1887
Volume 73, Issue 6
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Microseismic event detection and location are two primary components in microseismic monitoring, which offer us invaluable insights into the subsurface during reservoir stimulation and evolution. Conventional approaches for event detection and location often suffer from manual intervention and/or heavy computation, while current machine learning assisted approaches typically address detection and location separately; such limitations hinder the potential for real‐time microseismic monitoring. We propose an approach to unify event detection and source location into a single framework by adapting a convolutional neural network backbone and an encoder–decoder transformer with a set‐based Hungarian loss, which is applied directly to recorded waveforms. The proposed network is trained on synthetic data simulating multiple microseismic events corresponding to random source locations in the area of suspected microseismic activities. A synthetic test on a two‐dimensional profile of the SEG Advanced Modeling (SEAM) Time Lapse model illustrates the capability of the proposed method in detecting the events properly and locating them in the subsurface accurately; while, a field test using the Arkoma Basin data further proves its practicability, efficiency, and its potential in paving the way for real‐time monitoring of microseismic events.

© 2025 European Association of Geoscientists & Engineers. © 2025 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.70040
2025-07-09
2026-02-09

  • From This Site

    /content/journals/10.1111/1365-2478.70040
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Akram, J., and D. W.Eaton. 2016. “A Review and Appraisal of Arrival‐Time Picking Methods for Downhole Microseismic Data.” Geophysics81: KS71–KS91.
     [Google Scholar]
  2. Alkhalifah, T., H.Wang, and O.Ovcharenko. 2022. “MLReal: Bridging the Gap Between Training on Synthetic Data and Real Data Applications in Machine Learning.” Artificial Intelligence in Geosciences3: 101–114.
     [Google Scholar]
  3. Allen, R. V.1978. “Automatic Earthquake Recognition and Timing From Single Traces.” Bulletin of the Seismological Society of America68: 1521–1532.
     [Google Scholar]
  4. Anikiev, D., C.Birnie, U.bin Waheed, T.Alkhalifah, C.Gu, D. J.Verschuur, and L.Eisner. 2023. “Machine Learning in Microseismic Monitoring.” Earth‐Science Reviews239: 104371.
     [Google Scholar]
  5. Anikiev, D., J.Valenta, F.Staněk, and L.Eisner. 2014. “Joint Location and Source Mechanism Inversion of Microseismic Events: Benchmarking on Seismicity Induced by Hydraulic Fracturing.” Geophysical Journal International198: 249–258.
     [Google Scholar]
  6. Artman, B., I.Podladtchikov, and B.Witten. 2010. “Source Location Using Time‐Reverse Imaging.” Geophysical Prospecting58: 861–873.
     [Google Scholar]
  7. Berkner, K., and R.Wells. 1998. “Wavelet Transforms and Denoising Algorithms.” In Conference Record of Thirty‐Second Asilomar Conference on Signals, Systems and Computers, vol. 2, 1639–1643. IEEE.
  8. Birnie, C., and T.Alkhalifah. 2022. “Leveraging Domain Adaptation for Efficient Seismic Denoising.” In Energy in Data Conference, Austin, Texas, 20–23 February 2022Energy in Data, 11–15. SEG.
  9. Birnie, C., and F.Hansteen. 2022. “Bidirectional Recurrent Neural Networks for Seismic Event Detection.” Geophysics87: KS97–KS111.
     [Google Scholar]
  10. Birnie, C., H.Jarraya, and F.Hansteen. 2021. “An Introduction to Distributed Training of Deep Neural Networks for Segmentation Tasks With Large Seismic Data Sets.” Geophysics86: KS151–KS160.
     [Google Scholar]
  11. Bose, S., H.‐P.Valero, Q.Liu, R.Shenoy, and A.Ounadjela. 2009. “An Automatic Procedure To Detect Microseismic Events Embedded in High Noise.” In SEG Technical Program Expanded Abstracts 2009 January 2009, 1537–1541. Society of Exploration Geophysicists.
