Full-waveform associated identification method of ATEM 3D anomalies based on multiple linear regression analysis

Yanju Ji; Wanyu Huang; Mingmei Yu; Shanshan Guan; Yuan Wang; Yu Zhu

doi:10.1117/12.2265560

23 January 2017 Full-waveform associated identification method of ATEM 3D anomalies based on multiple linear regression analysis

Yanju Ji, Wanyu Huang, Mingmei Yu, Shanshan Guan, Yuan Wang, Yu Zhu

Author Affiliations +

Proceedings Volume 10322, Seventh International Conference on Electronics and Information Engineering; 1032225 (2017) https://doi.org/10.1117/12.2265560
Event: Seventh International Conference on Electronics and Information Engineering, 2016, Nanjing, China

Abstract

This article studies full-waveform associated identification method of airborne time-domain electromagnetic method (ATEM) 3-d anomalies based on multiple linear regression analysis method. By using convolution algorithm, full-waveform theoretical responses are computed to derive sample library including switch-off-time period responses and off-time period responses. Extract full-waveform attributes from theoretical responses to derive linear regression equations which are used to identify the geological parameters. In order to improve the precision ulteriorly, we optimize the identification method by separating the sample library into different groups and identify the parameter respectively. Performance of full-waveform associated identification method with field data of wire-loop test experiments with ATEM system in Daedao of Changchun proves that the full-waveform associated identification method is feasible practically.

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Yanju Ji, Wanyu Huang, Mingmei Yu, Shanshan Guan, Yuan Wang, and Yu Zhu "Full-waveform associated identification method of ATEM 3D anomalies based on multiple linear regression analysis", Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 1032225 (23 January 2017); https://doi.org/10.1117/12.2265560

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KEYWORDS

Error analysis

Data modeling

Electromagnetism

Calibration

Statistical modeling

3D modeling

Convolution

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