Noise Reduction in the Gamma-Ray Log by Means of Nonlinear Filtering

In January 1983, I returned to OSU to begin formal work on the degree of Doctor of Philosophy. My personal goal was to take a number of courses which I knew from experience would be useful in the burgeoning information age. Luckily for me, since without it I might never have collected and produced enough material to write this dissertation, I became involved with the Oklahoma State University Consortium for Enhancement of Well Log Data via Signal Processing. (We call it the Well Log Project for brevity.) This wonderful group of companies gave us much needed contact with researchers from companies such as Amoco Production Company; Arco Oil and Gas Company; Cities Service Oil and Gas Corporation; Conoco; Dresser-Atlas Company; Exxon Production Research Company; Gearhart Industries, Inc.; Halliburton; International Business Machines; Mobil Research and Development Corporation; Phillips Petroleum Corporation; Seismograph Service Corporation; Sohio Petroleum Company; Texaco Corporation; and the Oklahoma State University Center for Energy Research. My graduate research assistantship was funded by this consortium, and the support is gratefully acknowledged. Ironically, some experts, along the very helpful and extremely detailed discussions, also advised that everything possible had been done for the gamma-ray log, and that I should pursue a more fruitful avenue of research. Instead of the desired effect, this made me more determined to do something which, I hope, is useful in the field. After all, the history of technology is filled with ironies like Einstein working at a patent office during an era when serious suggestions were being made that patent offices be closed since everything possible had been invented. One thing for which I feel indebted to these researchers is that in spite of any doubts as to the fruitfulness of this field of my endeavor, they did their utmost to assist me in every way possible. After reading a large amount of literature on gamma-ray logging, I knew that the issue of what to do about the Poisson noise inherent in radioactive decay is an important problem to investigate. The problem is that this noise is small in comparison with the uncertainties involved in physical logging, so by late spring 1983 I began using the synthetic logs described here and measuring the results in Monte Carlo simulations. This was the turning point because it provided me with a relatively objective figure of merit of a filter. And, although I touch on the subject of what input parameters should be provided to the synthetic log generator, I have never changed them from that first spring day when the program ran. This I have purposely not experimented with for fear of coming up with an optimized log instead of an improved filter. The question of how the different synthetic log parameters affect filtering may provide another interesting topic of research if couched in slightly different tenns. The advent of the synthetic log brought with it some startling conclusions: Ordinary median filters increased the noise; recursive median filters improved the noise level more than did the optimal time-invariant linear filter; but my own best filter thus far did little in comparison. That filter has long since been confined to mass storage, but it did introduce me to a useful methodology in inventing filters. One indication of the pathological features of this filter appears to be that the histograms results of the Monte Carlo simulation are multimodal. By keeping a record of the input seeds to the random number subsequently be regenerated and examined manually for the features that cause unusually beneficial or pathological behavior. This may seem like it would produce an unwieldy quantity of output, but for any filter that improved the results, less than 10% of the histogram's data points fell outside the Gaussian-like center hump, and often the examination of only a few of these would be sufficient to conjecture what might be improved. Once the histogram began to appear Gaussian, the filters often began to produce reasonable results on actual log data, also. Perhaps the process could be repeated with the tails of the histograms, but this is left as a potentially interesting problem for future work.Electrical Engineerin

Radar mapping of Isunnguata Sermia, Greenland

This is the published version. Copyright 2013 International Glaciological SocietyIce thickness estimates using advanced nadir sounding and tomographic radar processing techniques are compared and combined in a study of Isunnguata Sermia glacier, Greenland. Using an ensemble of Operation IceBridge flight lines spaced at 500 m intervals and running approximately along the flow direction, we find there is a statistically excellent comparison between subglacial terrains derived from two-dimensional tomography and gridded nadir sounding. Analysis shows that tomographic data better capture short wavelength (1–2 km) patterns in basal terrain, but interpolated nadir sounding data yield more spatially extensive and continuous coverage across the glacier, especially in deep subglacial troughs. Using derived surface and basal topography maps, we find that driving stress and measured and modeled surface velocity comparisons indicate that basal sliding is an important component of the glacier motion, but is also only weakly coupled to the detailed bed topography save for the deepest troughs. As might be expected for this land-terminating, relatively slow-moving glacier, the subglacial and proglacial topography is similar, suggesting the erosional processes acting on the modern glacier bed once helped sculpt the now exposed land

