On the light variations of Seyfert galaxy 3C120

The fragmentary light-curve of the Seyfert galaxy 3C120, based on photometry of the bestavailable Harvard plates of the past 60 yr, has been subjected to a periodicity analysis using a method (Appendix) particularly suited to this type of irregularly-spaced data. The analysis yields convincing evidence for a period of about 350 d and some indication of a longer period of about 22.5 yr. The observational data often depart appreciably from the light curve synthesized from the two periodic components. In one comparatively well-documented segment (1934–1939), the agreement between observational data and the synthesized model improves if it is assumed that a pair of oppositelydirected phase shifts have occurred, disturbing the 350-day period and then causing it to recover.     

Forecasting cosmological constraints from redshift surveys

Observations of redshift-space distortions in spectroscopic galaxy surveys offer an attractive method for observing the build-up of cosmological structure, which depends both on the expansion rate of the Universe and our theory of gravity. In this paper, we present a formalism for forecasting the constraints on the growth of structure which would arise in an idealized survey. This Fisher matrix based formalism can be used to study the power and aid in the design of future surveys.     

A Method for Installing Miniature Multilevel Sampling Wells

Abstract. A method has been developed to install miniature multilevel ground-water sampling wells in shallow, unconsoli-dated aquifers at sites without truck access. An advantage of these wells over the traditional bundle-type multilevel is that this type of installation causes minimal aquifer disturbance. Thus, miniature multilevels can be installed while a tracer test is in progress. Furthermore, only hand tools are needed in the field, and equipment and materials are easily obtained and inexpensive. The time required for installation is comparable to other methods.     

Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field,Kern County,California, USA

Hydrogeology Journal - Groundwater total dissolved solids (TDS) distribution was mapped with a three-dimensional (3D) model, and it was found that TDS variability is largely controlled by...     

Deep learning for dune pattern mapping with the AW3D30 global surface model

In this paper we present a deep learning (U-Net)-based workflow for classifying linear dune landforms based on the discrete Laplacian convolution of a new global elevation dataset, the AW3D30 digital surface model. Crest vectors were then derived for landscape pattern analysis. The U-Net crest classification model was trained and evaluated on sample data from dunefields across the Australian continent. The resulting crest vectors and dune defect placement were then evaluated in typical semi-arid and arid dune landscapes in eastern central Australia where high-resolution (5 m horizontal) digital elevation models are available (for three out of our four study sites) as a reference dataset. The method was applied to quantify dune pattern metrics for the entire Simpson Desert dunefield, Australia. The U-Net does a very good job of segmenting dune crests, even where dunes are less clear in the Laplacian map (intersection over union score ≈ 0.68). When crest vectors and dune defects (network nodes) were derived, the defect predictions were typically correct (0.4 to 0.79 correctness) but incomplete (0.02 to 0.64 completeness). Much of the residual error was traced to the resolution of the input data. Through the application to the Simpson Desert, we nevertheless demonstrated that our method can effectively be used for regional-scale dune pattern analysis. Furthermore, we suggest that the combination of morphological filtering and a convolutional neural network could readily be adapted to target other geomorphic features, such as channel networks or geological lineaments. © 2020 John Wiley & Sons, Ltd.     

Testing Stokes-Helmert geoid model computation on a synthetic gravity field: experiences and shortcomings

We report on testing the UNB (University of New Brunswick) software suite for accurate regional geoid model determination by use of Stokes-Helmert’s method against an Australian Synthetic Field (ASF) as “ground truth”. This testing has taken several years and has led to discoveries of several significant errors (larger than 5mm in the resulting geoid models) both in the UNB software as well as the ASF. It was our hope that, after correcting the errors in UNB software, we would be able to come up with some definite numbers as far as the achievable accuracy for a geoid model computed by the UNB software. Unfortunately, it turned out that the ASF contained errors, some of as yet unknown origin, that will have to be removed before that ultimate goal can be reached. Regardless, the testing has taught us some valuable lessons, which we describe in this paper. As matters stand now, it seems that given errorless gravity data on 1′ by 1′ grid, a digital elevation model of a reasonable accuracy and no topographical density variations, the Stokes-Helmert approach as realised in the UNB software suite is capable of delivering an accuracy of the geoid model of no constant bias, standard deviation of about 25 mm and a maximum range of about 200 mm. We note that the UNB software suite does not use any corrective measures, such as biases and tilts or surface fitting, so the resulting errors reflect only the errors in modelling the geoid.