Linear Regression for Flooding Surface Identification in Well Log, and Outcrop Image

  • Epo Prasetya Kusumah Universitas Pertamina
  • Ridha Santika Riadi Pertamina Hulu Sanga-sanga
  • Teguh Surino Setiawan Pertamina Hulu Sanga-sanga


Defining parasequences manually would take a huge amount of time. Interpretation subjectivity has also become an issue among stratigrapher when they are dealing with parasequence boundary identification which may resulting in inconsistency of parasequence identification. This paper means to present the use of automation in parasequence boundary identification using simple linear regression method in synthetic data, well log data, as well as outcrop image data.

In stratigraphy, vertical succession of lithology holds a very important meaning. Vertical succession of lithology in paralic setting where deposition occurred in a certain sea level might shows coarsening upward vertical succession. In the event where flooding occurred and sea level abruptly rise, the coarsening vertical succession might be disturbed by sharp change of lithology into finer particle, or simply called vertical discontinuity. Stratigaphers may use vertical discontinuity to identify the presence of flooding surfaces or parasequence boundaries.

Linear regression can be used to identify vertical discontinuity by measuring error occurred due to linear regression prediction. Vertical succession that showing deposition continuity might  show small error number in the data where vertical disturbance occurred. The error value might increase significantly. Thus, it would be possible to determine flooding surface using linear regression by applying some threshold. This method has been proven to work using both well log data and outcrop image data which might ease stratigraphy analysis workflow in general.


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How to Cite
KUSUMAH, Epo Prasetya; RIADI, Ridha Santika; SETIAWAN, Teguh Surino. Linear Regression for Flooding Surface Identification in Well Log, and Outcrop Image. Bulletin of Geology, [S.l.], v. 6, n. 2, p. 978-993, july 2022. ISSN 2580-0752. Available at: <>. Date accessed: 29 may 2023.