  12. Carion, N., F.Massa, G.Synnaeve, N.Usunier, A.Kirillov, and S.Zagoruyko. 2020. “End‐to‐End Object Detection With Transformers.” In Proceedings of the 16th European Conference on Computer Vision (ECCV 2020), Glasgow, UK, August 23–28, 2020, Part I, 213–229. Springer.
  13. Chambers, K., J.‐M.Kendall, S.Brandsberg‐Dahl, and J.Rueda. 2010. “Testing the Ability of Surface Arrays To Monitor Microseismic Activity.” Geophysical Prospecting58: 821–830.
     [Google Scholar]
  14. Chamorro, D., J.Zhao, C.Birnie, M.Staring, M.Fliedner, and M.Ravasi. 2024. “Deep learning‐based extraction of surface wave dispersion curves from seismic shot gathers.” Near Surface Geophysics2, no. 4: 421–437.
     [Google Scholar]
  15. Chen, Y.. 2020. “Automatic Microseismic Event Picking Via Unsupervised Machine Learning.” Geophysical Journal International222: 1750–1764.
     [Google Scholar]
  16. Cieplicki, R., L.Eisner, and D.Abbott. 2012. “Correlation Detection and Location for Microseismic Events Induced By Hydraulic Fracturing.” In 74th EAGE Conference and Exhibition incorporating (EUROPEC 2012), cp–293. European Association of Geoscientists & Engineers.
  17. Dai, Z., B.Cai, Y.Lin, and J.Chen. 2021. “Up‐DETR: Unsupervised Pre‐Training for Object Detection With Transformers.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1601–1610. IEEE.
  18. Drew, J., D.Leslie, P.Armstrong, and G.Michaud. 2005. “Automated Microseismic Event Detection and Location By Continuous Spatial Mapping.” In SPE Annual Technical Conference and Exhibition, SPE–95513. SPE.
  19. Eisner, L., D.Abbott, W. B.Barker, J.Lakings, and M. P.Thornton. 2008. “Noise Suppression for Detection and Location of Microseismic Events Using a Matched Filter.” In SEG Technical Program Expanded Abstracts 2008, 1431–1435. Society of Exploration Geophysicists.
  20. Eisner, L., P. M.Duncan, W. M.Heigl, and W. R.Keller. 2009. “Uncertainties in Passive Seismic Monitoring.” The Leading Edge28: 648–655.
     [Google Scholar]
  21. Folesky, J., J.Kummerow, S. A.Shapiro, M.Häring, and H.Asanuma. 2016. “Rupture Directivity of Fluid‐Induced Microseismic Events: Observations from an Enhanced Geothermal System.” Journal of Geophysical Research: Solid Earth121: 8034–8047.
     [Google Scholar]
  22. Gajewski, D., C.Vanelle, E.Tessmer, D.Anikiev, and B.Kashtan. 2007. “Localization of Seismic Events by Diffraction Stacking.” In the SEG International Exposition and Annual Meeting, SEG–2007. Society of Exploration Geophysicists.
  23. Gharti, H. N., V.Oye, M.Roth, and D.Kühn. 2010. “Automated Microearthquake Location Using Envelope Stacking and Robust Global Optimization.” Geophysics75: MA27–MA46.
     [Google Scholar]
  24. Gibbons, S. J., and F.Ringdal. 2006. “The Detection of Low Magnitude Seismic Events Using Array‐Based Waveform Correlation.” Geophysical Journal International165: 149–166.
     [Google Scholar]
  25. Grady, T. J., R.Khan, M.Louboutin, Z.Yin, P. A.Witte, R.Chandra, R. J.Hewett, and F. J.Herrmann. 2022. “Model‐Parallel Fourier Neural Operators as Learned Surrogates for Large‐Scale Parametric PDEs.” Technical Report TR‐CSE‐2022‐1, April 2022. Computers & Geosciences.
     [Google Scholar]
  26. Grandi, S., and S.Oates. 2009. “Microseismic Event Location By Cross‐Correlation Migration of Surface Array Data for Permanent Reservoir Monitoring.” In the 71st EAGE Conference and Exhibition incorporating SPE (EUROPEC 2009), cp–127. European Association of Geoscientists & Engineers.
