This study presents an innovative approach to automated coral reef monitoring using satellite imagery, addressing challenges in image quality assessment and correction. The method employs Principal Component Analysis (PCA) coupled with clustering for efficient image selection and quality evaluation, followed by a machine learning-based cloud removal technique using an XGBoost model trained to detect land and cloudy pixels over water. The workflow incorporates depth correction using Lyzenga’s algorithm and superpixel analysis, culminating in an unsupervised classification of reef areas using KMeans. Results demonstrate the effectiveness of this approach in producing consistent, interpretable classifications of reef ecosystems across different imaging conditions. This study highlights the potential for scalable, autonomous monitoring of coral reefs, offering valuable insights for conservation efforts and climate change impact assessment in shallow marine environments.

The use of hyperspectral data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has uncovered a wide range of previously undetected minerals on the surface of the red planet, and has been instrumental in the selection of multiple rover mission landing sites. Yet, due to aging of the sensor and an increase in noise, a significant portion of CRISM data is deemed too degraded and remains unanalysed. We present a new data-driven model architecture, Noise-to-Noise-for-Mars (N2N4M), designed specifically to denoise hyperspectral data. Our model does not require zero-noise target data, making it well suited for use in Planetary Science applications where such data does not exist. We demonstrate its strong performance over benchmark methods, outperforming them on most metrics. We suggest that the reliance on spatial correlations in noise profiles for the deep learning benchmark methods may be part of the cause of their poor performance when applied to CRISM data. We apply our methodology to two case study sites, each previously studied for hydrated mineralogy, and demonstrate our model can increase the visual clarity of summary parameters used to highlight areas of interest. This has potential impacts for characterisation of landing sites for upcoming missions such as the ESA’s ExoMars rover mission.

Orbital remote sensing observations are a lynchpin of planetary science research. Hyperspectral infrared spectroscopy in particular is key for planetary mineralogical exploration, for example, CRISM for Mars, as this underpins our understanding of the distribution of specific lithologies and the geological process leading to their formation. Yet routine analysis workflows involving summary parameters have significant limitations and are highly time‐consuming. This work presents a novel methodology and framework for the analysis and classification of CRISM SWIR reflectance spectroscopy, leveraging Machine Learning (ML). We train a model to classify 37 minerals previously manually identified on the planet. We show this model is highly performant, with test data across Mars and a case study within Jezero crater, where ML results match previous manual analyses and rover observations. We also adapt Expected Cost (EC) to remote sensing data for use in geological context for the first time. We demonstrate that EC can be used to dynamically weight misclassification penalties based on geological context, as a rigorous measure of automated classification methods. We envision this model to make analysis of CRISM data more accessible to the planetary science community, allowing rapid searches for a vast range of minerals across a global/regional scale. As a result, areas of interest for further satellite or rover exploration can be quickly identified, leading to greater understanding of geological processes on Mars.

Due to a lack of pre‐Nectarian samples associated with specific impacts in the Apollo suite, the early Solar System's impact chronology is poorly constrained. We investigate where Artemis and other south polar lunar missions could sample impactites to determine the ages of pre‐Nectarian craters, focusing on the ancient craters Haworth, Shoemaker, and Faustini. We examine similarly sized non‐polar analogue craters containing mappable impact melt features and find no patterns that could be used to predict where similar deposits may have once existed in the south polar region. We also create a model of the ejecta stratigraphy of craters that post‐date the South Pole‐Aitken basin, from which we predict how younger, smaller craters may excavate ancient, buried impact melt to the surface. We find that, at most, primary impact melt from a source crater will comprise no more than 2%–3% of material at the surface above its buried rim, and <1% elsewhere. Furthermore, disruption of the ejecta stratigraphy by ballistic sedimentation and surface mixing due to impact gardening will make sampling specific sources more difficult. We conclude that determining the provenance of ancient impact melt samples will be difficult, but that sampling near the rim of a given source—such as in the “Haworth” Artemis candidate landing region—will maximize the chances of success.

All observed giant (≥ MW 8.5) earthquakes have occurred at subduction margins. Due to their long intermittence times, our instrumental and historical earthquake catalogues only contain a handful of giant earthquake occurrences, with no observations at all for some margins. This raises the question whether giant earthquakes may occur at all subduction margins or whether their nucleation requires a certain set of geological properties, which may be present at only some margins.Since the 1980s, numerous studies have focused on the search for a subduction margin property enabling giant earthquakes, with parameters such as sediment thickness, subducting plate age and hydration, seafloor roughness, convergence rate, and dip steepness amongst the most debated, many of them with contradicting hypotheses. Recent years have brought the hypothesis that giant earthquake occurrence may depend on a combination of margin properties to the forefront, with several studies taking multivariate statistics approaches to relating the two. These approaches are however limited by the incomplete nature of earthquake catalogues, specifically regarding giant earthquakes.We present an unsupervised approach to examining the connections between margin properties and seismicity, which allows us to uncover patterns in margin property data, excluding any earthquake occurrence data from the incomplete record. Considering sediment thickness, convergence rate, dip angle, and different measures of seafloor roughness, we “fingerprint” margin segments by applying Principal Component Analysis (PCA) to margin property data. Based on these “fingerprints”, we quantify similarity between the margins’ property combinations, and group them into different hazard groups regarding the possibility of giant earthquake occurrence. Using Kernel-PCA, a non-linear PCA variant, reveals non-linear patterns in margin properties, prompting us to suggest that connections between margin properties and seismicity are non-linear. Finally, we apply this method to characterise the seismic behaviour of subduction zones where seismic activity is less well-documented, such as the Makran, Hellenic, and Lesser Antilles margins.