Results 141 to 150 of about 15,948 (253)

Seismological Computing Centre, Edinburgh [PDF]

Nature, 1963
openaire   +1 more source

Estimating Mediterranean Cyclone Activity via Explainable Machine Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Intense cyclones in the Mediterranean drive most of the region's rainfall and wind‐wave extremes, exerting a significant socio‐economic impact. Currently there are no established analytical tools for estimating Mediterranean cyclone activity from climatological fields.
Guido Ascenso, Luca Proserpio, Leone Cavicchia, Enrico Scoccimarro, Matteo Giuliani, Andrea Castelletti   +5 more
wiley   +1 more source

Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Physics‐informed neural networks (PINNs) integrate physical constraints with neural architectures and leverage their nonlinear fitting capabilities to solve complex inverse problems. Tomography serves as a classic example, aiming to reconstruct subsurface velocity models to improve seismic exploration.
Yonghao Wang, Zhuo Jia, Bowu Jiang, Wenkai Lu   +3 more
wiley   +1 more source

FocoNet: Transformer‐Based Focal‐Mechanism Determination

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Traditional focal‐mechanism determination primarily relies on fitting the first‐motion polarities with grid‐search algorithms. We developed a machine‐learning model, FocoNet, to include more seismic information into focal mechanism determination.
Xiaohan Song, Men‐Andrin Meier, William L. Ellsworth, Gregory C. Beroza   +3 more
wiley   +1 more source

An Effective Physics‐Informed Neural Operator Framework for Predicting Wavefields

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Solving the wave equation is fundamental for many geophysical applications. However, numerical solutions of the Helmholtz equation face significant computational and memory challenges. Therefore, we introduce a physics‐informed convolutional neural operator (CNO) (PICNO) to solve the Helmholtz equation efficiently.
X. Ma, T. Alkhalifah
wiley   +1 more source

GeoFWI: A Large Velocity Model Data Set for Benchmarking Full Waveform Inversion Using Deep Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Full waveform inversion (FWI) plays an increasingly important role in the field of seismic imaging due to its strong ability to estimate subsurface properties. Specifically, data‐driven FWI (DDFWI) establishes a straightforward mapping relationship between seismic data and the corresponding velocity model, yielding promising results.
Chao Li, Yiran Shen, Sergey Fomel, Umair Bin Waheed, Alexandros Savvaidis, Yangkang Chen   +5 more
wiley   +1 more source

CNXT‐Ti‐LT–Based Multi‐Scale Feature–Aware Susceptibility Mapping of Rainfall‐Induced Clustered Landslides in Southeast China

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract This study focuses on the clustered landslide event triggered by intense rainfall on 16 June 2024 in the Fujian–Guangdong–Jiangxi border region, aiming to develop an efficient deep learning model for high‐accuracy landslide susceptibility mapping. Based on the mapped landslide distribution and insights from field investigations, we constructed
Senlin Luo, Wuwei Mao, Zhiqiang Yang, Guoming Zheng, Zhonghui He, Jiahuang Wang, Yu Huang   +6 more
wiley   +1 more source

TOMO4D Operator for Assimilation of GNSS Tomography‐Derived Water Vapor Fields Into the WRFDA 4DVAR System to Improve Regional Rainfall Forecasting

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 4, April 2026.
Abstract Accurate representation of atmospheric water vapor is crucial for improving numerical weather prediction, particularly over regions with complex topography and sparse observation networks. Although assimilation of Global Navigation Satellite System (GNSS)‐derived integrated products such as zenith total delay or precipitable water vapor can ...
Arash Tayfehrostami, Yazdan Amerian, Saeed Izanlou, Majid Azadi   +3 more
wiley   +1 more source

From Points to Planes: A Workflow for Converting Three‐Dimensional Point Cloud Data Into Discrete Fracture Network Flow and Transport Models

Water Resources Research, Volume 62, Issue 4, April 2026.
Abstract We present the Point cLoud Algorithm for NEtwork Extraction of Discrete Fracture Networks (PLANE‐DFN), a point cloud–based algorithm for automatic fracture network extraction designed to support discrete fracture network (DFN) modeling workflows. PLANE‐DFN segments three‐dimensional fracture planes from raw point cloud data using RANdom SAmple
Collin R. Sutton, Jeffrey D. Hyman, Aleksandra A. Pachalieva, Christopher Zahasky   +3 more
wiley   +1 more source

Fine‐Scale Segmentation and Spatiotemporal Variability of the 2010 Mw 8.8 Maule Aftershock Sequence Revealed by a Deep‐Learning‐Based Earthquake Catalog

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 4, April 2026.
Abstract We re‐examine the aftershock sequence of the Mw 8.8 Maule earthquake in south‐central Chile to understand how seismicity, magnitude‐frequency distribution, and fault structure vary along the rupture zone. Using the International Maule Aftershock Deployment (IMAD) data set, we analyze 10 months of continuous data from approximately 156 ...
Rodrigo Flores‐Allende, Léonard Seydoux, Éric Beaucé, Luis Fabian Bonilla, Philippe Gueguen, Claudio Satriano   +5 more
wiley   +1 more source