Geophysical simulation of landslide model based on electrical resistivity and refraction seismic tomography through unstructured meshing

Articles in Press

Document Type : Research Paper

Authors

School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.

10.22059/ijmge.2024.370406.595136

Abstract

Mass movements of land, such as landslides, pose significant threats to human safety and infrastructure. This study focuses on advancing the understanding of landslide dynamics through the application of geophysical surveys, specifically Electrical Resistivity Tomography (ERT) and Seismic Refraction Tomography (SRT). Unstructured meshing, as a pivotal technique in geophysics simulation studies, provides flexibility in discretizing complex geological structures. This method allows for refined mesh elements where needed, optimizing computational resources. In the field of geophysics, unstructured meshing is particularly advantageous for accurately representing subsurface heterogeneities. This study employs pyGIMLi, a Geophysical Inversion and Modeling Python library. This Python programming library, though devoid of a GUI, offers a comprehensive suite of tools for geophysical data analysis and inversion. This library incorporates unstructured meshing capabilities. This feature enhances the accuracy of simulations, enabling researchers to model intricate geological formations with more precision. Using this library empowers users to seamlessly generate, manipulate, and analyze unstructured meshes, facilitating robust simulations and detailed investigations of subsurface properties in geophysics. In this study, we present a novel approach to simulate a 3-layered landslide using ERT and SRT, coupled with inverse modeling through utilizing the unstructured meshing of the inversion area. The synthetic model produced has a depth of study extending to 65 meters. The SRT model reveals a dense coverage in layer 2, providing crucial information about the subsurface characteristics. The utilization of ERT and SRT in tandem allows for a comprehensive understanding of the landslide structure, offering insights into detecting the slip surface of the landslide. The study's innovative methodology provides a robust framework for the analysis of complex geological scenarios. The results obtained from this simulation contribute to the broader knowledge of landslide dynamics and offer a valuable tool for assessing and mitigating landslide risks in similar geological settings.

