Published

2022-11-29

A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion

Método de detección de anomalías geológicas subterráneas en el Reino Unido con base en la fusión de características

DOI:

https://doi.org/10.15446/esrj.v26n3.103605

Keywords:

feature fusion, British city, underground space, geology, abnormal body, detection (en)
fusión de características, Reino Unido, espacios urbanos subterráneos, geología, cuerpos anormales, detección (es)

Downloads

Authors

  • Xuemei Liu Department of Preschool Education

Engineering geological conditions include the nature of rock and soil, geological structure, landform, hydrogeological conditions, and adverse geological processes. Among them, faults, fissures, folds, karst, and lithology changes seriously affect the safety and construction cost of mountain tunnels, hydraulic tunnels, and other projects. For this reason, a new method based on feature fusion is proposed to detect the geological anomalies in London and Sheffield. It established a 3D raster data model oriented to attribute information modeling and visualization of urban underground space to obtain geological data. Based on this acquired data, authors adopted the feature-level fusion extraction method based on the multi-attribute geological abnormal body to extract, fuse, fill and surface the multi-attribute data of underground space geological data. Smooth processing can realize the detection of abnormal geological bodies in underground space. It has been proved that this method can be used in geological data display, feature extraction, feature fusion, and abnormal physical examination.

Las condiciones de ingeniería geológica incluyen la naturaleza de rocas y suelo, estructura geológica, formas de la tierra, condiciones hidrogeológicas y procesos geológicos adversos. Entre estas condiciones las fallas, fisuras, plegamientos, kársticos y los cambios de la litología afectan seriamente la seguridad y los costos de producción de túneles en montañas, túneles hidráulicos y otros proyectos. Por esta razón, este trabajo propone un nuevo método basado en la fusión de características para detectar las anomalías geológicas en Londres y Sheffield, en el Reino Unido. Este método parte de un modelo de datos de trama 3D para realizar un modelado de información y visualización del espacio urbano subterráneo con el fin de obtener información geológica. Con base en esta información adquirida, los autores adoptaron el método de extraccción de fusión de características con base en los multiatributos geológicos de un cuerpo anormal para extraer, fusionar, llenar y normalizar la información multiatributos del espacio urbano subterráneo. En el proceso de suavizado se pueden detectar los cuerpos anormales en el espacio subterráneo. Los resultados prueban que este método puede usarse en la presentación de información geológica, en las características de extracción, características de fusión, y en la examinación física de los cuerpos.

References

Bai, C. Y., He, L. Y., Li, X. W., & Sun, J. Y. (2018). Simultaneous travel time tomography for updating both velocity and reflector geometry in triangular/tetrahedral cell model. Journal of Seismology, 22, 559-574. https://doi.org/10.1007/s10950-017-9722-9 DOI: https://doi.org/10.1007/s10950-017-9722-9

Cotterill, C. J., Phillips, E., James, L., Forsberg, C. F., Tjelta, T. I., Carter, G., & Dove, D. (2017). The evolution of the Dogger Bank, North Sea: A complex history of terrestrial, glacial and marine environmental change. Quaternary Science Reviews, 171, 136-153. https://doi.org/10.1016/j.quascirev.2017.07.006 DOI: https://doi.org/10.1016/j.quascirev.2017.07.006

Gao, L. (2018). Urban strip road landscape sharp feature extraction integrity optimization simulation. Computer Simulation, 7, 261-264.

Gao, R. M., Yang, W.J., Dai, Q. (2018). Characteristic polynomials of graphical arrangements corresponding to wheel graphs and join graphs. Journal of Jilin University (Science Edition), 56, 859-863.

Han, X., Zhang, D., Yan, J., Zhao, S., & Liu, J. (2020). Process development of flue gas desulphurization wastewater treatment in coal-fired power plants towards zero liquid discharge: Energetic, economic and environmental analyses. Journal of Cleaner Production, 261, 121144. https://doi.org/10.1016/j.jclepro.2020.121144 DOI: https://doi.org/10.1016/j.jclepro.2020.121144

Li, B., Luo, Z. X., Chen, J. Y., Cai, H. Q., & Pang, X. (2019). Application research of UAV photogrammetry in geological disaster monitoring of hydropower station reservoir area. Automation & Instrumentation, 5, 209-211+227.

