소형루프 전자탐사 자료의 시간경과 역산

초록

Small loop electromagnetic (EM) method has the ability to provide continuous conductivity distribution at shallow depth. Especially, this method is very suitable for monitoring conductivity variation in the subsurface because data acquisition is very rapid and non-destructive. In this study, we developed a time-lapse inversion algorithm to effectively interpret monitoring data. The developed inversion algorithm imposes a big penalty on model parameter with small change, while a minimal penalty on model parameter with large change compared with a reference model. Consequently, inversion result shows a focused image where model parameters changed greatly. Applying the method on the inversion of synthetic 2.5D small loop EM monitoring data, we can ensure that it is effective in the detection of areas which show great change between a reference and time-lapse model.

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