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Application of Wavelet De-Noising for Travel-Time Based Hydraulic Tomography

1
Applied Geology, Geoscience Centre, University of Goettingen, Goldschmidtstr. 3, 37077 Goettingen, Germany
2
School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(6), 1533; https://doi.org/10.3390/w12061533
Received: 5 March 2020 / Revised: 21 April 2020 / Accepted: 24 May 2020 / Published: 27 May 2020
(This article belongs to the Section Hydraulics and Hydrodynamics)
Travel-time based hydraulic tomography is a promising method to characterize heterogeneity of porous-fractured aquifers. However, there is inevitable noise in field-scale experimental data and many hydraulic signal travel times, which are derived from the pumping test’s first groundwater level derivative drawdown curves and are strongly influenced by noise. The required data processing is thus quite time consuming and often not accurate enough. Therefore, an effective and accurate de-noising method is required for travel time inversion data processing. In this study, a series of hydraulic tomography experiments were conducted at a porous-fractured aquifer test site in Goettingen, Germany. A numerical model was built according to the site’s field conditions and tested based on diagnostic curve analyses of the field experimental data. Gaussian white noise was then added to the model’s calculated pumping test drawdown data to simulate the real noise in the field. Afterward, different de-noising methods were applied to remove it. This study has proven the superiority of the wavelet de-noising approach compared with several other filters. A wavelet de-noising method with calibrated mother wavelet type, de-noising level, and wavelet level was then determined to obtain the most accurate travel time values. Finally, using this most suitable de-noising method, the experimental hydraulic tomography travel time values were calculated from the de-noised data. The travel time inversion based on this de-noised data has shown results consistent with previous work at the test site. View Full-Text
Keywords: hydraulic travel time inversion; hydraulic tomography; porous-fractured aquifer; wavelet de-noising; numerical model hydraulic travel time inversion; hydraulic tomography; porous-fractured aquifer; wavelet de-noising; numerical model
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MDPI and ACS Style

Yang, H.; Hu, R.; Qiu, P.; Liu, Q.; Xing, Y.; Tao, R.; Ptak, T. Application of Wavelet De-Noising for Travel-Time Based Hydraulic Tomography. Water 2020, 12, 1533. https://doi.org/10.3390/w12061533

AMA Style

Yang H, Hu R, Qiu P, Liu Q, Xing Y, Tao R, Ptak T. Application of Wavelet De-Noising for Travel-Time Based Hydraulic Tomography. Water. 2020; 12(6):1533. https://doi.org/10.3390/w12061533

Chicago/Turabian Style

Yang, Huichen, Rui Hu, Pengxiang Qiu, Quan Liu, Yixuan Xing, Ran Tao, and Thomas Ptak. 2020. "Application of Wavelet De-Noising for Travel-Time Based Hydraulic Tomography" Water 12, no. 6: 1533. https://doi.org/10.3390/w12061533

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