地球物理：2025–2026 年度综述

An annual survey of the physics.geo-ph arXiv category, covering research published from September 2025 – August 2026. Built on a close-reading set of 120 papers and organised into 44 chapters, it maps the year by the field's research sub-directions.

Book framework (share of the year's output):

• Porous Media Flow and Transport Dynamics (17.8%) — Investigates fluid dynamics, heat transport, and shock interactions within porous media, including fractured rocks and soil structures.
• Earth Interior Structure and Dynamics (14.5%) — Explores the physical properties, convection, and rotational dynamics of the Earth's core, mantle, and interior using seismic and neutrino data.
• Earthquake Forecasting and Seismic Hazard (12.4%) — Focuses on real-time magnitude classification, probabilistic forecasting, and tsunami prediction associated with seismic events and fault activity.
• Spectral Modeling and Environmental Monitoring (11.3%) — Applies spectral analysis and stochastic modeling to monitor environmental phenomena such as coal fires, floods, and atmospheric responses.
• Surface and Internal Wave Propagation (10.1%) — Analyzes the generation, dispersion, and interaction of ocean surface waves, internal waves, and gravitational wave effects on planetary bodies.
• Hydrological and Cryospheric Modeling (9.2%) — Develops numerical models for subsurface flow, firn hydrology, ice velocity, and runoff forecasting using diffusion and neural operator techniques.
• Machine Learning for Geophysical Inversion (7.4%) — Utilizes deep learning and physics-informed neural networks for subsurface characterization, gravity inversion, and turbulence prediction.
• Physics-Informed Full-Waveform Inversion (7.1%) — Advances full-waveform inversion and PDE-constrained optimization using neural operators and Bayesian frameworks for subsurface imaging.
• Seismic Data Processing and Reconstruction (6.3%) — Addresses seismic data denoising, reconstruction, and facies translation using self-supervised learning and general-purpose datasets.
• Distributed Acoustic Sensing and Localization (3.9%) — Employs fiber-optic distributed acoustic sensing for high-resolution monitoring of geophysical events and infrastructure stability.

Each sub-direction is covered across three chapters — how it framed its questions, the strength of its key findings, and its open debates and frontiers — so the reader gets a traceable picture of what this part of the field actually produced over the year.