Automating the Hunt for Volcanoes on Venus

Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern recognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers

Automated analysis of radar imagery of Venus: handling lack of ground truth

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the “ground truth” is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in “ground truth” is essential in applications of this nature

Inferring Ground Truth from Subjective Labelling of Venus Images

In remote sensing applications "ground-truth" data is often used as the basis for training pattern recognition algorithms to generate thematic maps or to detect objects of interest. In practical situations, experts may visually examine the images and provide a subjective noisy estimate of the truth. Calibrating the reliability and bias of expert labellers is a non-trivial problem. In this paper we discuss some of our recent work on this topic in the context of detecting small volcanoes in Magellan SAR images of Venus. Empirical results (using the Expectation-Maximization procedure) suggest that accounting for subjective noise can be quite significant in terms of quantifying both human and algorithm detection performance

Fluid Outflows From Venus Impact Craters: Analysis From Magellan Data

Many impact craters on Venus have unusual outflow features originating in or under the continuous ejecta blankets and continuing downhill into the surrounding terrain. These features clearly resulted from flow of low-viscosity fluids, but the identity of those fluids is not clear. In particular, it should not be assumed a priori that the fluid is an impact melt. A number of candidate processes by which impact events might generate the observed features are considered, and predictions are made concerning the rheological character of flows produced by each mechanism. A sample of outflows was analyzed using Magellan images and a model of unconstrained Bingham plastic flow on inclined planes, leading to estimates of viscosity and yield strength for the flow materials. It is argued that at least two different mechanisms have produced outflows on Venus: an erosive, channel-forming process and a depositional process. The erosive fluid is probably an impact melt, but the depositional fluid may consist of fluidized solid debris, vaporized material, and/or melt

Chapter Machine Learning in Volcanology: A Review

A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches

NASA geology program bibliography

A bibliography of scientific papers, articles, and books based on research supported by the NASA Geology Program is given. The citations cover the period 1980 to 1990. An author index is included

MEVTV Workshop on Tectonic Features on Mars

The state of knowledge of tectonic features on Mars was determined and kinematic and mechanical models were assessed for their origin. Three sessions were held: wrinkle ridges and compressional structure; strike-slip faults; and extensional structures. Each session began with an overview of the features under discussion. In the case of wrinkle ridges and extensional structures, the overview was followed by keynote addresses by specialists working on similar structures on the Earth. The first session of the workshop focused on the controversy over the relative importance of folding, faulting, and intrusive volcanism in the origin of wrinkle ridges. The session ended with discussions of the origin of compressional flank structures associated with Martian volcanoes and the relationship between the volcanic complexes and the inferred regional stress field. The second day of the workshop began with the presentation and discussion of evidence for strike-slip faults on Mars at various scales. In the last session, the discussion of extensional structures ranged from the origin of grabens, tension cracks, and pit-crater chains to the origin of Valles Marineris canyons. Shear and tensile modes of brittle failure in the formation of extensional features and the role of these failure modes in the formation of pit-crater chains and the canyons of Valles Marineris were debated. The relationship of extensional features to other surface processes, such as carbonate dissolution (karst) were also discussed

Sedimentary Processes on Venus

The sedimentary cycle, including the processes of erosion, transport, and lithification, is a key part of how planets evolve over time. Early images of Venus’s vast volcanic plains, numerous volcanoes, and rugged tectonic regions led to the interpretation that Venus is a volcanic planet with little sediment cover and perhaps few processes for generating sedimentary rocks. However, in the years since the Magellan mission in the 1990s we have developed a better understanding of sedimentary process on Venus. Impact craters are the largest present-day source of sediments, with estimates from the current crater population suggesting an average sediment layer 8–63 cm in thickness if distributed globally. There is clear evidence of fine-grained material in volcanic summit regions that is likely produced through volcanism, and dune fields and yardangs indicate transport of sediments and erosion of rocks through wind. Landslides and fine-grained materials in highland tessera regions demonstrate erosive processes that move sediment downhill. It is clear that sediments are an important part of Venus’s geology, and it is especially important to realize that they mantle features that may be of interest to future landed or low-altitude imaging missions. The sinks of sediments are less well known, as it has been difficult to identify sedimentary rocks with current data. Layering observed in Venera images and in Magellan images of some tessera regions, as well as calculated rock densities, suggest that sedimentary rocks are present on Venus. New data is needed to fully understand and quantify the present-day sedimentary cycle and establish with certainty whether sedimentary rock packages do, in fact, exist on Venus. These data sets will need to include higher-resolution optical and radar imaging, experimental and geochemical measurements to determine how chemical weathering and lithification can occur, and topography to better model mesospheric winds. Sediments and sedimentary rocks are critical to understanding how Venus works today, but are also extremely important for determining how Venus’s climate has changed through time and whether it was once a habitable planet

