...Daniele Colombo; Ersan Turkoglu; Ernesto Sandoval-Curiel; Diego Rovetta; Weichang Li ABSTRACT We develop a novel physics-adaptive machine-learning (ML) inversion scheme showing optimal generalization capabilities for field data applications. We apply the physics-driven deep-learning inversion...

...Ehsan Zabihi Naeini; Kenton Prindle Abstract Machine learning has been around for decades or, depending on your view, centuries. To consider the tools and underpinnings of machine learning, one would need to go back to the work of Bayes and Laplace, the derivation of least squares, and Markov...

...‐dynamic (PD) approaches integrate the physical principles of dynamic rupture into a kinematic framework for efficient ground‐motion computation. To capture the complex nonlinear relationships between earthquake source parameters, machine learning (ML) approaches offer a suitable alternative to first‐order...

... methods have been introduced in the literature and operate under the supposition that background events occur independently whereas clusters are triggered by prior events. Here, we test the ability of supervised machine learning (SML) on the declustering problem by leveraging two popular statistical...

...Prithwijit Chowdhury; Ahmad Mustafa; Mohit Prabhushankar; Ghassan AlRegib ABSTRACT In geophysics, hydrocarbon prospect risking involves assessing the risks associated with hydrocarbon exploration by integrating data from various sources. Machine-learning-based classifiers trained on tabular data...

... of upholes and by multiple operators, the outcome is often an inconsistent velocity model. As machine learning (ML) algorithms continue to exhibit potential for advancing various subfields of geophysics, we introduce an ML-based approach for automatic interpretation of uphole and check-shot velocity profiles...

...Juan Wu; Renze Luo; Lei Luo; Canru Lei; Xingting Chen ABSTRACT Accurate prediction of porosity and permeability is crucial for understanding subsurface fluids. Traditional physical methodologies, however, are costly and time consuming. Moreover, existing machine-learning predictive methods require...

...Dipika Keshri; Shovan Lal Chattoraj; Rakesh Kumar Pandey; Kripamoy Sarkar ABSTRACT Numerous susceptibility modelling and mapping studies have been conducted in the past, aiming to mitigate landslides. In recent years, Machine Learning-assisted predictive modelling has gained tremendous attention...

... methods rely on complete waveform records, including earthquake epicenter distance and waveform amplitude, which can delay magnitude assessment. Machine learning techniques offer a promising avenue for capturing nonlinear relationships within seismic data, enhancing both information extraction...

.... However, the logging response to sparry calcite is weak and sample sizes are limited, making accurate identification challenging using conventional methods. To address this challenge, we develop data augmentation and attention-based machine-learning algorithms, specify logging curves as experimental data...

... shows that different time histories matching the same target spectrum can produce highly variable FRS estimates. This study examines the impact of spectrum‐matched time histories on biased FRS estimates using machine learning methods. We generated 1750 spectrum‐matched time histories based on five...

... of organic matter enrichment in Carboniferous Permian shales based on machine learning Donglin Lin1, Zhaodong Xi1,2,3 Shuheng Tang1,2,3 Gary G. Lash4, Yang Chen1, Qian Zhang2, Xiongxiong Yang2, and Tengfei Jia2 1S chool of Energy Resource, China University of Geosciences (Beijing), Beijing 100083, China 2K...

... challenges in association due to the vast numbers of observations and high likelihood of errant picks. In addition, the large number of stations can greatly increase the time it takes to perform the association. For this reason, machine learning (ML) methods might provide a more optimal method of association...

... the quality of a seismic reflector is poor. The uncertainty caused by inaccurate seismic horizon interpretations often misleads the volumetric analyses of subsurface reservoirs. To resolve these issues, we develop a four-step machine-learning-based semiautomated horizon interpretation workflow. In the first...

... – 1298 . Bravard , S. and Righi , D. ( 1990 ) Podzols in Amazonia . Catena , 17 , 461 – 475 . Breiman , L. ( 2001 ) Random forests . Machine Learning , 45 , 5 – 32 , https://doi.org/10.1023/A:1010933404324 . Brewer , A. , Teng , F.Z. , and Dethier , D...

... ), and Brazilian tensile strength (BTS) as the input parameters considering two statistical approaches i.e., multivariate linear regression (MVLR) and multivariate non-linear regression (MVNLR) and five machine learning (ML) algorithms i.e., random forests (RF), K-nearest neighbour (KNN), XG-Boost, support vector...

... based on machine learning (ML) to velocity model building has garnered significant attention due to their computational efficiency. However, most existing ML implementations provide only a single prediction for a given input, which may not adequately reflect the distribution of the testing data...

...I.A. Lisenkov; A.A. Soloviev; V.A. Kuznetsov; Yu.I. Nikolova The article presents a practical approach to the geological and geophysical spatial data collection and preliminary processing to use in machine learning models for geophysical applications. According to the established principles...

...Chengping Chai; Omar Marcillo; Monica Maceira; Junghyun Park; Stephen Arrowsmith; James O. Thomas; Joshua Cunningham Abstract Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities...

...Julián Luis Gómez; Emilio Camilion Abstract Machine learning is a powerful data-driven technology for regression and classification tasks in numerous branches of science and technology. With probabilistic machine learning, we can approximate the probability density function of a given digital data...