General & Applied Physics · Physics & Astronomy
Geophysics
The newest papers in Geophysics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
34 papers, from 7 September 2026 to 4 November 2026: 34 arXiv preprints.
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Newest papers
From hydrological laws to catchment models: A process-based model matches deep-learning accuracy on three continents
Dooge asked whether hydrology has laws at the catchment scale.Controls on the deformation and reorientation of mafic enclaves during magma mingling: A computational study
Mafic microgranular enclaves in granitic plutons record magma mingling, and their shapes are often interpreted as a record of the flow that deformed them.DeepWaveRL: Self-Supervised Full Waveform Inversion via Reinforcement Learning
Full Waveform Inversion (FWI) is a fundamental inverse problem in geophysics to estimate subsurface properties, such as velocity, from seismic measurements.A Geometry-Informed Neural Operator surrogate of LISFLOOD-FP for rapid pluvial-fluvial flood forecasting: the case of Forlì, Italy
Physically based two-dimensional hydraulic models are too slow for real-time inundation forecasting and large ensembles.Benchmark Study for Quantum Statevector Simulators for Scientific Computing
Quantum circuit simulators are essential for quantum algorithm development, yet systematic runtime benchmarks comparing major frameworks across CPU and GPU backends are limited.Forward-Backward Map Matching for Airborne Magnetic Anomaly Navigation
Airborne magnetic anomaly navigation provides a passive, unjammable alternative to Global Navigation Satellite Systems positioning by matching magnetometer measurements against a lithospheric…Expert lithological labels do not improve learning when used to predict marine sediment properties
Scientific drilling has produced large archives of physical, chemical and magnetic measurements linked to descriptions of the rock recovered.Satellite-derived bathymetry of Arabian Gulf ports under leakage-free validation: a multi-site benchmark of empirical, machine-learning and deep-learning models against 285,000 multibeam soundings, with an outlook for UAE waters
Satellite-derived bathymetry (SDB) is often proposed as a low-cost complement to multibeam surveys of ports, but its accuracy is seldom measured on an area the model has never seen.Space weather dependence of decameter-scale ionospheric irregularities spatial distribution
This paper describes an approach to constructing nowcast and forecast predictive models for the occurrence of high-latitude decameter ionospheric irregularities based on SuperDARN and SECIRA radar…HypoNet Nankai PS: rapid hypocenter determination for the Nankai Trough using physics-informed neural networks with three-dimensional P- and S-wave velocity models
Rapid and accurate hypocenter determination based on three-dimensional (3D) seismic velocity models is essential for seismic monitoring in subduction zones, yet it remains computationally demanding.Quantifying the Threat: A Bayesian Probabilistic Framework for Heavy Metal Health Risks among Children in the Densu Basin, Ghana
Groundwater in Ghana's Densu River Basin remains a key domestic water source, but multi-metal contamination threatens children's health.Ammonia-Hydrogen Phase Equilibria in Planetary Envelopes from First-Principles-Based Machine-Learning Molecular Dynamics
Ammonia and hydrogen are major constituents of planets within and beyond the solar system, yet how they interact in planetary envelopes is poorly known.Emergent Sparsity: How Many Effective Variables Survive Coarse-Graining in AlphaEarth Embeddings?
AlphaEarth encodes each 10 m land pixel as a 64-component vector intended to support diverse global mapping tasks.Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes
Deep learning models, particularly Long Short-Term Memory networks, are increasingly used for operational streamflow forecasting, with ensemble spread providing a signal of predictive uncertainty.Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent
Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, especially when the initial model is inaccurate…CartoVec: General-Purpose Location Embeddings from Open Map Data
Geographic coordinates are common inputs in applied machine learning, but latitude and longitude describe position rather than the surrounding environment.Event-Centric Earthquake Monitoring with a Multi-Station Transformer
Earthquake catalogs underpin studies such as fault structure, earthquake processes and seismic hazard, and their value depends on being both complete and reliable.Full Waveform Inversion with a Learned Preconditioner
Full-waveform inversion (FWI) is a powerful approach for reconstructing subsurface velocity models, but its practical performance is limited by ill-conditioning, sensitivity to the update direction…Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models
Deep-learning models increasingly rival numerical simulation in Earth-system forecasting, yet forecast error alone cannot determine whether their learned input-output dependencies are physically…Depth-progressive 3D seismic velocity model building via 2D generative diffusion models
Three-dimensional (3D) velocity model building (VMB) remains computationally challenging with conventional approaches and native 3D neural networks, largely due to the prohibitive memory and…
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