Geophysics
Side-scan sonar mosaicking plays a crucial role in large-scale seabed mapping but is challenged by complex non-linear, spatially varying distortions due to diverse sonar acquisition conditions. Existing rigid or affine registration methods…
Slow earthquakes differ from regular earthquakes in their slower moment release and size distribution dominated by smaller events. However, the physical origin of these slow earthquake statistics remains controversial. In this work, we…
We develop a site-specific ground-motion model (GMM) for crustal earthquakes in Japan that can directly model the probability distribution of ground motion acceleration time histories based on generative adversarial networks (GANs). The…
We investigate the possible outcomes of a subaqueous barchan moving over a crater-like depression in the bed. For that, we carried out experiments where we varied the dune size, crater and grain diameters, and flow velocities. We found that…
Not all nations on earth have previously been surveyed accurately enough to know for certain which peak is the national highpoint, the highest peak in the country. Knowledge of these peaks is important for understanding the physical…
In the context of global climate change, geological materials are increasingly destabilized by water flow and infiltration. We study the creeping dynamics of a densely monitored landslide in Western Norway to decipher the role of fluid flow…
P-wave first-motion polarity plays an important role in resolving focal mechanisms of small to moderate earthquakes (M <= 4.5). High-quality focal mechanism solutions for abundant small events can greatly improve our understanding of…
This study presents a coupled physical statistical framework for retrieving snow water equivalent (SWE) in forested areas using dual frequency X and Ku band SAR observations. The method combines a multilayer snow hydrology model (MSHM) with…
Subglacial blisters form due to the rapid drainage of supraglacial lakes into grounded ice sheets, and are characterised by elastic ice uplift and transient ice-velocity anomalies. Although blister occurrence is confirmed by observations,…
We develop a new, efficient, and accurate method to simulate frequency-domain borehole electromagnetic (EM) measurements acquired in the presence of three-dimensional (3D) variations of the anisotropic subsurface conductivity. The method is…
Networks of radiation detectors provide a platform for real-time radioactive source detection and identification in urban environments. Detection algorithms in these systems must adapt to naturally-occurring changes in background, which…
Seismic acoustic impedance inversion is a challenging problem in geophysical exploration, primarily due to the scarcity of well-logging data and the inherent nonlinearity of the task. Most existing inversion methods, including…
A comprehensive geoscientific downscaling model strategy is presented outlining an approach that has evolved over the last 20 years, together with an explanation for its development, its technical aspects, and evaluation scheme. This effort…
Seismic exploration is currently the most mature approach for studying subsurface structures, yet the presence of noise greatly restricts its imaging accuracy. Previous methods still face significant challenges: traditional computational…
Ecohydrological models are increasingly applied across multiple scenarios, yet their application remains constrained by high computational costs of fine-resolution simulations and structural inconsistencies in cross-scale modeling. This…
We propose the Diffusion-Inversion-Net (DIN) framework for inverse modeling of groundwater flow and solute transport processes. DIN utilizes an offline-trained Denoising Diffusion Probabilistic Model (DDPM) as a powerful prior leaner, which…
Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally…
Supervised deep learning methods typically require large datasets and high-quality labels to achieve reliable predictions. However, their performance often degrades when trained on imperfect labels. To address this challenge, we propose a…
Current Landsat Level 2 surface temperature products are derived using a single-channel (SC) methodology to estimate per-pixel surface temperature (ST) maps from Level~1 radiance data. A known issue with the Level 2 uncertainty, however, is…
Mountainous terrain is increasingly being measured and mapped by airplane-based LiDAR (Light Detection and Ranging) techniques, but the accuracy of these measurements in such topographically variable terrain is not well understood. For this…