Politics

Intelligent assessment of building damage of 2023 Turkey-Syria Earthquake by multiple remote sensing approaches – npj Natural Hazards


  • Türkoğlu, E., Unsworth, M., Bulut, F. & Çağlar, I. Crustal structure of the North Anatolian and East Anatolian Fault Systems from magnetotelluric data. Phys. Earth Planet. Inter. 241, 1–14 (2015).

    Article 
    ADS 

    Google Scholar 

  • Alpyurur, M. & Lav, M. A. An assessment of probabilistic seismic hazard for the cities in Southwest Turkey using historical and instrumental earthquake catalogs. Nat. Hazards 114, 335–365 (2022).

    Article 

    Google Scholar 

  • Nalbant, S. S., McCloskey, J., Steacy, S. & Barka, A. A. Stress accumulation and increased seismic risk in eastern Turkey. Earth Planet. Sci. Lett. 195, 291–298 (2002).

    Article 
    CAS 
    ADS 

    Google Scholar 

  • Faccenna, C., Bellier, O., Martinod, J. & Piromallo, C. & Regard, V. Slab detachment beneath eastern Anatolia: A possible cause for the formation of the North Anatolian fault. Earth Planet. Sci. Lett. 242, 85–97 (2006).

    Article 
    CAS 
    ADS 

    Google Scholar 

  • Emre, Ö. et al. Active fault database of Turkey. Bull. Earthq. Eng. 16, 3229–3275 (2018).

    Article 

    Google Scholar 

  • The World Bank. Earthquake Damage in Türkiye Estimated to Exceed $34 billion: World Bank Disaster Assessment Report. The World Bank (2023).

  • USGS Geologic Hazards Science Center and Collaborators. The 2023 Kahramanmaraş, Turkey, Earthquake Sequence. (2023).

  • Melgar, D. et al. Sub- and super-shear ruptures during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in SE Türkiye. Seismica. 2 (2023).

  • Jiang, X., Song, X., Li, T. & Wu, K. Moment magnitudes of two large Turkish earthquakes on February 6, 2023 from long-period coda. Earthq. Science. 36, 169–174 (2023).

    Google Scholar 

  • Shalal, A. World Bank estimates Feb. 6 earthquakes caused $34.2 bln in damage in Turkey. Reuters (2023).

  • Toksabay, E. & Butler, D. Turkey widens probe into building collapses as quake toll exceeds 50,000. Reuters (2023).

  • Leyendecker, E. V., Perkins, D. M., Algermissen, S. T., Thenhaus, P. C., & Hanson, S. L. USGS spectral response maps and their relationship with seismic design forces in building codes. (Open-File Report 95–596; Online only, Version 1.0) (1995).

  • Yang, S. et al. Analysis on public earthquake risk perception: based on questionnaire. In 3rd International Conference on Cartography and GIS (2010).

  • Xu, Q., Zhang, S. & Li, W. Spatial distribution of large-scale landslides induced by the 5.12 Wenchuan Earthquake. J. Mt. Sci. 8, 246–260 (2011).

    Article 

    Google Scholar 

  • Yuan, Y., Zomorodian, S., Hashim, M. & Lu, Y. Devastating earthquakes facilitating civil societies in developing countries: across-national analysis. Environ. Hazards 17, 352–370 (2018).

    Article 

    Google Scholar 

  • Fan, X. et al. Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts. Rev. Geophys. 57, 421–503 (2019).

    Article 
    ADS 

    Google Scholar 

  • Dell’Acqua, F. & Gamba, P. Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives. Proc. IEEE 100, 2876–2890 (2012).

    Article 

    Google Scholar 

  • Geiß, C. & Taubenböck, H. Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap. Nat. Hazards 68, 7–48 (2013).

    Article 

    Google Scholar 

  • Joshi, G., Natsuaki, R. & Hirose, A. Neural-Network Fusion Processing and Inverse Mapping to Combine Multi-Sensor Satellite Data and Analyze the Prominent Features. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 2819–2840 (2023).

    Article 
    ADS 

    Google Scholar 

  • Xiong, C., Li, Q. & Lu, X. Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network. Autom. Constr. 109, 102994 (2020).

    Article 

    Google Scholar 

  • Janalipour, M. & Mohammadzadeh, A. A novel and automatic framework for producing building damage map using post-event LiDAR data. Int. J. Disaster Risk Reduct. 39, 101238 (2019).

    Article 

    Google Scholar 

  • Polcari, M. et al. Using multi-band InSAR data for detecting local deformation phenomena induced by the 2016–2017 Central Italy seismic sequence. Remote Sens. Environ. 201, 234–242 (2017).

    Article 
    ADS 

    Google Scholar 

  • Stramondo, S., Bignami, C., Chini, M., Pierdicca, N. & Tertulliani, A. Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies. Int. J. Remote Sens. 27, 4433–4447 (2006).

    Article 

    Google Scholar 

  • Batool, S., Frezza, F., Mangini, F. & Simeoni, P. Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review. Atmosphere 11, 517 (2020).

    Article 
    ADS 

    Google Scholar 

  • Zhou, C. et al. Enhanced dynamic landslide hazard assessment using MT-InSAR method in the Three Gorges Reservoir Area. Landslides 19, 1585–1597 (2022).

    Article 

    Google Scholar 

  • He, L. et al. Coseismic kinematics of the 2023 Kahramanmaras, Turkey earthquake sequence from INSAR and optical data. Geophys. Res. Lett. 50, e2023GL104693 (2023).

    Article 
    ADS 

    Google Scholar 

  • Xu, X. et al. Surface deformation associated with fractures near the 2019 Ridgecrest earthquake sequence. Science 370, 605–608 (2020).

    Article 
    MathSciNet 
    CAS 
    PubMed 

    Google Scholar 

  • Song, C. et al. Triggering and recovery of earthquake accelerated landslides in Central Italy revealed by satellite radar observations. Nat. Commun. 13, 7278 (2022).

    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 

  • Arciniegas, G., Bijker, W., Kerle, N. & Tolpekin, V. A. Coherence- and Amplitude-Based Analysis of Seismogenic Damage in Bam, Iran, Using ENVISAT ASAR Data. IEEE Trans. Geosci. Remote Sens. 45, 1571–1581 (2007).

    Article 
    ADS 

    Google Scholar 

  • Yun, S. H. et al. Rapid Damage Mapping for the 2015 Mw 7.8 Gorkha Earthquake Using Synthetic Aperture Radar Data from COSMO–SkyMed and ALOS-2 Satellites. Seismol. Res. Lett. 86, 1549–1556 (2015).

    Article 

    Google Scholar 

  • Xu, S., Dimasaka, J., Wald, D. J. & Noh, H. Y. Seismic multi-hazard and impact estimation via causal inference from satellite imagery. Nat. Commun. 13, 7793 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 

  • Yu, X., Hu, X., Wang, G., Wang, K. & Chen, X. Machine‐Learning Estimation of Snow Depth in 2021 Texas Statewide Winter Storm Using SAR Imagery. Geophys. Res. Lett. 49, e2022GL099119 (2022).

    Article 
    ADS 

    Google Scholar 

  • Hu, X., Bürgmann, R., Fielding, E. J., Xu, X. & Zhen, L. Machine-learning characterization of tectonic, hydrological and anthropogenic sources of ground deformation in California. J. Geophys. Res. Solid Earth 126, e2021JB022373 (2021).

    Article 
    ADS 

    Google Scholar 

  • Madadi, M. R., Azamathulla, H. M. & Yakhkeshi, M. Application of Google earth to investigate the change of flood inundation area due to flood detention dam. Earth Sci. Inform 8, 627–638 (2015).

    Article 

    Google Scholar 

  • Ahn, D. et al. A human-machine collaborative approach measures economic development using satellite imagery. Nat Commun 14, 6811 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 

  • Wang, C. et al. Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery. Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS, 7–12 (2023).

  • Yu, X., Wang, G., Hu, X., Liu, Y. & Bao, Y. Land subsidence in Tianjin, China: Before and after the South-to-North Water Diversion. Remote Sens. 15, 1647 (2023).

    Article 
    ADS 

    Google Scholar 

  • Robinson, C. et al. Turkey Building Damage Assessment. Microsoft (2023).

  • Li, X. et al. DisasterNet: Causal Bayesian Networks with normalizing flows for cascading hazards estimation fromsatellite imagery. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4391–4403 (2023).

  • Xu, S., Dimasaka, J., Wald. D. J., & Noh, H. Y. Seismic multi-hazard and impact estimation via causal inference from satellite imagery. Nat. Commun. 13, 7793 (2022).

  • Douglas, J. Earthquake ground motion estimation using strong-motion records: a review of equations for the estimation of peak ground acceleration and response spectral ordinates. Earth-Sci. Rev. 61, 43–104 (2003).

    Article 
    ADS 

    Google Scholar 

  • Arjun, C. & Kumar, A. Artificial neural network-based estimation of peak ground acceleration. ISET J. Earthq. Technol. 501, 46 (2009).

    Google Scholar 

  • Montilla, J. A. P., Hamdache, M. & Casado, C. L. Seismic hazard in Northern Algeria using spatially smoothed seismicity. Results for peak ground acceleration. Tectonophysics 372, 105–119 (2003).

    Article 
    ADS 

    Google Scholar 

  • Barnhart, W. D., Brengman, C. M. G., Li, S. & Peterson, K. E. Ramp-flat basement structures of the Zagros Mountains inferred from co-seismic slip and afterslip of the 2017 Mw7.3 Darbandikhan, Iran/Iraq earthquake. Earth Planet. Sci. Lett. 496, 96–107 (2018).

    Article 
    CAS 
    ADS 

    Google Scholar 

  • Hason, M. M., Hanoon, A. N. & Abdulhameed, A. A. Particle swarm optimization technique based prediction of peak ground acceleration of Iraq’s tectonic regions. J. King Saud Univ. Eng. Sci. 35, 463–473 (2021).

    Google Scholar 

  • Wirth, E. A., Grant, A., Marafi, N. A. & Frankel, A. E. Ensemble ShakeMaps for Magnitude 9 Earthquakes on the Cascadia Subduction Zone. Seismol. Res. Lett. 92, 199–211 (2021).

    Article 

    Google Scholar 

  • Frankel, A. E., Wirth, E. A., Marafi, N. A., Vidale, J. E. & Stephenson, W. J. Broadband Synthetic Seismograms for Magnitude 9 Earthquakes on the Cascadia Megathrust Based on 3D Simulations and Stochastic Synthetics, Part 1: Methodology and Overall Results. Bull. Seismol. Soc. Am. 108, 2347–2369 (2018).

    Article 

    Google Scholar 

  • Athanasopoulos, G., Pelekis, P. C. & Leonidou, E. Effects of surface topography on seismic ground response in the Egion (Greece) 15 June 1995 earthquake. Soil Dyn. Earthq. Eng. 18, 135–149 (1999).

    Article 

    Google Scholar 

  • Ma, S., Archuleta, R. J. & Page, M. T. Effects of Large-Scale Surface Topography on Ground Motions, as Demonstrated by a Study of the San Gabriel Mountains, Los Angeles, California. Bull. Seismol. Soc. Am. 97, 2066–2079 (2007).

    Article 

    Google Scholar 

  • Zonno, G. et al. Assessing Seismic Damage Through Stochastic Simulation of Ground Shaking: The Case of the 1998 Faial Earthquake (Azores Islands). Surv. Geophys. 31, 361–381 (2010).

    Article 
    ADS 

    Google Scholar 

  • Toppozada, T. R. Earthquake magnitude as a function of intensity data in California and Western Nevada. Bull. Seismol. Soc. Am. 65, 1223–1238 (1975).

    Google Scholar 

  • Kanamori, H. Quantification of Earthquakes. Nature 271, 411–414 (1978).

    Article 
    ADS 

    Google Scholar 

  • Lee, K. & Monge, E. J. Effect of soil conditions on damage in the Peru earthquake of October 17, 1966. Bull. Seismol. Soc. Am. 58, 937–962 (1968).

    Article 

    Google Scholar 

  • Dalgıç, S. Factors affecting the greater damage in the Avcılar area of Istanbul during the 17 August 1999 Izmit earthquake. Bull. Eng. Geol. Environ. 63, 221–232 (2004).

    Article 

    Google Scholar 

  • Seed, H. B. & Lee, K. Liquefaction of Saturated Sands During. Cyclic Loading. J. Soil Mech. Found. Div. 92, 105–134 (1966).

    Article 

    Google Scholar 

  • Wang, C., Wong, A., Dreger, D. S. & Manga, M. Liquefaction Limit during Earthquakes and Underground Explosions: Implications on Ground-Motion Attenuation. Bull. Seismol. Soc. Am. 96, 355–363 (2006).

    Article 

    Google Scholar 

  • Wang, Y., Feng, W., Chen, K. & Samsonov, S. Source Characteristics of the 28 September 2018 Mw 7.4 Palu, Indonesia, Earthquake Derived from the Advanced Land Observation Satellite 2 Data. Remote Sens. 11, 1999 (2019).

    Article 
    ADS 

    Google Scholar 

  • Pratama, A., Fathani, T. F. & Satyarno, I. Liquefaction potential analysis on Gumbasa Irrigation Area in Central Sulawesi Province after 2018 earthquake. IOP Conf. 930, 012093 (2021).

    Google Scholar 

  • Dong, L. & Shan, J. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS J. Photogramm. Remote Sens. 84, 85–99 (2013).