Self-affine subglacial roughness: consequences for radar scattering and basal thaw discrimination in northern Greenland

Subglacial roughness can be determined at a variety of length scales from radio-echo sounding (RES) data either via statistical analysis of topography or inferred from basal radar scattering. Past studies have demonstrated that subglacial terrain exhibits self-affine (power law) roughness scaling behaviour, but existing radar scattering models do not take this into account. Here, using RES data from northern Greenland, we introduce a self-affine statistical framework that enables a consistent integration of topographic-scale roughness with the electromagnetic theory of radar scattering. We demonstrate that the degree of radar scattering, quantified using the waveform abruptness (pulse peakiness), is topographically controlled by the Hurst (roughness power law) exponent. Notably, specular bed reflections are associated with a lower Hurst exponent, with diffuse scattering associated with a higher Hurst exponent. Abrupt waveforms (specular reflections) have previously been used as a RES diagnostic for basal water, and to test this assumption we compare our radar scattering map with a recent prediction for the basal thermal state. We demonstrate that the majority of thawed regions (above pressure melting point) exhibit a diffuse scattering signature, which is in contradiction to the prior approach. Self-affine statistics provide a generalised model for subglacial terrain and can improve our understanding of the relationship between basal properties and ice-sheet dynamics

Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical. It is less clear how well these techniques may apply on structured prediction problems where fine-grained output with high precision is required, such as in scientific imaging domains. Here we consider the problem of segmenting echogram radar data collected from the polar ice sheets, which is challenging because segmentation boundaries are often very weak and there is a high degree of noise. We propose a multi-task spatiotemporal neural network that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice surface boundaries from sequences of tomographic radar images. We show that our model outperforms the state-of-the-art on this problem by (1) avoiding the need for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being about 6 times faster.Comment: 10 pages, 7 figures, published in WACV 201

Radio-echo sounding and waveform modeling reveal abundant marine ice in former rifts and basal crevasses within Crary Ice Rise, Antarctica

Crary Ice Rise formed after the Ross Ice Shelf re-grounded ~1 kyr BP. We present new ice-penetrating radar data from two systems operating at center frequencies of 7 and 750 MHz that confirm the ice rise is composed of a former ice shelf buried by subsequent accumulation. Stacks of englacial diffraction hyperbolas are present almost everywhere across the central ice rise and extend up to ~350 m above the bed. In many cases, bed reflections beneath the diffraction hyperbolas are obscured for distances up to 1 km. Waveform modeling indicates that the diffraction hyperbolas are likely caused by marine ice deposits in former basal crevasses and rifts. The in-filling of rifts and basal crevasses may have strengthened the connection between the ice rise and the surrounding ice shelf, which could have influenced local and regional ice dynamics. Three internal reflection horizons mark the upper limit of disturbed ice and diffraction hyperbolas in different sections of the ice rise, indicating at least three stages of flow stabilization across the ice rise. A surface lineation visible in MODIS imagery corresponds spatially to deepening and strong deformation of these layers, consistent with the characteristics of former grounding lines observed elsewhere in Antarctica

CReSIS airborne radars and platforms for ice and snow sounding

This work is licensed under a Creative Commons Attribution 4.0 International License.This paper provides an update and overview of the Center for Remote Sensing of Ice Sheets (CReSIS) radars and platforms, including representative results from these systems. CReSIS radar systems operate over a frequency range of 14–38 GHz. Each radar system's specific frequency band is driven by the required depth of signal penetration, measurement resolution, allocated frequency spectra, and antenna operating frequencies (often influenced by aircraft integration). We also highlight recent system advancements and future work, including (1) increasing system bandwidth; (2) miniaturizing radar hardware; and (3) increasing sensitivity. For platform development, we are developing smaller, easier to operate and less expensive unmanned aerial systems. Next-generation platforms will further expand accessibility to scientists with vertical takeoff and landing capabilities