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Fracture Network Modeling: Minkowski Functionals, Spatial Derivatives, and Gravitational Optimization
Effective modeling of fracture networks is essential for accurately simulating fluid dynamics and mass transfer processes in the subsurface...
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Unsupervised Machine Learning Methods
This chapter introduces unsupervised machine learning methods. It starts by describing the algorithms for dimensionality reduction, which include...
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Probability Theory
This chapter gives the axiomatic approach to probability theory, due to Kolmogorov. It defines the probability space and then introduces the...
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Fusion of Ground-Based and Spaceborne Radar Precipitation Based on Spatial Domain Regularization
High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and...
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Prediction of significant wave height using machine learning and its application to extreme wave analysis
Waves of large size can damage offshore infrastructures and affect marine facilities. In coastal engineering studies, it is essential to have the...
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Fracture network flow prediction with uncertainty using physics-informed graph features
The inherent uncertainty of subsurface fracture characteristics requires an ensemble-based approach where multiple network realizations are generated...
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Using edge detection techniques and machine learning classifications for accurate lithological discrimination and structure lineaments extraction: a comparative case study from Gattar area, Northern Eastern Desert of Egypt
Satellite images are used, among other functions, for geologic feature extraction. Scientists have developed advanced, cutting-edge, and reliable...
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Probability Density Functions and Their Use in Geology
Chapter 9 deals with probability density functions and their application to geology problems. It defines probability distributions and probability...
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Application of high-level Green–Naghdi theory to sill-controlled flows
Sill-controlled flows involving the acceleration across a crest or control section with significant flow curvature are characterized by the Euler...
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2DNMR data inversion using locally adapted multi-penalty regularization
Geologists and Reservoir Engineers routinely use time-domain nuclear magnetic resonance (NMR) to learn about the porous structure of rocks that hold...
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Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model
In the ensemble Kalman filter (EnKF) framework for data assimilation, a limited ensemble size results in a spurious sampling error and...
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Machine-learning-based prediction of regularization parameters for seismic inverse problems
Regularization parameter selection (RPS) is one of the most important tasks in solving inverse problems. The most common approaches seek the optimal...
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An improved dung beetle optimization with recurrent convolutional neural networks for efficient detection and classification of undersea water object images
The exploration of the underwater environment has become increasingly important due to the utilization and development of deep-sea resources in...
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EMT simulation and effect of TTI anisotropic media in EMT signal
An axisymmetric finite difference method is employed for the simulations of electromagnetic telemetry in the homogeneous and layered underground...
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A two-stage seismic data denoising network based on deep learning
Seismic data with a high signal-to-noise ratio is beneficial in the inversion and interpretation. Thus, denoising is an indispensable step in the...
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Simultaneous Monitoring of Different Drought Types Using Linear and Nonlinear Combination Approaches
Univariate drought indicators are insufficient for characterizing the complicated effects and conditions of droughts. Accordingly, this study aimed...
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Joint inversion of ocean-bottom pressure and GNSS data from the 2003 Tokachi-oki earthquake
When the 2003 Tokachi-oki earthquake generated a large tsunami off eastern Hokkaido, Japan, it was detected by cabled ocean-bottom pressure gauges...
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A new method of variational Bayesian slip distribution inversion
For slip distribution inversion with Bayesian theory, traditionally, the Markov Chain Monte Carlo (MCMC) method is well applied to generate a...
