Forecasting Volcanic Activity Using An Event Tree Analysis System And Logistic Regression

Forecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into a single set of models provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. A bootstrapping analysis of the training dataset allowed for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and high eruption frequency. The cross validation process produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78 - 0.81, which indicate the algorithm has good predictive capabilities. In addition, ROC curves also allowed for the determination of a false positive rate and optimum detection threshold for each stage of the algorithm. The results demonstrate the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information. The incorporation of source modeling results into the event tree’s decision making process has begun the transition of volcano monitoring applications from simple mechanized pattern recognition algorithms to a physical model based forecasting system

Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep temporal convolutional neural network is employed to learn the complex non-linear filters required to transform the single-trace waveforms into corresponding idealized characteristic functions. The training data consists of all arrivals in the International Seismological Centre Database for seven seismic arrays over a five year window from 1 January 2010 to 1 January 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 January 2015 to 1 January 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the generalization and transportability of the technique to new stations. Detection performance against this test set is outstanding, yielding significant improvements in recall over existing techniques. Fixing a type-I error rate of 0.001, the algorithm achieves an overall recall (true positive rate) of 56% against the 141,095 array-beam arrivals in the test set, yielding 78,802 correct detections. This is more than twice the 37,572 detections made by an STA/LTA detector over the same period, and represents a 35% improvement over the 58,515 detections made by a state-of-the-art kurtosis-based detector. Furthermore, DeepPick provides at least a 4 dB improvement in detector sensitivity across the board, and is more computationally efficient, with run-times an order of magnitude faster than either of the other techniques tested. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network

Chemical Fabrication Used to Produce Thin-Film Materials for High Power-to- Weight-Ratio Space Photovoltaic Arrays

The key to achieving high specific power (watts per kilogram) space solar arrays is the development of a high-efficiency, thin-film solar cell that can be fabricated directly on a flexible, lightweight, space-qualified durable substrate such as Kapton (DuPont) or other polyimide or suitable polymer film. Cell efficiencies approaching 20 percent at AM0 (air mass zero) are required. Current thin-film cell fabrication approaches are limited by either (1) the ultimate efficiency that can be achieved with the device material and structure or (2) the requirement for high-temperature deposition processes that are incompatible with all presently known flexible polyimide or other polymer substrate materials. Cell fabrication processes must be developed that will produce high-efficiency cells at temperatures below 400 degrees Celsius, and preferably below 300 degress Celsius to minimize the problems associated with the difference between the coefficients of thermal expansion of the substrate and thin-film solar cell and/or the decomposition of the substrate

Burn Scar Analysis Using Remotely Sensed Multispectral Imagery

This paper describes a simple image processing algorithm that estimates the area of burn scars using multispectral imagery acquired by space based remote sensing platforms. The algorithm is used to track the regeneration of biomass destroyed by the 2011, Central Florida, Iron Horse brush fire. Our results indicate approximately 6.98e7 m2 (17,257 acres) were destroyed by the fire and nearly 100% of the burned biomass regenerated in less than 1 year. © 2013 IEEE

Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep temporal convolutional neural network is employed to learn the complex non-linear filters required to transform the single-trace waveforms into corresponding idealized characteristic functions. The training data consists of all arrivals in the International Seismological Centre Database for seven seismic arrays over a five year window from 1 January 2010 to 1 January 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 January 2015 to 1 January 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the generalization and transportability of the technique to new stations. Detection performance against this test set is outstanding, yielding significant improvements in recall over existing techniques. Fixing a type-I error rate of 0.001, the algorithm achieves an overall recall (true positive rate) of 56% against the 141,095 array-beam arrivals in the test set, yielding 78,802 correct detections. This is more than twice the 37,572 detections made by an STA/LTA detector over the same period, and represents a 35% improvement over the 58,515 detections made by a state-of-the-art kurtosis-based detector. Furthermore, DeepPick provides at least a 4 dB improvement in detector sensitivity across the board, and is more computationally efficient, with run-times an order of magnitude faster than either of the other techniques tested. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network

First Observations with the UMass Dual-Beam

An dual-beam along-track interferometric synthetic aperture radar which is self contained within an aircraft pod has been developed to study coastal regions. System hardware is described. Initial test flights aboard the NOAA WP-3D research aircraft were performed to evaluate system performance over land and water surfaces. Notable look-angle dependences are observed in the sea surface NRCS under very low wind conditions

Cfrsl Rain Measurement Facility - Part-2 Rain Profiling Radar

The Central Florida Remote Sensing Laboratory has established a rain measurement facility in Central, Fl to study the statistical properties of tropical rainfall and the associated interaction with electromagnetic waves. The rain absorption coefficient is an important parameter, which governs the atmospheric propagation absorption loss during rainfall. This paper (part-2 of 2) describes this research facility and presents the theoretical design of a rain profiling radar system. This L-band pulsed radar will provide zenith-viewing reflectivity measurements over altitude increments of 150 m to augment a passive microwave radiometer that is used to estimate the rain absorption coefficient at C-band. A theoretical \u27Z-R\u27 relationship (radar reflectivity values associated with corresponding rain rate) is used in a MATLAB radar simulation to illustrate the radar measurement performance using a numerical weather model to provide a realistic thunderstorm rainfall event. © 2013 IEEE

Cfrsl Rain Measurement Facility, Part I: Radiometer And Rain Gauges

The Central Florida Remote Sensing Laboratory has developed a rain measurement facility to study atmospheric microwave noise emission during heavy rain events. This paper (part-1 of 2) describes the design and primarily results obtained from a 6.8 GHz microwave radiometer, rain rate, and rain drop size sensors. This instrument suite can be used to determine rain absorption coefficient during high rain rate events, tropical storms, and occasional hurricane that occur in Central Florida. Results from this facility will advance the development of an empirical model to predict atmospheric propagation loss during heavy rain (\u3e 50 mm/hr). Preliminary results show measurements obtained from the radiometer and rain gauges are correlated, where a maximum rate of approximately 82 mm/hr is observed. © 2013 IEEE

Detecting Developing Volcanic Unrest

In this paper we show how to optimize QAM signal constellation which minimizes bit error probabilities with nonuniform sources on AWGN and Rayleigh fading channel. The optimal signal constellation is not equally spaced. We show that the optimal system has a lower error probability than a conventional system, i.e., a system with source coding, equiprobable symbols, and equal space constellation. In several cases presented in this paper the error probability of the optimal system is significantly lower than in the conventional system. We prove that even with equal symbol probabilities the equal space constellation is not optimal for 16QAM, but only asymptotically optimal. Finally, we evaluate the robustness of constellations to estimation error in symbol probabilities. © 2012 IEEE