Acoustic impedance inversion via voting stacked regression (VStaR) algorithms
Abstract In this study, we focused on improving acoustic impedance (AI) in seismic exploration. AI is a crucial parameter estimated by multiplying the density of a material by the velocity of an acoustic wave passing through it. A low AI in sandstones and carbonates often indicates high porosity, wh...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-06332-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849334358603726848 |
|---|---|
| author | Lutfi Mulyadi Surachman Sanlin I. Kaka Abdullatif Al-Shuhail |
| author_facet | Lutfi Mulyadi Surachman Sanlin I. Kaka Abdullatif Al-Shuhail |
| author_sort | Lutfi Mulyadi Surachman |
| collection | DOAJ |
| description | Abstract In this study, we focused on improving acoustic impedance (AI) in seismic exploration. AI is a crucial parameter estimated by multiplying the density of a material by the velocity of an acoustic wave passing through it. A low AI in sandstones and carbonates often indicates high porosity, which enhances hydrocarbon accumulation. Accurate AI estimation is thus critical for reliable hydrocarbon exploration. To refine the AI estimation, we used stacking and voting regression algorithms, with depth, two-way travel time (TWTT), and nine seismic attributes as inputs. All models were implemented using scikit-learn . The VStaR model achieved superior predictive performance ( $$\hbox {R}^{2}$$ = 0.9973) and yielded a more accurate fitting parameter (a = 0.1584) in the acoustic impedance–porosity transformation compared to the VSR ( $$\hbox {R}^{2}$$ = 0.9775, a = 0.1583). The VSR approach made the voting of a top-performing base model with two less predictive base models, as used in the existing literature. Relative to the true and BLIMP-derived impedance, the fitting accuracy followed the order of true > VStaR > VSR > BLIMP. While VStaR required longer computation time ( $$\approx$$ 400 s), it reduced RMSE by 14.74% compared to the top-performing base model. VStaR outperformed all evaluated models based on MSE, RMSE, and $$\hbox {R}^{2}$$ metrics. The novelty of the VStaR method based on hyperparameters lies in its superior performance in obtaining a more precise prediction of acoustic impedance compared to the VSR and conventional BLIMP method, potentially improving the effectiveness of hydrocarbon exploration in Illam carbonate dataset. |
| format | Article |
| id | doaj-art-ea82a6edc6ae43c5818eba285383a8f3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ea82a6edc6ae43c5818eba285383a8f32025-08-20T03:45:35ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-06332-yAcoustic impedance inversion via voting stacked regression (VStaR) algorithmsLutfi Mulyadi Surachman0Sanlin I. Kaka1Abdullatif Al-Shuhail2Geoscience Department, College of Petroleum Engineering and Geoscience, King Fahd University of Petroleum and MineralsGeoscience Department, College of Petroleum Engineering and Geoscience, King Fahd University of Petroleum and MineralsGeoscience Department, College of Petroleum Engineering and Geoscience, King Fahd University of Petroleum and MineralsAbstract In this study, we focused on improving acoustic impedance (AI) in seismic exploration. AI is a crucial parameter estimated by multiplying the density of a material by the velocity of an acoustic wave passing through it. A low AI in sandstones and carbonates often indicates high porosity, which enhances hydrocarbon accumulation. Accurate AI estimation is thus critical for reliable hydrocarbon exploration. To refine the AI estimation, we used stacking and voting regression algorithms, with depth, two-way travel time (TWTT), and nine seismic attributes as inputs. All models were implemented using scikit-learn . The VStaR model achieved superior predictive performance ( $$\hbox {R}^{2}$$ = 0.9973) and yielded a more accurate fitting parameter (a = 0.1584) in the acoustic impedance–porosity transformation compared to the VSR ( $$\hbox {R}^{2}$$ = 0.9775, a = 0.1583). The VSR approach made the voting of a top-performing base model with two less predictive base models, as used in the existing literature. Relative to the true and BLIMP-derived impedance, the fitting accuracy followed the order of true > VStaR > VSR > BLIMP. While VStaR required longer computation time ( $$\approx$$ 400 s), it reduced RMSE by 14.74% compared to the top-performing base model. VStaR outperformed all evaluated models based on MSE, RMSE, and $$\hbox {R}^{2}$$ metrics. The novelty of the VStaR method based on hyperparameters lies in its superior performance in obtaining a more precise prediction of acoustic impedance compared to the VSR and conventional BLIMP method, potentially improving the effectiveness of hydrocarbon exploration in Illam carbonate dataset.https://doi.org/10.1038/s41598-025-06332-ySeismic attributesAcoustic impedanceHydrocarbon explorationBase ModelsFinal estimators3-fold cross-validation |
| spellingShingle | Lutfi Mulyadi Surachman Sanlin I. Kaka Abdullatif Al-Shuhail Acoustic impedance inversion via voting stacked regression (VStaR) algorithms Scientific Reports Seismic attributes Acoustic impedance Hydrocarbon exploration Base Models Final estimators 3-fold cross-validation |
| title | Acoustic impedance inversion via voting stacked regression (VStaR) algorithms |
| title_full | Acoustic impedance inversion via voting stacked regression (VStaR) algorithms |
| title_fullStr | Acoustic impedance inversion via voting stacked regression (VStaR) algorithms |
| title_full_unstemmed | Acoustic impedance inversion via voting stacked regression (VStaR) algorithms |
| title_short | Acoustic impedance inversion via voting stacked regression (VStaR) algorithms |
| title_sort | acoustic impedance inversion via voting stacked regression vstar algorithms |
| topic | Seismic attributes Acoustic impedance Hydrocarbon exploration Base Models Final estimators 3-fold cross-validation |
| url | https://doi.org/10.1038/s41598-025-06332-y |
| work_keys_str_mv | AT lutfimulyadisurachman acousticimpedanceinversionviavotingstackedregressionvstaralgorithms AT sanlinikaka acousticimpedanceinversionviavotingstackedregressionvstaralgorithms AT abdullatifalshuhail acousticimpedanceinversionviavotingstackedregressionvstaralgorithms |