{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T06:18:59Z","timestamp":1780640339965,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T00:00:00Z","timestamp":1633392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fiber-optic cables have recently gained popularity for use as Distributed Acoustic Sensing (DAS) arrays for borehole microseismic monitoring due to their physical robustness as well as high spatial and temporal resolutions. As a result, the sensors record large amounts of data, making it very difficult to process in real-\/semi-real-time using the conventional processing routines. We present a novel approach, based on deep learning, for handling the large amounts of DAS data in real-\/semi-real-time. The proposed neural network was trained on synthetic microseismic data contaminated with real-ambient noise from field data and was validated using field DAS microseismic data obtained from a hydraulic fracturing operation. The results indicate that the trained network is capable of detecting and locating microseismic events from DAS data and simultaneously update the velocity model to a high degree of precision. The mean absolute errors in the event locations and the velocity model parameters are 2.04, 0.72, 2.76, 4.19 and 0.97 percent for distance (x), depth (z), P-wave velocity, S-wave velocity and density, respectively. In addition to automation and computational efficiency, deep learning reduces human expert data handling during processing, thus preserving data integrity leading to more accurate and reproducible results.<\/jats:p>","DOI":"10.3390\/s21196627","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"6627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Deep Neural Networks for Detection and Location of Microseismic Events and Velocity Model Inversion from Microseismic Data Acquired by Distributed Acoustic Sensing Array"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0045-1162","authenticated-orcid":false,"given":"Daniel","family":"Wamriew","sequence":"first","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, 121205 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman","family":"Pevzner","sequence":"additional","affiliation":[{"name":"Department of Exploration Geophysics, Curtin University, 26 Dick Perry Avenue, Kensington, WA 6151, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2846-9049","authenticated-orcid":false,"given":"Evgenii","family":"Maltsev","sequence":"additional","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, 121205 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitri","family":"Pissarenko","sequence":"additional","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, 121205 Moscow, Russia"},{"name":"Total Energies, Research & Development, Lesnaya 7, 125047 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Del Villar, I., and Matias, I.R. (2020). Distributed sensors in the oil and gas industry. Optical Fibre Sensors, John Wiley & Sons.","DOI":"10.1002\/9781119534730"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hartog, A.H. (2017). An Introduction to Distributed Optical Fibre Sensors, CRC Press. [1st ed.].","DOI":"10.1201\/9781315119014"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1111\/1365-2478.12634","article-title":"High-resolution multi-component distributed acoustic sensing","volume":"66","author":"Ning","year":"2018","journal-title":"Geophys. Prospect."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11792","DOI":"10.1002\/2017GL075722","article-title":"Fiber-Optic Network Observations of Earthquake Wavefields","volume":"44","author":"Lindsey","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2436","DOI":"10.1038\/s41467-020-15824-6","article-title":"Distributed acoustic sensing of microseismic sources and wave propagation in glaciated terrain","volume":"11","author":"Walter","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_6","first-page":"1025","article-title":"Seismic monitoring leveraging existing telecom infrastructure at the SDASA: Active, passive, and ambient-noise analysis","volume":"36","author":"Martin","year":"2017","journal-title":"Geophysics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e2020JB020462","DOI":"10.1029\/2020JB020462","article-title":"Low-Magnitude Seismicity with a Downhole Distributed Acoustic Sensing Array\u2014Examples from the FORGE Geothermal Experiment","volume":"126","author":"Lellouch","year":"2021","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lior, I., Sladen, A., Rivet, D., Ampuero, J., Hello, Y., Becerril, C., Martins, H.F., Lamare, P., Jestin, C., and Tsagkli, S. (2021). On the Detection Capabilities of Underwater DAS. J. Geophys. Res. Solid Earth, 1\u201320.","DOI":"10.1002\/essoar.10504330.1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5777","DOI":"10.1038\/s41467-019-13793-z","article-title":"Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables","volume":"10","author":"Sladen","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2491","DOI":"10.1785\/0120190176","article-title":"Velocity-Based Earthquake Detection Using Downhole Distributed Acoustic Sensing\u2014Examples from the San Andreas Fault Observatory at Depth","volume":"109","author":"Lellouch","year":"2019","journal-title":"Bull. Seism. Soc. Am."