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Related Experiment Video

Updated: Aug 14, 2025

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

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Published on: August 5, 2016

Modelling parametric uncertainty in large-scale stratigraphic simulations.

A Mahmudova¹, A Civa², V Caronni²

¹Dipartimento di Ingegneria Civile ed Ambientale, Politecnico di Milano, Milan, Italy.

Scientific Reports

|January 16, 2023

Summary

This summary is machine-generated.

This study enhances sedimentary basin modeling by integrating forward stratigraphic models with uncertainty quantification and machine learning. This approach efficiently calibrates complex models, improving the characterization of geological formations.

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Last Updated: Aug 14, 2025

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Published on: September 7, 2019

Area of Science:

Geosciences
Computational Geology
Sedimentary Basin Analysis

Background:

Stratigraphic forward models (SFMs) are crucial for characterizing sedimentary successions but are computationally intensive.
Parameter uncertainty and high calibration costs limit the widespread application of SFMs in large-scale basin analysis.

Purpose of the Study:

To develop and demonstrate a computationally efficient approach for calibrating SFMs.
To assess the value of probabilistic modeling in understanding large-scale sedimentary basins.
To improve the characterization of sedimentary successions by addressing parameter uncertainty.

Main Methods:

Combined uncertainty quantification and stochastic model calibration algorithms with SFMs.
Employed a two-step parameter screening and sensitivity analysis.
Utilized model reduction techniques and machine learning for optimization.
Developed a data-driven reduced complexity model for efficient simulation.

Main Results:

Successfully calibrated SFMs by identifying key parameters and their distributions.
Quantified parameter uncertainty and assessed practical identifiability.
Characterized the spatial distribution of lithologies, including deep-sea sand bodies.
Demonstrated the approach using data from the Porcupine Basin, Ireland.

Conclusions:

The integrated probabilistic approach significantly reduces computational costs associated with SFM calibration.
This methodology enhances the characterization of sedimentary successions and geological formations.
The findings provide valuable insights into the information content of observational data for basin modeling.