{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:57:24Z","timestamp":1771041444711,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T00:00:00Z","timestamp":1709596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Distinguished Young Scholars Program of Sichuan","award":["2021JDJQ0022"],"award-info":[{"award-number":["2021JDJQ0022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings\u2019 polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = \u22125 dB, surpassing the residual U-Net, Akaike information criterion, and short\/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data.<\/jats:p>","DOI":"10.3390\/s24051682","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T08:35:54Z","timestamp":1709627754000},"page":"1682","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings"],"prefix":"10.3390","volume":"24","author":[{"given":"Xiangyun","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology, Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Haihang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Binhong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology, Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8615-5083","authenticated-orcid":false,"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology, Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8163-2699","authenticated-orcid":false,"given":"Huailiang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,5]]},"reference":[{"key":"ref_1","first-page":"31","article-title":"Fracture imaging of the surface based microseismic monitoring in shale gas fracking: Methods and application","volume":"37","author":"Yang","year":"2017","journal-title":"Nat. Gas Ind."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.tust.2017.01.006","article-title":"Performance and feasibility analysis of two microseismic location methods used in tunnel engineering","volume":"63","author":"Feng","year":"2017","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7996","DOI":"10.1109\/TGRS.2020.3032664","article-title":"Automated platform for microseismic signal analysis: Denoising, detection, and classification in slope stability studies","volume":"59","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.tust.2015.04.016","article-title":"Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station","volume":"49","author":"Ma","year":"2015","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ijrmms.2012.12.022","article-title":"Studies on temporal and spatial variation of microseismic activities in a deep metal mine","volume":"60","author":"Liu","year":"2013","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107","DOI":"10.15446\/esrj.v18n2.35887","article-title":"A semi-automatic approach to identify first arrival time: The cross-correlation technique (CCT)","volume":"18","author":"Senkaya","year":"2014","journal-title":"Earth Sci. Res. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S225","DOI":"10.1785\/BSSA07206B0225","article-title":"Automatic phase pickers: Their present use and future prospects","volume":"72","author":"Allen","year":"1982","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_9","first-page":"5907613","article-title":"Automatic first arrival time identification using fuzzy C-means and AIC","volume":"60","author":"Lan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/LGRS.2019.2952571","article-title":"Fast-AIC method for automatic first arrivals picking of microseismic event with multitrace energy stacking envelope summation","volume":"17","author":"Long","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4558","DOI":"10.1109\/TGRS.2013.2282422","article-title":"The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces","volume":"52","author":"Gaci","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.cageo.2012.12.002","article-title":"Seismic noise study for accurate P-wave arrival detection via MODWT","volume":"54","author":"Hafez","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1109\/TGRS.2002.800438","article-title":"PAI-S\/K: A robust automatic seismic P phase arrival identification scheme","volume":"40","author":"Saragiotis","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1088\/1742-2140\/aab30c","article-title":"The S-STK\/LTK algorithm for arrival time picking of microseismic signals","volume":"15","author":"Dong","year":"2018","journal-title":"J. Geophys. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1093\/jge\/gxab026","article-title":"Automatic first-arrival picking method via intelligent Markov optimal decision processes","volume":"18","author":"Luo","year":"2021","journal-title":"J. Geophys. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Leng, J., Yu, Z., Mao, Z., and He, C. (2022). Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events. Sensors, 22.","DOI":"10.3390\/s22114065"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, W., and Ren, H. (2022). Automatic Time Picking for Weak Seismic Phase in the Strong Noise and Interference Environment: An Hybrid Method Based on Array Similarity. Sensors, 22.","DOI":"10.3390\/s22249924"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1109\/LGRS.2013.2260720","article-title":"An automatic P-phase picking algorithm based on adaptive multiband processing","volume":"10","author":"Mota","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1093\/gji\/ggt516","article-title":"An earthquake detection algorithm with pseudo-probabilities of multiple indicators","volume":"197","author":"Ross","year":"2014","journal-title":"Geophys. J. Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1785\/0220190038","article-title":"A Reliable Strategy for Improving Automatic First-Arrival Picking of High-Noise Three-Component Microseismic Data","volume":"90","author":"Li","year":"2019","journal-title":"Seismol. