Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 0704.1504

The Formation of Lake Stars

Victor C. Tsai·J. S. Wettlaufer
View original source ↗Read source document ↗Export metadata ↓

Abstract

Star patterns, reminiscent of a wide range of diffusively controlled growth forms from snowflakes to Saffman-Taylor fingers, are ubiquitous features of ice covered lakes. Despite the commonality and beauty of these ``lake stars'' the underlying physical processes that produce them have not been explained in a coherent theoretical framework. Here we describe a simple mathematical model that captures the principal features of lake-star formation; radial fingers of (relatively warm) water-rich regions grow from a central source and evolve through a competition between thermal and porous media flow effects in a saturated snow layer covering the lake. The number of star arms emerges from a stability analysis of this competition and the qualitative features of this meter-scale natural phenomena are captured in laboratory experiments.

Explore related subjects

physics.geo-phphysics.flu-dyn

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Victor C. Tsai, J. S. Wettlaufer. 2007-04-11. The Formation of Lake Stars. https://arxiv.org/abs/0704.1504

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Constructive Reuse of Free-Surface Ghost Wavefields in Marine Seismic Data: A Propagation-Based Framework

Recent advances in marine seismic acquisition and processing have substantially improved the decomposition of free surface ghost wavefields. Building upon these developments, this study proposes a propagation-based framework in which decomposed ghost wavefields are treated as physically meaningful components that can be constructively reused as additional wavefield components. Starting from a finite inverse expansion of the ghost operator, a recursive formulation is derived and implemented through adaptive predictive decomposition. The formulation is subsequently extended to multidimensional wavefields through wavefield extrapolation and reciprocity, enabling decomposition into components associated with real and mirrored sources and receivers. The decomposed wavefields are then restored to a common acquisition geometry by survey sinking and combined through wavefield superposition, forming a preprocessing module that can be incorporated into conventional marine seismic processing workflows. Synthetic examples demonstrate increased signal strength through ghost reuse, spectral amplitude recovery of ghost-related notches including frequencies near DC, and clear decomposition of ghost-related wavefield components. Field-data examples acquired using a conventional shallow-towed airgun survey further demonstrate improvements in reflector continuity and coherent reflection energy, together with partial restoration of spectral amplitudes degraded by the free surface ghost, supporting the practical applicability of the proposed framework. The proposed framework therefore provides a processing-based complement to conventional ghost decomposition technologies and suggests a unified approach in which acquisition geometry and ghost processing are jointly optimised to maximise the usable wavefield information.

physics.geo-ph↗
arXiv2026

The NCS-Model: A seismic foundation model trained on the Norwegian repository of public data

We present the NCS-models, a family of seismic foundation models pretrained on a large curated share of full-stack seismic cubes from the Norwegian Continental Shelf (NCS) available through the public DISKOS database. The model weights are open-sourced for the wider geoscience community. Foundation models trained with large-scale self-supervision are emerging as a promising basis for automatic seismic interpretation. However, most existing seismic models rely on limited or proprietary datasets, and it remains unclear how well natural-image foundation models transfer to seismic data. Our goals are to develop basin-scale seismic foundation models, provide practical recipes for scalable 3D training, and compare the downstream performance of practical configurations trained under similar pretraining budgets. Using masked autoencoders with Vision Transformer backbones, we pretrain models on a DISKOS-derived corpus of 3D time- and depth-migrated seismic volumes. The NCS-model variants use 2D, 2.5D multi-view, and 3D tokenization and each variant is pretrained using approximately 250 GPU-hours on the same hardware. We evaluate the models using frozen backbones together with k-nearest neighbors and linear probing on NCS interpretation benchmarks and one out-of-basin benchmark. Baselines include an ImageNet-pretrained MAE, a frontier vision foundation model, and a globally pretrained seismic foundation model. Following large-scale pretraining on the curated DISKOS corpus, NCS-2.5D achieves the highest average performance among all evaluated models. The resulting embeddings additionally support similarity search for interactive interpretation.

physics.geo-ph↗
arXiv2026

GeoFWI3D: Large-scale 3D Velocity Model Dataset for Deep Learning-assisted Seismic Imaging

We introduce GeoFWI3D, a large-scale open-source benchmark dataset of geologically plausible 3D subsurface models designed to accelerate deep learning (DL) assisted seismic imaging and full waveform inversion (FWI). FWI is a physics-driven, wave-equation-based optimization technique used in seismic imaging to estimate the subsurface properties. However, traditional FWI is highly non-linear, non-unique, and ill-posed. The iterative process often converges to local minima when the starting model is insufficiently accurate. Most importantly, 3D FWI is prohibitively expensive. With the advent of DL, a direct mapping between the shot gathers and subsurface properties can be established by training a network on realistic models. The primary bottleneck for such approaches is the lack of large-scale, realistic training datasets. GeoFWI3D addresses this gap with 10,000 geologically diverse velocity models at 96x96x96 resolution, spanning four structural complexity classes: pure stratigraphy, faulted networks, salt diapirism, and complex coupled fault-salt systems. Each model is accompanied by co-registered multi-modal labels including compressional velocity (Vp), zero-offset seismic reflectivity, relative geologic time (RGT), and semantic fault/salt masks. To facilitate systematic evaluation, we introduce benchmark tasks covering fault detection, joint salt body segmentation and chronostratigraphy prediction, FWI, wavefield and traveltime surrogates using neural operators, and generative modeling with a 3D diffusion model. We present baseline results for each task to establish reference performance metrics for future users. The dataset is publicly available under Creative Commons Attribution 4.0 International.

physics.geo-ph↗