Hydrological characterization of cave drip waters in a porous limestone: Golgotha Cave, Western Australia

Cave drip water response to surface meteorological conditions is complex due to the heterogeneity of water movement in the karst unsaturated zone. Previous studies have focused on the monitoring of fractured rock limestones that have little or no primary porosity. In this study, we aim to further understand infiltration water hydrology in the Tamala Limestone of SW Australia, which is Quaternary aeolianite with primary porosity. We build on our previous studies of the Golgotha Cave system and utilize the existing spatial survey of 29 automated cave drip loggers and a lidar-based flow classification scheme, conducted in the two main chambers of this cave. We find that a daily sampling frequency at our cave site optimizes the capture of drip variability with the least possible sampling artifacts. With the optimum sampling frequency, most of the drip sites show persistent autocorrelation for at least a month, typically much longer, indicating ample storage of water feeding all stalactites investigated. Drip discharge histograms are highly variable, showing sometimes multimodal distributions. Histogram skewness is shown to relate to the wetter-than-average 2013 hydrological year and modality is affected by seasonality. The hydrological classification scheme with respect to mean discharge and the flow variation can distinguish between groundwater flow types in limestones with primary porosity, and the technique could be used to characterize different karst flow paths when high-frequency automated drip logger data are available. We observe little difference in the coefficient of variation (COV) between flow classification types, probably reflecting the ample storage due to the dominance of primary porosity at this cave site. Moreover, we do not find any relationship between drip variability and discharge within similar flow type. Finally, a combination of multidimensional scaling (MDS) and clustering by k means is used to classify similar drip types based on time series analysis. This clustering reveals four unique drip regimes which agree with previous flow type classification for this site. It highlights a spatial homogeneity in drip types in one cave chamber, and spatial heterogeneity in the other, which is in agreement with our understanding of cave chamber morphology and lithology. © Author(s) 201

Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion

Geophysical inversion should ideally produce geologically realistic subsurface models that explain the available data. Multiple-point statistics is a geostatistical approach to construct subsurface models that are consistent with site-specific data, but also display the same type of patterns as those found in a training image. The training image can be seen as a conceptual model of the subsurface and is used as a non-parametric model of spatial variability. Inversion based on multiple-point statistics is challenging due to high nonlinearity and time-consuming geostatistical resimulation steps that are needed to create new model proposals. We propose an entirely new model proposal mechanism for geophysical inversion that is inspired by texture synthesis in computer vision. Instead of resimulating pixels based on higher-order patterns in the training image, we identify a suitable patch of the training image that replace a corresponding patch in the current model without breaking the patterns found in the training image, that is, remaining consistent with the given prior. We consider three cross-hole ground-penetrating radar examples in which the new model proposal mechanism is employed within an extended Metropolis Markov chain Monte Carlo (MCMC) inversion. The model proposal step is about 40 times faster than state-of-the-art multiple-point statistics resimulation techniques, the number of necessary MCMC steps is lower and the quality of the final model realizations is of similar quality. The model proposal mechanism is presently limited to 2-D fields, but the method is general and can be applied to a wide range of subsurface settings and geophysical data types

Ice Dynamics and Morphological Changes During Proglacial Lake Development at Exploradores Glacier, Patagonia

Proglacial lakes are ubiquitous features formed during deglaciarization and are currently increasing in number in Patagonia and elsewhere. Proglacial lakes can affect glacier dynamics, catchment hydrology and have the potential to cause glacial lake outburst floods. Therefore, monitoring the onset and development of proglacial lake formation is relevant to understand glacial processes and anticipate glacier response to climate change. In this study, we integrate geomorphological and ice-dynamic information to assess proglacial lake development in Exploradores Glacier, Chilean Patagonia. We monitor recent spatial and temporal changes in the lower trunk of Exploradores Glacier (10 km2) to provide a 20-year observation record by combining eight uncrewed aerial vehicles (UAV) surveys between 2019 and 2020, with high-medium resolution satellite imagery (Rapid Eye and Landsat) between 2000 and 2018. We use feature tracking techniques, digital surface elevation model analysis and field data to create a multi-temporal scale (inter-annual and seasonal) and a multi-spatial (cm to km) data set. Our analysis shows that surface velocity overall trend has not changed over the last 20 years and that surface velocity near the terminus is significant (>10 m a−1). Moreover, an exceptional advance over moraine deposits was detected. We also found low downwasting rates (<0.5 m a−1) close to the glacier terminus which are attributed to sufficient ice flux and the insulation effect of the debris-covered surface. However, hundreds of supraglacial ponds were observed and are currently coalescing and expanding by ice-cliff backwasting favoring glacier disintegration. Lastly, it was found that calving losses at the east marginal lake equaled ice-flux input into the lake for the UAV monitored period. This study contributes to a better understanding of glacial lake dynamics during proglacial lake development, and our results may help ice modelling efforts to predict glacier response to future climate scenarios

