A combination of seismic refraction and ambient noise methods to detect landslide-prone materials
Contenido principal del artículo
Resumen
Una porción del poniente de la Ciudad de México está densamente poblada en una topografía abrupta. Los materiales en esa zona son volcano-sedimentarios, los cuales, debido a procesos naturales y antropogénicos, aumentan la probabilidad de deslizamientos de tierra. En este estudio explotamos la geometría de un arreglo cuadrangular de geófonos mediante los métodos de Tomografía de Refracción Sísmica (TRS) y Tomografía de Ruido Ambiental (TRA) para explorar la extensión de los materiales propensos a deslizamientos de tierra. Los resultados muestran áreas de baja velocidad (Vs < 100 m/s) asociadas con materiales que han perdido su resistencia debido al aumento de la presión de poro, y áreas donde eventualmente ocurrirán más deslizamientos de tierra (120 < Vs < 200 m/s) si no se realizan trabajos de mitigación. Las zonas más estables corresponden a materiales con valores de velocidad superiores a 250 m/s que sobreyacen a un subestrato irregular con profundidad media de 8 m. Por lo tanto, cuando no es aconsejable realizar experimentos de fuente activa, TRA puede proporcionar resultados prácticos para determinar la extensión de los materiales propensos a deslizamiento.
Publication Facts
Reviewer profiles N/D
Author statements
- Editor & editorial board
- profiles
- Academic society
- Geofísica Internacional
Para obtener más información sobre un dato, haga clic
PF is maintained by the Public Knowledge Project
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Citas
Advanced Geosciences, Inc. (2014). Instruction manual for EarthImager 2D, version 1.9.0, Resistivity and IP inversion software: Advanced Geosciences, Inc.,134 p., accessed at http://www.agiusa.com/
Alimohammadlou, Y., Najafi, A., & Yalcin, A. (2013). Landslide process and impacts: A proposed classification method. Catena, 104, 219-232. doi: https://doi.org/10.1016/j.catena.2012.11.013
Arce, J. L., Paul, W. L., Luis Macías, J., Morales-Casique, E., García-Palomo, A., Jiménez-Domínguez, F. J., Benowitz, J., & Vásquez-Serrano, A. (2019). Geology and Stratigraphy of the Mexico Basin (Mexico City) Central Trans-Mexican Volcanic Belt. Journal of Maps 15(2), 320–32. doi: https://doi.org/10.1080/17445647.2019.1593251
Bakulin, A., & Calvert, R. (2006). The virtual source method: Theory and case study. Geophysics, 71(4), SI139-SI150. doi: https://doi.org/10.1190/1.2216190
Bensen, G. D., Ritzwoller, M. H., Barmin, M. P., Levshin, A. L., Lin, F., Moschetti, M. P., Shapiro, N. M., & Yang, Y. (2007). Processing Seismic Ambient Noise Data to Obtain Reliable Broad-Band Surface Wave Dispersion Measurements. Geophysical Journal International 169(3), 1239–60. doi: https://doi.org/10.1111/j.1365-246x.2007.03374.x
Breton, M. L., Bontemps, N., Guillemot, A., Baillet, L., & Larose, E. (2021). Landslide Monitoring Using Seismic Ambient Noise Correlation: Challenges and Applications. Earth-Science Reviews 216 (May): 103518. doi: https://doi.org/10.1016/j.earscirev.2021.103518
Campillo, M., & Paul, A. (2003). Long-Range Correlations in the Diffuse Seismic Coda. Science 299 (5606), 547–49. doi: https://doi.org/10.1126/science.1078551
Cárdenas-Soto, M., Ramos-Saldaña, H., & Vidal-García, M. C. (2016). Interferometría de ruido sísmico para la caracterización de la estructura de velocidad 3D de un talud en la 3ª Sección del Bosque de Chapultepec, Ciudad de México. Boletín de la Sociedad Geológica Mexicana, 68(2), 173-186. doi: https://doi.org/10.18268/bsgm2016v68n2a1
