Transient electromagnetic wave logging is one of the important methods for subsurface media detection, especially suitable for detecting anomalies near wells. However, the electromagnetic response of wellbore anomalies is diverse and complex, demanding a precise positioning and quantitative characterization method. This method needs to accurately identify key attributes of the anomalies, such as their position, shape, size, and electrical properties, and provide quantitative descriptions. In this study, the three-dimensional Finite Difference Time Domain(FDTD)method in the time domain is employed to investigate the transient electromagnetic field responses of homogeneous formations, horizontally layered anomalies, and three-dimensional block anomalies. Factors such as anomaly resistivity and position are examined to understand their influence on the measurement responses. By analyzing the responses generated by scattering bodies, important information regarding the extension of layered media and the position, size, and response magnitude of three-dimensional isolated anomalies is revealed. A layered wellbore anomaly electrical profile inversion technique is proposed based on the full-area apparent resistivity inversion method. To address the issue of late signals in the full-area apparent resistivity inversion method that cannot accurately reflect the true variation of formation resistivity, a direct vertical partition inversion combining gradient optimization algorithm is proposed. Additionally, a smoke ring inversion algorithm is employed for three-dimensional block anomaly electrical profile inversion. The improved detection method achieves quantitative characterization of wellbore anomalies, significantly improving the accuracy of positioning and electrical property extraction of isolated anomalies near wells, with an overall characterization error of less than 5%. This improved detection method is of great significance for petroleum and solid mineral exploration, aiding in determining the distribution and reserves of oil, gas, and solid mineral resources. It can guide decision-making in exploration activities and optimize resource development plans, thereby enhancing the accuracy and reliability of wellbore anomaly detection. Simultaneously, accurate detection of underground anomalies contributes to understanding the characteristics of subsurface structures, guiding engineering design and construction, and improving the efficiency and safety of engineering projects.