The semiairborne transient electromagnetic method (SATEM) is a geophysical method that combines the advantages of high-power ground-based transmitting and efficient airborne observation. In recent years, the threshold for SATEM application has been significantly reduced through the integration of multirotor unmanned aerial vehicles and systems obtained by the simple redevelopment of existing ground-based systems. However, it is still difficult to obtain good detection results with such systems for the following specific reasons: in terms of system design, there is a lack of method for estimating the key parameters of the system, resulting in the designed system being unable to effectively meet the requirements of the SATEM method and observation scenarios; in terms of technology, there is a lack of effective means of ensuring a high signal-to-noise ratio (high-current transmitting and effective suppression of major external noise) while meeting the demand for distortion-free observation. In response to these issues, a system bandwidth estimation method based on the analysis of information-carrying capacity is developed. Based on this, solutions for large current and fast turn-off transmitter and for overcoming major external noise are provided. Through a survey located in a karst landform area, the overall application performance of the system is discussed based on the inversion results.

The presence of shallow conductive heterogeneities can cause static shift in controlled source audio-frequency magnetotellurics (CSAMT) apparent resistivity sounding curves. This is observed as a shift along the apparent resistivity axis in double logarithmic coordinates. Such effect can cause difficulties in CSAMT data interpretation. In this letter, we established a new method to identify and correct the CSAMT static shift based on the high-resolution Radon transformation. We took advantage of the property that the static shift of apparent resistivity curves behaves as a point in the Radon domain. We presented 3-D synthetic study to demonstrate that the static shift can be effectively removed from the apparent resistivity curve. However, due to the low resolution and precision, the traditional Radon transform can generate ``scissor-tail'' and then, the static shift may not be completely removed. We proposed to use a new high-resolution Radon transform by improving the regularization matrix using the least squares inversion. Our numerical simulation shows that by using this high-resolution Radon transform, the static shift converges accurately to a point without showing the ``scissor-tail.'' In the application to field CSAMT data, the high-resolution Radon transform method was able to correct the static shift effectively; thus, it can improve the precision and accuracy in data interpretation.

The inversion of artificial source electromagnetic (EM) method data fundamentally involves constructing a mathematical relationship between observable data and geological structures. The aim of imaging and inversion is to construct a geophysical model that matches the observable results, thereby realizing the identification of subsurface targets. The results of EM data inversion, due to the simplicity of geophysical models, limit inversion computing efficiency. Moreover, complexity of actual geological structures, and lack of onsite observable data, are often hindered by non-uniqueness. The challenge in the interpretation of artificial source EM data is in enhancing both the precision and expeditiousness of the inversion process. It can be classified into three main types for EM data inversion: direct imaging inversion, deterministic inversion, and stochastic inversion. To enhance computational efficiency and reduce non-uniqueness in the results, effective inversion methods, prior geological information, geophysical data, and comprehensive analysis can help mitigate the issue of non-uniqueness in EM data inversion, thereby leading to more rational geophysical interpretation results. With the progress of technology such as computing centers and the development of artificial intelligence methods, future inversion techniques will become faster, more efficient, and more intelligent, and will be applied to the interpretation of artificial source EM data.