Liu, X. L., Chang, J. C., & Liu, R. N. (2017). Synthesis method of initial noise samples of oil pump cavitation fault based on characteristic frequency analysis. Automation & Instrumentation, 12, 62-164.

Montanari, A., Farley, K., Claeys, P., De Vleeschouwer, D., de Winter, N., Vansteenberge, S., Sinnesael, M., & Koeberl, C. (2017). Stratigraphic record of the Asteroidal veritas breakup in the Tortonian Monte Dei Corvi section (Ancona, Italy). Geological Society of America Bulletin, 129, 9-10. https://doi.org/10.1130/B31476.1 DOI: https://doi.org/10.1130/B31476.1

Olson, H. N., Dilles, J. H., Kent, A. J. R., & Lang, J. R. (2017). Geochemistry of the Cretaceous Kaskanak Batholith and genesis of the Pebble porphyry Cu-Au-Mo deposit, Southwest Alaska. American Mineralogist, 102(8), 1597-1621. https://doi.org/10.2138/am-2017-6053 DOI: https://doi.org/10.2138/am-2017-6053

Pang, R., Xu, B., Kong, X., & Zou, D. (2018). Seismic fragility for high CFRDs based on deformation and damage index through incremental dynamic analysis. Soil Dynamics and Earthquake Engineering, 104, 432-436. https://doi.org/10.1016/j.soildyn.2017.11.017 DOI: https://doi.org/10.1016/j.soildyn.2017.11.017

Pang, R., Xu, B., Zhou, Y., Zhang, X., & Wang, X. (2020). Fragility analysis of high CFRDs subjected to mainshock-aftershock sequences based on plastic failure. Engineering Structures, 206, 110152. https://doi.org/10.1016/j.engstruct.2019.110152 DOI: https://doi.org/10.1016/j.engstruct.2019.110152

Peng, Y. W., Gu, X. X., Cheng, W. B., Zhao, X. B., Wang, G. N., Lu, L. T., Han, J. M. (2018). Metallogenesis of the Late Palaeozoic Axi-Tawuerbieke Au-Pb-Zn district in the Tulasu Basin, Western Tianshan, China: Constraints from geological characteristics and isotope geochemistry. Geological Journal, 53, 3030-3050. DOI: https://doi.org/10.1002/gj.3141

Ping, P. Z., Qiu, M. C., & Zhen, J. Z. (2017). Genesis and implications of the late Jurassic Hailesitai granites in the northern Greater Khingan Range: Evidence from zircon U-Pb dating and Hf isotope. Geological Magazine, 154, 963-982. DOI: https://doi.org/10.1017/S0016756816000534

Sheth, H., Vanderkluysen, L., Demonterova, E. I., Ivanov, A. V., Savatenkov, V. M. (2019). Geochemistry and 40Ar/39Ar geochronology of the Nandurbar-Dhule mafic dyke swarm: Dyke-sill-flow correlations and stratigraphic development across the Deccan flood basalt province. Geological Journal, 54, 157– 176. https://doi.org/10.1002/gj.3167 DOI: https://doi.org/10.1002/gj.3167

Shrestha, S., Larson, K. P., Guilmette, C. (2017). The P-T-t evolution of the exhumed Himalayan metamorphic core in the Likhu Khola region, East Central Nepal. Journal of Metamorphic Geology, 35, 663-693. DOI:10.1111/jmg.12250 DOI: https://doi.org/10.1111/jmg.12250

Singh, A. K., Nayak, R., Khogenkumar, S., Subramanyam, K. S. V., Thakur, S. S., Bikramaditya Singh, R. K., & Satyanarayanan, M. (2017). Genesis and tectonic implications of cumulate pyroxenites and tectonite peridotites from the Nagaland-Manipur ophiolites, Northeast India: Constraints from mineralogical and geochemical characteristics. Geological Journal, 52, 415-436. DOI: 10.1002/gj.2769. DOI: https://doi.org/10.1002/gj.2769