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108867","DOI":"10.1016\/j.ymssp.2022.108867","article-title":"An improved AIC onset-time picking method based on regression convolutional neural network","volume":"171","author":"Li","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","first-page":"7504605","article-title":"Time series segmentation clustering: A new method for S-phase picking in microseismic data","volume":"19","author":"Zhu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"KS93","DOI":"10.1190\/geo2015-0213.1","article-title":"Improved methods for detection and arrival picking of microseismic events with low signal-to-noise ratiosMS event detection and arrival picking","volume":"81","author":"Tan","year":"2016","journal-title":"Geophysics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"KS71","DOI":"10.1190\/geo2014-0500.1","article-title":"A review and appraisal of arrival-time picking methods for downhole microseismic data","volume":"81","author":"Akram","year":"2016","journal-title":"Geophysics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1109\/LGRS.2018.2861218","article-title":"Automatic arrival time detection for earthquakes based on stacked denoising autoencoder","volume":"15","author":"Saad","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","first-page":"5910110","article-title":"Novel wavelet threshold denoising method to highlight the first break of noisy microseismic recordings","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"eabm4470","DOI":"10.1126\/science.abm4470","article-title":"Deep-learning seismology","volume":"377","author":"Mousavi","year":"2022","journal-title":"Science"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, C., Yan, W., Xu, W., Li, T., Ran, X., Wan, J., Tong, K., and Lin, Y. (2023). Parallel Processing Method for Microseismic Signal Based on Deep Neural Network. Remote Sens., 15.","DOI":"10.3390\/rs15051215"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1109\/LGRS.2017.2785834","article-title":"Seismic waveform classification and first-break picking using convolution neural networks","volume":"15","author":"Yuan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107560","DOI":"10.1016\/j.soildyn.2022.107560","article-title":"Automatic arrival-time picking of P-and S-waves of microseismic events based on object detection and CNN","volume":"164","author":"Li","year":"2023","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_31","first-page":"7505105","article-title":"Microseismic first-arrival picking using fine-tuning feature pyramid networks","volume":"19","author":"Liu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.1109\/TGRS.2020.3010541","article-title":"AEnet: Automatic picking of P-wave first arrivals using deep learning","volume":"59","author":"Guo","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","first-page":"5904311","article-title":"CapsPhase: Capsule neural network for seismic phase classification and picking","volume":"60","author":"Omar","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6234","DOI":"10.1109\/TGRS.2020.3019520","article-title":"Earthquake detection and P-wave arrival time picking using capsule neural network","volume":"59","author":"Saad","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"261","article-title":"PhaseNet: A deep-neural-network-based seismic arrival-time picking method","volume":"216","author":"Zhu","year":"2019","journal-title":"Geophys. J. Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6612","DOI":"10.1029\/2019JB017536","article-title":"Deep learning for picking seismic arrival times","volume":"124","author":"Wang","year":"2019","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"V415","DOI":"10.1190\/geo2019-0792.1","article-title":"Automated arrival-time picking using a pixel-level network","volume":"85","author":"Ma","year":"2020","journal-title":"Geophysics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104175","DOI":"10.1016\/j.compgeo.2021.104175","article-title":"An arrival time picker for microseismic rock fracturing waveforms and its quality control for automatic localization in tunnels","volume":"135","author":"Zhang","year":"2021","journal-title":"Comput. Geotech."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5120","DOI":"10.1029\/2017JB015251","article-title":"P wave arrival picking and first-motion polarity determination with deep learning","volume":"123","author":"Ross","year":"2018","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3952","DOI":"10.1038\/s41467-020-17591-w","article-title":"Earthquake transformer\u2014An attentive deep-learning model for simultaneous earthquake detection and phase picking","volume":"11","author":"Mousavi","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"V299","DOI":"10.1190\/geo2020-0308.1","article-title":"Automatic seismic phase picking based on unsupervised machine-learning classification and content information analysis","volume":"86","author":"Cano","year":"2021","journal-title":"Geophysics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1093\/gji\/ggaa186","article-title":"Automatic microseismic event picking via unsupervised machine learning","volume":"222","author":"Chen","year":"2020","journal-title":"Geophys. J. Int."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3972","DOI":"10.1038\/s41467-020-17841-x","article-title":"Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning","volume":"11","author":"Seydoux","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6315","DOI":"10.1016\/j.eswa.2015.04.032","article-title":"Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization","volume":"42","author":"Pimentel","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"115216","DOI":"10.1016\/j.eswa.2021.115216","article-title":"Stable first-arrival picking through adaptive threshold determination and spatial constraint clustering","volume":"182","author":"Gao","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.1109\/LGRS.2016.2616510","article-title":"Automatic time picking for microseismic data based on a fuzzy C-means clustering algorithm","volume":"13","author":"Zhu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, Z., Wang, J., Sui, Q., Li, S., Wang, H., and Cao, Z. (2021). First arrival picking on microseismic signals based on K-means with a ReliefF algorithm. Symmetry, 13.","DOI":"10.3390\/sym13050790"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3821","DOI":"10.1785\/0220200439","article-title":"PolarGUI: A MATLAB-Based Tool for Polarization Analysis of the Three-Component Seismic Data Using Different Algorithms","volume":"92","author":"Li","year":"2021","journal-title":"Seismol. Res. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1111\/j.1365-246X.1980.tb04308.x","article-title":"Some comments on the descriptions of the polarization states of waves","volume":"61","author":"Samson","year":"1980","journal-title":"Geophys. J. Int."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1682\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:09:35Z","timestamp":1760105375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1682"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,5]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24051682"],"URL":"https:\/\/doi.org\/10.3390\/s24051682","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,5]]}}}