NMR Log Data De-noising Method Based on a Variable Order Wavelet Packet Domain Adaptive Filtering
| LEADER | caa a22 4500 | ||
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| 001 | 605546304 | ||
| 003 | CHVBK | ||
| 005 | 20210128100940.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00723-015-0715-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00723-015-0715-y | ||
| 245 | 0 | 0 | |a NMR Log Data De-noising Method Based on a Variable Order Wavelet Packet Domain Adaptive Filtering |h [Elektronische Daten] |c [Xiangning Meng, Ranhong Xie, Mi Liu] |
| 520 | 3 | |a To improve the de-noising effects of low signal to noise ratio (SNR) nuclear magnetic resonance (NMR) log data, improve the calculating precision of the porosity parameters of the reservoirs, this paper attempts to apply the wavelet packet domain adaptive filtering algorithm to de-noise the NMR log data. First of all, the algorithm is interpreted in detail. And then, the de-noise off phenomenon is analyzed in the de-noising process of simulant and NMR echo data using the wavelet (packet) domain adaptive filtering algorithm. The factors affecting the occasion of the phenomenon are studied systematically, and a variable order processing scheme is proposed to eliminate the influence of the existence of the de-noise off phenomenon on the inversed T2 spectra. As a result, the effectiveness of the algorithm is verified by the application in the numerical simulation and NMR log data, respectively. The results indicated that, comparing with wavelet domain adaptive filtering algorithm, wavelet packet domain adaptive filtering algorithm is more suitable for low SNR-NMR echo data de-noising. | |
| 540 | |a Springer-Verlag Wien, 2015 | ||
| 700 | 1 | |a Meng |D Xiangning |u State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, 102249, Beijing, People's Republic of China |4 aut | |
| 700 | 1 | |a Xie |D Ranhong |u State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, 102249, Beijing, People's Republic of China |4 aut | |
| 700 | 1 | |a Liu |D Mi |u State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, 102249, Beijing, People's Republic of China |4 aut | |
| 773 | 0 | |t Applied Magnetic Resonance |d Springer Vienna |g 46/11(2015-11-01), 1265-1282 |x 0937-9347 |q 46:11<1265 |1 2015 |2 46 |o 723 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00723-015-0715-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00723-015-0715-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Meng |D Xiangning |u State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, 102249, Beijing, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Xie |D Ranhong |u State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, 102249, Beijing, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Liu |D Mi |u State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, 102249, Beijing, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Applied Magnetic Resonance |d Springer Vienna |g 46/11(2015-11-01), 1265-1282 |x 0937-9347 |q 46:11<1265 |1 2015 |2 46 |o 723 | ||