Sun, X., Liu, Z., Hu, Y., Zhang, L., & Zimmermann, R. (2018). Perceptual multi-channel visual feature fusion for scene categorization. Information Sciences, 429, 37-48. https://doi.org/10.1016/j.ins.2017.10.051 DOI: https://doi.org/10.1016/j.ins.2017.10.051

Wang, M., Zhang, D. Q., Cheng, Y. N., & Tan, S. K. (2019). Assessing performance of porous pavements and bioretention cells for stormwater management in response to probable climatic changes. Journal of Environmental Management, 243, 157-167. DOI: https://doi.org/10.1016/j.jenvman.2019.05.012

Wu, R., Calner, M., & Lehnert, O. (2017). Integrated conodont biostratigraphy and carbon isotope chemostratigraphy in the Lower-Middle Ordovician of southern Sweden reveals a complete record of the MDICE. Geological Magazine, 154, 334-353. https://doi.org/10.1017/S0016756816000017 DOI: https://doi.org/10.1017/S0016756816000017

Xu, B., Pang, R., & Zhou, Y. (2020). Verification of stochastic seismic analysis method and seismic performance evaluation based on multi-indices for high CFRDs. Engineering Geology, 264, 105412. https://doi.org/10.1016/j.enggeo.2019.105412 DOI: https://doi.org/10.1016/j.enggeo.2019.105412

Xu, X., & Chen, L. (2019). Projection of Long-Term Care Costs in China, 2020–2050: Based on the Bayesian Quantile Regression Method. Sustainability, 11(13), 3530. http://dx.doi.org/10.3390/su11133530 DOI: https://doi.org/10.3390/su11133530

Zhang, K., Wang, Q., Chao, L., Ye, J., Li, Z., Yu, Z., Yang, T., & Ju, Q. (2019). Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China. Journal of Hydrology, 574, 903-914. https://doi.org/10.1016/j.jhydrol.2019.04.087 DOI: https://doi.org/10.1016/j.jhydrol.2019.04.087

Zhu, Z. G., He, B. B., & Xue, R. (2018). Image feature recognition method for dangerous situation in intelligent substation. Chinese Journal of Power Sources, 42, 597-600.

How to Cite

APA

Liu, X. (2022). A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion. Earth Sciences Research Journal, 26(3), 255–262. https://doi.org/10.15446/esrj.v26n3.103605

ACM

[1]
Liu, X. 2022. A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion. Earth Sciences Research Journal. 26, 3 (Nov. 2022), 255–262. DOI:https://doi.org/10.15446/esrj.v26n3.103605.

ACS

(1)
Liu, X. A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion. Earth sci. res. j. 2022, 26, 255-262.

ABNT

LIU, X. A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion. Earth Sciences Research Journal, [S. l.], v. 26, n. 3, p. 255–262, 2022. DOI: 10.15446/esrj.v26n3.103605. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/103605. Acesso em: 7 mar. 2025.

Chicago

Liu, Xuemei. 2022. “A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion”. Earth Sciences Research Journal 26 (3):255-62. https://doi.org/10.15446/esrj.v26n3.103605.

Harvard

Liu, X. (2022) “A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion”, Earth Sciences Research Journal, 26(3), pp. 255–262. doi: 10.15446/esrj.v26n3.103605.

IEEE

[1]
X. Liu, “A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion”, Earth sci. res. j., vol. 26, no. 3, pp. 255–262, Nov. 2022.

MLA

Liu, X. “A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion”. Earth Sciences Research Journal, vol. 26, no. 3, Nov. 2022, pp. 255-62, doi:10.15446/esrj.v26n3.103605.

Turabian

Liu, Xuemei. “A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion”. Earth Sciences Research Journal 26, no. 3 (November 29, 2022): 255–262. Accessed March 7, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/103605.

Vancouver

1.
Liu X. A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion. Earth sci. res. j. [Internet]. 2022 Nov. 29 [cited 2025 Mar. 7];26(3):255-62. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/103605

Download Citation

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Earth Sciences Research Journal holds a Creative Commons Attribution license. 

You are free to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.