Keywords

Main Subjects

References

[1]      S. Morelli, S. Utili, V. Pazzi, R. Castellanza, and X. Fan, ‘Landslides and Geophysical Investigations: Advantages and Limitations’, International Journal of Geophysics, vol. 2019. Hindawi Limited, 2019. doi: 10.1155/2019/8732830.
[2]      D. Jongmans and S. Garambois, ‘Geophysical investigation of landslides : a review’, vol. 178, no. 2, pp. 101–112, 2007, doi: 10.2113/gssgfbull.178.2.101ï.
[3]      J. S. Whiteley et al., ‘Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations’, Eng Geol, vol. 268, p. 105525, Apr. 2020, doi: 10.1016/J.ENGGEO.2020.105525.
[4]      S. Carpentier, M. Konz, R. Fischer, G. Anagnostopoulos, K. Meusburger, and K. Schoeck, ‘Geophysical imaging of shallow subsurface topography and its implication for shallow landslide susceptibility in the Urseren Valley, Switzerland’, J Appl Geophy, vol. 83, pp. 46–56, Aug. 2012, doi: 10.1016/J.JAPPGEO.2012.05.001.
[5]      P. Imani, A. Abd El-Raouf, and G. Tian, ‘Landslide investigation using seismic refraction tomography method: A review’, Annals of Geophysics, vol. 64, May 2021, doi: 10.4401/AG-8633.
[6]      G. Pappalardo, S. Imposa, M. S. Barbano, S. Grassi, and S. Mineo, ‘Study of landslides at the archaeological site of Abakainon necropolis (NE Sicily) by geomorphological and geophysical investigations’, Landslides, vol. 15, no. 7, pp. 1279–1297, Jul. 2018, doi: 10.1007/s10346-018-0951-y.
[7]      J. S. Whiteley et al., ‘Rapid characterisation of landslide heterogeneity using unsupervised classification of electrical resistivity and seismic refraction surveys’, Eng Geol, vol. 290, Sep. 2021, doi: 10.1016/j.enggeo.2021.106189.
[8]      S. Rezaei, I. Shooshpasha, and H. Rezaei, ‘Reconstruction of landslide model from ERT, geotechnical, and field data, Nargeschal landslide, Iran’, Bulletin of Engineering Geology and the Environment, vol. 78, no. 5, pp. 3223–3237, Jul. 2019, doi: 10.1007/s10064-018-1352-0.
[9]      M. Zainal, B. Munir, M. Yanis, and A. Muhni, ‘Characterization of Landslide geometry using Seismic Refraction Tomography in the GayoLues, Indonesia’, 2021. [Online]. Available: https://ejournal2.undip.ac.id/index.php/jpa/index
[10]    Q. U. Rehman, W. Ahmed, M. Waseem, S. Khan, A. Farid, and S. H. A. Shah, ‘Geophysical investigations of a potential landslide area in mayoon, hunza district, gilgit-baltistan, pakistan’, Rudarsko Geolosko Naftni Zbornik, vol. 36, no. 3, pp. 127–141, 2021, doi: 10.17794/rgn.2021.3.9.
[11]    J. Yang et al., ‘Seismic Characterization of a Landslide Complex: A Case History from Majes, Peru’, Sustainability (Switzerland), vol. 15, no. 18, Sep. 2023, doi: 10.3390/su151813574.
[12]    K. Damavandi, M. Abedi, G. H. Norouzi, and M. Mojarab, ‘Geoelectrical modelling of a landslide surface through an unstructured mesh’, Bulletin of Geophysics and Oceanography, vol. 63, no. 2, pp. 337–356, Jun. 2022, doi: 10.4430/bgo00384.
[13]    Q. Chen, S. Zhang, S. Chang, B. Liu, J. Liu, and J. Long, ‘Geophysical Interpretation of a Subsurface Landslide in the Southern Qinshui Basin’, J Environ Eng Geophys, vol. 24, no. 3, pp. 433–449, Sep. 2019, doi: 10.2113/JEEG24.3.433.
[14]    B. Pasierb, M. Grodecki, and R. Gwóźdź, ‘Geophysical and geotechnical approach to a landslide stability assessment: a case study’, Acta Geophysica, vol. 67, no. 6, pp. 1823–1834, Dec. 2019, doi: 10.1007/s11600-019-00338-7.
[15]    M. Himi et al., ‘Application of Resistivity and Seismic Refraction Tomography for Landslide Stability Assessment in Vallcebre, Spanish Pyrenees’, Remote Sens (Basel), vol. 14, no. 24, Dec. 2022, doi: 10.3390/rs14246333.
[16]    D. Huntley, P. Bobrowsky, M. Hendry, R. Macciotta, and M. Best, ‘Multi-technique Geophysical Investigation of a Very Slow-moving Landslide near Ashcroft, British Columbia, Canada’, J Environ Eng Geophys, vol. 24, no. 1, pp. 87–110, Mar. 2019, doi: 10.2113/JEEG24.1.87.
