Abstract
The advancements in remote sensing (RS) satellite applications have revolutionized natural disaster surveillance and prediction in the earthquake monitoring by delineating various precursors at the Earth’s surface and in atmosphere. In this paper, the earthquake precursors comprising land surface temperature, outgoing longwave radiations, relative humidity, and air temperature for both the daytime and nighttime are investigated for two Mw > 6.0 events in USA. Interestingly, we noticed surface and atmospheric parameters anomalies in 6–8 days window prior to both the events by using standard deviation method. Moreover, these abrupt deviations are also validated by the recurrent neural networks like autoregressive network with exogenous inputs and long short-term memory inputs. The findings of this study demonstrate the potential of using modern analysis tools to further develop our knowledge of the linked dynamics of the lithosphere and atmosphere preceding seismic occurrences. This study implements substantially the developing of natural hazard surveillance and earthquake prediction capabilities for future researches as a valuable addition of reference in the field of RS.
Similar content being viewed by others
References
Abbasi AR, Shah M, Ahmed A, Naqvi NA (2021) Possible ionospheric anomalies associated with the 2009 Mw 6.4 Taiwan earthquake from DEMETER and GNSS TEC. Acta Geod Geophys 56:77–91. https://doi.org/10.1007/s40328-020-00325-1
Adhikari B, Klausner V, Cândido CMN et al (2024) Lithosphere–atmosphere–ionosphere coupling during the september 2015 Coquimbo earthquake. J Earth Syst Sci 133:35. https://doi.org/10.1007/s12040-023-02222-x
Adil MA, Senturk A, Shah M, Naqvi NA, Saqib M, Abbasi AR (2021a) Atmospheric and ionospheric disturbances assocaited with M > 6.0 earthquakes in the East Asian regions: a case study from Taiwan. J Asian Earth Sci 220:104918. https://doi.org/10.1016/j.jseaes.2021.104918
Adil MA, Abbas A, Ehsan M, Shah M, Naqvi NA (2021b) Investigation of ionospheric and atmospheric anomalies associated with three Mw > 6.5 EQs in New Zealand. J Geodyn 145:101841. https://doi.org/10.1016/j.jog.2021.101841
Ahmed J, Shah M, Zafar WA, Amin MA, Iqbal T (2018) Seismo ionospheric anomalies associated with earthquakes from the analysis of the ionosonde data. J Atmos Sol-Terr Phys 179:450–458. https://doi.org/10.1016/j.jastp.2018.10.004
Ahmed J, Shah M, Awais M, Jin S, Zafar WA, Ahmed N, Amin A, Shah MA, Ali I (2021) Seismo-ionospheric anomalies before the 2019 Mirpur earthquake from ionosonde measurements. Adv Space Sci. https://doi.org/10.1016/j.asr.2021.07.030
Cai Y, Shyu ML, Tu YX et al (2019) Anomaly detection of earthquake precursor data using long short-term memory networks. Appl Geophys 16:257–266. https://doi.org/10.1007/s11770-019-0774-1
Colonna R, Filizzola C, Genzano N et al (2023) Optimal setting of earthquake-related ionospheric TEC (total electron content) anomalies detection methods: long-term validation over the Italian Region. Geosciences (switzerland) 13:150. https://doi.org/10.3390/geosciences13050150
Dai Z, Li X, Lan B (2023) Three-dimensional modeling of tsunami waves triggered by submarine landslides based on the smoothed particle hydrodynamics method. J Mar Sci Eng 11(10):2015. https://doi.org/10.3390/jmse11102015
De Santis A, Abbattista C, Alfonsi L et al (2019) Geosystemics view of earthquakes. Entropy 21:412. https://doi.org/10.3390/e21040412
Dobrovolsky IP, Zubkov SI, Miachkin VI (1979) Estimation of the size of earthquake preparation zones. Pure Appl Geophys 117:1025–1044
Draz MU, Shah M, Jamjareegulgarn P et al (2023) Deep machine learning based possible atmospheric and ionospheric precursors of the 2021 Mw 7.1 Japan earthquake. Remote Sens (basel) 15:1904
Du W, Wang G (2013) Intra-event spatial correlations for cumulative absolute velocity, arias intensity, and spectral accelerations based on regional site conditions. Bull Seismol Soc Am 103(2A):1117–1129. https://doi.org/10.1785/0120120185
