Reference health
Leak-Off Pressure Using Weakly Correlated Geospatial Information and Machine Learning Algorithms
19/19 checkable references clean · checked 2026-07-26
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
18 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 19 checked references that resolve
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What can machine learning do for seismic data processing? An interpolation application
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Deep Neural Network Based Prediction of Leak-Off Pressure in Offshore Norway
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Crustal stress in and around Norway: an evaluation of stress-generating mechanisms
resolves10.1007/s11004-013-9511-0
Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
resolves10.1016/j.ecolind.2019.02.026
Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation
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Probabilistic geomechanical analysis of compartmentalization at the Snøhvit CO<sub>2</sub> sequestration project
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no DOI — not checkedGlorot, X., Bordes, A., and Bengio, Y. (2011, January 11–13). Deep Sparse Rectifier Neural Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA.
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no DOI — not checkedHinkle, D.E., Wiersma, W., and Jurs, S.G. (2003). Applied Statistics for the Behavioral Sciences, Houghton Mifflin.
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no DOI — not checkedBrown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language Models Are Few-Shot Learners. arXiv.
no DOI — not checkedNg, A. (2018). CS229 Course Notes: Deep Learning, Stanford University.
no DOI — not checkedAndrews, J., Fintland, T.G., Helstrup, O.A., Horsrud, P., and Raaen, A.M. (2016). Use of Unique Database of Good Quality Stress Data to Investigate Theories of Fracture Initiation, Fracture Propagation and the Stress State in the Subsurface, American Rock Mechanics Association.
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