Stochastic simulation of geological cross-sections from boreholes: A random field approach with Markov Chain Monte Carlo method

Yang, Hao-Qing; Chu, Jian; Qi, Xiaohui; Wu, Shifan; Chiam, Kiefer

doi:10.1016/j.enggeo.2023.107356

Engineering Geology

2023

DOI: 10.1016/j.enggeo.2023.107356

|View full text |Cite

Stochastic simulation of geological cross-sections from boreholes: A random field approach with Markov Chain Monte Carlo method

Hao-Qing Yang,

Jian Chu,

Xiaohui Qi

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 10 publications

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

Atangana Njock,

Yin,

Zhang

2024

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

Contemporary geoengineering challenges grapple with the plateauing of both existing algorithms and their depth of insights, a phenomenon exacerbated by the scarcity of high‐fidelity data. Although existing solutions such as Monte‐Carlo method can generate abundant data, they are not sufficiently robust for ensuring the high fidelity of data. This study proposes a novel data augmentation framework that combines statistical and machine learning methods to generate high‐fidelity synthetic data, which closely align with field data in terms of the statistical and empirical attributes. The innovations of the proposed approach lie in the integration of Copulas theory for data generation, a developed geo‐regression anomaly detection (GRAD) for adjusting data attributes, and an evolutionary polynomial regression for data consistency enforcement. The multilayer perceptron (MLP) and a wide‐and‐deep (WaD) networks are applied to assess the effectiveness of high‐fidelity data augmentation using jet grouting data. The outcomes reveal the robustness of the synthetic data generation framework, achieving satisfactory fidelity in both empirical and statistical attributes. The proposed data augmentation improved the R2 and MAE achieved by MLP and WaD up to 28.37% under data fractions ranging from 0.2 to 1. MLP and WaD yielded comparable results in terms of accuracy and generalization ability across various augmented fractions. This indicates that the accuracy of synthetic data plays a pivotal role, suggesting improving data quality can be highly effective in boosting performance, regardless of the model complexity. This study contributes valuable insights to addressing the challenges of scare high‐fidelity data in geoengineering.

show abstract

High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

Atangana Njock,

Yin,

Zhang

2024

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

show abstract

A flexible and efficient model coupling multi-type data for 2D/3D stratigraphic modeling

Yan,

Guan,

Zhou

et al. 2024

Bull Eng Geol Environ

View full text Add to dashboard Cite

Predicting long-term displacements of deep tunnels using an artificial neural network optimized by sand cat swarm optimization with Chebyshev map

Zaheri,

Li,

Ranjbarnia

et al. 2024

Environ Earth Sci

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Stochastic simulation of geological cross-sections from boreholes: A random field approach with Markov Chain Monte Carlo method

Cited by 10 publications

References 45 publications

High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

A flexible and efficient model coupling multi-type data for 2D/3D stratigraphic modeling

Predicting long-term displacements of deep tunnels using an artificial neural network optimized by sand cat swarm optimization with Chebyshev map

Contact Info

Product

Resources

About