Matching application of logging data based on DTW and its improved algorithm

Chunhua Zhou; Jun Feng; Tianyu Huang; Chao Ma

doi:10.1117/12.2656767

13 January 2023 Matching application of logging data based on DTW and its improved algorithm

Chunhua Zhou, Jun Feng, Tianyu Huang, Chao Ma

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125101H (2023) https://doi.org/10.1117/12.2656767
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

For the geophysical logging data acquired in reality, in order to calibrate the acquired logging information, it is necessary to compare the acquired multiple logging time series data. DTW algorithm has great advantages in measuring the similarity of two time series data. However, there will be more data points "one to many" or "many to many", so the overall accuracy of curve alignment will be reduced. In order to solve this problem, this paper adopts an improved DTW algorithm. The DTW algorithm of segmented mode distance is used to correspond to the stratigraphic division. Based on DTW, the weight improvement algorithm is introduced to optimize the corresponding accuracy of the sequence and improve the matching accuracy of the corresponding points. The experimental results show that the DTW algorithm based on segmented pattern distance plays an important role in matching the changing trend of sequence features. Compared with WDTW and DTW, the accuracy of the TDTW algorithm is obviously improved, and the corresponding goal of optimizing logging data is achieved.

Citation Download Citation

Chunhua Zhou, Jun Feng, Tianyu Huang, and Chao Ma "Matching application of logging data based on DTW and its improved algorithm", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125101H (13 January 2023); https://doi.org/10.1117/12.2656767

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