Results 121 to 130 of about 24,679 (283)

Learning Wave Scattering Properties From Seismograms

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Heterogeneities in the Earth's crust scatter seismic waves at many scales, trapping seismic energy and producing coda waves that encode valuable information on geological structures. In regions such as volcanoes and fault systems, analyzing coda waves is essential for characterizing non‐uniform subsurface heterogeneity, improving ...
Reza Esfahani, Mirko Bracale, Léonard Seydoux, Michel Campillo   +3 more
wiley   +1 more source

Testing Audio Compression Autoencoders for Seismology: Moving Toward Foundation Models

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Efforts to develop foundation models (FMs) for seismic waveform analysis are beginning to advance with progress still needed for geophysics applications. FMs learn a generalized representation of the data using a self‐supervised approach, thus allowing several downstream tasks to be performed in a unified framework.
Laura Laurenti, Christopher W. Johnson, Daniele Trappolini, Elisa Tinti, Fabio Galasso, Chris Marone   +5 more
wiley   +1 more source

Signal characteristics of surface seismic explosive sources near the West Antarctic Ice Sheet divide

Journal of Glaciology
Seismic imaging in 3-D holds great potential for improving our understanding of ice sheet structure and dynamics. Conducting 3-D imaging in remote areas is simplified by using lightweight and logistically straightforward sources.
Marianne S. Karplus, Nori Nakata, Galen M. Kaip, Steven H. Harder, Lucia F. Gonzalez, Adam D. Booth, Emma C. Smith, Stephen A. Veitch, Jacob I. Walter, Poul Christoffersen   +9 more
doaj   +1 more source

Neural Operators for Continuous Bias Correction of Water Level Forecast Guidance

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract The National Oceanic and Atmospheric Administration's (NOAA) Global Two‐Dimensional Surge and Tide Operational Forecast System (STOFS‐2D‐Global) provides global operational tidal, subtidal, and total water level forecast guidance with a 7.5‐day horizon.
Atieh Alipour, Fariborz Daneshvar, Soroosh Mani, Gregory Seroka, Lei Shi, Zizang Yang, Yuji Funakoshi, Georgios Britzolakis, Edward Payson Myers, Kamyar Azizzadenesheli, Saeed Moghimi   +10 more
wiley   +1 more source

A Unified Seismicity Catalog Development for Saudi Arabia: Multi-Network Fusion and Machine Learning-Based Anomaly Detection

Applied Sciences
This investigation concentrates on refining the accuracy of earthquake parameters as reported by various Saudi seismic networks, addressing the significant challenges arising from data discrepancies in earthquake location, depth, and magnitude ...
Sayed S. R. Moustafa, Mohamed H. Yassien, Mohamed Metwaly, Ahmad M. Faried, Basem Elsaka   +4 more
doaj   +1 more source

Earthquake Source Depth Determination Using Single Station Waveforms and Deep Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract In areas with limited station coverage, earthquake depth constraints are much less accurate than their latitude and longitude. Traditional travel‐time‐based location methods struggle to constrain depths due to imperfect station distribution and the strong trade‐off between source depth and origin time.
Wenda Li, Miao Zhang
wiley   +1 more source

The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today

Risks
I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio
Nicolas Houlié
doaj   +1 more source

Forecasting the Rate of Induced Seismicity as a Neural Temporal Point Process

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Forecasting is an essential part of risk mitigation, where the mitigation efficacy depends strongly on the quality of forecasts. We explore the neural temporal point process as a deep learning framework to forecast induced earthquakes. We train our deep learning model using numerous enhanced geothermal systems and hydraulic fracturing test ...
Ryan Schultz, Stefan Wiemer
wiley   +1 more source

Applying spectral decomposition to seismic facies clustering with unsupervised machine learning

Геофизический журнал
Seismic facies analysis, essential for subsurface geological exploration, has traditionally challenged the ability to capture subtle variations in complex stratigraphic environments.
Ruslan Malikov, Gulam Babayev
doaj   +1 more source

Addressing Known Challenges in Solar Flare Forecasting I: Limb‐Flare Prediction With a 4π Full‐Heliosphere Framework

Space Weather, Volume 24, Issue 2, February 2026.
Abstract A demonstrated failure mode for operational solar flare forecasting is the inability to forecast flares that occur near, or just beyond, the solar limb. To address this shortcoming, we develop a “4π $4{\uppi }$” full‐heliosphere event forecasting framework and evaluate its statistical classification ability against this specific challenge.
K. D. Leka, Eric L. Wagner, Lisa Upton, Bibhuti Kumar Jha, Kiran Jain, Sara Petty   +5 more
wiley   +1 more source