Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications

Predicting Soil Influence on the Performance of Metal Detectors: Magnetic Properties of Tropical Soils

Mine detection and clearance are costly and time-consuming procedures necessary to benefit the communities these weapons affect. A complication surrounding mine detection is the influence of the soil on landmine detection, but little research has been done on the subject. This article discusses how soil can affect mine detectors and research plans to improve mine-detection efficiency

Modeling of GPR Clutter Caused by Soil Heterogeneity

In small-scale measurements, ground-penetrating radar (GPR) often uses a higher frequency to detect a small object or structural changes in the ground. GPR becomes more sensitive to the natural heterogeneity of the soil when a higher frequency is used. Soil heterogeneity scatters electromagnetic waves, and the scattered waves are in part observed as unwanted reflections that are often referred to as clutter. Data containing a great amount of clutter are difficult to analyze and interpret because clutter disturbs reflections from objects of interest. Therefore, modeling GPR clutter is useful to assess the effectiveness of GPR measurements. In this paper, the development of such a technique is discussed. This modeling technique requires the permittivity distribution of soil (or its geostatistical properties) and gives a nominal value of clutter power. The paper demonstrates the technique with the comparison to the data from a GPR time-lapse measurement. The proposed technique is discussed in regard to its applicability and limitations based on the results

Influence of Soil Properties on the Performance of Metal Detectors and GPR

This article examines the effects of four soil types on metal detector and GPR performance and proposes the development of a classification system based on soil type to aid in the selection of effective methods for manual demining

Über die kleinräumige Variabilität elektrischer Bodeneigenschaften und ihr Einfluss auf geophysikalische Messungen