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6931","DOI":"10.1029\/2019JB017533","article-title":"Seismic Velocity Estimation Using Passive Downhole Distributed Acoustic Sensing Records: Examples from the San Andreas Fault Observatory at Depth","volume":"124","author":"Lellouch","year":"2019","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11620","DOI":"10.1038\/s41598-017-11986-4","article-title":"Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study","volume":"7","author":"Dou","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1038\/s41598-018-36675-8","article-title":"Distributed Acoustic Sensing Using Dark Fiber for Near-Surface Characterization and Broadband Seismic Event Detection","volume":"9","author":"Dou","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1038\/s41467-018-04860-y","article-title":"Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features","volume":"9","author":"Jousset","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e2020JB020421","DOI":"10.1029\/2020JB020421","article-title":"Depth Constraints on Coseismic Velocity Changes from Frequency-Dependent Measurements of Repeating Earthquake Waveforms","volume":"126","author":"Sheng","year":"2021","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2019JB018656","DOI":"10.1029\/2019JB018656","article-title":"Urban Seismic Site Characterization by Fiber-Optic Seismology","volume":"125","author":"Spica","year":"2020","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1126\/science.aay5881","article-title":"Illuminating seafloor faults and ocean dynamics with dark fiber distributed acoustic sensing","volume":"366","author":"Lindsey","year":"2019","journal-title":"Science"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5613","DOI":"10.1038\/s41598-021-84845-y","article-title":"Utilizing distributed acoustic sensing and ocean bottom fiber optic cables for submarine structural characterization","volume":"11","author":"Cheng","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e2020GL089931","DOI":"10.1029\/2020GL089931","article-title":"City-Scale Dark Fiber DAS Measurements of Infra-structure Use During the COVID-19 Pandemic","volume":"47","author":"Lindsey","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1190\/tle39090646.1","article-title":"Near-surface characterization using a roadside distributed acoustic sensing array","volume":"39","author":"Yuan","year":"2020","journal-title":"Lead. Edge"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5778","DOI":"10.1038\/s41467-019-13262-7","article-title":"Distributed sensing of microseisms and teleseisms with submarine dark fibers","volume":"10","author":"Williams","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12810","DOI":"10.1029\/2019JD031453","article-title":"Characterizing Thunder-Induced Ground Motions Using Fiber-Optic Distributed Acoustic Sensing Array","volume":"124","author":"Zhu","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1190\/tle32060699.1","article-title":"Field testing of fiber-optic distributed acoustic sensing (DAS) for subsurface seismic monitoring","volume":"32","author":"Daley","year":"2013","journal-title":"Lead. Edge"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1111\/1365-2478.12324","article-title":"Field testing of modular borehole monitoring with simultaneous distributed acoustic sensing and geophone vertical seismic profiles at Citronelle, Alabama","volume":"64","author":"Daley","year":"2016","journal-title":"Geophys. Prospect."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"A25","DOI":"10.1190\/geo2018-0528.1","article-title":"Comparison of geophone and surface-deployed distributed acoustic sensing seismic data","volume":"84","author":"Spikes","year":"2019","journal-title":"Geophysics"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Webster, P., Wall, J., Perkins, C., and Molenaar, M. (2013). Micro-seismic detection using distributed acoustic sensing. SEG Technical Program Expanded Abstracts 2013, Society of Exploration Geophysicists.","DOI":"10.1190\/segam2013-0182.1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hull, R.A., Meek, R., Bello, H., and Miller, D. (2017, January 24\u201326). Case history of DAS fiber-based microseismic and strain data, monitoring horizontal hydraulic stimulations using various tools to highlight physical deformation processes (Part A). Proceedings of the SPE\/AAPG\/SEG Unconventional Resources Technology Conference, Austin, TX, USA.","DOI":"10.15530\/urtec-2017-2695282"},{"key":"ref_28","first-page":"38","article-title":"DAS Microseismic Fiber-Optic Locating DAS Microseismic Events and Errors","volume":"41","author":"Webster","year":"2016","journal-title":"CSEG Rec."