Results 101 to 110 of about 1,345 (197)

Three‐Dimensional Forward Modeling of Vector and Tensor Gravitational Fields Using the FFT‐Accelerated Finite‐Difference Scheme

Earth and Space Science, Volume 13, Issue 3, March 2026.
Abstract Three‐dimensional gravity forward modeling with conventional numerical methods requires solving large‐scale linear system using direct matrix inversion or iterative solvers, incurring substantial computational costs that critically limit large‐scale three‐dimensional inversions.
Xiaozhong Tong, Yanfang Hu, Pu Yang, Kanggui Wei   +3 more
wiley   +1 more source

Fourier Neural Operator for Moonquake Detection

Earth and Space Science, Volume 13, Issue 3, March 2026.
Abstract Moonquakes provide critical observations for probing the lunar interior, yet their analysis is hindered by the limited number of recordings and their inherently low signal‐to‐noise ratio (S/N). Conventional detection methods such as Short‐Term Average/Long‐Term Average (STA/LTA) perform poorly on lunar data, while standard deep learning models
Basem Al‐Qadasi, Umair Bin Waheed
wiley   +1 more source

A Random Walker Algorithm for Plate Boundary Detection in Spherical Mantle Convection Models and Global Geophysical Data Sets: Application to Euler Vector Determination

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 3, March 2026.
Abstract As spherical shell mantle convection models become increasingly commonplace, understanding how plates are generated has raised the issue of how to recognize whether rigid plates are present in model output. Tectonocists have long recognized that intraplate regions are not rigid without exception.
P. Javaheri, J. P. Lowman
wiley   +1 more source

Guided Unconditional and Conditional Generative Models for Super‐Resolution and Inference of Quasi‐Geostrophic Turbulence

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 3, March 2026.
Abstract Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy. We apply four generative diffusion modeling approaches to super‐resolution and inference of forced two‐dimensional quasi‐geostrophic turbulence on the β $\beta $‐plane from coarse, sparse, and gappy observations.
Anantha Narayanan Suresh Babu, Akhil Sadam, Pierre F. J. Lermusiaux   +2 more
wiley   +1 more source

Modeling Transient Groundwater Flow in Unconfined Aquifers Under Dynamic Conditions Using Physics‐Informed Neural Networks

Water Resources Research, Volume 62, Issue 3, March 2026.
Abstract Deep learning neural networks (DLNNs) hold great potential for modeling groundwater flow, but their performance depends on data availability. Physics‐informed neural networks (PINNs) help to reduce the reliance of DLNNs on data by integrating physical laws into the training process. This approach is increasingly used in applications related to
Adhish Virupaksha, François Lehmann, Hussein Hoteit, Anis Younes, Marwan Fahs, Thomas Nagel   +5 more
wiley   +1 more source

CUQIpy: II. Computational uncertainty quantification for PDE-based inverse problems in Python

Inverse Problems
Abstract Inverse problems, particularly those governed by Partial Differential Equations (PDEs), are prevalent in various scientific and engineering applications, and uncertainty quantification (UQ) of solutions to these problems is essential for informed decision-making.
Amal M A Alghamdi, Nicolai A B Riis, Babak M Afkham, Felipe Uribe, Silja L Christensen, Per Christian Hansen, Jakob S Jørgensen   +6 more
openaire   +4 more sources

Multichannel Wavefield Reconstruction With Physics‐Informed Neural Networks and Transfer Learning

Geophysical Prospecting, Volume 74, Issue 3, March 2026.
ABSTRACT Multi‐component seismic data contain rich information essential for accurate subsurface imaging, but they are often sparse due to acquisition limitations and include noise. Robust interpolation techniques are therefore crucial to reconstruct missing traces and preserve wavefield integrity for reliable analysis and inversion. Thus, we propose a
Francesco Brandolin, Matteo Ravasi, Tariq Alkhalifah   +2 more
wiley   +1 more source

On Qualitative Experimental Design for PDE Parameter Identification Inverse Problems [PDF]

Der Erfolg der Rekonstruktion eines Modellparameters aus indirekten experimentellen Messungen durch Lösen des inversen Problems hängt ma{\ss}geblich von der Qualität der zur Verfügung stehenden Daten ab. Ziel der Versuchsplanung ist daher die Auswahl von geeigneten experimentellen Setups, die für diese Aufgabe informative Daten erzeugen.
openaire   +1 more source

A Deep Learning‐Based Time Shift Objective Function for Full Waveform Inversion

Geophysical Prospecting, Volume 74, Issue 3, March 2026.
ABSTRACT Full waveform inversion (FWI) is a powerful technique for estimating high‐resolution subsurface velocity models by minimizing the discrepancy between modelled and observed seismic data. However, the oscillatory nature of seismic waveforms makes point‐wise discrepancy measures highly prone to cycle skipping, especially when the initial velocity
Mustafa Alfarhan, Fuqiang Chen, George Turkiyyah, David Keyes, Ivan Vasconcelos, Matteo Ravasi   +5 more
wiley   +1 more source

Quasi‐Fuchsian flows and the coupled vortex equations

Bulletin of the London Mathematical Society, Volume 58, Issue 3, March 2026.
Abstract We provide an alternative construction of the quasi‐Fuchsian flows introduced by Ghys. Our approach is based on the coupled vortex equations that allows to see these flows as thermostats on the unit tangent bundle of the Blaschke metric, uniquely determined by a conformal class and a holomorphic quadratic differential.
Mihajlo Cekić, Gabriel P. Paternain
wiley   +1 more source