[17]    M. Wróbel et al., ‘Integrated Geophysical Imaging and Remote Sensing for Enhancing Geological Interpretation of Landslides with Uncertainty Estimation—A Case Study from Cisiec, Poland’, Remote Sens (Basel), vol. 15, no. 1, Jan. 2023, doi: 10.3390/rs15010238.
[18]    V. Pupatenko, Y. Sukhobok, G. Stoyanovich, A. Stetsyuk, and L. Verkhovtsev, ‘GPR data interpretation in the landslides and subgrade slope surveys’, MATEC Web of Conferences, vol. 265, p. 03003, 2019, doi: 10.1051/matecconf/201926503003.
[19]    G. Zhang, F. Tu, Y. Tang, X. Chen, K. Xie, and S. Dai, ‘Application of geophysical prospecting methods ERT and MASW in the landslide of Daofu County, China’, Front Earth Sci (Lausanne), vol. 10, Jan. 2023, doi: 10.3389/feart.2022.1054394.
[20]    A. Hojat et al., ‘Geoelectrical characterization and monitoring of slopes on a rainfall-triggered landslide simulator’, J Appl Geophy, vol. 170, Nov. 2019, doi: 10.1016/j.jappgeo.2019.103844.
[21]    Y. Hussain et al., ‘Review on the Geophysical and UAV-Based Methods Applied to Landslides’, Remote Sensing, vol. 14, no. 18. MDPI, Sep. 01, 2022. doi: 10.3390/rs14184564.
[22]    Adella Syavira, Y Yatini, and Wrego Seno Giamboro, ‘Identification of landslide potential based on Ground Penetrating Radar (GPR) data in Prambanan District, Sleman, Yogyakarta’, Global Journal of Engineering and Technology Advances, vol. 13, no. 2, pp. 071–078, Nov. 2022, doi: 10.30574/gjeta.2022.13.2.0193.
[23]    G. Blanchy, S. Saneiyan, J. Boyd, P. McLachlan, and A. Binley, ‘ResIPy, an intuitive open source software for complex geoelectrical inversion/modeling’, Comput Geosci, vol. 137, p. 104423, Apr. 2020, doi: 10.1016/J.CAGEO.2020.104423.
[24]    C. Rücker, T. Günther, and F. M. Wagner, ‘pyGIMLi: An open-source library for modelling and inversion in geophysics’, Comput Geosci, vol. 109, pp. 106–123, Dec. 2017, doi: 10.1016/j.cageo.2017.07.011.
[25]    R. Rochlitz, M. Becken, and T. Günther, ‘Three-dimensional inversion of semi-airborne electromagnetic data with a second-order finite-element forward solver’, Geophys J Int, vol. 234, no. 1, pp. 528–545, Jul. 2023, doi: 10.1093/gji/ggad056.
[26]    M. Steiner and A. Flores Orozco, ‘formikoj: A flexible library for data management and processing in geophysics—Application for seismic refraction data’, Comput Geosci, vol. 176, p. 105339, Jul. 2023, doi: 10.1016/J.CAGEO.2023.105339.
[27]    R. Cockett, S. Kang, L. J. Heagy, A. Pidlisecky, and D. W. Oldenburg, ‘SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications’, Comput Geosci, vol. 85, pp. 142–154, Dec. 2015, doi: 10.1016/J.CAGEO.2015.09.015.
[28]    V. J. C. B. Guedes, S. T. R. Maciel, and M. P. Rocha, ‘Refrapy: A Python program for seismic refraction data analysis’, Comput Geosci, vol. 159, Feb. 2022, doi: 10.1016/j.cageo.2021.105020.
[29]    Douglas W. Oldenburg and Yaoguo Li, ‘Fundamentals of Inversion Near-Surface Geophysics Part 1: Concepts and Fundamentals’, 2005. [Online]. Available: http://library.seg.org/
[30]    T. Chen and G. Zhang, ‘Forward modeling of gravity anomalies based on cell mergence and parallel computing’, Comput Geosci, vol. 120, pp. 1–9, Nov. 2018, doi: 10.1016/j.cageo.2018.07.007.
[31]    T. M. Hansen, K. S. Cordua, B. H. Jacobsen, and K. Mosegaard, ‘Accounting for imperfect forward modeling in geophysical inverse problems - Exemplified for crosshole tomography’, Geophysics, vol. 79, no. 3, pp. H1–H21, Mar. 2014, doi: 10.1190/GEO2013-0215.1.
[32]    S. L. Butler and G. Sinha, ‘Forward modeling of applied geophysics methods using Comsol and comparison with analytical and laboratory analog models’, Comput Geosci, vol. 42, pp. 168–176, May 2012, doi: 10.1016/j.cageo.2011.08.022.
[33]    V. Pereyra, H. B. Keller, and W. H. K. Lee, ‘COMPUTATIONAL METHODS FOR INVERSE PROBLEMS IN GEOPHYSICS: INVERSION OF TRAVEL TIME OBSERVATIONS’, 1980.
[34]    O. Portniaguine and M. S. Zhdanov, ‘Focusing geophysical inversion images’, 1999. [Online]. Available: http://library.seg.org/
