[1] AKI, K. Space and time spectra of stationary stochastic waves, with special referenceto microtremors[J]. Bulletin of the Earthquake Research Institute, 1957, 35, 415–456.
[2] ARFKEN, G., WEBER, H., & HARRIS, F. Mathematical methods for physicists: Acomprehensive guide 7th ed[M]. London: Academic Press, 2012.
[3] BADRINARAYANAN, V., KENDALL, A., & CIPOLLA, R. Segnet: A deepconvolutional encoder-decoder architecture for image segmentation[J]. IEEETransactions on Pattern Analysis and Machine Intelligence, 2017, 39(12), 2481–2495.doi: 10.1109/TPAMI.2016.2644615
[4] BENSEN, G. D., RITZWOLLER, M. H., BARMIN, M. P., et al. Processing seismicambient noise data to obtain reliable broad-band surface wave dispersionmeasurements[J]. Geophysical Journal International, 2007, 169(3), 1239–1260. doi:https://doi.org/10.1111/j.1365-246X.2007.03374.x
[5] BENSEN, G. D., RITZWOLLER, M. H., & YANG, Y. A 3-D shear velocity model ofthe crust and uppermost mantle beneath the United Statesfrom ambient seismic noise[J].Geophysical Journal International, 2009, 177(3), 1177–1196.https://doi.org/10.1111/j.1365-246X.2009.04125.x
[6] BI, Z., WU, X., GENG, Z., et al. Deep relative geologic time: A deep learning methodfor simultaneously interpreting 3-D seismic horizons and faults[J]. Journal ofGeophysical Research: Solid Earth, 2021, 126, e2021JB021882.https://doi.org/10.1029/2021JB021882
[7] BORGWARDT, K. M., GRETTON, A., RASCH, M. J., et al. Integrating structuredbiological data by kernel maximum mean discrepancy[J]. Bioinformatics, 2006, 22(14),e49-e57.
[8] CAI, A., QIU, H., & NIU, F. Semi‐Supervised Surface Wave Tomography WithWasserstein Cycle‐Consistent GAN: Method and Application to Southern CaliforniaPlate Boundary Region[J]. Journal of Geophysical Research: Solid Earth, 2022, 127(3),e2021JB023598.
[9] CAMPILLO, M., & PAUL, A. Long-range correlations in the diffuse seismic coda[J].Science, 2003, 299(5606), 547–549. https://doi.org/10.1126/science.1078551.
[10] CHEN, L. C., PAPANDREOU, G., KOKKINOS, I., et al. DeepLab: Semantic imagesegmentation with deep convolutional nets, atrous convolution, and fully connectedCRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4),834–848. doi: 10.1109/TPAMI.2017.2699184
[11] CHEN, X. A systematic and efficient method of computing normal modes formultilayered half-space[J]. Geophysical Journal International, 1993, 115(2), 391–409.doi: 10.1111/j.1365-246X.1993.tb01194.x
[12] CHEN, X. Seismogram synthesis in multi-layered half-space Part I. Theoreticalformulations[J]. Earthquake Research in China, 1999, 13(2), 149-174.
[13] CHEN, X., XIA, J., PANG, J., et al. Deep learning inversion of Rayleigh-wavedispersion curves with geological constraints for near-surfaceinvestigations[J]. Geophysical Journal International, 2022, 231(1), 1-14.
[14] CHENG, F., XIA, J., ZHANG, K., et al. Phase-weighted slant stacking for surface wavedispersion measurement[J]. Geophysical Journal International, 2021, 226(1), 256–269.
[15] DAI, T., XIA, J., NING, L., et al. Deep learning for extracting dispersioncurves[J]. Surveys in Geophysics, 2021, 42, 69-95.
[16] DERODE, A., LAROSE, E., CAMPILLO, M., et al. How to estimate the Green'sfunction of a heterogeneous medium between two passive sensors? Application toacoustic waves[J]. Applied Physics Letters, 2003, 83(15), 3054–3056.https://doi.org/10.1063/1.1617373
[17] DING, H., JIANG, X., SHUAI, B., et al. Semantic segmentation with context encodingand multi-path decoding[J]. IEEE Transactions on Image Processing, 2020, 29, 3520-3533.
