Instrumentation of Low-Cost IoT-Agnostic Data-Capturing Platform With Enhanced Sampling and Bandwidth Capabilities for Subsurface Characterization

Applications that involve subsurface characterization and monitoring such as landslide detection or geotechnical condition monitoring typically require continuous and high-quality geoscience data to be collected from remote areas where sampling frequency and power consumption pose major challenges. New advanced platforms are required which seamlessly blend accuracy, affordability, and practicality in sensor data collection and transfer. Here, we introduce and evaluate an agnostic platform, tailored for proficient data acquisition via its four-channel differential analog-to-digital converters (ADC), thereby enabling the integration of four sensors with differential outputs for comprehensive data collection. Utilizing the ESP32 Wemos board as its cornerstone CPU, the platform presents a dual-faceted capability to efficiently stream geoscience data to the cloud where signal strength is sufficient, safeguarding data integrity and providing near-real-time accessibility, and to store it locally when the site does not allow connectivity. Surpassing current systems, this platform delivers a sampling rate of 3.2 kHz samples per second (800 samples per second per channel) and provides a robust bandwidth response critical for geotechnical monitoring, while maintaining a highly cost-effective production framework for scalable monitoring. The platform’s utility is validated through its application in subsurface ground characterization, employing the Nakamura method to determine initial layer depths and then monitoring through time to analyze property changes related to ground saturation, a primary catalyst for slope instabilities. The findings demonstrate the devised platform as a potent, economically viable tool, promising substantial applicability across various research domains necessitating precise data capture and transmission.