CDU-Net: An adaptive conditional denoising model for microseismic signals in complex environments

Abstract

Microseismic signals in complex geological environments are frequently contaminated by multiple non-stationary noise. Current denoising models often struggle to effectively balance noise suppression with accurate signal preservation, compromising the reliability of early warning systems. A conditional denoising U-Net (CDU-Net) model is proposed in this study to address this challenge. The model augments the U-Net backbone with a Transformer block to capture long-range temporal dependencies and a feature-wise linear modulation (FiLM) layer to adaptively adjust feature representations in different signal-to-noise ratio (SNR) environments. Microseismic monitoring data obtained through field measurements at the Hanjiang-to-Weihe River Diversion Project were employed to establish the research dataset. The denoising performances of five methods, wavelet transform (WT), variational mode decomposition (VMD), convolutional neural network (CNN), U-Net, and CDU-Net, are systematically compared from two perspectives: quantitative evaluation metrics and qualitative analysis. Significant advantages were demonstrated by the CDU-Net model across all quantitative evaluation indicators and waveform reconstruction quality, as shown by the experimental results. Particularly excellent performance was achieved, with the peak signal-to-noise ratio (PSNR) exceeding 30 dB and the structural similarity index measure (SSIM) reaching 0.82. At the same time, the denoising performance of the CDU-Net model was verified through visual analysis. Furthermore, ablation studies further confirm the complementary benefits of the U-Net backbone, Transformer, and FiLM components, reducing loss and synergistically improving output SNR. This study provides a solid theoretical foundation and reliable technical support for the long-term stable identification and high-precision early warning of microseismic signals in deep engineering.

Keywords

Microseismic signal / Self-adaption / Transformer / Feature-wise linear modulation / Denoising / Visualization

References

[1] Anikiev, D., Birnie, C., Bin Waheed, U., Alkhalifah, T., Gu, C., Verschuur, D., & Eisner, L. (2023). Machine learning in microseismic monitoring. Earth-Science Reviews, 239, 104371.

[2] Baloch, G., Ozkaramanli, H., & Yu, R. (2018). Residual correlation regularization based image denoising. IEEE Signal Processing Letters, 25(2), 298-302.

[3] Birnie, C., & Alkhalifah, T. (2022). Transfer learning for self-supervised, blind-spot seismic denoising. Frontiers in Earth Science, 10, 1053279.

[4] Dai, F., Li, B., Xu, N., Zhu, Y., & Xiao, P. (2015). Stability evaluation on surrounding rocks of underground powerhouse based on microseismic monitoring. Shock and Vibration, 2015(1), 937181.

[5] Dong, L. J., Tang, Z., Li, X., Chen, Y., & Xue, J. C. (2020). Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform. Journal of Central South University, 27(10), 3078-3089.

[6] Dong, L., Shu, H., Tang, Z., & Yan, X. (2023). Microseismic event waveform classification using CNN-based transfer learning models. International Journal of Mining Science and Technology, 33(10), 1203-1216.

[7] Dong, X., Li, Y., Zhong, T., Wu, N., & Wang, H. (2022). Random and coherent noise suppression in DAS-VSP data by using a supervised deep learning method. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.

[8] Gaci, S. (2014). The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces. IEEE Transactions on Geoscience Remote Sensing, 52(8), 4558-4563.

[9] Ge, W., Mao, Q., Zhou, W., Gui, Z., & Wang, P. (2024). Signal enhancement for downhole microseismic data using improved attention mechanism based on autoencoder network. IEEE Access, 12, 156390-156400.

[10] Geetha, K., & Hota, M. K. (2023). Microseismic signal denoising based on variational mode decomposition with adaptive non-local means filtering. Pure and Applied Geophysics, 180(11), 3709-3731.

[11] Goertz-Allmann, B. P., Langet, N., Iranpour, K., Kühn, D., Baird, A., Oates, S., Rowe, C., Harvey, S., Oye, V., & Nakstad, H. (2024). Effective microseismic monitoring of the Quest CCS site, Alberta, Canada. International Journal of Greenhouse Gas Control, 133, 104100.

[12] Gong, B., Wang, Y., Chen, X., Song, W., Wang, K., & Yu, J. (2025). Analysis of sensitivity factors on CJB mechanical behavior from specimen scale to engineering scale. Engineering Fracture Mechanics, 314, 110673.

[13] He, Z., Jia, M., & Wang, L. (2023). Uacnet: A universal automatic classification network for microseismic signals regardless of waveform size and sampling rate. Engineering Applications of Artificial Intelligence, 126, 107088.

[14] Herrera, R. H., Han, J., & van der Baan, M. (2014). Applications of the synchrosqueezing transform in seismic time-frequency analysis. Geophysics, 79(3), V55-V64.

