Applicability of machine learning for shear-wave velocity prediction from conventional well logs: the LSBoost approach.
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| Title: | Applicability of machine learning for shear-wave velocity prediction from conventional well logs: the LSBoost approach. |
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| Authors: | WIBOWO, Rahmat Catur1 rahmat.caturwibowo@eng.unila.ac.id, ARBY, Fadsyah Muhammad2, MULYATNO, Bagus Sapto2, DEWANTO, Ordas2, KUMALASARI, Isti Nur2, SARKOWI, Muh2 |
| Source: | Turkish Journal of Earth Sciences. 2026, Vol. 35 Issue 3, p236-249. 18p. |
| Subjects: | Boosting algorithms, Geophysical well logging, Geotechnical engineering, Machine learning, Multiple regression analysis, Model validation, Support vector machines |
| Abstract: | Shear-wave velocity (Vs) is one of the most critical parameters for determining geomechanical properties and basin overpressure. However, assessing Vs via techniques like core analysis requires considerable effort and expense. This study predicts Vs using several approaches and compares the accuracy levels of all models. For this objective, the multiple linear regression, multiple linear stepwise regression, support vector machine, and least-squares boost (LSBoost) methodologies were selected. The six well-logging data inputs of density (RHOB), gamma-ray (GR), deep resistivity (ILD), acoustic wave velocity (Vp), shale volume (VCL), and water saturation (SW) were selected as effective variables, whereas Vs was regarded as the output. The model was developed using data from the RCW-1 well and evaluated through 5-fold cross-validation and independent blind cross-well validation. The LSBoost model demonstrated robust and stable in-well performance, achieving a mean coefficient of determination (R² ) of 0.958 ± 0.003 and root mean square error (RMSE) of 56.48 ± 1.58 m/s, indicating effective learning without evidence of overfitting. Feature importance and Spearman rank correlation analyses consistently identified ILD and Vp as the most influential predictors, confirming the presence of nonlinear monotonic relationships between the input logs and Vs. However, blind application of the RCW-1-trained model to the independent RCW-2 well resulted in reduced predictive accuracy (R² = 0.266, RMSE = 287.95 m/s), reflecting geological and petrophysical domain shifts between wells rather than model inadequacy. Comparisons with empirical Castagna and Greenberg–Castagna correlations showed that while the empirical models provided relatively stable baseline predictions across wells, LSBoost significantly outperformed them within a consistent geological domain. These results highlight the potential of LSBoost for accurate Vs prediction when representative training data are available and emphasize the importance of multiwell datasets to improve cross-well generalization. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Journal of Earth Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194017265 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Applicability of machine learning for shear-wave velocity prediction from conventional well logs: the LSBoost approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22WIBOWO%2C+Rahmat+Catur%22">WIBOWO, Rahmat Catur</searchLink><relatesTo>1</relatesTo><i> rahmat.caturwibowo@eng.unila.ac.id</i><br /><searchLink fieldCode="AR" term="%22ARBY%2C+Fadsyah+Muhammad%22">ARBY, Fadsyah Muhammad</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22MULYATNO%2C+Bagus+Sapto%22">MULYATNO, Bagus Sapto</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22DEWANTO%2C+Ordas%22">DEWANTO, Ordas</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22KUMALASARI%2C+Isti+Nur%22">KUMALASARI, Isti Nur</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22SARKOWI%2C+Muh%22">SARKOWI, Muh</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Journal+of+Earth+Sciences%22">Turkish Journal of Earth Sciences</searchLink>. 2026, Vol. 35 Issue 3, p236-249. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Geophysical+well+logging%22">Geophysical well logging</searchLink><br /><searchLink fieldCode="DE" term="%22Geotechnical+engineering%22">Geotechnical engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+regression+analysis%22">Multiple regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Shear-wave velocity (Vs) is one of the most critical parameters for determining geomechanical properties and basin overpressure. However, assessing Vs via techniques like core analysis requires considerable effort and expense. This study predicts Vs using several approaches and compares the accuracy levels of all models. For this objective, the multiple linear regression, multiple linear stepwise regression, support vector machine, and least-squares boost (LSBoost) methodologies were selected. The six well-logging data inputs of density (RHOB), gamma-ray (GR), deep resistivity (ILD), acoustic wave velocity (Vp), shale volume (VCL), and water saturation (SW) were selected as effective variables, whereas Vs was regarded as the output. The model was developed using data from the RCW-1 well and evaluated through 5-fold cross-validation and independent blind cross-well validation. The LSBoost model demonstrated robust and stable in-well performance, achieving a mean coefficient of determination (R² ) of 0.958 ± 0.003 and root mean square error (RMSE) of 56.48 ± 1.58 m/s, indicating effective learning without evidence of overfitting. Feature importance and Spearman rank correlation analyses consistently identified ILD and Vp as the most influential predictors, confirming the presence of nonlinear monotonic relationships between the input logs and Vs. However, blind application of the RCW-1-trained model to the independent RCW-2 well resulted in reduced predictive accuracy (R² = 0.266, RMSE = 287.95 m/s), reflecting geological and petrophysical domain shifts between wells rather than model inadequacy. Comparisons with empirical Castagna and Greenberg–Castagna correlations showed that while the empirical models provided relatively stable baseline predictions across wells, LSBoost significantly outperformed them within a consistent geological domain. These results highlight the potential of LSBoost for accurate Vs prediction when representative training data are available and emphasize the importance of multiwell datasets to improve cross-well generalization. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Journal of Earth Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.55730/1300-0985.2017 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 236 Subjects: – SubjectFull: Boosting algorithms Type: general – SubjectFull: Geophysical well logging Type: general – SubjectFull: Geotechnical engineering Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Multiple regression analysis Type: general – SubjectFull: Model validation Type: general – SubjectFull: Support vector machines Type: general Titles: – TitleFull: Applicability of machine learning for shear-wave velocity prediction from conventional well logs: the LSBoost approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: WIBOWO, Rahmat Catur – PersonEntity: Name: NameFull: ARBY, Fadsyah Muhammad – PersonEntity: Name: NameFull: MULYATNO, Bagus Sapto – PersonEntity: Name: NameFull: DEWANTO, Ordas – PersonEntity: Name: NameFull: KUMALASARI, Isti Nur – PersonEntity: Name: NameFull: SARKOWI, Muh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13000985 Numbering: – Type: volume Value: 35 – Type: issue Value: 3 Titles: – TitleFull: Turkish Journal of Earth Sciences Type: main |
| ResultId | 1 |
