

Shang Q F , Huang D . Optical fiber vibration signal denoising method based on MELMD-ICA. Semiconductor Optoelectronics, 2023, 44 (2): 312- 318. https://doi.org/10.16818/j.issn1001-5868.2022120102

Shao J , Wang Y B , Liang X , et al. Siamese network based noise elimination of artificial seismic data recorded by distributed fiber-optic acoustic sensing. Chinese Journal of Geophysics, 2022, 65 (9): 3599- 3609. https://doi.org/10.6038/cjg2022P0919 Cited in this article [1]

Sladen A , Rivet D , Ampuero J P , et al. Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables. Nature Communications, 2019, 10: 5777. https://doi.org/10.1038/s41467-019-13793-z Cited in this article [1]

Spikes K T , Tisato N , Hess T E , et al. Comparison of geophone and surface-deployed distributed acoustic sensing seismic data. Geophysics, 2019, 84 (2): A25- A29. https://doi.org/10.1190/geo2018-0528.1 Cited in this article [2]

Sun W H , Zhu S , Li W , et al. Noise suppression of distributed acoustic sensing based on f-x deconvolution and wavelet transform. IEEE Photonics Journal, 2020, 12 (1): 1- 8. https://doi.org/10.1109/JPHOT.2020.2972645 Cited in this article [1]

Tang H H , Cheng S J , Li W Q , et al. Simultaneous reconstruction and denoising for DAS-VSP seismic data by RRU-net. Frontiers in Earth Science, 2023, 10: 993465. https://doi.org/10.3389/feart.2022.993465 Cited in this article [1]

Tian C W , Fei L K , Zheng W X , et al. Deep learning on image denoising: An overview. Neural Networks, 2020, 131: 251- 275. https://doi.org/10.1016/j.neunet.2020.07.025 Cited in this article [1]

Tian Y N , Lin T , Li Y , et al. Efficient SPSNet for downhole weak DAS signals recovery. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 7508305. https://doi.org/10.1109/LGRS.2022.3226759 Cited in this article [1]

Tian Y N , Sun H R , Song M S , et al. Fading noise elimination of DAS exploration data based on ADNet. Journal of Jilin University (Information Science Edition), 2022, 40 (4): 525- 530. https://doi.org/10.19292/j.cnki.jdxxp.2022.04.003

van den Ende M , Ferrari A , Sladen A , et al. Deep deconvolution for traffic analysis with distributed acoustic sensing data. IEEE Transactions on Intelligent Transportation Systems, 2023a, 24 (3): 2947- 2962. https://doi.org/10.1109/TITS.2022.3223084 Cited in this article [1]

van den Ende M , Lior I , Ampuero J P , et al. A self-supervised deep learning approach for blind denoising and waveform coherence enhancement in distributed acoustic sensing data. IEEE Transactions on Neural Networks and Learning Systems, 2023b, 34 (7): 3371- 3384. https://doi.org/10.1109/TNNLS.2021.3132832 Cited in this article [4]

Walter F , Gräff D , Lindner F , et al. Distributed acoustic sensing of microseismic sources and wave propagation in glaciated terrain. Nature Communications, 2020, 11: 2436. https://doi.org/10.1038/s41467-020-15824-6 Cited in this article [1]

Wang H , Chen Y F , Min R , et al. Urban DAS data processing and its preliminary application to city traffic monitoring. Sensors, 2022, 22 (24): 9976. https://doi.org/10.3390/s22249976 Cited in this article [3]

Wang H Z , Lin J , Shao D , et al. Multi-scale interactive network in the application of DAS seismic data processing. Frontiers in Earth Science, 2023, 10: 991860. https://doi.org/10.3389/feart.2022.991860 Cited in this article [1]

Wang M N , Deng L , Zhong Y Z , et al. Rapid response DAS denoising method based on deep learning. Journal of Lightwave Technology, 2021, 39 (8): 2583- 2593. https://doi.org/10.1109/JLT.2021.3052651 Cited in this article [1]

Wang M N , Li Z Q , Zhang J W , et al. Vehicle trajectory extraction method using distributed optical acoustic sensing. Advanced Engineering Sciences, 2021, 53 (2): 141- 150. https://doi.org/10.15961/j.jsuese.202000301

Wang W J , Chen L , Wang Y B , et al. Fiber-optic vibration sensing-Ⅱ: Intrinsic sensing with scattered or transmitted light and their seismological applications. Reviews of Geophysics and Planetary Physics, 2022, 53 (2): 119- 137. https://doi.org/10.19975/j.dqyxx.2021-048 Cited in this article [1]

Wang Y Q , Lu W K , Liu J L , et al. Random seismic noise attenuation based on data augmentation and CNN. Chinese Journal of Geophysics, 2019, 62 (1): 421- 433. https://doi.org/10.6038/cjg2019M0385

Wu H J , Yang S Q , Liu X Y , et al. Simultaneous extraction of multi-scale structural features and the sequential information with an end-to-end mCNN-HMM combined model for fiber distributed acoustic sensor. Journal of Lightwave Technology, 2021, 39 (20): 6606- 6616. https://doi.org/10.1109/JLT.2021.3102265 Cited in this article [1]