[18] DONG, S., LI, Z., CHEN, X., et al. DisperNet: An effective method of extracting andclassifying the dispersion curves in the frequency–bessel dispersionspectrum[J]. Bulletin of the Seismological Society of America, 2021, 111(6), 3420-3431.
[19] FU, L., PAN, L., MA, Q., et al. Retrieving S-wave velocity from surface wavemultimode dispersion curves with DispINet[J]. Journal of Applied Geophysics, 2021,193, 104430.
[20] GOODFELLOW, I., POUGET-ABADIE, J., MIRZA, M., et al. Generative adversarialnetworks[J]. Communications of the ACM, 2020, 63(11), 139-144.
[21] GANIN, Y., USTINOVA, E., AJAKAN, H., et al. Domain-adversarial training of neuralnetworks[J]. The journal of machine learning research, 2016, 17(1), 2096-2030.
[22] GAO, H., WU, X., & LIU, G. ChannelSeg3D: Channel simulation and deep learningfor channel interpretation in 3D seismic images[J]. Geophysics, 2021, 86(4), IM73-IM83.
[23] GAO, L., ZHANG, W., ZHANG, Z., et al. Extraction of multimodal dispersion curvesfrom ambient noise with compressed sensing[J]. Journal of Geophysical Research:Solid Earth, 2021, e2020JB021472.
[24] GENG, Z., WU, X., SHI, Y., et al. Deep learning for relative geologic time and seismichorizonsDL for RGT and horizons[J]. Geophysics, 2020, 85(4), WA87-WA100.
[25] GENG, Z., Z. HU, X. WU, et al. Semi-supervised salt segmentation using meanteacher[J]. Interpretation, 2022, Vol. 10(3), SE21-SE29.
[26] GHIFARY, M., KLEIJN, W. B., ZHANG, M., et al. Deep reconstruction-classificationnetworks for unsupervised domain adaptation[C]. In Proceedings of the 14th EuropeanConference on Computer Vision. Amsterdam, Netherlands, 2016.
[27] HE, K., ZHANG, X., REN, S., et al. Deep residual learning for image recognition[C].In Proceedings of the 29th Conference on Computer Vision and Pattern Recognition.Las Vegas, America, 2016.
[28] HERRMANN, R. B. Computer programs in seismology: An evolving tool forinstruction and research[J]. Seismological Research Letters, 2013, 84(6), 1081–1088.doi: https://doi.org/10.1785/0220110096
[29] HU, J., QIU, H., ZHANG, H., et al. Using deep learning to derive shear‐wave velocitymodels from surface‐wave dispersion data[J]. Seismological Research Letters, 2020,91(3), 1738-1751.
[30] JEDDI, Z., GUDMUNDSSON, O., & TRYGGVASON, A. Ambient-noise tomographyof Katla volcano, south Iceland[J]. Journal of Volcanology and Geothermal Research,2017, 347, 264–277. https://doi.org/10.1016/j.jvolgeores.2017.09.019
[31] KIM, T., CHA, M., KIM, H., et al. Learning to discover cross-domain relations withgenerative adversarial networks[C]. In Proceedings of 34th International Conferenceon Machine Learning. Sydney, Australia, 2017.
[32] KUANG, W., YUAN, C., & ZHANG, J. Real-time determination of earthquake focalmechanism via deep learning[J]. Nature communications, 2021, 12(1), 1432.
[33] LI, Y., & PENG, X. Network architecture search for domain adaptation[J]. arXivpreprint arXiv:2008.05706, 2020.
[34] LI, Z., MEIER, M. A., HAUKSSON, E., et al. Machine learning seismic wavediscrimination: Application to earthquake early warning[J]. Geophysical ResearchLetters, 2018, 45(10), 4773-4779.
[35] LIN, F. C., MOSCHETTI, M. P., & RITZWOLLER, M. H. Surface wave tomographyof the western United States from ambient seismic noise: Rayleigh and Love wavephase velocity maps[J]. Geophysical Journal International, 2008, 173(1), 281–298.https://doi.org/10.1111/j.1365-246X.2008.03720.x
[36] LIN, F. C., RITZWOLLER, M. H., & SHEN, W. On the reliability of attenuationmeasurements from ambient noise cross-correlations[J]. Geophysical Research Letters,2011, 38(11). https://doi.org/10.1029/2011GL047366
[37] LIN, G., MILAN, A., SHEN, C., et al. Refinenet: Multi-path refinement networks forhigh-resolution semantic segmentation[C]. In Proceedings of the 29th Conference onComputer Vision and Pattern Recognition. Las Vegas, America, 2016.