  27. Grubas, S., S.Yaskevich, and A.Duchkov. 2021. “Localization of Microseismic Events Using the Physics‐Informed Neural‐Network for Traveltime Computation.” In the 82nd EAGE Annual Conference & Exhibition vol. 2021, 1–5. EAGE Publications.
  28. Horne, S., A.Baird, A.Stork, and G.Naldrett. 2019. “Machine Learning for DAS Microseismic Event Detection.” In the 81st EAGE Conference and Exhibition 2019 Workshop Programme, vol. 2019, 1–5. EAGE Publications.
  29. Huang, L., J.Li, H.Hao, and X.Li. 2018. “Micro‐Seismic Event Detection and Location in Underground Mines By Using Convolutional Neural Networks (CNN) and Deep Learning.” Tunnelling and Underground Space Technology81: 265–276.
     [Google Scholar]
  30. Huang, X., and T.Alkhalifah. 2023. “Microseismic Source Imaging Using Physics‐Informed Neural Networks with Hard Constraints.” Preprint, arXiv, 14 February, https://doi.org/10.48550/arXiv.2304.04315.
  31. Izzatullah, M., I. E.Yildirim, U. B.Waheed, and T.Alkhalifah. 2022. “Laplace HypoPINN: Physics‐Informed Neural Network for Hypocenter Localization and Its Predictive Uncertainty.” Machine Learning: Science and Technology3: 045001.
     [Google Scholar]
  32. Kaven, J. O., S. H.Hickman, A. F.McGarr, and W. L.Ellsworth. 2015. “Surface Monitoring of Microseismicity at the Decatur, Illinois, CO2 Sequestration Demonstration Site.” Seismological Research Letters86: 1096–1101.
     [Google Scholar]
  33. Kumar, S., R.Vig, and P.Kapur. 2018. “Development of Earthquake Event Detection Technique Based on STA/LTA Algorithm for Seismic Alert System.” Journal of the Geological Society of India92: 679–686.
     [Google Scholar]
  34. Larmat, C., J.‐P.Montagner, M.Fink, Y.Capdeville, A.Tourin, and E.Clévédé. 2006. “Time‐Reversal Imaging of Seismic Sources and Application to the Great Sumatra Earthquake.” Geophysical Research Letters33: L19312.
     [Google Scholar]
  35. Li, F., H.Zhang, S.Liu, J.Guo, L. M.Ni, and L.Zhang. 2022. “Dn‐detr: Accelerate Detr Training By Introducing Query Denoising.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13619–13627. IEEE.
  36. Li, T., Y. J.Gu, D. C.Lawton, H.Gilbert, M.Macquet, G.Savard, J.Wang, K. A.Innanen, and N.Yu. 2022. “Monitoring CO2 Injection at the CaMI Field Research Station Using Microseismic Noise Sources.” Journal of Geophysical Research: Solid Earth127: e2022JB024719.
     [Google Scholar]
  37. Liu, E., L.Zhu, A. G.Raj, J. H.McClellan, A.Al‐Shuhail, S. I.Kaka, and N.Iqbal. 2017. “Microseismic Events Enhancement and Detection in Sensor Arrays Using Autocorrelation‐Based Filtering.” Geophysical Prospecting65: 1496–1509.
     [Google Scholar]
  38. Liu, Y., T.Wang, X.Zhang, and J.Sun. 2022. “PETR: Position Embedding Transformation for Multi‐View 3D Object Detection.” In Proceedings of the 17th European Conference on Computer Vision (ECCV 2022), Tel Aviv, Israel, October 23–27, 2022, Part XXVII, 531–548. Springer.
  39. Luo, Y., and F.Yang. 2014. “Deep Learning with Noise.” https://api.semanticscholar.org/CorpusID:42426462.
  40. Maxwell, S. C., J.Rutledge, R.Jones, and M.Fehler. 2010. “Petroleum Reservoir Characterization Using Downhole Microseismic Monitoring.” Geophysics75: 75A129–75A137.
