Joint inversion of one-dimensional time-frequency electromagnetic data with constraints
QI Zhipeng1, TANG Wenwu1, HE Zhanxiang2,3, ZHANG Li1,4
1. School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, Jiangxi 330013, China; 2. Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China; 3. Guangdong Provincial Key Laboratory of Geophysical High-Resolution Imaging Technology, SouthernUniversity of Science and Technology, Shenzhen, Guangdong 518055, China; 4. Key Laboratory of Geophysical Electromagnetic Probing Technologies of Ministry of Natural Resources, Langfang, Hebei 065000, China
Abstract：Time-frequency electromagnetic data can be used to simultaneously obtain electromagnetic information in the time domain and frequency domain. Compared with electromagnetic signals in a single time domain or frequency domain, comprehensive time-frequency electromagnetic information can significantly improve the inversion accuracy of electrical properties. In order to obtain a refined geoelectric model, this paper proposes a one-dimensional adaptive time-frequency electromagnetic joint inversion algorithm by using the regularization inversion method and combining data constraints, model constraints, and step size constraints. The algorithm adopts an adaptive adjustment scheme for weighting balance factors and regularization factors of time domain and frequency domain data. At the same time, the model parameter search range constraint is combined with the WOLFE criterion to constrain the iterative step size while limiting the parameter search range, which ensures the stability and accuracy of the inversion process. The classical layered model, shale gas geo-electric model with noise, and measured data are used to test and analyze the inversion method. The results show that the constrained time-frequency electromagnetic joint inversion method can effectively improve the resolution of resistivity inversion of complex models. Therefore, it is of practical significance to improve the recognition accuracy of time-frequency electromagnetic methods for oil and gas reservoirs.
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