Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 11; p. 4193
• Main Authors: Idehen, Igbinigie Philip, You, Qingyu, Xu, Xiqiang, Li, Shaoqing, Zhang, Yan, Hu, Yaoxing, Wang, Yuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.05.2022; MDPI
• Subjects: 5G module; Algorithms; ambient noise tomography; Analog to digital converters; Channels; Clustering; Data acquisition; Data acquisition systems; IoT; Methods; multichannel seismograph; Multiprocessing; Noise; Power supply; Raspberry Pi; Real time operation; real-time data transfer; Seismic stability; Seismology; Software; Technology application; China; United States > US; Texas; real-time data transfer; Raspberry Pi; 5G module; machine learning; multichannel seismograph; ambient noise tomography; IoT
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22114193

A seismograph was designed based on Raspberry Pi. Although comprising 8 channels, the seismograph can be expanded to 16, 24, or 32 channels by using a USB interfacing with a microcontroller. In addition, by clustering more than one Raspberry Pi, the number of possible channels can be extended beyond 32. In this study, we also explored the computational intelligence of Raspberry Pi for running real-time systems and multithreaded algorithms to process raw seismic data. Also integrated into the seismograph is a Huawei MH5000-31 5G module, which provided high-speed internet real-time operations. Other hardware peripherals included a 24 bit ADS1251 analog-to-digital converter (ADC) and a STM32F407 microcontroller. Real-time data were acquired in the field for ambient noise tomography. An analysis tool called spatial autocorrelation (SPAC) was used to analyze the data, followed by inversion, which revealed the subsurface velocity of the site location. The proposed seismograph is prospective for small, medium, or commercial data acquisition. In accordance with the processing power and stability of Raspberry Pi, which were confirmed in this study, the proposed seismograph is also recommended as a template for developing high-performance computing applications, such as artificial intelligence (AI) in seismology and other related disciplines.