[38] LIN, J. T., MELGAR, D., THOMAS, A. M., et al. Early warning for great earthquakesfrom characterization of crustal deformation patterns with deep learning[J]. Journal ofGeophysical Research: Solid Earth, 2021, 126(10), e2021JB022703.
[39] LIU, A. H., LIU, Y. C., YEH, Y. Y., et al. A unified feature disentangler for multidomain image translation and manipulation[C]. In Proceedings of 32nd Conference onNeural Information Processing System. Montréal, Canada, 2018.
[40] LOBKIS, O. I., & WEAVER, R. L. On the emergence of the Green's function in thecorrelations of a diffuse field[J]. The Journal of the Acoustical Society of America,2001, 110(6), 3011–3017. https://doi.org/10.1121/1.1417528
[41] LONG, J., SHELHAMER, E., & DARRELL, T. Fully convolutional networks forsemantic segmentation[C]. In Proceedings of the 28th Conference on Computer Visionand Pattern Recognition. Boston, America, 2015.
[42] LUC, P., COUPRIE, C., CHINTALA, S., et al. Semantic segmentation usingadversarial networks[C]. In Proceedings of 30th Conference on Neural InformationProcessing System. Barcelona, Spain, 2016.
[43] LUO, Y., HUANG, Y., YANG, Y., et al. Constructing shear velocity models fromsurface wave dispersion curves using deep learning[J]. Journal of AppliedGeophysics, 2022, 196, 104524.
[44] LUO, Y., XU, Y., LIU, Q., et al. Rayleigh-wave dispersive energy imaging and modeseparating by high-resolution linear Radon transform[J]. The Leading Edge, 2008,27(11), 1536–1542. https://doi.org/10.1190/1.3011026
[45] MEIER, M.‐A., ROSS, Z. E., RAMACHANDRAN, A., et al. Reliable real‐time seismicsignal/noise discrimination with machine learning[J]. Journal of Geophysical Research:Solid Earth, 2019, 124, 788–800. https://doi.org/ 10.1029/2018JB016661
[46] MOUSAVI, S. M., ELLSWORTH, W. L., ZHU, W., et al. Earthquake transformer—anattentive deep-learning model for simultaneous earthquake detection and phasepicking[J]. Nature communications, 2020, 11(1), 3952.
[47] MOSCHETTI, M. P., RITZWOLLER, M. H., & SHAPIRO, N. M. Surface wavetomography of the western United States from ambient seismic noise: Rayleigh wavegroup velocity maps[J]. Geochemistry, Geophysics, Geosystems, 2007, 8(8).https://doi.org/10.1029/2007GC001655
[48] MÜNCHMEYER, J., WOOLLAM, J., RIETBROCK, A., et al. Which picker fits mydata? A quantitative evaluation of deep learning based seismic pickers[J]. Journal ofGeophysical Research: Solid Earth, 2022, 127, e2021JB023499.
[49] OKADA, H., & SUTO, K. The microtremor survey method[C]. Society of ExplorationGeophysicists with the cooperation of Society of Exploration Geophysicists of Japan[and] Australian Society of Exploration Geophysicists, 2003.
[50] PARK, C. B., MILLER, R. D., & XIA, J. Imaging dispersion curves of surface waveson multichannel record[C]. In SEG Technical Program Expanded Abstracts 1998 (pp.1377–1380). Society of Exploration Geophysicists, 1998.
[51] PAN, S. J., & YANG, Q. A survey on transfer learning[J]. IEEE Transactions onKnowledge and Data Engineering, 2009, 22(10), 1345–1359. doi:10.1109/TKDE.2009.191
[52] PAN, Y., XIA, J., XU, Y., et al. Delineating shallow S wave velocity structure usingmultiple ambient-noise surface wave methods: An example from Western Junggar,China[J]. Bulletin of the Seismological Society of America, 2016, 106(2), 327–336.https://doi.org/10.1785/0120150014
[53] RAISSI, M., PERDIKARIS, P., & KARNIADAKIS, G. E. Physics-informed neuralnetworks: A deep learning framework for solving forward and inverse problemsinvolving nonlinear partial differential equations[J]. Journal of Computationalphysics, 2019, 378, 686-707.
