Xie, T. et al. Seismic monitoring of rockfalls using distributed acoustic sensing. Eng. Geol. https://doi.org/10.1016/J.ENGGEO.2023.107285 (2023).
Chen, Z. et al. Eavesdropping on wastewater…
Xie, T. et al. Seismic monitoring of rockfalls using distributed acoustic sensing. Eng. Geol. https://doi.org/10.1016/J.ENGGEO.2023.107285 (2023).
Chen, Z. et al. Eavesdropping on wastewater…

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Wu, X., Liang, L., Shi, Y. & Fomel, S. FaultSeg3D: using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. GEOPHYSICS 84, IM35–IM45 (2019).
Suping Peng. Current status and prospects of research on geological assurance system for coal mine safe and high efficient mining. J. China Coal Soc. 45, 2331–2345 (2020).
Mousavi, S. M. & Beroza, G. C. Deep-learning seismology. Science 377, eabm4470 (2022).
Lin, P., Peng, S., Zhao, J., Cui, X. & Du, W. Accurate diffraction imaging for detecting small-scale geologic discontinuities. GEOPHYSICS 83, S447–S457 (2018).
Zou, G., Ren, K., Sun, Z., Peng, S. & Tang, Y. Fault interpretation using a support vector machine: A study based on 3D seismic mapping of the Zhaozhuang coal mine in the Qinshui Basin, China. J. Appl. Geophys. 171, 103870 (2019).
Ren, K. et al. Fault identification based on the KPCA-GPSO-SVM algorithm for seismic attributes in the Sihe Coal Mine, Qinshui Basin, China. Interpretation 1–58 (2022). https://doi.org/10.1190/int-2022-0039.1
Yang, Y. et al. Feature Extraction, Selection, and K-Nearest neighbors algorithm for shark behavior classification based on imbalanced dataset. IEEE Sens. J. 21, 6429–6439 (2021).
Ren, K. et al. Fault identification and reliability evaluation using an SVM model based on 3-D seismic data volume. Geophys. J. Int. 234, 755–768 (2023).
Zuo, R. & Carranza, E. J. M. Support vector machine: A tool for mapping mineral prospectivity. Comput. Geosci. 37, 1967–1975 (2011).
Han, C. et al. Intelligent fault prediction with wavelet-SVM fusion in coal mine. Comput. Geosci. 194, 105744 (2025).
Xiong, W. et al. Seismic fault detection with convolutional neural network. GEOPHYSICS 83, O97–O103 (2018).
Pochet, A., Diniz, P. H. B., Lopes, H. & Gattass, M. Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps. IEEE Geosci. Remote Sens. Lett. 16, 352–356 (2019).
Di, H., Li, Z., Maniar, H. & Abubakar, A. Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow. GEOPHYSICS 85, WA77–WA86 (2020).
Geng, Z. & Wang, Y. Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification. Nat. Commun. 11, 3311 (2020).
Zhang, G., Lin, C. & Chen, Y. Convolutional neural networks for microseismic waveform classification and arrival picking. GEOPHYSICS 85, WA227–WA240 (2020).
Yu, S. & Ma, J. Deep learning for geophysics: current and future trends. Rev. Geophys. 59, e2021RG000742 (2021).
Zou, G., Liu, H., Ren, K., Deng, B. & Xue, J. Automatic recognition of faults in mining areas based on convolutional neural network. Energies 15, 3758 (2022).
Deng, B. et al. An approach of 2D convolutional neural network–based seismic data fault interpretation with linear annotation and pixel thinking. Geophys. Prospect. 72, 3350–3370 (2024).
An, Y. et al. Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review. Earth-Sci. Rev. 243, 104509 (2023).
An, Y. et al. Deep convolutional neural network for automatic fault recognition from 3D seismic datasets. Comput. Geosci. 153, 104776 (2021).
Kay, S. M. & Marple, S. L. Spectrum analysis—A modern perspective. Proc. IEEE 69, 1380–1419 (1981).
Hubral, P., Tygel, M. & Schleicher, J. Seismic image waves. Geophys. J. Int. 125, 431–442 (1996).
Cao, S. & Chen, X. The second-generation wavelet transform and its application in denoising of seismic data. Appl. Geophys. 2, 70–74 (2005).
Neut, J. V. D., Sen, M. K. & Wapenaar, K. Seismic reflection coefficients of faults at low frequencies: a model study. Geophys. Prospect. 56, 287–292 (2008).
Liu, W., Wang, Z. & Cao, S. Stratigraphic interfaces identification based on wavelet transform. SEG Tech. Program. Expanded Abstracts. 2012, 1–5. https://doi.org/10.1190/segam2012-0247.1 (2012). Society of Exploration Geophysicists.
Botter, C., Cardozo, N., Qu, D., Tveranger, J. & Kolyukhin, D. Seismic characterization of fault facies models. Interpretation 5, SP9–SP26 (2017).