Du W, Wang G (2014) Fully probabilistic seismic displacement analysis of spatially distributed slopes using spatially correlated vector intensity measures. Earthquake Eng Struct Dynam 43(5):661–679. https://doi.org/10.1002/eqe.2365
Freund FT, Takeuchi A, Lau BWS et al (2007) Stimulated infrared emission from rocks: assessing a stress indicator. eEarth 2:7–16
González J, Yu W, Telesca L (2019) Earthquake magnitude prediction using recurrent neural networks. Proceedings 24(1):22. https://doi.org/10.3390/IECG2019-06213
Hafeez A, Ehsan M, Abbas A, et al (2022) Machine learning-based thermal anomalies detection from MODIS LST associated with the M w 7.7 Awaran, Pakistan earthquake. Natural Hazards 1–19
Haider SF, Shah M, Li B et al (2024) Synchronized and co-located ionospheric and atmospheric anomalies associated with the 2023 Mw 7.8 Turkey earthquake. Remote Sens (basel) 16:222. https://doi.org/10.3390/rs16020222
Hereher M, Bantan R, Gheith A, El-Kenawy A (2022) Spatio-temporal variability of sea surface temperatures in the red sea and their implications on Saudi Arabia coral reefs. Geocarto Int 37:5636–5652. https://doi.org/10.1080/10106049.2021.1922513
Jiao Z, Shan X (2022) Pre-seismic temporal integrated anomalies from multiparametric remote sensing data. Remote Sens (basel) 14:2343
Jing F, Shen XH, Kang CL, Xiong P (2013) Variations of multi-parameter observations in atmosphere related to earthquake. Nat Hazard 13:27–33
Khan MM, Ghaffar B, Shahzad R et al (2022) Atmospheric anomalies associated with the 2021 M w 7.2 Haiti earthquake using machine learning from multiple satellites. Sustainability 14:14782
Kiyani A, Shah M, Ahmed A et al (2020) Seismo ionospheric anomalies possibly associated with the 2018 Mw 8.2 Fiji earthquake detected with GNSS TEC. J Geodyn 140:101782
Li J, Liu Y, Lin G (2023) Implementation of a coupled FEM-SBFEM for soil-structure interaction analysis of large-scale 3D base-isolated nuclear structures. Comput Geotech 162:105669. https://doi.org/10.1016/j.compgeo.2023.105669
Liu X, Zhang QY, Shah M, Hong Z (2017) Atmospheric-ionospheric disturbances following the April 2015 Calbuco volcano from GPS and OMI observations. Adv Space Res. https://doi.org/10.1016/j.asr.2017.07.007
Lizunov G, Skorokhod T, Hayakawa M, Korepanov V (2020) Formation of ionospheric precursors of earthquakes—probable mechanism and its substantiation. Open J Earthquake Res 09:142–169. https://doi.org/10.4236/ojer.2020.92009
Mahmood I, Iqbal MF, Shahzad MI, Qaiser S (2017) Investigation of atmospheric anomalies associated with Kashmir and awaran Earthquakes. J Atmos Sol Terr Phys 154:75–85
Maletckii B, Astafyeva E, Sanchez SA et al (2023) The 6 February 2023 Türkiye earthquake sequence as detected in the ionosphere. J Geophys Res Space Phys. https://doi.org/10.1029/2023JA031663
Mohamed EK, Elrayess M, Omar K (2022) Evaluation of thermal anomaly preceding northern red sea earthquake, the 16th June 2020. Arab J Sci Eng 47:7387–7406. https://doi.org/10.1007/s13369-021-06524-4
Nekoee M, Shah-Hosseini R (2020) Thermal anomaly detection using NARX neural network method to estimate the earthquake occurrence time. Earth Obs Geomat Eng 4:98–108. https://doi.org/10.22059/eoge.2021.292253.1067
Nugroho HA, Joelianto E, Widiyantoro S (2013) Time series estimation of earthquake occurrences in Bali and its surroundings using NARX network model. In: Proceedings of 2013 3rd international conference on instrumentation, control and automation, ICA 2013. IEEE Computer Society, pp. 251–256
Pulinets S, Mironova I, Miklyaev P, Petrova T, Shitov A, Karagodin A (2024) Radon variability as a result of interaction with the environment. Atmosphere 15(2):167. https://doi.org/10.3390/atmos15020167
