Quantitative logging data clustering with hidden Markov model to assist log unit classification
Abstract Revealing subsurface structures is a fundamental task in geophysical and geological studies. Logging data are usually acquired through drilling projects, which constrain the subsurface structure, and together with the description of drill core samples, are used to distinguish geological uni...
Những tác giả chính: | Suguru Yabe, Yohei Hamada, Rina Fukuchi, Shunichi Nomura, Norio Shigematsu, Tsutomu Kiguchi, Kenta Ueki |
---|---|
Định dạng: | Bài viết |
Ngôn ngữ: | English |
Được phát hành: |
SpringerOpen
2022-06-01
|
Loạt: | Earth, Planets and Space |
Những chủ đề: | |
Truy cập trực tuyến: | https://doi.org/10.1186/s40623-022-01651-0 |
Những quyển sách tương tự
-
A new method for the empirical conversion of logging data to clay mineral fraction in the Nankai accretionary prism
Bằng: Suguru Yabe, et al.
Được phát hành: (2020-10-01) -
Simultaneous estimation of in situ porosity and thermal structure from core sample measurements and resistivity log data at Nankai accretionary prism
Bằng: Suguru Yabe, et al.
Được phát hành: (2019-11-01) -
Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models
Bằng: Stephen Adams, et al.
Được phát hành: (2016-01-01) -
Visual tracking using interactive factorial hidden Markov models
Bằng: Jin Wook Paeng, et al.
Được phát hành: (2021-08-01) -
UTILIZING DISCRETE HIDDEN MARKOV MODELS TO ANALYZE TETRAPLOID PLANT BREEDING
Bằng: Nahrul Hayati, et al.
Được phát hành: (2024-10-01)