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Paper ID:IJSREM63572

Big Data For Early Earthquake And Seismic Pattern Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63572

Volume:

10 |

5 |

May 2026

Pages:

1--

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RS

RAKSHITHA S

Master Of Computer Applications

Bapuji Institute Of Engineering And Technology

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APM

Anusha P M

Master Of Computer Applications

Bapuji Institute Of Engineering And Technology

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Abstract

Earthquakes are natural disasters that cause massive damage to life and property. Predicting earthquakes early has always been a big challenge because of the complex and unpredictable nature of seismic activities. This project volumes of seismic data from different sources, such as sensors, Big Data for Early Earthquake and Seismic Pattern Detection, focuses on using big data technologies to collect and analyse large satellite and historical records. By applying big data analytics, machine learning and data visualisation techniques, the system can identify hidden patterns and detect early warning signs of possible earthquakes. The project aims to create a data-driven model that continuously monitors seismic signals and predicts potential earthquake occurrences based on real-time data. This approach helps researchers and disaster management teams make faster and more accurate decisions, reducing the risk to human life and infrastruct

KEYWORDS

Keywords : Seismic Data Earthquake Prediction Real-time Monitoring Early Warning Systems GPS Data Satellite Data Seismographs IOT Devices Historical Seismic Records.

Cite This Article

RAKSHITHA S, Anusha P M, "Big Data For Early Earthquake And Seismic Pattern Detection," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1--, May 2026.

Full Article/Conclusion

The Big Data for Early Earthquake and Seismic   Pattern Detection  System highlights the powerful role of large-scale data processing, machine learning, and automated pattern analysis in providing accurate and early alerts for natural disaster management. By integrating real-time seismic data with predictive analytics, the system enhances safety by identifying abnormal ground vibrations earlier than traditional methods. Unlike conventional manual monitoring, this Big Data-driven approach increases accuracy, reduces human error, and provides continuous monitoring that supports quicker decision-making.

References

The development of this framework is grounded in established geophysical theory and contemporary big data research. 1.        United States Geological Survey (USGS). (n.d.). Methodologies for Automated Seismic Analysis and Early Warning Dissemination. USGS Technical Manuals. http://pubs.usgs.gov/of/2014/1097/ 2.        Kanamori, H. (2005). Real-Time Seismology and Earthquake Early Warning. Annual Review of Earth and Planetary Sciences, 33, 195–214. [DOI: 10.1146/annurev.earth.33.092203.122455] (2002). Quantitative Seismology. University Science Books. http://doi.org/10.1146/annurev.earth.33.092 203.122626 3 Allen, R., Gasparini, P., Kamigaichi, O., “Earthquake Early Warning Systems,” Seismological Research Letters, 2009. 4.  IRIS Consortium, “Global Seismic Data and Tools,” Official Data Repository, n.d. 5.  Shelly, P., Peng, Z., “Machine Learning Approaches in Seismic Event Detection,” Nature Geoscience, 2018. 6.            OpenSeismo, “Seismic Signal Processing Toolkit,” Open-Source Documentation, n.d. 7.   European-Mediterranean Seismological Centre (EMSC), “Seismic Hazard and Real-Time Data,” Official Website, n.d. http://www.emsc-csem.org/ 8.  Gutenberg, B., Richter, C., Seismicity of the Earth, Princeton University Press, 1954. https://books.google.com/books?id=YOcK AQAAIAAJ 9      World Meteorological Organization (WMO), “Guidelines on Earthquake and Geological Hazard Monitoring,” Technical Reports, n.d. http://public.wmo.int/en/resources/docume nts

Editorial & Review Transparency

Publication Date

5/21/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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