Qasim M, Shah M, Shahzad R, Jamjareegulgarn P (2023) Atmospheric precursors from multiple satellites associated with the 2020 Mw 6.5 Idaho (USA) earthquake. Adv Space Res. https://doi.org/10.1016/j.asr.2023.09.057
Quan J, Chen Y, Zhan W et al (2014) A hybrid method combining neighborhood information from satellite data with modeled diurnal temperature cycles over consecutive days. Remote Sens Environ 155:257–274. https://doi.org/10.1016/j.rse.2014.08.034
Rawat V, Saraf AK, Das J et al (2011) Anomalous land surface temperature and outgoing long-wave radiation observations prior to earthquakes in India and Romania. Nat Hazards 59:33–46
Saqib M, Şentürk E, Sahu SA, Adil MA (2022) Comparisons of autoregressive integrated moving average (ARIMA) and long short term memory (LSTM) network models for ionospheric anomalies detection: a study on Haiti (Mw = 7.0) earthquake. Acta Geod Geoph 57:195–213. https://doi.org/10.1007/s40328-021-00371-3
Satti MS, Ehsan M, Abbas A et al (2022) Atmospheric and ionospheric precursors associated with Mw≥ 6.5 earthquakes from multiple satellites. J Atmos Sol Terr Phys 227:105802
Shah M, Jin SG (2015) Statistical characteristics of seismo-ionospheric GPS TEC disturbances prior to global Mw≥ 5.0 earthquakes (1998–2014). J Geodyn 92:42–49. https://doi.org/10.1016/j.jog.2015.10.002
Shah M, Khan M, Ullah H, Ali S (2018) thermal anomalies prior to the 2015 Gurkha (Nepal) earthquake from MODIS land surface temperature and outgoing longwave radiations. Geodyn Tectonophys 9(1):123–138. https://doi.org/10.5800/gt-2018-9-1-0341
Shah M, Tariq MA, Naqvi NA (2019a) Atmospheric anomalies associated with Mw>6.0 earthquakes in Pakistan and Iran during 2010–2017. J Atmos Sol Terr Phys 191:105056. https://doi.org/10.1016/j.jastp.2019.06.003
Shah M, Tariq MA, Ahmad J, Naqvi NA, Jin S (2019b) Seismo ionospheric anomalies before the 2007 M7.7 Chile earthquake from GPS TEC and DEMETER. J Geodyn 127:42–51
Shah M, Ahmed A, Ehsan M, Khan M, Tariq MA, Calabia A, Rahman Z (2020a) Total electron content anomalies associated with earthquakes occurred during 1998–2019. Acta Astronaut 175:268–276. https://doi.org/10.1016/j.actaastro.2020.06.005
Shah M, Inyurt S, Ehsan M, Ahmed A, Shakir M, Ullah S, Shahid Iqbal M (2020b) Seismo ionospheric anomalies in Turkey associated with M ≥ 6.0 earthquakes detected by GPS stations and GIM TEC. Adv Space Res 65(11):2540–2550. https://doi.org/10.1016/j.asr.2020.03.005
Shah M, Aibar AC, Tariq MA et al (2020c) Possible ionosphere and atmosphere precursory analysis related to Mw > 6.0 earthquakes in Japan. Remote Sens Environ 239:111620
Shah M, Abbas A, Ehsan M et al (2021a) Ionospheric–thermospheric responses in south America to the august 2018 geomagnetic storm based on multiple observations. IEEE J Sel Top Appl Earth Obs Remote Sens 15:261–269
Shah M, Ehsan M, Abbas A et al (2021b) Possible thermal anomalies associated with global terrestrial earthquakes during 2000–2019 based on MODIS-LST. IEEE Geosci Remote Sens Lett 19:1–5
Shah M, Qureshi RU, Khan NG et al (2021c) Artificial neural network based thermal anomalies associated with earthquakes in Pakistan from MODIS LST. J Atmos Sol Terr Phys 215:105568
Shah M, Ehsan M, Abbas A et al (2022) Possible thermal anomalies associated with global terrestrial earthquakes during 2000–2019 based on MODIS-LST. IEEE Geosci Remote Sens Lett 19:1–5. https://doi.org/10.1109/LGRS.2021.3084930
Shah M, Draz MU, Saleem T (2023a) A comprehensive study on the synchronized outgoing longwave radiation and relative humidity anomalies related to global Mw ≥ 6.5 earthquakes. Nat Hazards. https://doi.org/10.1007/s11069-023-06262-w
Shah M, Shahzad R, Jamjareegulgarn P et al (2023b) Machine-learning-based lithosphere-atmosphere-ionosphere coupling associated with Mw > 6 earthquakes in America. Atmosphere (basel) 14:1236. https://doi.org/10.3390/atmos14081236
