Open AccessCoal velocity and proximate analysis relationship using Multiple Linear Regression
Author(s) -
Sarah M. Erfani,
Fajri Marindra Siregar,
Arif Zaenudin,
Rustadi,
Ida Bagus Suanand Yogi,
Rahmat Catur Wibowo
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1173/1/012010
Subject(s) - linear regression , coal , regression analysis , proximate , regression , statistics , mathematics , volume (thermodynamics) , coal mining , mineralogy , soil science , environmental science , geology , chemistry , physics , thermodynamics , food science , organic chemistry
Coal properties such a velocity (Vp) are essential to building a lateral distribution of coal seam using seismic data. The experimental determination of velocity analysis is sophisticated, long time consumed, and expensive. On the contrary, statistical approaches such as linear regression can be run rapidly. The study’s two main objectives were to develop models for coal velocity using well log data variables (density and natural Gamma-Ray) and found the relationship between velocity with proximate analysis results. Multiple linear regression (MLR) methods were applied to estimate Vp’s relationship between estimated and proximate analysis. By conducting cross-validation, the prediction analysis of the models has been tested by using R 2 . The result showed that between Vp estimated versus Vp log have R 2 0.80 and Vp estimated versus proximate analysis that reflected have R 2 of 0.52. Correlations can estimate the relationship between Vp and proximate analysis, then applied that correlation to distributed in seismic volume to obtain coal seam characteristic.