Xiao H , Tanimoto T , Spica Z J , et al. Locating the precise sources of high-frequency microseisms using distributed acoustic sensing. Geophysical Research Letters, 2022, 49 (17): e2022GL099292. https://doi.org/10.1029/2022GL099292 Cited in this article [1]

Yang L Q , Fomel S , Wang S D , et al. Denoising distributed acoustic sensing data using unsupervised deep learning. Geophysics, 2023a, 88 (4): V317- V317. https://doi.org/10.1190/geo2022-0460.1 Cited in this article [1]

Yang L Q , Fomel S , Wang S D , et al. Denoising of distributed acoustic sensing data using supervised deep learning. Geophysics, 2023b, 88 (1): WA91- WA104. https://doi.org/10.1190/geo2022-0138.1 Cited in this article [3]

Yu S W , Ma J W . Complex variational mode decomposition for slop-preserving denoising. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56 (1): 586- 597. https://doi.org/10.1109/TGRS.2017.2751642 Cited in this article [1]

Yu S W , Ma J W , Wang W L . Deep learning for denoising. Geophysics, 2019, 84 (6): V333- V350. https://doi.org/10.1190/geo2018-0668.1 Cited in this article [1]

Zeng Z Y , Lu T X , Han P , et al. Microseismic data denoising in the sychrosqueezed domain by integrating the wavelet coefficient thresholding and pixel connectivity. Geophysical Journal International, 2023, 232 (2): 1113- 1128. https://doi.org/10.1093/gji/ggac378 Cited in this article [1]

Zhang H , Xu T W , Pei S P , et al. Application of distributed acoustic sensing in structural investigation of Lake Yigong in Tibet. Earth Science Frontiers, 2021, 28 (6): 227- 234. https://doi.org/10.13745/j.esf.sf.2021.11.10

Zhang H , Lü Q T , Zhang Y , et al. Recent advances in geoscience using Fiber Bragg Grating (FBG) and Distrusted Acoustic Sensing(DAS) and the road ahead. Progress in Geophysics, 2023, 38 (3): 1416- 1454. https://doi.org/10.6038/pg2023GG0365 Cited in this article [1]

Zhang X , Yang W X , Li X B , et al. Analysis of the effect of using batch normalization layers in convolutional neural networks on seismic data denoising. Progress in Geophysics, 2024, 39 (1): 183- 196. https://doi.org/10.6038/pg2024HH0045

Zhang Z D , White M C A , Bai T , et al. Characterizing microearthquakes induced by hydraulic fracturing with hybrid borehole DAS and three-component geophone data. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4501315. https://doi.org/10.1109/TGRS.2023.3264931

Zhao Y , Zhong Z C , Li Y , et al. Ensemble empirical mode decomposition and stacking model for filtering borehole distributed acoustic sensing records. Geophysics, 2023, 88 (1): WA319- WA334. https://doi.org/10.1190/geo2022-0273.1 Cited in this article [1]

Zhao Y X , Li Y , Wu N . Coupled noise reduction in distributed acoustic sensing seismic data based on convolutional neural network. IEEE Geoscience and Remote Sensing Letters, 2022a, 19: 8025605. https://doi.org/10.1109/LGRS.2022.3144421 Cited in this article [1]

Zhao Y X , Li Y , Wu N . Data augmentation and its application in distributed acoustic sensing data denoising. Geophysical Journal International, 2022b, 228 (1): 119- 133. https://doi.org/10.1093/gji/ggab345 Cited in this article [1]

Zhao Y X , Li Y , Wu N . Distributed acoustic sensing vertical seismic profile data denoiser based on convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 2022c, 60: 5900511. https://doi.org/10.1109/TGRS.2020.3042202 Cited in this article [3]

Zhao Y X , Li Y , Wu N , et al. Sample2Sample: an improved self-supervised denoising framework for random noise suppression in distributed acoustic sensing vertical seismic profile data. Geophysical Journal International, 2023, 232 (3): 1515- 1532. https://doi.org/10.1093/gji/ggac400 Cited in this article [3]

Zhong T , Li Y . Statistical analysis for background noise of borehole distributed acoustic sensing. Journal of Jilin University (Information Science Edition), 2021, 39 (1): 8- 14.

