首页|A rapid imaging approach for transient electromagnetic data based on gradient features
A rapid imaging approach for transient electromagnetic data based on gradient features
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Taylor & Francis
Data processing techniques in transient electromagnetic (TEM) methods face numerouschallenges, such as the problem of 3-D imaging and the demand for new data processing techniquesarising from the development of small-loop TEM devices. We have developed an imagingalgorithm based on gradient features derived from TEM data to address these challenges. Wefound that normalising the gradient field of TEM responses under appropriate backgroundconditions enables imaging of the target. Numerical experiments conducted under theoreticalmodels have validated the feasibility of this approach and highlighted the significance of backgroundfields.Wefurther designed more complex models and achieved 3-D imaging of syntheticdata within seconds, demonstrating the method’s high efficiency and applicability to highdimensionaldata. Subsequently, we applied the algorithm to challenging small-loop TEM dataand demonstrated its superior performance compared to the inversion method, which presentsa novel approach for processing data from small-loop TEM devices. This study demonstrates thatthe algorithm possesses characteristics of high efficiency and wide applicability, suitable for TEMdata from various dimensions and types of devices. Furthermore, it holds promising potential inassisting the enhancement of the efficiency of the three-dimensional inversion of TEM data.
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, People’s Republic of China||Center forExcellence in Comparative Planetology, Chinese Academy of Sciences, Hefei, People’s Republic of China
School of Resources andEnvironmental Engineering, Hefei University of Technology, Hefei, People’s Republic of China
College of Transportation Engineering,Nanjing Tech University, Nanjing, People’s Republic of China
NETL
NSTL
Taylor & Francis