Cárdenas-Soto, M., Piña-Flores, J., Escobedo-Zenil, D., Sánchez-González, J., & Martínez-González, J. A. (2021a). Ambient seismic noise tomography to build up a 3D shear-wave velocity model. Ingeniería, investigación y tecnología, 22(2), 1-9. doi: https://doi.org/10.22201/fi.25940732e.2021.22.2.009
Cárdenas-Soto, M., Piña-Flores, J., Escobedo-Zenil, D., Vidal-Garcia, M. C., Natarajan, T., Hussain, Y., and Sánchez-Sesma F. J. (2021b). Seismic Ambient Noise Tomography to Retrieve near-Surface Properties in Soils with Significant 3D Lateral Heterogeneity: The Case of Quinta Colorada Building in Chapultepec Mexico. Natural Hazards 108(1), 129–45. doi: https://doi.org/10.1007/s11069-021-04735-4
Cárdenas-Soto, M., Gámez Lindoro, J. A., Peña Gaspar, V., Aguirre Díaz, J. P., & García Serrano, A. (2022). A Pseudo 3D Seismic Refraction Tomography for Exploring Archaeological Structures. Ingeniería Investigación y Tecnología 23(1), 1–9. doi: https://doi.org/10.22201/fi.25940732e.2022.23.1.003
Chávez-García, F. J., Natarajan, T., Cárdenas-Soto, M., & Rajendran, K. (2021). Landslide characterization using active and passive seismic imaging techniques: a case study from Kerala, India. Natural Hazards, 105, 1623-1642. doi: https://doi.org/10.1007/s11069-020-04369-y
Del Gaudio, V., Wasowski, J., & Muscillo, S. (2013). New developments in ambient noise analysis to characterise the seismic response of landslide-prone slopes. Natural hazards and earth system sciences, 13(8), 2075-2087. doi: https://doi.org/10.5194/nhess-13-2075-2013
Everett, Mark E. (2013). Near-Surface Applied Geophysics. Cambridge University Press. doi: https://doi.org/10.1017/CBO9781139088435
Gabàs, A., Macau, A., Benjumea, B., Bellmunt, F., Figueras, S., & Vilà, M. (2014). Combination of geophysical methods to support urban geological mapping. Surveys in Geophysics, 35, 983-1002. doi: https://doi.org/10.1007/s10712-013-9248-9
Giroux, B., & Larouche, B. (2013). Task-parallel implementation of 3D shortest path raytracing for geophysical applications. Computers & geosciences, 54, 130-141. doi: https://doi.org/10.1016/j.cageo.2012.12.005
Guedes, V. J. C. B., Maciel, S. T. R., & Rocha, M. P. (2022). Refrapy: A Python program for seismic refraction data analysis. Computers & Geosciences, 159, 105020. doi: https://doi.org/10.1016/j.cageo.2021.105020
Günther, T., Rücker, C., & Spitzer, K. (2006). Three-dimensional modelling and inversion of DC resistivity data incorporating topography—II. Inversion. Geophysical Journal International, 166(2), 506-517. doi: https://doi.org/10.1111/j.1365-246X.2006.03011.x
Harba, P., Pilecki, Z., & Krawiec, K. (2019). Comparison of MASW and seismic interferometry with use of ambient noise for estimation of S-wave velocity field in landslide subsurface. Acta Geophysica, 67(6), 1875-1883. doi: https://doi.org/10.1007/s11600-019-00344-9
Herrmann, R. B. (2013). Computer programs in seismology: An evolving tool for instruction and research. Seismological Research Letters, 84(6), 1081-1088. doi: https://doi.org/10.1785/0220110096
Jongmans, D., & Garambois, S. (2007). Geophysical investigation of landslides: a review. Bulletin de la Société Géologique de France, 178(2), 101-112. doi: https://doi.org/10.2113/gssgfbull.178.2.101
Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L., Guéguen, P., & Massey, C. (2015). Environmental seismology: What can we learn on earth surface processes with ambient noise? Journal of Applied Geophysics, 116, 62-74. doi: https://doi.org/10.1016/j.jappgeo.2015.02.001
Loke, M. H. (2004). Tutorial: 2-D and 3-D electrical imaging surveys.