[54] RONNEBERGER, O., FISCHER, P., & BROX, T. U-Net: Convolutional networks forbiomedical image segmentation[C]. In Proceedings of International Conference onMedical Image Computing and Computer-Assisted Intervention, 2015.
[55] ROSS, Z. E., MEIER, M. A., HAUKSSON, E., et al. Generalized seismic phasedetection with deep learning[J]. Bulletin of the Seismological Society ofAmerica, 2018, 108(5A), 2894-2901.
[56] ROSS, Z. E., MEIER, M.-A., & HAUKSSON, E. P wave arrival picking and firstmotion polarity determination with deep learning[J]. Journal of Geophysical Research:Solid Earth, 2018, 123, 5120–5129. https://doi.org/10.1029/2017JB015251
[57] SAAD, O. M., HUANG, G., CHEN, Y., et al. Scalodeep: A highly generalized deeplearning framework for real‐time earthquake detection[J]. Journal of GeophysicalResearch: Solid Earth, 2021, 126(4), e2020JB021473.
[58] SÁNCHEZ-SESMA, F. J., & CAMPILLO, M. Retrieval of the greens function fromcross correlation: The canonical elastic problem[J]. Bulletin of the SeismologicalSociety of America, 2006, 96(3), 1182–1191.
[59] SATO, H., FEHLER, M. C., & MAEDA, T. Green's function retrieval from the crosscorrelation function of random waves, Seismic wave propagation and scattering in theheterogeneous Earth: Second edition [M]. Berlin, Heidelberg: Springer, 2012.
[60] SHEN, W., & RITZWOLLER, M. H. Crustal and uppermost mantle structure beneaththe United States[J]. Journal of Geophysical Research: Solid Earth, 2016, 121(6),4306–4342. doi: https://doi.org/10.1002/2016JB012887
[61] SHEN, W., RITZWOLLER, M. H., KANG, D., et al. A seismic reference model for thecrust and uppermost mantle beneath China from surface wavedispersion[J]. Geophysical Journal International, 2016, 206(2), 954–979.
[62] SHI, Y., WU, X., & FOMEL, S. SaltSeg: Automatic 3D salt segmentation using a deepconvolutional neural network[J]. Interpretation, 2019, 7(3), SE113-SE122.
[63] SHI, Y., WU, X., & FOMEL, S. Waveform embedding: Automatic horizon picking withunsupervised deep learning[J]. Geophysics, 2020, 85(4), WA67-WA76.
[64] SHAPIRO, N. M., & CAMPILLO, M. Emergence of broadband Rayleigh waves fromcorrelations of the ambient seismic noise[J]. Geophysical Research Letters, 2004, 31(7).https://doi.org/10.1029/2004GL019491
[65] SHAPIRO, N. M., CAMPILLO, M., STEHLY, L., et al. High-resolution surface wavetomography from ambient seismic noise[J]. Science, 2005, 307(5715), 1615–1618.https://doi.org/10.1126/science.1108339
[66] SONG, W., FENG, X., WU, G., et al. Convolutional neural network, Res‐Unet++,‐based dispersion curve picking from noise cross‐correlations [J]. Journal ofGeophysical Research: Solid Earth, 2021, 126(11), e2021JB022027.
[67] SONG, W., FENG, X., ZHANG, G., et al. Domain adaptation in automatic picking ofphase velocity dispersions based on deep learning[J]. Journal of Geophysical Research:Solid Earth, 2022, 127(6), e2021JB023389.
[68] SUN, B., & SAENKO, K. Deep coral: Correlation alignment for deep domainadaptation[C]. In Proceedings of the 14th European conference on computer vision.Amsterdam, Netherlands, 2016.
[69] TAIGMAN, Y., POLYAK, A., & WOLF, L. Unsupervised cross-domain imagegeneration[J]. arXiv preprint arXiv:1611.02200, 2016.