[15] Hu, Z. F., Kang, H. X., Tian, Y. K., Song, H. J., Zhou, H., Ma, Y. Y., & Wang, Y. F. (2023). Quantitative comparative analysis of monitoring effect of microseismic fracturing borehole and surface. Applied Geophysics, 20(4), 507-517.

[16] Iqbal, N. (2023). Deepseg: Deep segmental denoising neural network for seismic data. IEEE Transactions on Neural Networks and Learning Systems, 34(7), 3397-3404.

[17] Jia, R. S., Liang, Y. Q., Hua, Y. C., Sun, H. M., & Xia, F. F. (2016). Suppressing non-stationary random noise in microseismic data by using ensemble empirical mode decomposition and permutation entropy. Journal of Applied Geophysics, 133, 132-140.

[18] Jia, R. S., Zhao, T. B., Sun, H. M., & Yan, X. H. (2015). Micro-seismic signal denoising method based on empirical mode decomposition and independent component analysis. Chinese Journal of Geophysics, 58(3), 1013-1023 (in Chinese).

[19] Johnson, C. W., Ben-Zion, Y., Meng, H., & Vernon, F. (2020). Identifying different classes of seismic noise signals using unsupervised learning. Geophysical Research Letters, 47(15), e2020GL088353.

[20] Lan, Z., Gao, P., Wang, P., Wang, Y., Liang, J., & Hu, G. (2022). Improved wavelet packet noise reduction for microseismic data via fuzzy partition. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.

[21] Li, J., Li, Y., Li, Y., & Qian, Z. (2018). Downhole microseismic signal denoising via empirical wavelet transform and adaptive thresholding. Journal of Geophysics and Engineering, 15(6), 2469-2480.

[22] Li, J., Tang, S., Li, T., Zhang, S., Tang, L., & Sun, K. (2025a). Study on rockburst control of deep-buried tunnel by combining advanced stress release borehole and hydraulic fracturing. Tunnelling and Underground Space Technology, 160, 106517.

[23] Li, J., Tang, S., Zhang, J., Tang, B., & Zhang, S. (2025b). Multitask intelligent early warning of rockbursts based on classification-regression cascaded network model. International Journal of Rock Mechanics and Mining Sciences, 189, 106103.

[24] Li, J., Tang, S., Zhang, S., Tang, B., Huang, X., & Liu, W. (2025c). The failure process of high stress rock with through-water disturbance based on acoustic emission. International Journal of Mining Science and Technology, 35(5), 753-765.

[25] Li, L., Jin, H., Chen, Y., Cheng, S., Hu, H., & Wang, S. (2021). Noise reduction method of microseismic signal of water inrush in tunnel based on variational mode method. Bulletin of Engineering Geology and the Environment, 80(8), 6497-6512.

[26] Li, M., Yan, X., & Hu, C. (2024). A diffusion model-based framework USDDM for random noise elimination of seismic signal. Earth Science Informatics, 17(4), 3191-3213.

[27] Li, X., Dong, L., Li, B., Lei, Y., & Xu, N. (2020). Microseismic signal denoising via empirical mode decomposition, compressed sensing, and soft-thresholding. Applied Sciences, 10(6), 2191.

[28] Lin, J., Zheng, J., Li, D., & Wu, Z. (2023). Research on microseismic denoising method based on CBDNet. Artificial Intelligence in Geosciences, 4, 28-38.

[29] Liu, F., Wang, Y., Kou, M., & Liang, C. (2024). Applications of microseismic monitoring technique in coal mines: A state-of-the-art review. Applied Sciences, 14(4), 1509.

[30] Liu, H., Zhao, Z., Ma, Q., Du, J., & Liu, X. (2025). Cross-scale correlation analysis of water-induced deterioration on soft rock in coal mine underground reservoir engineering based on deep learning algorithm. International Journal of Coal Science & Technology, 12(1), 48.

[31] Meng, L., Zheng, J., Yang, R., Peng, S., Sun, Y., Xie, J., & Li, D. (2023). Microseismic monitoring technology developments and prospects in CCUS injection engineering. Energies, 16(7), 3101.

[32] Mousavi, S. M., & Langston, C. A. (2016). Adaptive noise estimation and suppression for improving microseismic event detection. Journal of Applied Geophysics, 132, 116-124.

[33] Nurdiyah, D., Yuniarno, E. M., Wulandari, S. A., Surapto, Y. K., & Purnomo, M. H. (2024). Deep semantic feature extraction to overcome overlapping frequencies for instrument recognition in Indonesian traditional music orchestras. IEEE Access, 12, 76936-76954.