Physical soil properties feature high spatial variabilities which are known to affect geophysical measurements. However, these variations are not considered in most cases. The challenging task is to quantify the influence of soil heterogeneities on geophysical data. This question is analysed for DC resistivity and GPR measurements which are frequently used for near-surface explorations. To determine the pattern of electric soil properties in situ with the required high spatial resolution, geophysical measuring techniques are methodically enhanced. High-resolution dipole-dipole resistivity measurements are used to determine the electric conductivity distribution of the topsoil. Due to the small electrode separations, the actual electrode geometry has to be considered and an analytic expression for geometric factors is derived instead of assuming point electrodes. Two methods are used to determine soil permittivity with GPR:(i) the coefficient of reflection at the interface air-soil is measured with an air-launched horn antenna, (ii) the velocity of the groundwave is measured with a new setup using two receiver antennas enhancing the lateral resolution from in the best case 0.5 m for standard techniques to approximately 0.1 m with the new technique. With the optimised measuring techniques, the electric properties of sandy soils are determined in the field. Conductivity and permittivity show high spatial variability with correlation lengths of a few decimetres. Geostatistical simulation techniques are used to generate synthetic random media featuring the same statistical properties as in the field. FD calculations are carried out with this media to provide realistic synthetic data of resistivity and GPR measurements. Conductivity variations as determined in the field generate significant variations of simulated Schlumberger sounding curves resulting in uncertainties of the inverted models. Even in pedologically homogeneous sandy soil, moisture pattern and resulting permittivity variations cause strong GPR diffractions as demonstated by FD calculations. This influences the detectability of small objects such as e.g. landmines or of large reflectors as e.g. the groundwater table. Conductivity variations as typical for soils showed to have a minor effect on GPR measurements than variations of permittivity. In summary, geostatistical analysis and simulation provide a powerful tool to simulate geophysical measurements under field conditions including soil heterogeneity which can be used to quantify the uncertainty of field measurements by geologic noise.Geophysikalische Messungen werden häufig durch die hohe räumliche und zeitliche Variabilität der physikalischen Eigenschaften von Böden beeinflusst. Diese Variabilität wird jedoch in den meisten Fällen nicht berücksichtigt. Es drängt sich die Frage auf, in welchem Ausmaß geophysikalische Messungen von Bodenheterogenitäten beeinflusst werden. Dieser Frage wird speziell für den Fall von Geoelektrik- und Georadar-Messungen nachgegangen, die häufig für oberflächennahe Untersuchungen des Untergrunds verwendet werden. Um in situ die räumliche Verteilung der elektrischen Bodeneigenschaften mit der erforderlichen hohen räumlichen Auflösung zu bestimmen, werden die geophysikalischen Messtechniken methodisch verbessert. Hochauflösende Dipol-Dipol-Geoelektrikmessungen werden zur Bestimmung der elektrischen Leitfähigkeitsverteilung im Oberboden verwendet. Aufgrund der kleinen Elektrodenabstände muss die tatsächliche Elektrodengeometrie berücksichtigt werden. Anstatt Punktelektroden anzunehmen wird ein analytischer Ausdruck für den Geometriefaktor hergeleitet. Es werden zwei Methoden zur Bestimmung des Dielektrizitätskoeffizienten des Bodens mit dem Georadar (GPR) verwendet: (i) mit einer Hornantenne wird der Reflexionskoeffizient an der Grenzschicht Luft-Boden gemessen, (ii) die Ausbreitungsgeschwindigkeit der Bodenwelle wird mit einer neuartigen Aufstellung mit zwei Empfangsantennen bestimmt. Die laterale Auflösung wird dabei von bestenfalls 0,5 m für die Standardaufstellung auf ungefähr 0,1 m mit der neuen Aufstellung verbessert. Mit den optimierten Messtechniken werden die elektrischen Eigenschaften von Sandböden im Gelände bestimmt. Die elektrische Leitfähigkeit und der Dielektrizitätskoeffizient zeigen beide eine hohe räumliche Variabilität mit Korrelationslängen von wenigen Dezimetern. Mittels geostatistischer Simulationstechniken werden zufallsverteilte synthetische Medien mit den selben Eigenschaften wie im Feld generiert. Diese Medien werden für FD-Simulationen herangezogen, um realistische synthetische Geoelektrik- und Georadar-Daten zu erhalten. Heterogenitäten der elektrischen Leitfähigkeit, wie sie im Feld bestimmt wurden, rufen signifikante Variationen in simulierten Schlumberger Sondierungskurven hervor, was zu einer Unsicherheit in den invertierten Modellen führt. Selbst in Sandböden, die unter bodenkundlichen Gesichtspunkten homogenen sind, führen die Feuchteverteilung und die damit einhergehenden Variationen des Dielektrizitätskoeffizienten zu starken Diffraktionen im Radargramm, wie mit Hilfe von FD-Simulationen gezeigt wird. Das beeinflusst die Detektierbarkeit von kleinen Objekten, wie beispielsweise Landminen oder auch größeren Reflektoren, wie beispielsweise dem Grundwasserspiegel. Es zeigt sich weiterhin, dass die für Böden typischen Variationen der elektrischen Leitfähigkeit einen geringeren Einfluss auf GPR-Messungen haben als die Variationen des Dielektrizitätskoeffizienten. Zusammenfassend läßt sich konstatieren, dass mit geostatistischen Analysemethoden und Simulationstechniken ein Instrumentarium zur Verfügung steht, das es ermöglicht, realistische Simulationen von geophysikalischen Messungen unter Feldbedingungen zu erzeugen, die auch die Bodenheterogenität beinhalten. Diese Simulationen können dazu verwendet werden, die Unsicherheit von Feldmessungen, die durch „geologischen Noise“ hervorgerufen werden, zu quantifizieren

3D architecture of cyclic-step and antidune deposits in glacigenic subaqueous fan and delta settings: Integrating outcrop and ground-penetrating radar data

