Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2204.14253

Reconstruction of the collapse of the 'Azure Window' natural arch via photogrammetry

Joseph Caruana·John Wood·Erica Nocerino·Fabio Menna·Aaron Micallef·Timmy Gambin
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Azure Window was a natural arch situated in the west coast of Gozo (Maltese Archipelago) that collapsed in March 2017. We employ a Diver Propulsion Vehicle-mounted camera system to capture data for the 3D-modelling of this collapsed arch via photogrammetry. We demonstrate use of this method to document complex underwater geomorphology spread across a large area, and draw up a geomorphic assessment of the site and collapse event on the basis of this 3D-model. The methodology enables a reconstruction and understanding of the collapse event. On account of the high-resolution attained, we are able to cross-match the principal submerged components with their pre-collapse location; this enables an understanding of the dynamics of the collapse, confirmation of rock break-up along existing joints, and mapping the distribution of the rock and debris from the collapse event. We conclude that the key stages in the collapse of the Azure Window entailed erosion at the base of the pillar, leading to the latter's collapse in the southwest direction, breaking into two main sections that separated along the lithological boundary. We also find clear evidence that separation of some sections of the pillar followed pre-existing joints. The bridge collapsed vertically upon loss of support from the pillar, breaking into two main components and many other fragments. We also document further changes at the site post-collapse. We show that this approach can be utilised to understand and characterise such events even when significant time has elapsed since collapse, and rocks have already undergone erosion and significant marine growth.

Explore related subjects

physics.geo-phphysics.ao-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Joseph Caruana, John Wood, Erica Nocerino, Fabio Menna, Aaron Micallef, Timmy Gambin. 2022-04-29. Reconstruction of the collapse of the 'Azure Window' natural arch via photogrammetry. https://doi.org/10.1016/j.geomorph.2022.108250

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

KEEP EXPLORING

Related papers

arXiv2026

Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dense geophone deployments, distributed acoustic sensing (DAS) arrays, and large-scale 2D and 3D surveys. This expansion makes complex noise suppression increasingly challenging, especially when signal fidelity must be preserved. Conventional supervised deep learning methods are often task-specific, require large paired datasets, and can suffer from domain shift under new acquisition conditions. Foundation models offer a promising alternative, but pre-training seismic foundation models from scratch requires massive domain-specific data and substantial computation. We propose an efficient framework that repurposes general-purpose Vision Foundation Models (VFMs) for geophysical tasks through Parameter-Efficient Fine-Tuning. The architecture uses a pre-trained VFM, a DINOv3 encoder, adapted with Low-Rank Adaptation (LoRA) to enable effective feature adaptation with few additional parameters. To improve robustness under unseen field conditions without ground truth, we introduce a kurtosis-guided unsupervised test-time adaptation module that updates only LoRA parameters during inference. This module self-calibrates the model to site-specific noise by identifying information-rich regions via kurtosis and performing self-training without labeled data. Experiments on public exploration seismic images and DAS vertical seismic profiling data from the Utah FORGE site show that the framework matches or outperforms domain-specific models. Tests on unseen cross-site data from a land survey in China and the Groß Schönebeck geothermal site in Germany further demonstrate strong generalization and effective signal-noise separation. These results highlight the potential of adapting pre-trained VFMs to data-intensive problems in exploration seismology.

physics.geo-ph↗
arXiv2026

Shallow-to-deep velocity model building via diffusion models-Part I: Method and Proof of concept

Seismic velocity model building (VMB) is fundamental for understanding subsurface structures. Traditional methods demand high-quality starting models and, also, remain limited in resolution in coverage and computationally intensive. Recent generative diffusion model-based approaches capture statistical priors to support traditional inversion methods, but these approaches do not account for the top to bottom progression of information (layer stripping) involved in surface recorded data, where deep velocity information depends on the shallow. To address this issue, we propose a depth-progressive diffusion framework that constructs velocity models incrementally from shallow to deep by propagating prior information. Our method trains on paired shallow-deep velocity patches with variable overlap and explicit depth encoding, integrating multiple geophysical constraints including well logs and seismic images (representing structural information). During inference, we synthesize overlapping depth slices using a progressive algorithm and merge them with Gaussian-weighted blending to eliminate boundary artifacts. This approach leverages both learned geological distributions and observed shallow priors while providing uncertainty quantification. Extensive numerical experiments on in-distribution tests and an out-of-distribution test demonstrate excellent VMB accuracy with a strong correlation between predicted uncertainty and actual errors. As a proof of concept, this part I employs idealized structural constraints derived from vertical reflectivity to validate the methodological framework. The companion paper (Part II) extends the approach to realistic structural constraints relying on migrated images with field data applications.

physics.geo-ph↗
arXiv2026

Shallow-to-deep velocity model building via diffusion models-Part II: Realistic scenarios

Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth-progressive diffusion framework introduced in Part~I, which relied on idealized reflectivity constraints, this work adapts the methodology to realistic exploration scenarios. We replace perfect structural information with migration-derived attributes extracted from seismic images, and introduce smooth background velocity models from tomography as additional conditioning inputs. The framework jointly leverages background/migration velocity, migrated structural information, and sparse well measurements to synthesize high-resolution velocity models through depth-progressive generation. Validation on synthetic examples demonstrates superior accuracy compared to conventional interpolation and alternative deep learning methods, with generated models successfully initializing FWI and mitigating cycle-skipping even in complex geological structures. Field data confirms practical applicability: despite training on synthetic data, the method generalizes effectively to field conditions, producing velocity models with synthetic data response that nearly match observed seismic data. As a result, this framework establishes a practical pathway to deploy generative diffusion models for velocity model building under realistic constraints.

physics.geo-ph↗