Abstracts of the Annual Meeting of Planetary Geologic Mappers, Flagstaff, AZ, 2008

Topics discussed include: Merging of the USGS Atlas of Mercury 1:5,000,000 Geologic Series; Geologic Mapping of the V-36 Thetis Regio Quadrangle: 2008 Progress Report; Structural Maps of the V-17 Beta Regio Quadrangle, Venus; Geologic Mapping of Isabella Quadrangle (V-50) and Helen Planitia, Venus; Renewed Mapping of the Nepthys Mons Quadrangle (V-54), Venus; Mapping the Sedna-Lavinia Region of Venus; Geologic Mapping of the Guinevere Planitia Quadrangle of Venus; Geological Mapping of Fortuna Tessera (V-2): Venus and Earth's Archean Process Comparisons; Geological Mapping of the North Polar Region of Venus (V-1 Snegurochka Planitia): Significant Problems and Comparisons to the Earth's Archean; Venus Quadrangle Geological Mapping: Use of Geoscience Data Visualization Systems in Mapping and Training; Geologic Map of the V-1 Snegurochka Planitia Quadrangle: Progress Report; The Fredegonde (V-57) Quadrangle, Venus: Characterization of the Venus Midlands; Formation and Evolution of Lakshmi Planum (V-7), Venus: Assessment of Models using Observations from Geological Mapping; Geologic Map of the Meskhent Tessera Quadrangle (V-3), Venus: Evidence for Early Formation and Preservation of Regional Topography; Geological Mapping of the Lada Terra (V-56) Quadrangle, Venus: A Progress Report; Geology of the Lachesis Tessera Quadrangle (V-18), Venus; Geologic Mapping of the Juno Chasma Quadrangle, Venus: Establishing the Relation Between Rifting and Volcanism; Geologic Mapping of V-19, V-28, and V-53; Lunar Geologic Mapping Program: 2008 Update; Geologic Mapping of the Marius Quadrangle, the Moon; Geologic Mapping along the Arabia Terra Dichotomy Boundary: Mawrth Vallis and Nili Fossae, Mars: Introductory Report; New Geologic Map of the Argyre Region of Mars; Geologic Evolution of the Martian Highlands: MTMs -20002, -20007, -25002, and -25007; Mapping Hesperia Planum, Mars; Geologic Mapping of the Meridiani Region, Mars; Geology of Holden Crater and the Holden and Ladon Multi-Ring Impact Basins, Margaritifer Terra, Mars; Geologic Mapping of Athabasca Valles; Geologic Mapping of MTM -30247, -35247 and -40247 Quadrangles, Reull Vallis Region of Mars; Geologic Mapping of the Martian Impact Crater Tooting; Geology of the Southern Utopia Planitia Highland-Lowland Boundary Plain: First Year Results and Second Year Plan; Mars Global Geologic Mapping: Amazonian Results; Recent Geologic Mapping Results for the Polar Regions of Mars; Geologic Mapping of the Medusae Fossae Formation on Mars (MC-8 SE and MC-23 NW) and the Northern Lowlands of Venus (V-16 and V-15); Geologic Mapping of the Zal, Hi'iaka, and Shamshu Regions of Io; Global Geologic Map of Europa; Material Units, Structures/Landforms, and Stratigraphy for the Global Geologic Map of Ganymede (1:15M); and Global Geologic Mapping of Io: Preliminary Results

Significant achievements in the planetary geology program, 1981

Recent developments in planetology research are summarized. Important developments are summarized in topics ranging from solar system evolution, comparative planetology, and geologic processes, to techniques and instrument development for future exploration