Mainsant, G., Larose, E., Brönnimann, C., Jongmans, D., Michoud, C., & Jaboyedoff, M. (2012). Ambient seismic noise monitoring of a clay landslide: Toward failure prediction. Journal of Geophysical Research: Earth Surface, 117(F1). doi: https://doi.org/10.1029/2011jf002159
Martínez, J., & Qi, L. (1995). Inexact Newton methods for solving nonsmooth equations. Journal of Computational and Applied Mathematics, 60(1-2), 127-145. doi: https://doi.org/10.1016/0377-0427(94)00088-I
Moser, T. J. (1991). Shortest path calculation of seismic rays. Geophysics, 56(1), 59-67. doi: https://doi.org/10.1190/1.1442958
Nakata, N., Gualtieri, L., & Fichtner, A. (Eds.). (2019). Seismic ambient noise. Cambridge University Press. doi: https://doi.org/10.1017/9781108264808
Perrone, A., Lapenna, V., & Piscitelli, S. (2014). Electrical resistivity tomography technique for landslide investigation: A review. Earth-Science Reviews, 135, 65-82. doi: https://doi.org/10.1016/j.earscirev.2014.04.002
Pilz, M., Parolai, S., Bindi, D., Saponaro, A., & Abdybachaev, U. (2014). Combining seismic noise techniques for landslide characterization. Pure and Applied Geophysics, 171, 1729-1745. doi: https://doi.org/10.1007/s00024-013-0733-3
Renalier, F., Jongmans, D., Campillo, M., & Bard, P. Y. (2010). Shear wave velocity imaging of the Avignonet landslide (France) using ambient noise cross correlation. Journal of Geophysical Research: Earth Surface, 115(F3). doi: https://doi.org/10.1029/2009jf001538
Ritzwoller, M. H. (2009). Ambient noise seismic imaging. Recent Development in World Seismology, 9(31), 315-328. doi: https://doi.org/10.1036/1097-8542.YB090130
Romero‐Ruiz, A., Linde, N., Keller, T., & Or, D. (2018). A review of geophysical methods for soil structure characterization. Reviews of Geophysics, 56(4), 672-697. doi: https://doi.org/10.1029/2018rg000611
Rücker, C., Günther, T., & Wagner, F. M. (2017). pyGIMLi: An open-source library for modelling and inversion in geophysics. Computers & Geosciences, 109, 106-123. doi: https://doi.org/10.1016/j.cageo.2017.07.011
Schuster, G. T. (2014). Seismic interferometry. In Encyclopedia of exploration geophysics (pp. Q1-1). Society of Exploration Geophysicists. doi: https://doi.org/10.1190/1.9781560803027
Shapiro, N. M., & Campillo, M. (2004). Emergence of broadband Rayleigh waves from correlations of the ambient seismic noise. Geophysical Research Letters, 31(7). doi: https://doi.org/10.1029/2004gl019491
Stefani, J. P. (1995). Turning-ray tomography. Geophysics, 60(6), 1917-1929. doi: https://doi.org/10.1190/1.1443923
Telford, W. M., Geldart, L. P., & Sheriff, R. E. (1990). Applied Geophysics Cambridge University Press Cambridge. United Kingdom. doi: https://doi.org/10.1017/cbo9781139167932
Tsai, V. C. (2011). Understanding the amplitudes of noise correlation measurements. Journal of Geophysical Research: Solid Earth, 116(B9). doi: https://doi.org/10.1029/2011jb008483
Uhlemann, S., Hagedorn, S., Dashwood, B., Maurer, H., Gunn, D., Dijkstra, T., & Chambers, J. (2016). Landslide characterization using P-and S-wave seismic refraction tomography—The importance of elastic moduli. Journal of Applied Geophysics, 134, 64-76. doi: https://doi.org/10.1016/j.jappgeo.2016.08.014
Varnes, D. J. (1958). Landslide types and processes: Landslides and Engineering Practice by the Committee on Landslide Investigations: in Eckel. Highway Research Board Special Report, 29.
Zainal, M., Munir, B., Marwan, M., Yanis, M., & Muhni, A. (2021). Characterization of Landslide geometry using Seismic Refraction Tomography in the GayoLues, Indonesia. Journal of Physics and Its Applications, 3(2), 148-154. doi: https://doi.org/10.14710/jpa.v3i2.10601