Modular Software Architecture for a Raspberry Pi-Based Geophysical Data Acquisition System
DOI:
https://doi.org/10.29303/goescienceed.v7i4.3217Keywords:
Geophysical Data Acquisition, MEMS Accelerometer, Raspberry Pi, Software Architecture, Fault ToleranceAbstract
Continuous geophysical data acquisition requires software that can maintain sampling, timing, data storage, and system operation when individual components become unavailable. This paper describes a modular software architecture developed for a Raspberry Pi-based 24-bit geophysical data acquisition system. The application is divided into six modules covering system orchestration, hardware abstraction, signal processing, data persistence, REST services, and the web interface. External configuration, hybrid timestamping using GPS, a DS3231 real-time clock, and a monotonic counter are used together with deadline-based scheduling and layered fault handling. The software was evaluated through dependency analysis, startup measurement, induced fault tests, dashboard checks, and a one-hour acquisition test at 100 SPS. The system reached a record-ready state in 7.840 s and recorded 360,000 samples per channel during the one-hour test, with no dropped samples, MiniSEED gaps, or overlaps. Acquisition also continued during watchdog access denial, temporary database disconnection, GPS unavailability, and REST service interruption. The results show that a modular software structure can support continuous geophysical data acquisition on a general-purpose Linux platform without a real-time kernel.
References
Alessandro, A. D., B, S. S., & Vitale, G. (n.d.). Optimization of Low-Cost Monitoring Systems for On-Site Earthquake Early-Warning of Critical Infrastructures. 1, 1–13.
Ali Bakir, R., Elksasy, M., Salam, M., Saraya, M., & Abdelsalam, M. (2023). Low Cost MEMS accelerograph: structure, operation and application to seismology. Delta University Scientific Journal, 6(1), 193–204. https://doi.org/10.21608/dusj.2023.291042
Crognale, M., Rinaldi, C., Potenza, F., Gattulli, V., Colarieti, A., & Franchi, F. (2024). Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers. Sensors, 24(8), 1–19. https://doi.org/10.3390/s24082435
Knapp, A., & Bloom, A. J. (2022). Easy as piadcs: A low-cost, ultra-high-resolution data acquisition system using a Raspberry Pi. Applications in Plant Sciences, 10(3), 1–6. https://doi.org/10.1002/aps3.11485
Nof, R. N., Chung, A. I., Rademacher, H., Dengler, L., & Allen, R. M. (2019). MEMS accelerometer mini-array (MAMA): A low-cost implementation for earthquake early warning enhancement. Earthquake Spectra, 55(1), 21–38. https://doi.org/10.1193/021218EQS036M
Özcebe, A. G., Tiganescu, A., Ozer, E., Negulescu, C., Galiana-merino, J. J., Tubaldi, E., Toma-danila, D., Molina, S., Kharazian, A., Bozzoni, F., Borzi, B., & Balan, S. F. (2022). Raspberry Shake-Based Rapid Structural Identification of Existing Buildings Subject to Earthquake Ground Motion: The Case Study of Bucharest. Sensors, 22(13), 1–24. https://doi.org/10.3390/s22134787
Özdemir, K., & Kömeç Mutlu, A. (2024). Cost-Effective Data Acquisition Systems for Advanced Structural Health Monitoring. Sensors, 24(13). https://doi.org/10.3390/s24134269
Pratama, Y. A., & Suharsono, S. (2025). Performance Evaluation of ADS 1256 for Geoelectric Data Acquisition System: Laboratory Scale Comparative Study. Jurnal Geofisika, 23(1), 23. https://doi.org/10.36435/jgf.v23i1.674
Ramdeane, A., & Lynch, L. (2020). Low Cost Seismic Data Acquisition System Based on Open Source Hardware and Software Tools. 496–505. https://doi.org/10.47412/vycb8830
Ringler, A. T., Anthony, R. E., Bastien, P., & Pascale, A. (2023). Introduction to the Digitization of Seismic Data : A User ’ s Guide. 94(4). https://doi.org/10.1785/0220220158.Introduction
Vlachos, I., Anagnostou, M. N., Avlonitis, M., & Karakostas, V. (2025). Upgrading a Low-Cost Seismograph for Monitoring Local Seismicity. GeoHazards, 6(1), 1–32. https://doi.org/10.3390/geohazards6010004