Shahzad F, Shah M, Riaz S et al (2023a) Integrated analysis of lithosphere-atmosphere-ionospheric coupling associated with the 2021 M w 7.2 Haiti earthquake. Atmosphere (basel) 14:347
Shahzad R, Shah M, Tariq MA, Calabia A, Melgarejo-Morales A, Jamjareegulgarn P, Liu L (2023b) Ionospheric-thermospheric responses to geomagnetic storms from multi-instrument space weather data. Remote Sens 15:2687. https://doi.org/10.3390/rs15102687
Su B, Li H, Ma W et al (2021) The outgoing longwave radiation analysis of medium and strong earthquakes. IEEE J Sel Top Appl Earth Obs Remote Sens 14:6962–6973. https://doi.org/10.1109/JSTARS.2021.3090777
Sun R, Wang J, Cheng Q et al (2021) A new IMU-aided multiple GNSS fault detection and exclusion algorithm for integrated navigation in urban environments. GPS Solut 25:1–17
Tariq MA, Shah M, Hernández-P M, Iqbal T (2019a) Pre-earthquake ionospheric anomalies before three major earthquakes by GPS-TEC and GIM-TEC data during 2015–2017. Adv Space Res 63(7):2088–2099. https://doi.org/10.1016/j.asr.2018.12.028
Tariq MA, Shah M, Hernández-P M, Iqbal T (2019b) Ionospheric VTEC variations over Pakistan in the descending phase of solar activity during 2016–17. Astrophys Space Sci 364:99. https://doi.org/10.1007/s10509-019-3591-3
Tariq MA, Shah M, Ulukavak M, Iqbal T (2019c) Comparison of TEC from GPS and IRI-2016 model over different regions of Pakistan during 2015–2017. Adv Space Res 64(3):707–718. https://doi.org/10.1016/j.asr.2019.05.019
Vesnin A, Yasyukevich Y, Perevalova N, Şentürk E (2023) Ionospheric response to the 6 February 2023 Turkey-Syria earthquake. Remote Sens (basel) 15:2336. https://doi.org/10.3390/rs15092336
Xie X, Xie B, Cheng J, Chu Q, Dooling T (2021) A simple Monte Carlo method for estimating the chance of a cyclone impact. Nat Hazards 107(3):2573–2582. https://doi.org/10.1007/s11069-021-04505-2
Xiong P, Shen XH, Bi YX et al (2010) Study of outgoing longwave radiation anomalies associated with Haiti earthquake. Nat Hazard 10:2169–2178
Xu Y, Wang E, Yang Y, Chang Y (2022) A unified collaborative representation learning for neural-network based recommender systems. IEEE Trans Knowl Data Eng 34(11):5126–5139. https://doi.org/10.1109/TKDE.2021.3054782
Yin H, Wu Q, Yin S, Dong S, Dai Z, Soltanian MR (2023a) Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest. J Hydrol 616:128813. https://doi.org/10.1016/j.jhydrol.2022.128813
Yin L, Wang L, Li J, Lu S, Tian J, Yin Z, Zheng W (2023b) YOLOV4_CSPBi: enhanced land target detection model. Land 12(9):1813. https://doi.org/10.3390/land12091813
Zhou G, Zhao D, Zhou X, Xu C, Liu Z, Wu G, Zou L (2022a) An RF amplifier circuit for enhancement of echo signal detection in bathymetric LiDAR. IEEE Sens J 22(21):20612–20625. https://doi.org/10.1109/JSEN.2022.3206763
Zhou G, Xu C, Zhang H, Zhou X, Zhao D, Wu G, Zhang L (2022b) PMT gain self-adjustment system for high-accuracy echo signal detection. Int J Remote Sens 43(19–24):7213–7235. https://doi.org/10.1080/01431161.2022.2155089
Zhou G, Zhang H, Xu C, Zhou X, Liu Z, Zhao D, Wu G (2023) A real-time data acquisition system for single-band bathymetric LiDAR. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2023.3282624
Acknowledgements
The authors express their gratitude to USGS community for making earthquake information data available. We are extremely grateful to NASA for their provision of MODIS data and other atmospheric parameters data. The authors acknowledged the efforts of anonymous reviewers for constructive comments.
Funding
This paper received no external funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There are no financial or other conflicts of interest among the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Khalid, Z., Shah, M., Riaz, S. et al. Atmospheric precursors associated with two Mw > 6.0 earthquakes using machine learning methods. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06562-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11069-024-06562-9