{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T11:12:01Z","timestamp":1776337921039,"version":"3.51.2"},"reference-count":22,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41731284"],"award-info":[{"award-number":["41731284"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Migration imaging is a key step in tunnel seismic data processing. Due to the limitation of tunnel space, tunnel seismic data are small-quantity, multi-component, and have a small offset. Kirchhoff migration based on the ray theory is limited to the migration aperture and has low migration imaging accuracy. Kirchhoff migration can no longer meet the requirements of high-precision migration imaging. The reverse time migration (RTM) method is used to realize cross-correlation imaging by reverse-time recursion principle of the wave equation. The 3-D RTM method cannot only overcome the effect of small offset, but also realize multi-component data imaging, which is the most accurate migration method for tunnel seismic data. In this paper, we will study the 3-D RTM method for multi-component tunnel seismic data. Combined with the modeled data and the measured data, the imaging accuracy of the 3-D Kirchhoff migration and 3-D RTM is analyzed in detail. By comparing single-component and multi-component Kirchhoff migration and RTM profile, the advantages of the multi-component RTM method are summarized. Compared with the Kirchhoff migration method, the 3-D RTM method has the following advantages: (1) it can overcome the effect of small offset and expand the range of migration imaging; (2) multi-component data can be realized to improve the energy of anomalous interface; (3) it can make full use of multiple waves to realize migration imaging and improve the resolution of the anomalous interface. The modeled data and the measured data prove the advantages of the 3-D multi-component RTM method.<\/jats:p>","DOI":"10.3390\/s21093244","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T22:36:24Z","timestamp":1620426984000},"page":"3244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["3-D Multi-Component Reverse Time Migration Method for Tunnel Seismic Data"],"prefix":"10.3390","volume":"21","author":[{"given":"Peng","family":"Guan","sequence":"first","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Lumo Road 388, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuifa","family":"Shao","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Lumo Road 388, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuyong","family":"Jiao","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Lumo Road 388, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Lumo Road 388, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Lumo Road 388, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Lumo Road 388, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Lumo Road 388, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.tust.2008.01.001","article-title":"Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks","volume":"23","author":"Andisheh","year":"2008","journal-title":"Tunn. 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