IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i18p13574-d1237478.html
   My bibliography  Save this article

Seismic Characterization of a Landslide Complex: A Case History from Majes, Peru

Author

Listed:
  • Jihyun Yang

    (Geophysics Department, Colorado School of Mines, Golden, CO 80401, USA
    Current address: Nvidia Corp., Austin, TX 78717, USA.)

  • Jeffrey Shragge

    (Geophysics Department, Colorado School of Mines, Golden, CO 80401, USA)

  • Aaron J. Girard

    (Geophysics Department, Colorado School of Mines, Golden, CO 80401, USA)

  • Edgard Gonzales

    (School of Geophysical Engineering, Universidad Nacional de San Augstín, Arequipa 04001, Peru)

  • Javier Ticona

    (School of Geophysical Engineering, Universidad Nacional de San Augstín, Arequipa 04001, Peru)

  • Armando Minaya

    (School of Geophysical Engineering, Universidad Nacional de San Augstín, Arequipa 04001, Peru)

  • Richard Krahenbuhl

    (Geophysics Department, Colorado School of Mines, Golden, CO 80401, USA)

Abstract

Seismic characterization of landslides offers the potential for developing high-resolution models on subsurface shear-wave velocity profile. However, seismic methods based on reflection processing are challenging to apply in such scenarios as a consequence of the disturbance to the often well-defined structural and stratigraphic layering by the landslide process itself. We evaluate the use of alternative seismic characterization methods based on elastic full waveform inversion (E-FWI) to probe the subsurface of a landslide complex in Majes, southern Peru, where recent agricultural development and irrigation activities have altered the hydrology and groundwater table and are thought to have contributed to increased regional landslide activities that present continuing sustainability community development challenges. We apply E-FWI to a 2D near-surface seismic data set for the purpose of better understanding the subsurface in the vicinity of a recent landslide location. We use seismic first-arrival travel-time tomography to generate the inputs required for E-FWI to generate the final high-resolution 2D compressional- and shear-wave (P- and S-wave) velocity models. At distances greater than 140 m from the cliff, the inverted models show a predominantly vertically stratified velocity structure with a low-velocity near-surface layer between 5–15 m depth. At distances closer than 140 m from the cliff, though, the models exhibit significantly reduced shear-wave velocities, stronger heterogeneity, and localized shorter wavelength structure in the top 20 m. These observations are consistent with those expected for a recent landslide complex; however, follow-on geotechnical analysis is required to confirm these assertions. Overall, the E-FWI seismic approach may be helpful for future landslide characterization projects and, when augmented with additional geophysical and geotechnical analyses, may allow for improved understanding of the hydrogeophysical properties associated with suspected ground-water-driven landslide activity.

Suggested Citation

  • Jihyun Yang & Jeffrey Shragge & Aaron J. Girard & Edgard Gonzales & Javier Ticona & Armando Minaya & Richard Krahenbuhl, 2023. "Seismic Characterization of a Landslide Complex: A Case History from Majes, Peru," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13574-:d:1237478
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/18/13574/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/18/13574/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hakan Tanyaş & Tolga Görüm & Dalia Kirschbaum & Luigi Lombardo, 2022. "Could road constructions be more hazardous than an earthquake in terms of mass movement?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 639-663, May.
    2. Yongwei Li & Xianmin Wang & Hang Mao, 2020. "Influence of human activity on landslide susceptibility development in the Three Gorges area," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(3), pages 2115-2151, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jie Liu & Zhen Wu & Huiwen Zhang, 2021. "Analysis of Changes in Landslide Susceptibility according to Land Use over 38 Years in Lixian County, China," Sustainability, MDPI, vol. 13(19), pages 1-23, September.
    2. Fanyu Zhang & Jianbing Peng & Xiaowei Huang & Hengxing Lan, 2021. "Hazard assessment and mitigation of non-seismically fatal landslides in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 785-804, March.
    3. Liying Sun & Bingjuan Ma & Liang Pei & Xiaohang Zhang & John L. Zhou, 2021. "The relationship of human activities and rainfall-induced landslide and debris flow hazards in Central China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 147-169, May.
    4. Jinming Zhang & Jianxi Qian & Yuefeng Lu & Xueyuan Li & Zhenqi Song, 2024. "Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    5. Siti Norsakinah Selamat & Nuriah Abd Majid & Aizat Mohd Taib, 2023. "A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-21, January.
    6. Xin Wei & Lulu Zhang & Junyao Luo & Dongsheng Liu, 2021. "A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 471-497, October.
    7. Hakan Tanyaş & Tolga Görüm & Dalia Kirschbaum & Luigi Lombardo, 2022. "Could road constructions be more hazardous than an earthquake in terms of mass movement?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 639-663, May.
    8. Haoran Fang & Yun Shao & Chou Xie & Bangsen Tian & Chaoyong Shen & Yu Zhu & Yihong Guo & Ying Yang & Guanwen Chen & Ming Zhang, 2023. "A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    9. Haishan Wang & Jian Xu & Shucheng Tan & Jinxuan Zhou, 2023. "Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example," Sustainability, MDPI, vol. 15(16), pages 1-17, August.
    10. Li Zhuo & Yupu Huang & Jing Zheng & Jingjing Cao & Donghu Guo, 2023. "Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance," Sustainability, MDPI, vol. 15(11), pages 1-23, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13574-:d:1237478. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.