Bedforms related to supercritical flows are increasingly recognised as important constituents of many depositional environments, but outcrop studies are commonly hampered by long bedform wavelengths and complex three-dimensional geometries. We combined outcrop-based facies analysis with ground-penetrating radar (GPR) surveys to analyse the 3D facies architecture of subaqueous ice-contact fan and glacifluvial delta deposits. The studied sedimentary systems were deposited at the margins of the Middle Pleistocene Scandinavian ice sheets in Northern Germany. Glacifluvial Gilbert-type deltas are characterised by steeply dipping foreset beds, comprising cyclic-step deposits, which alternate with antidune deposits. Deposits of cyclic steps consist of lenticular scours infilled by backset cross-stratified pebbly sand and gravel. The GPR sections show that the scour fills form trains along the delta foresets, which can locally be traced for up to 15 m. Perpendicular and oblique to palaeoflow direction, these deposits appear as troughs with concentric or low-angle cross-stratified infills. Downflow transitions from scour fills into sheet-like low-angle cross-stratified or sinusoidally stratified pebbly sand, deposited by antidunes, are common. Cyclic steps and antidunes were deposited by sustained and surge-type supercritical density flows, which were related to hyperpycnal flows, triggered by major meltwater discharge or slope-failure events. Subaqueous ice-contact fan deposits include deposits of progradational scour fills, isolated hydraulic jumps, antidunes and (humpback) dunes. The gravel-rich fan succession consists of vertical stacks of laterally amalgamated pseudo-sheets, indicating deposition by pulses of waning supercritical flows under high aggradation rates. The GPR sections reveal the large-scale architecture of the sand-rich fan succession, which is characterised by lobe elements with basal erosional surfaces associated with scours filled with backsets related to hydraulic jumps, passing upwards and downflow into deposits of antidunes and (humpback) dunes. The recurrent facies architecture of the lobe elements and their prograding and retrograding stacking pattern are interpreted as related to autogenic flow morphodynamics

Modeling of GPR Clutter Caused by Soil Heterogeneity

In small-scale measurements, ground-penetrating radar (GPR) often uses a higher frequency to detect a small object or structural changes in the ground. GPR becomes more sensitive to the natural heterogeneity of the soil when a higher frequency is used. Soil heterogeneity scatters electromagnetic waves, and the scattered waves are in part observed as unwanted reflections that are often referred to as clutter. Data containing a great amount of clutter are difficult to analyze and interpret because clutter disturbs reflections from objects of interest. Therefore, modeling GPR clutter is useful to assess the effectiveness of GPR measurements. In this paper, the development of such a technique is discussed. This modeling technique requires the permittivity distribution of soil (or its geostatistical properties) and gives a nominal value of clutter power. The paper demonstrates the technique with the comparison to the data from a GPR time-lapse measurement. The proposed technique is discussed in regard to its applicability and limitations based on the results

Ice-marginal forced regressive deltas in glacial lake basins: geomorphology, facies variability and large-scale depositional architecture

This study presents a synthesis of the geomorphology, facies variability and depositional architecture of ice-marginal deltas affected by rapid lake-level change. The integration of digital elevation models, outcrop, borehole, ground-penetrating radar and high-resolution shear-wave seismic data allows for a comprehensive analysis of these delta systems and provides information about the distinct types of deltaic facies and geometries generated under different lake-level trends. The exposed delta sediments record mainly the phase of maximum lake level and subsequent lake drainage. The stair-stepped profiles of the delta systems reflect the progressive basinward lobe deposition during forced regression when the lakes successively drained. Depending on the rate and magnitude of lake-level fall, fan-shaped, lobate or more digitate tongue-like delta morphologies developed. Deposits of the stair-stepped transgressive delta bodies are buried, downlapped and onlapped by the younger forced regressive deposits. The delta styles comprise both Gilbert-type deltas and shoal-water deltas. The sedimentary facies of the steep Gilbert-type delta foresets include a wide range of gravity-flow deposits. Delta deposits of the forced-regressive phase are commonly dominated by coarse-grained debrisflow deposits, indicating strong upslope erosion and cannibalization of older delta deposits. Deposits of supercritical turbidity currents are particularly common in sand-rich Gilbert-type deltas that formed during slow rises in lake level and during highstands. Foreset beds consist typically of laterally and vertically stacked deposits of antidunes and cyclic steps. The trigger mechanisms for these supercritical turbidity currents were both hyperpycnal meltwater flows and slope-failure events. Shoal-water deltas formed at low water depths during both low rates of lake-level rise and forced regression. Deposition occurred from tractional flows. Transgressive mouthbars form laterally extensive sand-rich delta bodies with a digitate, multi-tongue morphology. In contrast, forced regressive gravelly shoal-water deltas show a high dispersion of flow directions and form laterally overlapping delta lobes. Deformation structures in the forced-regressive ice-marginal deltas are mainly extensional features, including normal faults, small graben or half-graben structures and shear-deformation bands, which are related to gravitational delta tectonics, postglacial faulting during glacial-isostatic adjustment, and crestal collapse above salt domes. A neotectonic component cannot be ruled out in some cases. © 2018 The Authors. Boreas published by John Wiley & Sons Ltd on behalf of The Boreas Collegiu

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc