An adaptive Butterworth spectral-based graph neural network for detecting ionospheric total electron content precursor prior to the Wenchuan earthquake on 12 May 2008

References

• Ahmedt-Aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. 2021. Graph-based deep learning for medical diagnosis and analysis: past, present and future. Sensors. 21(14):4758.
   PubMed Web of Science ®Google Scholar
• Aji BAS, Liong TH, Muslim B. 2017. Detection precursor of sumatra earthquake based on ionospheric total electron content anomalies using N-model articial neural network. 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 17738122. https://ieeexplore.ieee.org/document/8355045.
   Google Scholar
• Akyol AA, Arikan O, Arikan F. 2020. A machine learning-based detection of earthquake precursors using ionospheric data. Radio Sci. 55(11):e2019RS006931.
   Web of Science ®Google Scholar
• Al-Ameen Z, Muttar A, Al-Badrani G. 2019. Improving the sharpness of digital image using an amended unsharp mask filter. IJIGSP. 11(3):1–9.
   Google Scholar
• Bar-Sever Y, Young L, Stocklin F, Rush J. 2004. NASA’s global differential GPS system and the TDRSS augmentation service for satellites. 2nd ESA Workshop on Satellite Navigation User Equipment Technologies NAVITEC 2004, Noordwijk, The Netherlands.
   Google Scholar
• Belkin M, Hsu D, Ma S, Mandal S. 2019. Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proc Natl Acad Sci USA. 116(32):15849–15854.
   PubMed Web of Science ®Google Scholar
• Berge C, Doig A. 1964. The theory of graphs and its applications. Hoboken (NJ): Wiley; p. 247.
   Google Scholar
• Bernhardt C. 2011. Vertex maps on graphs-trace theorems. Fixed Point Theory Appl. 2011(1):8.
   Google Scholar
• Borovsky JE, Shprits YY. 2017. Is the DST index sufficient to define all geospace storms? J Geophys Res Space Phys. 122(11):11543–11547.
   Web of Science ®Google Scholar
• Brum D, Veronez MR, de Souza EM, Koch IE, Gonzaga L, Klein I, Matsuoka MT, Rofatto VF, Junior AM, dos Reis Racolte GE, et al. 2019. A proposed earthquake warning system based on ionospheric anomalies derived from GNSS measurements and artificial neural networks. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 19137221. https://ieeexplore.ieee.org/document/8900197.
   Google Scholar
• Carbone V, Piersanti M, Materassi M, Battiston R, Lepreti F, Ubertini P. 2021. A mathematical model of lithosphere-atmosphere coupling for seismic events. Sci Rep. 11(1):8682.
   PubMed Web of Science ®Google Scholar
• Chandrasekhar E, Prabhudesai SS, Seemala GK, Shenvi N. 2016. Multifractal detrended fluctuation analysis of ionospheric total electron content data during solar minimum and maximum. J Atmos Sol Terr Phys. 149:31–39.
   Web of Science ®Google Scholar
• Chen H, Miao M, Chang Y, Wang Q, Shen X, Hattori K, Han P. 2021. Singular spectrum analysis of the total electron content changes prior to M ≥ 6.0 earthquakes in the Chinese Mainland during 1998–2013. Front Earth Sci. 9:677163.
   Web of Science ®Google Scholar
• Chen Z, Sun Z, Wang W. 2011. Design and implementation of Kalman filter. IET International Conference on Communication Technology and Application (ICCTA 2011), 12570408. https://ieeexplore.ieee.org/document/6192999,
   Google Scholar
• Doroudi S. 2020. The bias-variance tradeoff: how data science can inform educational debates. AERA Open. 6(4):233285842097720–233285842097718.
   Web of Science ®Google Scholar
• Fallani F, Babiloni F. 2010. The graph theoretical approach in brain functional networks: theory and applications. Morgan & Claypool Publishers; p. 92. https://www.morganclaypool.com/doi/abs/10.2200/S00279ED1V01Y201004BME036
   Google Scholar
• Guo J, Li W, Liu X, Kong Q, Zhao C, Guo B. 2015. Temporal-spatial variation of global GPS-derived total electron content, 1999-2013. PLoS One. 10(7):e0133378.
   PubMed Web of Science ®Google Scholar
• Harary F, Gupta G. 1997. Dynamic graph models. Math Comput Modell. 25(7):79–87.
   Web of Science ®Google Scholar
• He L, Heki K. 2016. Three-dimensional distribution of ionospheric anomalies prior to three large earthquakes in Chile. Geophys Res Lett. 43(14):7287–7293.
   Web of Science ®Google Scholar
• Heki K. 2011. Ionospheric electron enhancement preceding the 2011 Tohoku-Oki earthquake. Geophys Res Lett. 38(17):L17312. doi:10.1029/2011GL047908.
   Google Scholar
• Heki K. 2021. Ionospheric disturbances related to earthquakes: ionospheric dynamics and applications geophysical monograph. Vol. 260. New York (NY): Wiley; p. 511–526.
   Google Scholar
• Hensel F, Moor M, Rieck B. 2021. A survey of topological machine learning methods. Front Artif Intell. 4:681108.
   PubMedGoogle Scholar
• Hernández-Pajares M, Juan JM, Sanz J, Orus R, Garcia-Rigo A, Feltens J, Komjathy A, Schaer SC, Krankowski A. 2009. The IGS VTEC maps: a reliable source of ionospheric information since 1998. J Geod. 83(3–4):263–275.
   Web of Science ®Google Scholar
• Huber MF. 2013. Chebyshev polynomial Kalman filter. Digital Signal Process. 23(5):1620–1629.
   Web of Science ®Google Scholar
• Igarashi G, Saeki S, Takahata N, Sumikawa K, Tasaka S, Sasaki Y, Takahashi M, Sano Y. 1995. Ground-water radon anomaly before the kobe earthquake in Japan. Science. 269(5220):60–61.
   PubMed Web of Science ®Google Scholar
• Kelley MC, Swartz WE, Heki K. 2017. Apparent ionospheric total electron content variations prior to major earthquakes due to electric fields created by tectonic stresses. J Geophys Res Space Phys. 122(6):6689–6695.
   Web of Science ®Google Scholar
• Lee TT, Jeng JT. 1998. The Chebyshev-polynomials-based unified model neural networks for function approximation. IEEE Trans Syst Man Cybern B Cybern. 28(6):925–935.
   PubMed Web of Science ®Google Scholar
• Lin JW. 2014. Ionospheric precursor of 2008 China Wenchuan earthquake using two-dimensional principal component analysis. HKIE Trans. 21(3):192–194.
   Google Scholar
• Lin JW. 2022. Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances. Nat Hazards. 111(2):1245–1270.
   Web of Science ®Google Scholar
• Lin JW, Chiou JS. 2020. Detecting total electron content precursors before earthquakes by examining total electron content images based on Butterworth filter in convolutional neural networks. IEEE Access. 8:110478–110494.
   Web of Science ®Google Scholar
• Little JD, Sandall H, Walegur MT, Nelson FE. 2003. Application of differential global positioning systems to monitor frost heave and thaw settlement in tundra environments. Permafrost Periglac Process. 14(4):349–357.
   Web of Science ®Google Scholar
• Liu JY, Chen YI, Chen CH, Liu CY, Chen CY, Nishihashi M, Li JZ, Xia YQ, Oyama KI, Hattori K, et al. 2009. Seismoionospheric GPS total electron content anomalies observed before the 12 May 2008 Mw = 7.9 Wenchuan Earthquake. J Geophys Res. 114(A4). https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008JA013698
   Google Scholar
• Liu Z, Zhou J. 2020. Introduction to graph neural networks. Williston, Vermont: Morgan & Claypool Publishers; p. 109.
   Google Scholar
• Lu H, Uddin S. 2021. A weighted patient network-based framework for predicting chronic diseases using graph neural networks. Sci Rep. 11(1):22607.
   PubMed Web of Science ®Google Scholar
• Ma G, Maruyama T. 2003. Derivation of TEC and estimation of instrumental biases from GEONET in Japan. Ann Geophys. 21(10):2083–2093.
   Web of Science ®Google Scholar
• Muafiry IN, Heki K. 2020. 3-D tomography of the ionospheric anomalies immediately before and after the 2011 Tohoku-Oki (Mw9.0) earthquake. J Geophys Res Space Phys. 125(10):e2020JA027993.
   Web of Science ®Google Scholar
• Narayan A, O’N Roe PH. 2018. Learning Graph Dynamics using Deep Neural Networks. IFAC-PapersOnLine. 51(2):433–438.
   Google Scholar
• Oikonomou C, Haralambous H, Muslim B. 2016. Investigation of ionospheric TEC precursors related to the M7.8 Nepal and M8.3 Chile earthquakes in 2015 based on spectral and statistical analysis. Nat Hazards. 83(S1):97–116.
   Google Scholar
• Oppenheim A, Schafer R. 2009. Discrete-time signal processing (Prentice-Hall Signal Processing Series). 3rd ed. London, UK: Pearson; p. 1144.
   Google Scholar
• Pulinets S, Ouzounov D. 2011. Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model – an unified concept for earthquake precursors validation. J Asian Earth Sci. 41(4–5):371–382.
   Web of Science ®Google Scholar
• Pulinets SA. 2004. Ionospheric precursors of earthquakes: recent advances in theory and practical applications. Terr Atmos Ocean Sci. 15(3):413–435.
   Web of Science ®Google Scholar
• Ren X, Zhang X, Xie W, Zhang K, Yuan Y, Li X. 2016. Global ionospheric modelling using multi-GNSS: BeiDou, Galileo, GLONASS and GPS. Sci Rep. 6:33499.
   PubMed Web of Science ®Google Scholar
• Rutkowski L, Scherer R, Korytkowski M, Pedrycz W, Tadeusiewicz R, Zurada JM. 2018. Artificial intelligence and soft computing. 17th International Conference, ICAISC 2018; June 3–7; Zakopane, Poland, Proceedings, Part I, Springer, Cham, Switzerland; p.774.
   Google Scholar
• Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G. 2009. Computational capabilities of graph neural networks. IEEE Trans Neural Netw. 20(1):81–102.
   PubMed Web of Science ®Google Scholar
• Thakkar V, Tewary S, Chakraborty C. 2018. Batch normalization in convolutional neural networks — a comparative study with CIFAR-10 data. 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), 18115489.
   Google Scholar
• Tsai LC, Tsai WH, Schreiner WS, Berkey FT, Liu JY. 2001. Comparisons of GPS/MET retrieved ionospheric electron density and ground based ionosonde data. Earth Planet Sp. 53(3):193–205.
   Web of Science ®Google Scholar
• Urschel JC, Zikatanov LT. 2021. Discrete trace theorems and energy minimizing spring embeddings of planar graphs. Linear Algebra Appl. 609:73–107.
   Web of Science ®Google Scholar
• Ye L, Lay T, Bai Y, Cheung KF, Kanamori H. 2017. The 2017 Mw 8.2 chiapas, mexico, earthquake: energetic slab detachment. Geophys Res Lett. 44(23):11824–11832.
   Web of Science ®Google Scholar
• Zhang S, Tong H, Xu J, Maciejewski R. 2019. Graph convolutional networks: a comprehensive review. Comput Soc Netw. 6(1):11.
   Google Scholar