Zhou Z H . A brief introduction to weakly supervised learning. National Science Review, 2018, 5 (1): 44- 53. Cited in this article [1]

包 丰 , 曾 祥方 , 林 融冰 , 等. 分布式光纤高密度地震观测揭示门源MS6.9地震余震活动与隐伏断层. 科学通报, 2022, 67 (27): 3340- 3347. Cited in this article [1]

蔡 海文 , 叶 青 , 王 照勇 , 等. 基于相干瑞利散射的分布式光纤声波传感技术. 激光与光电子学进展, 2020, 57 (5): 050001. https://doi.org/10.3788/LOP57.050001 Cited in this article [1]

董 旭日 , 冯 晅 , 刘 财 , 等. 基于DAS信号和CNN分类算法的人员运动轨迹监测方法. 吉林大学学报(地球科学版), 2022, 52 (3): 1004- 1015. https://doi.org/10.13278/j.cnki.jjuese.20210381 Cited in this article [1]

董 新桐 , 李 月 , 刘 飞 , 等. 基于卷积神经网络的井中分布式光纤传感器地震数据随机噪声压制新技术. 地球物理学报, 2021, 64 (7): 2554- 2565. https://doi.org/10.6038/cjg2021O0274 Cited in this article [1]

郭 继坤 , 陈 司晗 . 矿井下分布式光纤定位精度及感测距离研究. 吉林大学学报(信息科学版), 2018, 36 (5): 382- 387. https://doi.org/10.19292/j.cnki.jdxxp.2018.05.005 Cited in this article [1]

黄 斌 , 张 宏兵 , 王 强 , 等. 广义S变换与短时傅里叶变换在地震时频分析中的对比研究. 中国煤炭地质, 2017, 29 (1): 59- 63. 59-63, 78 Cited in this article [1]

贾 瑞生 , 赵 同彬 , 孙 红梅 , 等. 基于经验模态分解及独立成分分析的微震信号降噪方法. 地球物理学报, 2015, 58 (3): 1013- 1023. https://doi.org/10.6038/cjg20150326 Cited in this article [1]

李 月 , 杨 宝俊 , 林 红波 , 等. 地震资料中随机强噪声压制——时频峰值滤波. 中国科学: 地球科学, 2013, 43 (7): 1123- 1131. Cited in this article [1]

李 志辰 , 刘 琨 , 江 俊峰 , 等. 光纤周界安防系统的高准确度事件识别方法. 红外与激光工程, 2018, 47 (9): 167- 172. Cited in this article [1]

林 融冰 , 包 丰 , 谢 军 , 等. 光缆布设方式对DAS主、被动源记录的影响. 地球物理学报, 2022, 65 (10): 4087- 4098. https://doi.org/10.6038/cjg2022P0444 Cited in this article [1]

陆 天然 , 段 梦乔 , 李 子怡 , 等. 人工智能地震分类研究进展综述. 地球物理学进展, 2025, 40 (1): 25- 47. https://doi.org/10.6038/pg2025HH0559

尚 秋峰 , 黄 达 . 基于MELMD-ICA的光纤振动信号降噪方法. 半导体光电, 2023, 44 (2): 312- 318. https://doi.org/10.16818/j.issn1001-5868.2022120102 Cited in this article [1]

邵 婕 , 王 一博 , 梁 兴 , 等. 基于孪生网络的人工震源分布式光纤传感数据噪声压制. 地球物理学报, 2022, 65 (9): 3599- 3609. https://doi.org/10.6038/cjg2022P0919 Cited in this article [2]

田 雅男 , 孙 浩然 , 宋 明绅 , 等. 基于ADNet实现DAS数据衰落噪声压制. 吉林大学学报(信息科学版), 2022, 40 (4): 525- 530. https://doi.org/10.19292/j.cnki.jdxxp.2022.04.003 Cited in this article [1]

王 茂宁 , 李 钟麒 , 张 建伟 , 等. 基于分布式光纤传感系统的车辆轨迹提取方法. 工程科学与技术, 2021, 53 (2): 141- 150. https://doi.org/10.15961/j.jsuese.202000301 Cited in this article [1]

王 伟君 , 陈 凌 , 王 一博 , 等. 光纤振动传感之二: 基于散射或透射光的本征传感及其地震学应用. 地球与行星物理论评, 2022, 53 (2): 119- 137. https://doi.org/10.19975/j.dqyxx.2021-048 Cited in this article [1]

王 钰清 , 陆 文凯 , 刘 金林 , 等. 基于数据增广和CNN的地震随机噪声压制. 地球物理学报, 2019, 62 (1): 421- 433. https://doi.org/10.6038/cjg2019M0385 Cited in this article [1]

张 衡 , 徐 团伟 , 裴 顺平 , 等. 利用分布式光纤声传感设备开展青藏高原易贡湖浅层结构探测. 地学前缘, 2021, 28 (6): 227- 234. https://doi.org/10.13745/j.esf.sf.2021.11.10 Cited in this article [1]

张 辉 , 吕 庆田 , 张 毅 , 等. 光纤布拉格光栅(FBG)和分布式声波传感器(DAS)在地学中的应用进展及发展方向. 地球物理学进展, 2023, 38 (3): 1416- 1454. https://doi.org/10.6038/pg2023GG0365 Cited in this article [1]

张 鑫 , 杨 万祥 , 李 小斌 , 等. 卷积神经网络中批量规范化层的使用对地震数据去噪的影响分析. 地球物理学进展, 2024, 39 (1): 183- 196. https://doi.org/10.6038/pg2024HH0045 Cited in this article [1]

钟 铁 , 李 月 . 分布式声传感器井中背景噪声统计特性分析. 吉林大学学报(信息科学版), 2021, 39 (1): 8- 14. Cited in this article [1]