[70] TORREY, L., & SHAVLIK, J. Transfer learning[M]. In Handbook of research onmachine learning applications and trends: algorithms, methods, and techniques (pp.242–264). IGI global, 2010.
[71] TZENG, E., HOFFMAN, J., ZHANG, N., et al. Deep domain confusion: Maximizingfor domain invariance[J]. arXiv preprint arXiv:1412.3474, 2014.
[72] TZENG, E., HOFFMAN, J., SAENKO, K., et al. Adversarial discriminative domainadaptation[C]. In Proceedings of the 30th conference on computer vision and patternrecognition. Hawaii, America, 2017.
[73] VILLASENOR, A., YANG, Y., RITZWOLLER, M. H., et al. Ambient noise surfacewave tomography of the Iberian Peninsula: Implications for shallow seismicstructure[J]. Geophysical Research Letters, 2007, 34(11).https://doi.org/10.1029/2007GL030164
[74] WANG, F., WU, X., & WANG, H. Seismic horizon identification using semisupervised learning with virtual adversarial training[J]. IEEE Transactions onGeoscience and Remote Sensing, 2022, 60, 1-11.
[75] WANG, J., WU, G., & CHEN, X. Frequency-Bessel transform method for effectiveimaging of higher-mode Rayleigh dispersion curves from ambient seismic noise data[J].Journal of Geophysical Research: Solid Earth, 2019, 124(4), 3708–3723.https://doi.org/10.1029/2018JB016595
[76] WANG, J., XIAO, Z., LIU, C., et al. Deep learning for picking seismic arrivaltimes[J]. Journal of Geophysical Research: Solid Earth, 2019, 124(7), 6612-6624.
[77] WANG, K., LU, L., MAUPIN, V., et al. Surface wave tomography of NortheasternTibetan Plateau using beamforming of seismic noise at a dense array [J]. Journal ofGeophysical Research: Solid Earth, 2020, 125(4), e2019JB018416
[78] WANG, X., CHEN, Q. F., LI, J., et al. Seismic sensor misorientation measurementusing P-wave particle motion: An application to the NECsaids array[J]. SeismologicalResearch Letters, 2016, 87(4), 901–911.
[79] WANG, W., MCMECHAN, G. A., MA, J., et al. Automatic velocity picking fromsemblances with a new deep-learning regression strategy: Comparison with aclassification approach[J]. Geophysics, 2021, 86(2), U1–U13.https://doi.org/10.1190/geo2020-0423.1
[80] WEAVER, R. L., & LOBKIS, O. I. Ultrasonics without a source: Thermal fluctuationcorrelations at MHz frequencies[J]. Physical Review Letters, 2001, 87(13), 134301.
[81] WEISS, K., KHOSHGOFTAAR, T. M., & WANG, D. D. A survey of transferlearning[J]. Journal of Big Data, 2016, 3(1), 9. doi: https://doi.org/10.1186/s40537-016-0043-6
[82] WU, G. X., PAN, L., WANG, J. N., et al. Shear velocity inversion using multimodaldispersion curves from ambient seismic noise data of USArray transportable array [J].Journal of Geophysical Research: Solid Earth, 2020, 125(1), e2019JB018213.https://doi.org/10.1029/2019JB018213
[83] WU, X., LIANG, L., SHI, Y., et al. FaultSeg3D: Using synthetic data sets to train anend-to-end convolutional neural network for 3D seismic faultsegmentation[J]. Geophysics, 2019, 84(3), IM35-IM45.
[84] WU, X., LIANG, L., SHI, Y., et al. Multitask learning for local seismic imageprocessing: fault detection, structure-oriented smoothing with edge-preserving, andseismic normal estimation by using a single convolutional neuralnetwork[J]. Geophysical Journal International, 2019, 219(3), 2097-2109.
[85] WU, X., SHI, Y., FOMEL, S., et al. FaultNet3D: Predicting fault probabilities, strikes,and dips with a single convolutional neural network[J]. IEEE Transactions onGeoscience and Remote Sensing, 2019, 57(11), 9138-9155.
[86] WU, X., YAN, S., QI, J., et al. Deep learning for characterizing paleokarst collapsefeatures in 3‐D seismic images[J]. Journal of Geophysical Research: Solid Earth, 2020,125(9), e2020JB019685.
