Results 151 to 160 of about 1,357,139 (273)

Automatic Extraction of a Generalized Physics‐Based Feature From Time‐Lapse Active Source Ultrasonic Waveforms for Prediction of Shear Stress in Laboratory Friction Experiments

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Physics‐based handcrafted features, such as P‐wave amplitude derived from early parts of active source ultrasonic waveforms, successfully predict shear stress evolution. This study investigates whether feature extraction can be automated by leveraging full active source ultrasonic waveforms recorded during a laboratory friction experiment.
Prabhav Borate, Jacques Rivière, Daniel Kifer, Parisa Shokouhi   +3 more
wiley   +1 more source

Dimensionality Reduction Techniques for Analyzing Tsunami Simulations in Hazard Assessment and Forecasting Applications

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract A wide selection of linear and non‐linear dimensionality reduction techniques is evaluated on synthetic tsunami data that is the basis for tsunami hazard assessment and computational forecasting. The data were computed from earthquake rupture forecasts (ERFs) supplying initial generation conditions and a linear long‐wave Green's function ...
Eric L. Geist, Tom Parsons
wiley   +1 more source

Active protothrusts and fluid highways: Seismic noise reveals hidden subduction dynamics in Cascadia. [PDF]

Sci Adv
Kidiwela M, Denolle MA, Wilcock WSD, Feng KF.   +3 more
europepmc   +1 more source

Physics‐Constrained Variational Autoencoder for Uncertainty Quantification of Full Waveform Inversion

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract We propose a variational autoencoder framework to directly assess uncertainties in subsurface models produced by single‐ and multiparameter full waveform inversion (FWI). The new method does not require pretraining on labeled data, thus it significantly reduces computational cost and storage requirements.
Abdelrahman Elmeliegy, Mrinal K. Sen, Arnab Dhara, Jennifer Harding, Hongkyu Yoon   +4 more
wiley   +1 more source

DEMETRA-A Seismic Noise Survey at the Maccalube di Aragona Mud Volcanoes (Southern Italy): Results and Perspectives. [PDF]

Sensors (Basel)
Petrosino S, Madonia P, Gucciardo D, Cusano P.   +3 more
europepmc   +1 more source

Earthquake Seismogram Denoising Across Time, Time‐Frequency, and Hybrid Domain Approaches

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Deep learning earthquake signal denoisers commonly operate directly on waveform data or on time‐frequency representations, yet the impact of this design choice has not been systematically assessed under controlled conditions. Here we design and benchmark four comparable deep learning architectures to assess the effect of input data ...
Nikolaj L. Dahmen
wiley   +1 more source

Physics‐Informed Reservoir Characterization From Bulk and Extreme Pressure Events With a Differentiable Simulator

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Accurate characterization of subsurface heterogeneity is challenging but essential for applications such as reservoir pressure management, geothermal energy extraction and CO2 ${\text{CO}}_{2}$, H2 ${\mathrm{H}}_{2}$, and wastewater injection operations.
Harun Ur Rashid, Mingxin Li, Aleksandra Pachalieva, Georg Stadler, Daniel O’Malley   +4 more
wiley   +1 more source

Sources of seismic noise in an open-pit mining environment. [PDF]

Sci Rep
Diaz J, Torne M, Schimmel M, Rodríguez S, Martí D, Ruiz M, Seivane H, Sánchez-Pastor P, Davoise D.   +8 more
europepmc   +1 more source

Deep Network Reparameterized Full‐Waveform Inversion From Sequential to Simultaneous Sources With Adjoint‐State Method

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Full‐waveform inversion (FWI) relies on sequential‐source (SQ) workflows for high imaging precision, while simultaneous‐source (SML) encoding drastically accelerates computation but introduces irreversible wavenumber discontinuity artifacts in hybrid‐domain implementations.
Jinwei Fang, Jie Chen, Lutz Gross, Qingchen Zhang, Ning Wang   +4 more
wiley   +1 more source

A Machine‐Learning Surrogate for Differentiable Phase Equilibrium Modeling of Mantle Rocks on a Planetary Scale

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Modeling processes within Earth's and other planetary bodies' mantle requires constraints from phase equilibrium to infer how changes in phase abundance and composition affect physical properties and geochemical processes. When applying such thermodynamic models on a planetary scale, performance becomes especially crucial.
Philip Hartmeier, Pierre Lanari
wiley   +1 more source