3 papers
physics.geo-ph2026
Physics-Informed Neural Networks for Viscoacoustic Wave Propagation: Forward Modelling, Inversion and Discretization Sensitivity
Chaohua Liang, Xingliang Peng, Jun Matsushima
Seismic wave forward and inverse modeling are fundamental tools for subsurface imaging and geological hazard assessment. Conventional grid-based numerical methods, such as finite-d…
physics.geo-ph2026
SeisBind: Physics-Aware Tri-Modal Representation Binding for Seismic Data via Contrastive Learning
Chaohua Liang, Jun Matsushima
This letter proposes a physics-aware multi-modal contrastive learning framework designed to transform complex seismic wavefields into human-readable physical representations. Tradi…
physics.soc-ph2025
Oil Peak and the Decline of Net Energy: Policy Implications from an EROI-Entropy Perspective
Shunsuke Nakaya, Jun Matsushima
Net energy - the energy obtained from a resource after accounting for the energy expended in its acquisition - fundamentally determines the capacity of societies to sustain and exp…