[34] Palanisamy, B., Hassija, V., Chatterjee, A., Mandal, A., Chakraborty, D., Pandey, A., Chalapathi, G. S. S., & Kumar, D. (2025). Transformers for vision: A survey on innovative methods for computer vision. IEEE Access, 13, 95496-95523.

[35] Qiu, Y., Liu, Y., Zhang, L., Lu, H., & Xu, J. (2024). Boosting salient object detection with transformer-based asymmetric bilateral U-Net. IEEE Transactions on Circuits and Systems for Video Technology, 34(4), 2332-2345.

[36] Saad, O. M., Ravasi, M., & Alkhalifah, T. (2024). Noise attenuation in distributed acoustic sensing data using a guided unsupervised deep learning network. Geophysics, 89(6), V573-V587.

[37] Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020). Grad-cam: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision, 128(2), 336-359.

[38] Shang, L., & Cao, F. (2006). Adaptive denoising using a modified sparse coding shrinkage method. Neural Processing Letters, 24(2), 153-162.

[39] Shao, J., Zhang, H., & Miao, J. (2025). GPLM: Enhancing underwater images with global pyramid linear modulation. Image and Vision Computing, 154, 105361.

[40] Tang, S., Li, J., Tang, L., & Zhang, L. (2023). Microseismic monitoring and experimental study on rockburst in water-rich area of tunnel. Tunnelling and Underground Space Technology, 141, 105366.

[41] Wang, H., Alkhalifah, T., Waheed, U. B., & Birnie, C. (2022). Data-driven microseismic event localization: An application to the Oklahoma Arkoma basin hydraulic fracturing data. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-12.

[42] Wang, H., Li, Y., & Dong, X. (2021). Generative adversarial network for desert seismic data denoising. IEEE Transactions on Geoscience and Remote Sensing, 59(8), 7062-7075.

[43] Wang, Q., Liu, M., & Jia, M. (2025). Multitask frequency-time feature-wise linear modulation for piano transcription with pedal. IEEE Transactions on Audio, Speech and Language Processing, 33, 1049-1062.

[44] Wang, Z., Zhang, Q., Xiong, J., Xiao, M., Sun, G., & He, J. (2017). Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sensors Journal, 17(17), 5581-5588.

[45] Xiao, Y., Chen, S., Liu, L., Feng, G., Zhou, J., Hou, D., Li, S., & Guo, J. (2024). Determining S-wave arrival times for microseismic monitoring in tunnels based on instantaneous phase difference of sensors. Rock Mechanics and Rock Engineering, 57(3), 1781-1797.

[46] Xu, N. W., Tang, C. A., Li, L. C., Zhou, Z., Sha, C., Liang, Z. Z., & Yang, J. Y. (2011). Microseismic monitoring and stability analysis of the left bank slope in Jinping first stage hydropower station in southwestern china. International Journal of Rock Mechanics and Mining Sciences, 48(6), 950-963.

[47] Xu, P., Zhu, X., & Clifton, D. A. (2023). Multimodal learning with transformers: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12113-12132.

[48] Yang, J., Lu, G., Li, L., & Zhang, D. (2024). Microseismic electronic fencing for monitoring of transboundary mining in mines. Applied Sciences, 14(23), 11043.

[49] Yang, X., Liu, Y., Cao, A., Liu, Y., Wang, C., Zhao, W., & Niu, Q. (2025). Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network. International Journal of Coal Science & Technology, 12(1), 11.

[50] Yin, X., Liu, Q., Huang, X., & Pan, Y. (2022). Development and application of a novel hybrid CEEMDAN-LWT denoising approach in microseismic monitoring. Pure and Applied Geophysics, 179(9), 3279-3294.

[51] Zhan, K., Wen, X., Xu, R., Wang, X., Wang, C., Song, P., & Kong, C. (2024). LGF SeismoLocator: A deep learning model for precision microseismic event localization in coal mines. Rock Mechanics and Rock Engineering, 57(12), 10717-10730.

[52] Zhang, J., Li, J., Tang, B., Zhang, S., & Tang, S. (2025). Adaptive classification model based on dynamic variations in microseismic signal characteristics. Rock Mechanics and Rock Engineering, 1, 1-17.

[53] Zhang, J., & Sheng, G. (2020). First arrival picking of microseismic signals based on nested U-Net and Wasserstein generative adversarial network. Journal of Petroleum Science and Engineering, 195, 107527.

[54] Zhang, P., Dai, Y., Wang, R., & Tan, Y. (2017). A quantitative evaluation method based on EMD for determining the accuracy of time-varying seismic wavelet extraction. Journal of Seismic Exploration, 26(3), 267-292.

[55] Zhang, X., Lu, X., Jia, R., & Kan, S. (2018). Micro-seismic signal denoising method based on variational mode decomposition and energy entropy. Journal of China Coal Society, 2018(2), 356-363.