Research on key technologies of the low-noise system for Semi-airborne electromagnetic method

Semi-airborne electromagnetic method (SAEM) is a novel method that is favorable for efficient and low-cost exploration under complex terrain and surface conditions, especially suitable for China's national conditions. SAEM data is affected by internal and external noise of various systems, and the signal quality is relatively limited. The different noises will be processed one by one in the traditional denoising methods, with many steps and strong subjective factors. In view of the above problems, this research considers focusing on both suppressing noise sources and enhancing noise removal methods, so as to propose a whole process low-noise solution. The noise reduction carrying structure is used to suppress the main external noise, the low-noise hybrid amplifier circuit is used to suppress the main internal noise, and the one-stop noise removal method based on deep learning is mainly used to overcome the influence of subjective factors, to achieve low-noise in the whole SAEM observation and processing process. The test shows that in areas with complex terrain and airflow conditions, the developed SAEM system can work stably with high data reliability and consistency, which is expected to provide more powerful support for resource exploration, deep engineering, and other work in China in the future.