Investigation
on Estimation and Prediction of Resistivity of Limestone Rocks based on Physico-Mechanical
Properties of Rocks
Varalakshmi P., Reddy S.K. and Ch. S.N. Murthy
Disaster Advances; Vol. 18(5); 139-147;
doi: https://doi.org/10.25303/185da1390147; (2025)
Abstract
Prediction of rock resistivity indirectly is of paramount importance in several
geophysical and civil engineering applications. Physico-mechanical properties such
as p-wave velocity, porosity and dry density tend to have a good correlation with
electrical resistivity of rocks. Conventional approaches for measuring resistivity
produce results which may consume more time and efforts and are not accessible every
location. To overcome this, an Artificial Neural Network (ANN) model was evolved
in this study, using Python and TensorFlow. The model was trained using known values
to predict electrical resistivity of unknown and similar materials. Actual results
of resistivity were compared with resistivity values obtained from ANN model. The
obtained values were evaluated for reliability using non-linear regression models.
It was observed that predicted resistivity values generated using p-wave velocity
were more reliable. Also, validations made based on the ANN model, using mean absolute
error (MAE) and average residuals indicate that P-wave velocity is the most reliable
predictor, achieving the lowest MAE (4.638) and near-zero residuals (-0.005), while
porosity and dry density showed higher errors and weaker correlations. This study
revealed that the ANN model developed results in reliable predictions of rock resistivity
based on p-wave values.
