Analysis on stable imaging and inverse algorithm for artificial source EM data

The inversion of artificial source electromagnetic method (EM) 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, limited 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 the 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 center 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.