# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 7 Conclusions  
This study successfully developed an magnetotelluric signal acquisition and monitoring system based on an IoT cloud platform. Through hardware and software co-optimization, efficient acquisition, processing, and cloud management of magnetotelluric signals were achieved. In the hardware design, FPGA and DSP collaborative control, combined with lightning protection, ESD protection, and multi-band processing functions, significantly improved the system's anti-interference capability and stability. The integration of the embedded subsystem and IoT module supports multiple network protocols, ensuring real-time data transmission and remote control. On the software side, the layered architecture design enhanced the system's scalability and maintainability. The cloud platform backend, built on the Spring Boot framework, achieved data storage, processing, and visualization functions. The test results showed that the system performed well in stress testing, with good concurrent processing capability. The consistency testing verified the system's data acquisition accuracy, and that the cloud platform functions ran stably with a low error rate.  
The cloud-based geomagnetic monitoring system is of great scientific significance, as it significantly improves the temporal and spatial resolution of geophysical exploration and research efficiency through real-time data aggregation and intelligent analysis capabilities. The cloud-based collaborative computing that can be developed in the future will be able to support global research teams in sharing data and carrying out joint inversion, which provides a technical foundation for geophysical big data.  
In our tests, we used a simpler monitoring structure, but the concurrency of our instrumentation will grow as the servers are updated, so it will gradually become a large volume system. In the future, this system will be applied to geothermal monitoring. Future research can further optimize anti-interference algorithms for high-frequency signal processing, expand the scale of the test scenarios, and explore the application of AI technology in automatic data analysis and anomaly detection to further enhance the system's intelligence level and application scope.  
Conceptualization, R.C.; methodology, R.C.; software, Q.L.; validation, Q.L. and H.Y.; formal analysis, Q.L.; investigation, Q.L.; data acquisition and processing, W.S. and X.M.; writing--original draft preparation, Q.L.; writing--review and editing, R.C.; supervision, R.C.; project administration, H.Y.; funding acquisition, W.S. and X.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research and Technology Development Project of the China National Petroleum Corporation under the project \"Research on Key Technologies for the Development and Utilization of Geothermal Resources\" (project number 2021 DJ5502).  
Not applicable.  
Data Availability Statement: Data are contained within the article; further inquiries can be directed to the corresponding author.  
We would like to thank Bureau of Geophysical Prospecting Inc., China National Petroleum Corporation, for providing the test site and financial support.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [PERSON] and [PERSON] are employees of China National Petroleum Corporation. The authors declare that this study received funding from China National Petroleum Corporation. The funder had no role in the design of the study; in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.