More properties of (β,γ)-Chebyshev functions and points

Recently, (β,γ)-Chebyshev functions, as well as the corresponding zeros, have been introduced as a generalization of classical Chebyshev polynomials of the first kind and related roots. They consist of a family of orthogonal functions on a subset of [−1,1], which indeed satisfies a three-term recurrence formula. In this paper we present further properties, which are proven to comply with various results about classical orthogonal polynomials. In addition, we prove a conjecture concerning the Lebesgue constant's behavior related to the roots of (β,γ)-Chebyshev functions in the corresponding orthogonality interval

Quantifying year-round nocturnal bird migration with a fluid dynamics model.

To understand the influence of biomass flows on ecosystems, we need to characterize and quantify migrations at various spatial and temporal scales. Representing the movements of migrating birds as a fluid, we applied a flow model to bird density and velocity maps retrieved from the European weather radar network, covering almost a year. We quantified how many birds take-off, fly, and land across Western Europe to (1) track bird migration waves between nights, (2) cumulate the number of birds on the ground and (3) quantify the seasonal flow into and out of the study area through several regional transects. Our results identified several migration waves that crossed the study area in 4 days only and included up to 188 million (M) birds that took-off in a single night. In spring, we estimated that 494 M birds entered the study area, 251 M left it, and 243 M birds remained within the study area. In autumn, 314 M birds entered the study area while 858 M left it. In addition to identifying fundamental quantities, our study highlights the potential of combining interdisciplinary data and methods to elucidate the dynamics of avian migration from nightly to yearly time scales and from regional to continental spatial scales

Multisensory 3D saliency for artficial attention systems

In this paper we present proof-of-concept for a novel solution consisting of a short-term 3D memory for artificial attention systems, loosely inspired in perceptual processes believed to be implemented in the human brain. Our solution supports the implementation of multisensory perception and stimulus-driven processes of attention. For this purpose, it provides (1) knowledge persistence with temporal coherence tackling potential salient regions outside the field of view, via a panoramic, log-spherical inference grid; (2) prediction, by using estimates of local 3D velocity to anticipate the effect of scene dynamics; (3) spatial correspondence between volumetric cells potentially occupied by proto-objects and their corresponding multisensory saliency scores. Visual and auditory signals are processed to extract features that are then filtered by a proto-object segmentation module that employs colour and depth as discriminatory traits. We consider as features, apart from the commonly used colour and intensity contrast, colour bias, the presence of faces, scene dynamics and also loud auditory sources. Combining conspicuity maps derived from these features we obtain a 2D saliency map, which is then processed using the probability of occupancy in the scene to construct the final 3D saliency map as an additional layer of the Bayesian Volumetric Map (BVM) inference grid

Dealing with non-stationarity in sub-daily stochastic rainfall models

Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeller. It is therefore desirable to establish quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space–time–intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities.Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution.</p

A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research

Dataset representing a 3D geological reconstruction of the a section of the Nappe de Morlces, Switzerland. Associated with "A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research" (Thornton et al.

AutoQS v1: automatic parametrization of QuickSampling based on training images analysis

Multiple-point geostatistics are widely used to simulate complex spatial structures based on a training image. The practical applicability of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. While methods for automatically selecting training images are available, parametrization can be cumbersome. Here, we propose to find an optimal set of parameters using only the training image as input. The difference between this and previous work that used parametrization optimization is that it does not require the definition of an objective function. Our approach is based on the analysis of the errors that occur when filling artificially constructed patterns that have been borrowed from the training image. Its main advantage is to eliminate the risk of overfitting an objective function, which may result in variance underestimation or in verbatim copy of the training image. Since it is not based on optimization, our approach finds a set of acceptable parameters in a predictable manner by using the knowledge and understanding of how the simulation algorithms work. The technique is explored in the context of the recently developed QuickSampling algorithm, but it can be easily adapted to other pixel-based multiple-point statistics algorithms using pattern matching, such as direct sampling or single normal equation simulation (SNESIM).</p