Studies on lithofacies sequences in the Hoanh Bo basin, Quang Ninh province by using Markov chain model and Entropy function

  • Affiliations:

    1 Hanoi University of Mining and Geology, Hanoi, Vietnam
    2 Phuong Dong University, Hanoi, Vietnam

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  • Received: 18th-Sept-2021
  • Revised: 29th-Dec-2021
  • Accepted: 11st-Jan-2022
  • Online: 28th-Feb-2022
Pages: 15 - 26
Views: 4852
Downloads: 3110
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The succession of lithofacies in the Dong Ho and Tieu Giao formation's Hoanh Bo basin was statistically analyzed using the modified Markov chain model and the function of Entropy. Based on the field definitions, petrographic investigation, and their borehole logs, the lithofacies study was carried out to determine the sediment deposition system and the deposition environment. Seventeen sub - lithofacies organized within the succession are recognized in three lithofacies associations. The analysis result of the Markov chain and chi-square or X2 test indicates that the deposition of the lithofacies is a non - markovian process and represents cyclic deposition of asymmetric fining - upward. To evaluate the randomness of the occurrence of lithofacies in a succession, entropy analysis was performed. Each state is associated with two types of entropy; one is relevant to the Markov matrix expressing upward transitions (entropy after deposition) and the other is relevant to the downward transition matrix (entropy before deposition). The energy regime calculated from the maximum randomness entropy analysis indicates that changing patterns in a deposition has resulted from rapid to steady flow. This results in a change in the depositional pattern from alluvial - fluvial to lacustrine environments, specifically from conglomerate facies (A1) → sandstone facies (A2)→ fine-grained and non - debris facies (A3).

How to Cite
Khuong, H.The and Lai, T.Thuy Thi 2022. Studies on lithofacies sequences in the Hoanh Bo basin, Quang Ninh province by using Markov chain model and Entropy function (in Vietnamese). Journal of Mining and Earth Sciences. 63, 1 (Feb, 2022), 15-26. DOI:

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