     [Google Scholar]
  41. McMechan, G. A.1982. “Determination of Source Parameters By Wavefield Extrapolation.” Geophysical Journal International71: 613–628.
     [Google Scholar]
  42. Michaud, G., and S.Leaney. 2008. “Continuous Microseismic Mapping for Real‐Time Event Detection and Location.” In the SEG International Exposition and Annual Meeting, SEG‐2008. Society of Exploration Geophysicists.
  43. Mousavi, S. M., W. L.Ellsworth, W.Zhu, L. Y.Chuang, and G. C.Beroza. 2020. “Earthquake Transformer–An Attentive Deep‐Learning Model for Simultaneous Earthquake Detection and Phase Picking.” Nature Communications11: 3952.
     [Google Scholar]
  44. Perol, T., M.Gharbi, and M.Denolle. 2018. “Convolutional Neural Network for Earthquake Detection and Location.” Science Advances4: e1700578.
     [Google Scholar]
  45. Rezatofighi, S. H., V. K.BG, A.Milan, E.Abbasnejad, A.Dick, and I.Reid. 2017. “Deepsetnet: Predicting Sets with Deep Neural Networks.” In 2017 IEEE International Conference on Computer Vision (ICCV), 5257–5266. IEEE.
     [Google Scholar]
  46. ShaheenA., U. b.Waheed, M.Fehler, L.Sokol, and S.Hanafy. 2021. “GroningenNet: Deep Learning for Low‐Magnitude Earthquake Detection on a Multi‐Level Sensor Network.” Sensors21: 8080.
     [Google Scholar]
  47. Shapiro, S., C.Dinske, and E.Rothert. 2006. “Hydraulic‐Fracturing Controlled Dynamics of Microseismic Clouds.” Geophysical Research Letters33: L14312.
     [Google Scholar]
  48. Skoumal, R. J., M. R.Brudzinski, and B. S.Currie. 2016. “An Efficient Repeating Signal Detector To Investigate Earthquake Swarms.” Journal of Geophysical Research: Solid Earth121: 5880–5897.
     [Google Scholar]
  49. Song, C., Z.Wu, and T.Alkhalifah. 2019. “Passive Seismic Event Estimation Using Multiscattering Waveform Inversion.” Geophysics84: KS59–KS69.
     [Google Scholar]
  50. Staněk, F., D.Anikiev, J.Valenta, and L.Eisner. 2015. “Semblance for Microseismic Event Detection.” Geophysical Journal International201: 1362–1369.
     [Google Scholar]
  51. Staněk, F., and L.Eisner. 2017. “Seismicity Induced By Hydraulic Fracturing in Shales: A Bedding Plane Slip Model.” Journal of Geophysical Research: Solid Earth122: 7912–7926.
     [Google Scholar]
  52. Stork, A. L., A. F.Baird, S. A.Horne, G.Naldrett, S.Lapins, J.‐M.Kendall, J.Wookey, J. P.Verdon, A.Clarke, and A.Williams. 2020. “Application of Machine Learning to Microseismic Event Detection in Distributed Acoustic Sensing Data.” Geophysics85: KS149–KS160.
     [Google Scholar]
  53. Sun, J., Z.Xue, T.Zhu, S.Fomel, and N.Nakata. 2016. “Full‐Waveform Inversion of Passive Seismic Data for Sources and Velocities.” In 2016 SEG International Exposition and Annual Meeting, 1405–1410. Society of Exploration Geophysicists.
  54. Vaezi, Y., and M.Van der Baan. 2015. “Comparison of the STA/LTA and Power Spectral Density Methods for Microseismic Event Detection.” Geophysical Supplements to the Monthly Notices of the Royal Astronomical Society203: 1896–1908.
     [Google Scholar]
  55. Waldhauser, F., and W. L.Ellsworth. 2000. “A Double‐Difference Earthquake Location Algorithm: Method and Application to the Northern Hayward Fault, California.” Bulletin of the Seismological Society of America90: 1353–1368.