[87] XIAO, Z., WANG, J., LIU, C., et al. Siamese Earthquake Transformer: A pairinputdeep-learning model for earthquake detection and phase picking on a seismic array[J].Journal of Geophysical Research: Solid Earth, 2021, 126, e2020JB02144
[88] XU, M., ZHANG, J., NI, B., et al. Adversarial domain adaptation with domainmixup[C]. In Proceedings of the AAAI Conference on Artificial Intelligence. New York,America, 2020.
[89] YANG, S., ZHANG, H., GU, N., et al. Automatically Extracting Surface‐Wave Groupand Phase Velocity Dispersion Curves from Dispersion Spectrograms Using aConvolutional Neural Network[J]. Seismological Society of America, 2022, 93(3),1549-1563.
[90] YANG, Y., RITZWOLLER, M. H., LEVSHIN, A. L., et al. Ambient noise Rayleighwave tomography across Europe[J]. Geophysical Journal International, 2007, 168(1),259–274. https://doi.org/10.1111/j.1365-246X.2006.03203.x
[91] YANG, Y., & RITZWOLLER, M. H. Characteristics of ambient seismic noise as asource for surface wave tomography[J]. Geochemistry, Geophysics, Geosystems, 2008,9(2). https://doi.org/10.1029/2007GC001814
[92] YANG, Y., SHEN, W., & RITZWOLLER, M. H. Surface wave tomography on a largescale seismic array combining ambient noise and teleseismic earthquake data[J].Earthquake Science, 2011, 24(1), 55–64. https://doi.org/10.1007/s11589-011-0769-3
[93] YANG, Y., & SOATTO, S. FDA: Fourier domain adaptation for semanticsegmentation[C]. In Proceedings of the 33rd Conference on Computer Vision andPattern Recognition. Seattle, America, 2020.
[94] YILMAZ, O. Seismic data processing[M]. Investigation in Geophysics, 1987, 2, 526.
[95] ZHAN, W., PAN, L., & CHEN, X. A widespread mid-crustal low-velocity layer beneathNortheast China revealed by the multimodal inversion of Rayleigh waves from ambientseismic noise[J]. Journal of Asian Earth Sciences, 2020, 196, 104372.https://doi.org/10.1016/j.jseaes.2020.104372
[96] ZHANG, G., LIU, Q., & CHEN, X. Enhancing the Frequency–Bessel Spectrogram ofAmbient Noise Cross‐Correlation Functions[J]. Bulletin of the Seismological Societyof America, 2023, 113(1), 361-377.
[97] ZHANG, Q., ZHANG, W., WU, X., et al. Deep Learning for Efficient MicroseismicLocation Using Source Migration‐Based Imaging[J]. Journal of Geophysical Research:Solid Earth, 2022, 127(3), e2021JB022649.
[98] ZHANG, X., JIA, Z., ROSS, Z. E., et al. Extracting dispersion curves from ambientnoise correlations using deep learning[J]. IEEE Transactions on Geoscience andRemote Sensing, 2022, 58(12), 8932-8939.
[99] ZHENG, X. F., YAO, Z. X., LIANG, J. H., et al. The role played and opportunitiesprovided by IGP DMC of China National Seismic Network in Wenchuan earthquakedisaster relief and research[J]. Bulletin of the Seismological Society of America, 2010,100(5B), 2866–2872.
[100] ZHOU, Z., SIDDIQUEE, M. M. R., TAJBAKHSH, N., et al. Unet++: A nested unet architecture for medical image segmentation[C]. In Proceedings of 4th DeepLearning in Medical Image Analysis. Granada, Spain, 2018.
[101] ZHU, W., & BEROZA, G. C. Phasenet: a deep-neural-network-based seismicarrival-time picking method[J]. Geophysical Journal International, 2018, 216(1), 261-273
[102] ZHU, W., TAI, K. S., MOUSAVI, S. M., et al. An end‐to‐end earthquake detectionmethod for joint phase picking and association using deep learning[J]. Journal ofGeophysical Research: Solid Earth, 2022, 127(3), e2021JB023283.
[103] 张功恒. 利用背景噪声高阶频散曲线研究四川盆地结构[D]: 哈尔滨工业大学,2019.
修改评论