    Article 
    ADS 

    Google Scholar 

  • Zhang, Y., Roffey, M. & Leblanc, S. G. A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information. Remote Sens 13, 3297 (2021).

    Article 
    ADS 

    Google Scholar 

  • NISAR. NASA-ISRO SAR (NISAR) Mission science users’ handbook. NASA Jet Propulsion Laboratory. (2018).

  • Li, P., Song, B. & Xu, H. Urban building damage detection from very high-resolution imagery by One-Class SVM and shadow information. Int. Geosci. Remote Sens. Symp. 1409-1412 (2011).

  • Yazilim, G., Cizenler, Y. & Haritası, I. 2023 Turkey Earthquakes – Building Damage Assessment Map (2023).

  • Tracy, K. C., Mosalam, K., Prevatt, D., Robertson, I. & Roueche, D. StEER-February 6, 2023, Kahramanmaras, Türkiye, Mw 7.8 Earthquake. DesignSafe-CI (2023).

  • Touzi, R., Lopes, A., Bruniquel, J. & Vachon, P. W. Coherence estimation for SAR imagery. IEEE Trans. Geosci. Remote Sens. 37, 135–149 (1999).

    Article 
    ADS 

    Google Scholar 

  • López-Martínez, C. & Pottier, E. Coherence estimation in synthetic aperture radar data based on speckle noise modeling. Appl. Opt. 46, 544–558 (2007).

    Article 
    PubMed 
    ADS 

    Google Scholar 

  • Zebker, H. A. & Villasenor, J. Decorrelation in interferometric radar echoes. IEEE Trans. Geosci. Remote Sens. 30, 950–959 (1992).

    Article 
    ADS 

    Google Scholar 

  • Hoen, E. W. & Zebker, H. A. Penetration depths inferred from interferometric volume decorrelation observed over the Greenland Ice Sheet. IEEE Trans. Geosci. Remote Sens. 38, 2571–2583 (2000).

    Article 
    ADS 

    Google Scholar 

  • Rott, H., Nagler, T. & Scheiber, R. Snow mass retrieval by means of SAR interferometry. Proceedings of the FRINGE 2003 Workshop (ESA SP-550) (2003).

  • Closson, D. & Milisavljevic, N. Mine Action – The Research Experience of the Royal Military Academy of Belgium 6 (IntechOpen, 2017).

  • Massonnet, D. & Feigl, K. L. Radar interferometry and its application to changes in the Earth’s surface. Rev. Geophys. 36, 441–500 (1998).

    Article 
    ADS 

    Google Scholar 

  • Sandwell, D., Mellors, R., Tong, X., Wei, M. & Wessel, P. Open Radar Interferometry Software for Mapping Surface Deformation. Eos Trans. AGU 92, 234 (2011).

    Article 
    ADS 

    Google Scholar 

  • Horn, B. K. P., Woodham, R. J. & Destriping, L. A. N. D. S. A. T. MSS Images by Histogram Modification. Comput. Graph. Image Process. 10, 69–83 (1979).

    Article 

    Google Scholar 

  • Castleman, K. R. Digital image processing (Prentice-Hall, New Jersey, 1996).

  • Gonzalez, R. C. & Woods, R. E. Digital Image Processing (3rd Edition) (Prentice-Hall, New Jersey, 2006).

  • Ferretti, A., Prati, C. & Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 39, 8–20 (2001).

    Article 
    ADS 

    Google Scholar 

  • Esmaeili, M. & Motagh, M. Improved Persistent Scatterer analysis using Amplitude Dispersion Index optimization of dual polarimetry data. ISPRS J. Photogramm. Remote Sens. 117, 108–114 (2016).

    Article 
    ADS 

    Google Scholar 

  • Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594 (2003).

    Article 

    Google Scholar 

  • Hu, Y. & Tang, H. X. On the Generalization Ability of a Global Model for Rapid Building Mapping from Heterogeneous Satellite Images of Multiple Natural Disaster Scenarios. Remote Sens. 13, 984 (2021).

    Article 
    ADS 

    Google Scholar 

  • Shao, X. et al. Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake. Remote Sens. 11, 978 (2019).

    Article 
    ADS 

    Google Scholar 

  • Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006).

    Article 
    ADS 

    Google Scholar 

  • Alatorre, L. C., Sánchez-Andrés, R., Cirujano, S., Beguería, S. & Sánchez-Carrillo, S. Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery. Remote Sens. 3, 1568–1583 (2011).

    Article 
    ADS 

    Google Scholar 

  • Chang, Z. et al. Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sens. 12, 502 (2020).

    Article 
    ADS 

    Google Scholar 

  • Pedregosa et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    MathSciNet 

    Google Scholar 



  • Source link