Mapping of Subsurface Geological Features for Predicting Cavities Based on True Resistivity using ARIMA and ALM Models

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Published: 2022-03-12

Page: 164-170


A. Arisona *

Department of Geological Engineering, Faculty of Earth Science and Technology, Haluoleo University, Kendari, Indonesia.

K. S. Ishola

Department of Geosciences, University of Lagos, Akoka, Lagos, Nigeria.

. Muliddin

Department of Geological Engineering, Faculty of Earth Science and Technology, Haluoleo University, Kendari, Indonesia.

. Hasria

Department of Geological Engineering, Faculty of Earth Science and Technology, Haluoleo University, Kendari, Indonesia.

L. D. Ngkoimani

Department of Geological Engineering, Faculty of Earth Science and Technology, Haluoleo University, Kendari, Indonesia.

. Masri

Department of Geological Engineering, Faculty of Earth Science and Technology, Haluoleo University, Kendari, Indonesia.

Ali Okto

Department of Geological Engineering, Faculty of Earth Science and Technology, Haluoleo University, Kendari, Indonesia.

L. A. Hamimu

Department of Geophysical Engineering, Haluoleo University, Kendari, Indonesia.

L. D. Safiuddin

Department of Mining Engineering, Haluoleo University, Kendari, Indonesia.

Erzam S. Hasan

Department of Geophysical Engineering, Haluoleo University, Kendari, Indonesia.

*Author to whom correspondence should be addressed.


Abstract

This study is an attempt to examine the emprical analysis of accuracy assessment of the true resistivity using the ARIMA and ALM models based on the RMSE and MAPE criterias. These involves F-calculated (F-cal), determinant coefficient (R2), and significance-α (Sig.α). Three parameters were used to show positive significance and best fit the field measurement data. However, the emphasis is on the applying ERI techniques for mapping of subsurface geological features to identify the presence of cavities in the cover layers. The prediction model shows that the smaller values of RMSE and MAPE are considered the best model. In addition, the ability of the ARIMA and ALM models to predict field measurement data (true resistivity) was tested and these result could be provide backups for future work with relevant data.

Keywords: ARIMA, ALM, RMSE, MAPE, true resistivity, subsurface geological, ERI


How to Cite

Arisona, A., Ishola, K. S., Muliddin, ., Hasria, ., Ngkoimani, L. D., Masri, ., Okto, A., Hamimu, L. A., Safiuddin, L. D., & Hasan, E. S. (2022). Mapping of Subsurface Geological Features for Predicting Cavities Based on True Resistivity using ARIMA and ALM Models. Asian Basic and Applied Research Journal, 4(1), 164–170. Retrieved from https://globalpresshub.com/index.php/ABAARJ/article/view/1490

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