     [Google Scholar]
  56. Wamriew, D. S., M.Charara, and E.Maltsev. 2020. “Deep Neural Network for Real‐Time Location and Moment Tensor Inversion of Borehole Microseismic Events Induced by Hydraulic Fracturing.” In the SPE Russian Petroleum Technology Conference. OnePetro.
     [Google Scholar]
  57. Wang, H., and T.Alkhalifah. 2018. “Microseismic Imaging Using A Source Function Independent Full Waveform Inversion Method.” Geophysical Journal International214: 46–57.
     [Google Scholar]
  58. Wang, H., and T.Alkhalifah. 2021. “Direct Microseismic Event Location and Characterization from Passive Seismic Data Using Convolutional Neural Networks.” Geophysics86: KS109–KS121.
     [Google Scholar]
  59. Wang, H., T.Alkhalifah, U.bin Waheed, and C.Birnie. 2021. “Data‐Driven Microseismic Event Localization: An Application To the Oklahoma Arkoma Basin Hydraulic Fracturing Data.” IEEE Transactions on Geoscience and Remote Sensing60: 1–12.
     [Google Scholar]
  60. Wang, T., L.Yuan, Y.Chen, J.Feng, and S.Yan. 2021. “PNP‐DETR: Towards Efficient Visual Analysis with Transformers.” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 4661–4670. IEEE.
  61. Warpinski, N.2009. “Microseismic Monitoring: Inside and Out.” Journal of Petroleum Technology61: 80–85.
     [Google Scholar]
  62. Wilkins, A. H., A.Strange, Y.Duan, and X.Luo. 2020. “Identifying Microseismic Events in A Mining Scenario Using A Convolutional Neural Network.” Computers & Geosciences137: 104418.
     [Google Scholar]
  63. Ye, J., T.Gui, Y.Luo, Y.Xu, and Q.Zhang. 2021. “One2Set: Generating Diverse Keyphrases as a Set.” Preprint, arXiv, May 24, https://doi.org/10.48550/arXiv.2105.11134.
  64. Young, R., and S.Maxwell. 1992. “Seismic Characterization of a Highly Stressed Rock Mass Using Tomographic Imaging and Induced Seismicity.” Journal of Geophysical Research: Solid Earth97: 12361–12373.
     [Google Scholar]
  65. Zhang, G., Z.Luo, Y.Yu, K.Cui, and S.Lu. 2022. “Accelerating DETR Convergence Via Semantic‐Aligned Matching.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 949–958. IEEE.
  66. Zhang, H., T.Alkhalifah, Y.Liu, C.Birnie, and X.Di. 2022. “Improving the Generalization of Deep Neural Networks in Seismic Resolution Enhancement.” IEEE Geoscience and Remote Sensing Letters20: 1–5.
     [Google Scholar]
  67. Zhang, X., C.Yuan, J.Zhang, S.Liu, Z.Chen, and W.Li. 2018. “Automatic Microseismic Detection and Location Via the Deep‐Convolutional Neural Network.” in SEG Technical Program Expanded Abstracts 2018, 3057–3061. Society of Exploration Geophysicists.
     [Google Scholar]
  68. Zheng, J., J.Lu, S.Peng, and T.Jiang. 2018. “An Automatic Microseismic Or Acoustic Emission Arrival Identification Scheme with Deep Recurrent Neural Networks.” Geophysical Journal International212: 1389–1397.
     [Google Scholar]
  69. Zhu, X., W.Su, L.Lu, B.Li, X.Wang, and J.Dai. 2020. “Deformable Detr: Deformable Transformers for End‐To‐End Object Detection.” Preprint, arXiv, March 18, https://doi.org/10.48550/arXiv.2010.04159.
/content/journals/10.1111/1365-2478.70040
Loading
Joint Microseismic Event Detection and Location With a Detection Transformer
Geophysical Prospecting 73, e70040 (2025); https://doi.org/10.1111/1365-2478.70040
/content/journals/10.1111/1365-2478.70040
/content/journals/10.1111/1365-2478.70040
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): monitoring |$|$ passive method |$|$ signal processing |$|$ seismics

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error