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3997\/1365-2397.35.4.87841","article-title":"Detecting microseismicity using distributed vibration","volume":"35","author":"Molteni","year":"2017","journal-title":"First Break"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1190\/tle36100837.1","article-title":"Hydraulic-fracturing-induced strain and microseismic using in situ distributed fiber-optic sensing","volume":"36","author":"Karrenbach","year":"2017","journal-title":"Lead. Edge"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3256","DOI":"10.1785\/0220200149","article-title":"Comparison between Distributed Acoustic Sensing and Geophones: Downhole Microseismic Monitoring of the FORGE Geothermal Experiment","volume":"91","author":"Lellouch","year":"2020","journal-title":"Seism. Res. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3997\/1365-2397.n0040","article-title":"Application of distributed fibre-optic sensing to geothermal reservoir characterization and monitoring","volume":"37","author":"Mondanos","year":"2019","journal-title":"First Break"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"D11","DOI":"10.1190\/geo2017-0396.1","article-title":"Fiber-optic distributed acoustic sensing of microseismicity, strain and temperature during hydraulic fracturing","volume":"84","author":"Karrenbach","year":"2019","journal-title":"Geophysics"},{"key":"ref_34","first-page":"KS89","article-title":"Microseismic monitoring using a fibre-optic Distributed Acoustic Sensor (DAS) array","volume":"85","author":"Verdon","year":"2020","journal-title":"Geophysics"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1111\/j.1365-2478.2010.00867.x","article-title":"Comparison of surface and borehole locations of induced seismicity","volume":"58","author":"Eisner","year":"2010","journal-title":"Geophys. Prospect."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"KS149","DOI":"10.1190\/geo2019-0774.1","article-title":"Application of machine learning to microseismic event detection in distributed acoustic sensing data","volume":"85","author":"Stork","year":"2020","journal-title":"Geophysics"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2770","DOI":"10.1111\/1365-2478.13027","article-title":"Convolutional neural networks for automated microseismic detection in downhole distributed acoustic sensing data and comparison to a surface geophone array","volume":"68","author":"Binder","year":"2020","journal-title":"Geophys. Prospect."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lellouch, A., and Biondi, B.L. (2021). Seismic Applications of Downhole DAS. Sensors, 21.","DOI":"10.3390\/s21092897"},{"key":"ref_39","unstructured":"Martin, T., and Nash, G. (2021, May 05). Energy and Geoscience Institute at the University of Utah. Utah FORGE: High-Resolution DAS Microseismic Data from Well 78-32 [data set]. Available online: http:\/\/gdr.openei.org\/submissions\/1185."},{"key":"ref_40","unstructured":"Moore, J., Jones, C.A., Skowron, G.A., Wannamaker, P.A., Nash, G.A., Hardwick, C.A., Hurlbut, W.A., Allis, R.A., Kirby, S.A., and Erickson, B.A. (2021, May 05). Energy and Geoscience Institute at the University of Utah. Utah FORGE: Phase 2C Topical Report [data set]. Available online: https:\/\/utahforge.com\/2019\/12\/12\/phase-2c-topical-report\/."},{"key":"ref_41","unstructured":"Pankow, K., Mesimeri, M., McLennan, J., Wannamaker, P., and Moore, J. (2020, January 10\u201312). Seismic Monitoring at the Utah Frontier Observatory for Research in Geothermal Energy. Proceedings of the 45th Workshop on Geothermal Reservoir Engineering, Stanford, CA, USA. SGP-TR-216."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1190\/1.1442051","article-title":"Weak elastic anisotropy","volume":"51","author":"Thomsen","year":"1986","journal-title":"Geophysics"},{"key":"ref_43","first-page":"7","article-title":"Ricker, Ormsby, Klauder, Butterworth A choice of wavelets","volume":"19","author":"Ryan","year":"1994","journal-title":"CSEG Rec."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gupta, H.K. (2011). Seismic Ray Theory. Encyclopedia of Solid Earth Geophysics, Springer.","DOI":"10.1007\/978-90-481-8702-7"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"103317","DOI":"10.1016\/j.ijggc.2021.103317","article-title":"An automated system for continuous monitoring of CO2 geosequestration using multi-well offset VSP with permanent seismic sources and receivers: Stage 3 of the CO2CRC Otway Project","volume":"108","author":"Isaenkov","year":"2021","journal-title":"Int. J. Greenh. Gas Control"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:09:38Z","timestamp":1760166578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,5]]},"references-count":48,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196627"],"URL":"https:\/\/doi.org\/10.3390\/s21196627","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,5]]}}}