Yeh, H. G., Sim, S. & Bravo, R. J. Wavelet and denoising techniques for Real-Time HIF detection in 12-kV distribution circuits. IEEE Syst. J. 13, 4365–4373 (2019).
Li, X., Chen, S., Wang, E. & Li, Z. Rockburst mechanism in coal rock with structural surface and the microseismic (MS) and electromagnetic radiation (EMR) response. Eng. Fail. Anal. 124, 105396 (2021).
Li, X., Chen, S., Liu, S. & Li, Z. AE waveform characteristics of rock mass under uniaxial loading based on Hilbert-Huang transform. J. Cent. South. Univ. 28, 1843–1856 (2021).
Li, X. et al. Rock burst monitoring by integrated microseismic and electromagnetic radiation methods. Rock. Mech. Rock. Eng. 49, 4393–4406 (2016).
Sang, E. F. & Yeh, H. G. The use of transform domain LMS algorithm to adaptive equalization. in Proceedings of IECON ’93–19th Annual Conference of IEEE Industrial Electronics –2064 vol.3 2061. https://doi.org/10.1109/IECON.1993.339393 (1993).
Daubechies, I. Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41, 909–996 (1988).
Larsonneur, J. L. & Morlet, J. Wavelets and seismic interpretation. In Wavelets (eds Combes, J. M. et al.) 126–131 (Springer, 1990). https://doi.org/10.1007/978-3-642-75988-8_7.
Chakraborty, A. & Okaya, D. Frequency-time decomposition of seismic data using wavelet-based methods. GEOPHYSICS 60, 1906–1916 (1995).
Graps, A. An introduction to wavelets. IEEE Comput. Sci. Eng. 2, 50–61 (1995).
Torrence, C. & Compo, G. P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).
Singh, G. K. & Sa’ad Ahmed, S. A. K. Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine. Electr. Power Syst. Res. 68, 119–136 (2004).
Osowski, S. & Garanty, K. Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng. Appl. Artif. Intell. 20, 745–755 (2007).
Gumus, E., Kilic, N., Sertbas, A. & Ucan, O. N. Evaluation of face recognition techniques using PCA, wavelets and SVM. Expert Syst. Appl. 37, 6404–6408 (2010).
Liu, W., Cao, S. & Chen, Y. Seismic Time–Frequency analysis via empirical wavelet transform. IEEE Geosci. Remote Sens. Lett. 13, 28–32 (2016).
Wang, Z., Zhang, B., Gao, J., Wang, Q. & Liu, Q. H. Wavelet transform with generalized beta wavelets for seismic time-frequency analysis. Geophysics 82, O47–O56 (2017).
Liu, S. et al. Experimental study of effect of liquid nitrogen cold soaking on coal pore structure and fractal characteristics. Energy 275, 127470 (2023).
Li, H. et al. Experimental study on compressive behavior and failure characteristics of imitation steel fiber concrete under uniaxial load. Constr. Build. Mater. 399, 132599 (2023).
Liu, N. et al. Seismic data reconstruction via Wavelet-Based residual deep learning. IEEE Trans. Geosci. Remote Sens. 60, 1–13 (2022).
Shen, S., Li, H., Chen, W., Wang, X. & Huang, B. Seismic fault interpretation using 3-D scattering wavelet transform CNN. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022).
Jiang, J., Stankovic, V., Stankovic, L., Parastatidis, E. & Pytharouli, S. Microseismic event classification with Time-, Frequency-, and Wavelet-Domain convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 61, 1–14 (2023).
Fujieda, S., Takayama, K. & Hachisuka, T. Wavelet Convolutional Neural Networks. Preprint at https://doi.org/10.48550/arXiv.1805.08620 (2018).
Yeh, H. G., Corona, A. & Ramirez, T. Data-Driven Adaptive Modulation Classification Systems. in IEEE International systems Conference (SysCon) 1–7 (2025). https://doi.org/10.1109/SysCon64521.2025.11014808 (2025).
Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708 (2017).
Springenberg, J. T., Dosovitskiy, A., Brox, T. & Riedmiller, M. Striving for Simplicity: The All Convolutional Net. Preprint at (2015). https://doi.org/10.48550/arXiv.1412.6806
Zhang, L. et al. Signal modulation classification based on deep learning and Software-Defined radio. IEEE Commun. Lett. 25, 2988–2992 (2021).
Huang, G., Liu, Z., Pleiss, G., van der Maaten, L. & Weinberger, K. Q. Convolutional networks with dense connectivity. IEEE Trans. Pattern Anal. Mach. Intell. 44, 8704–8716 (2022).
Hsiao, T. Y., Chang, Y. C., Chou, H. H. & Chiu, C. T. Filter-based deep-compression with global average pooling for convolutional networks. J. Syst. Archit. 95, 9–18 (2019).
Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE. 86, 2278–2324 (1998).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM. 60, 84–90 (2017).
Simonyan, K. & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. Preprint at (2015). https://doi.org/10.48550/arXiv.1409.1556
Mallat, S. A Wavelet Tour of Signal Processing.