QuakeApp: A Comprehensive Earthquake Detection and Monitoring System
Keywords:
Earthquake detection, Crowdsourced sensing, Mobile computing, Seismic monitoring, Distributed systemsAbstract
Traditional earthquake monitoring networks are limited by their sparse coverage and high deployment costs, leaving many at-risk areas without timely alerts. This work addresses whether smartphone accelerometers can be reliably used for real-time earthquake detection and how crowdsourced data can enhance the spatial density of seismic networks. QuakeApp was developed as a distributed sensing system, combining a mobile app for event detection, a scalable backend for data aggregation, and a dashboard for visualization. Previous research in similar crowdsourced systems has reported detection accuracies exceeding 90% under laboratory conditions, but QuakeApp’s results remain to be validated. The main contributions of this work are: (1) a distributed earthquake detection algorithm for smartphones, (2) a backend architecture for real-time crowdsourced aggregation, and (3) a quantitative assessment of citizen-based seismic sensing.
Downloads
References
Q. Kong, R. M. Allen, and L. Schreier, “Myshake: A smartphone seismic network for earthquake early warning and beyond,” Science Advances, vol. 2, no. 2, p. e1501055, 2016.
S. Liu, K. F. Hew, and B. Huang, “Does gamification improve student learning outcomes?” Educational Research Review, vol. 30, p. 100322, 2020.
T. Anastasiadis, G. Lampropoulos, and K. Siakas, “Digital game-based learning and serious games in education,” International Journal of Advanced Scientific Research and Engineering (IJASRE), 2018. [Online]. Available: https://www.georgioslampropoulos.com/publications/anastasiadis_2018_digital/
I. M. Garcia-Lopez et al., “The impact of gamification on emergency system performance,” Education Sciences, vol. 13, no. 8, p. 813, 2023.
. Ali, C. Sheng-Chang, and M. Shah, “Continuous wavelet transformation of seismic data for feature extraction,” SN Applied Sciences, vol. 2, p. 1835, 2020.
F. Finazzi and A. Fassò, “A statistical approach for the detection of earthquakes using accelerometric data,” Environmetrics, vol. 28, no. 5, p. e2437, 2017.
F. Y. Massoda Tchoussi and F. Finazzi, “A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems,” Frontiers in Applied Mathematics and Statistics, 2023.
Q. Kong, R. M. Allen, L. Schreier, and Y.-W. Kwon, “Myshake: Initial observations from a global smartphone seismic network,” Geophysical Research Letters, vol. 43, no. 19, pp. 9588–9594, 2016.
N. Leelawat, J. Tang, A. Kodaka et al., “The impact of smartphone applications on disaster response: Case studies from japan,” International Journal of Disaster Risk Reduction, vol. 53, p. 102006, 2021.
Z. Erbaşı, B. Tural, and İ. Coşkuner, “The role of interactive content in emergency training,” https://medium.com/@hussainkyteway/the-role-of-interactive-content-in-enhancing-elearning-07c99d3c0783, 2023.
A. Aloudat, K. Michael, X. Chen, and M. M. Al-Debei, “Social acceptance of location-based mobile government emergency services,” Government Information Quarterly, vol. 31, no. 1, pp. 153–161, 2014.
U. G. Survey, “Api documentation – earthquake catalog (fdsn web service),” https://earthquake.usgs.gov/fdsnws/event/1/, 2025, accessed: 2025-10-18.
——, “Earthquake hazards program – web services apis,” https://earthquake.usgs.gov/ws/, 2025, accessed: 2025-10-18.
Google Developers, “Android developers documentation: Workmanager, retrofit, and location services,” https://developer.android.com, 2023, accessed: 2025-10-18.
C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
R. Bossu, F. Roussel, M. Landès, R. Steed, G. Mazet-Roux, and A. Dupont, “Citizen seismology without seismologists? lessons learned from the european-mediterranean seismological centre (emsc) and lastquake app,” Seismological Research Letters, vol. 89, no. 1, pp. 2–11, 2018.
T. Heaton and R. M. Allen, “Global earthquake detection and warning using android phones,” Science, vol. 369, no. 6503, pp. 1163–1169, 2025.
D. Roberts. (2024) How your phone can detect earthquakes. Accessed Nov 2025. [Online]. Available: https://www.bbc.com/future/article/20230405-the-phones-that-detect-earthquakes
T. Editorial. (2020) Challenges of earthquake early warning. Accessed Nov 2025. [Online]. Available: https://temblor.net/earthquake-insights/challenges-of-earthquake-early-warning-11099/
D. J. Wald, “Practical limitations of earthquake early warning,” Earthquake Spectra, vol. 36, no. 4, pp. 2087–2094, 2020.
Downloads
Published
How to Cite
Issue
Section
ARK
License
Copyright (c) 2025 Mohammed Kazaz, Yassine Safsouf

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright on any article published in the International Journal of Computer Engineering and Data Science (IJCEDS) is retained by the author(s). All articles are published under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits any non-commercial use, distribution, and reproduction in any medium, provided that the original work is properly cited.
License Agreement
By submitting and publishing their work in IJCEDS, the authors:
-
Grant IJCEDS the non-exclusive right to publish the article and to identify IJCEDS as the original publisher.
-
Authorize any third party to use, share, and reproduce the article for non-commercial purposes, provided that appropriate credit is given to the original authors and source, and a link to the license is included.