[35]    M. Giudici et al., ‘A conceptual framework for discrete inverse problems in geophysics’, Jan. 2019, [Online]. Available: http://arxiv.org/abs/1901.07937
[36]    Mitsuhiro MATSU’URA, ‘Development of Inversion Theory in Geophysics’, 1991.
[37]    A. Tamburrino and G. Rubinacci, ‘A new non-iterative inversion method for electrical resistance tomography’, Inverse Probl, vol. 18, no. 6, pp. 1809–1829, Dec. 2002, doi: 10.1088/0266-5611/18/6/323.
[38]    M. Fallah Safari and R. Ghanati, ‘DC Electrical Resistance Tomography Inversion’, Journal of the Earth and Space Physics, vol. 47, no. 4, pp. 87–98, Dec. 2022, doi: 10.22059/JESPHYS.2021.323911.1007321.
[39]    M. Cozzolino, P. Mauriello, and D. Patella, ‘The Extended Data-Adaptive Probability-Based Electrical Resistivity Tomography Inversion Method (E-PERTI) for the Characterization of the Buried Ditch of the Ancient Egnazia (Puglia, Italy)’, Applied Sciences (Switzerland), vol. 12, no. 5, Mar. 2022, doi: 10.3390/app12052690.
[40]    D. J. LaBrecque and D. Casale, ‘Experience With Anisotropic Inversion For Electrical Resistivity Tomography’, in 15th EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems, European Association of Geoscientists & Engineers, Feb. 2002, p. cp-191-00010. doi: 10.3997/2214-4609-pdb.191.11ELE6.
[41]    R. Poormirzaee, R. H. Moghadam, and A. Zarean, ‘Inversion seismic refraction data using particle swarm optimization: a case study of Tabriz, Iran’, Arabian Journal of Geosciences, vol. 8, no. 8, pp. 5981–5989, Aug. 2015, doi: 10.1007/s12517-014-1662-x.
[42]    D. Mooney and W. M. Kohler, ‘Inversion of seismic refraction data in planar dipping structure’, 1985. [Online]. Available: https://academic.oup.com/gji/article/82/1/81/551363
[43]    D. J. White, ‘Two‐Dimensional Seismic Refraction Tomography’, Geophys J Int, vol. 97, no. 2, pp. 223–245, 1989, doi: 10.1111/j.1365-246X.1989.tb00498.x.
[44]    P. Mora, ‘Inversion-migration-t-tomography’, 1988.
[45]    J. W. D. Hobro, S. C. Singh, and T. A. Minshull, ‘Three-dimensional tomographic inversion of combined reflection and refraction seismic traveltime data’, 2003.
[46]    A. Alimoradi and A. Moradzadeh, ‘Reservoir Porosity Determination from 3D Seismic Data-Application of Two Machine Learning Techniques’, 2012. [Online]. Available: https://www.researchgate.net/publication/258871568
[47]    A. Alimoradi, A. Moradzadeh, and M. R. Bakhtiari, ‘Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data’, 2013.
[48]    M. S. Jeshvaghani and M. Darijani, ‘Two-dimensional geomagnetic forward modeling using adaptive finite element method and investigation of the topographic effect’, J Appl Geophy, vol. 105, pp. 169–179, 2014, doi: 10.1016/j.jappgeo.2014.03.016.
[49]    A. Binley, ‘Tools and Techniques: Electrical Methods’, Treatise on Geophysics: Second Edition, vol. 11, pp. 233–259, Jan. 2015, doi: 10.1016/B978-0-444-53802-4.00192-5.
[50]    K. Damavandi, M. Abedi, G. H. Norouzi, and M. Mojarab, ‘Geoelectrical modelling of a landslide surface through an unstructured mesh’, Bulletin of Geophysics and Oceanography, vol. 63, no. 2, pp. 337–356, Jun. 2022, doi: 10.4430/bgo00384.
[51]    J. Milsom and A. Eriksen, ‘Field Geophysics, Fourth Edition’, Environmental & Engineering Geoscience, vol. 19, no. 2, pp. 205–206, May 2013, doi: 10.2113/gseegeosci.19.2.205.
[52]    T. Günther, C. Rücker, and K. Spitzer, ‘Three-dimensional modelling and inversion of dc resistivity data incorporating topography - II. Inversion’, Geophys J Int, vol. 166, no. 2, pp. 506–517, Aug. 2006, doi: 10.1111/j.1365-246X.2006.03011.x.
[53]    V. der, G. Klaus Spitzer, B. Freiberg aD Peter Weidelt, and T. A. Braunschweig Maxwell Meju, ‘Inversion Methods and Resolution Analysis for the 2D/3D Reconstruction of Resistivity Structures from DC Measurements’, 2004.
International Journal of Mining and Geo-Engineering

Articles in Press, Accepted Manuscript
Available Online from 12 February 2024

Files

Share

How to cite

Statistics