Extending Waveform Correlation Techniques to Broad Regional Monitoring: Processing over 3 years of MKAR data.
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In this paper we report a data analysis protocol for ultrasonic velocity measurements carried out in a synchrotron x-ray facility based multi-anvil high-pressure apparatus. Synthetic ultrasonic signals for the time period between echoes from the two ends of the sample assembly are created with a few simple parameters as the echo waveform is modeled by the first echo from the anvil-assembly boundary. Each echo is modeled as a delta function with an amplitude and arrival time and convolved with this waveform. The final waveform, fit to the data, enables analysis of signals with overlapped echoes as they are common in small cell assemblies and high pressure.
Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, K-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.
This report includes research and data that the author's state demonstrates "the technique from [Shao et al., Nature Geoscience, 2012] can be used with ENTLN waveforms. Furthermore, an "automated processing has been developed to produce averaged waveforms and associated measurements." Ideas for future work is also presented.
Light Detection And Ranging (LiDAR) provides unique data on the 3-D structure of atmosphere constituents and the Earth's surface. Simulating LiDAR returns for different laser technologies and Earth scenes is fundamental for evaluating and interpreting signal and noise in LiDAR data. Different types of models are capable of simulating LiDAR waveforms of Earth surfaces. Semi-empirical and geometric models can be imprecise because they rely on simplified simulations of Earth surfaces and light interaction mechanisms. On the other hand, Monte Carlo ray tracing (MCRT) models are potentially accurate but require long computational time. Here, we present a new LiDAR waveform simulation tool that is based on the introduction of a quasi-Monte Carlo ray tracing approach in the Discrete Anisotropic Radiative Transfer (DART) model. Two new approaches, the so-called "box method" and "Ray Carlo method", are implemented to provide robust and accurate simulations of LiDAR waveforms for any landscape, atmosphere and LiDAR sensor configuration (view direction, footprint size, pulse characteristics, etc.). The box method accelerates the selection of the scattering direction of a photon in the presence of scatterers with non-invertible phase function. The Ray Carlo method brings traditional ray-tracking into MCRT simulation, which makes computational time independent of LiDAR field of view (FOV) and reception solid angle. Both methods are fast enough for simulating multi-pulse acquisition. Sensitivity studies with various landscapes and atmosphere constituents are presented, and the simulated LiDAR signals compare favorably with their associated reflectance images and Laser Vegetation Imaging Sensor (LVIS) waveforms. The LiDAR module is fully integrated into DART, enabling more detailed simulations of LiDAR sensitivity to specific scene elements (e.g., atmospheric aerosols, leaf area, branches, or topography) and sensor configuration for airborne or satellite LiDAR sensors.
Advances in the field of numerical relativity now make it possible to calculate the final, most powerful merger phase of binary black-hole coalescence for generic binaries. The state of the art has advanced well beyond the equal-mass case into the unequal-mass and spinning regions of parameter space. We present a study of the nonspinning portion of parameter space, primarily using an analytic waveform model tuned to available numerical data, with an emphasis on observational implications. We investigate the impact of varied mass-ratio on merger signal-to-noise ratios for several detectors, and compare our results with expectations from the test-mass limit. We note a striking similarity of the waveform phasing of the merger waveform across the available mass ratios. Motivated by this, we calculate the match between our 1:1 (equal-mass) and 4:1 mass-ratio waveforms during the merger as a function of location on the source sky, using a new formalism for the match that accounts for higher harmonics. This is an indicator of the amount of degeneracy in mass-ratio for mergers of moderate-mass-ratio systems.
Advances in the field of numerical relativity now make it possible to calculate the final, most powerful merger phase of binary black-hole coalescence for generic binaries. The state of the art has advanced well beyond the equal-mass case into the unequal-mass and spinning regions of parameter space. We present a study of the nonspinning portion of parameter space, primarily using an analytic waveform model tuned to available numerical data, with an emphasis on observational implications. We investigate the impact of varied m8BS ratio on merger signal-to-noise ratios (SNR~) for several detectors, and compare our results with expectations from the test-mass limit. We note a striking similarity of the waveform phasing of the merger waveform across the available mass ratios. Motivated by this, we calculate the match between our equal-mass and 4:1 mass-ratio waveforms during the merger as a function of location on the source sky, using a new formalism for the match that accounts for higher harmonics. This is an indicator of the amount of degeneracy in mass ratio for mergers of moderate mass ratio systems.
A motor has a rotor that rotates in response to one or more alternating current input signals. A position sensor is adapted to generate a substantially saw-tooth waveform indicative of a position of the rotor. A primary processing module or data processor is capable of providing position data and speed data for the rotor based on the substantially saw-tooth waveform. A notch filter can reject one or more selected frequency components in the substantially saw-tooth waveform to reduce distortion off the substantially saw-tooth waveform that would otherwise tend to cause inaccuracy in the provided position data and speed data.
Abstract We investigate the potential of using Global Navigation Satellite System (GNSS) observations to directly forecast full tsunami waveforms in real time. We train convolutional neural networks to use less than 9 min of GNSS data to forecast the full tsunami waveforms over 6 hr at select locations, and obtain accurate forecasts on a test data set. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone using the MudPy software, and corresponding tsunami waveforms in Puget Sound computed using GeoClaw. We use the same suite of synthetic earthquakes and waveforms as in earlier work where tsunami waveforms were used for forecasting, and provide a comparison. We also explore varying the number of GNSS stations, their locations, and their observation durations.
The single-phase liquid argon time projection chamber (LArTPC) provides a large amount of detailed information in the form of fine-grained drifted ionization charge from particle traces. To fully utilize this information, the deposited charge must be accurately extracted from the raw digitized waveforms via a robust signal processing chain. Enabled by the ultra-low noise levels associated with cryogenic electronics in the MicroBooNE detector, the precise extraction of ionization charge from the induction wire planes in a single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event display images, and quantitatively demonstrated via waveform-level and track-level metrics. Improved performance of induction plane calorimetry is demonstrated through the agreement of extracted ionization charge measurements across different wire planes for various event topologies. In addition to the comprehensive waveform-level comparison of data and simulation, a calibration of the cryogenic electronics response is presented and solutions to various MicroBooNE-specific TPC issues are discussed. This work presents an important improvement in LArTPC signal processing, the foundation of reconstruction and therefore physics analyses in MicroBooNE.
Eliminating redundancy in fixed frame television, and relationship between spectral properties of original sampled waveform and interpolation error
The goal of this project was to determine structural and stratigraphic controls on hydrate occurrence and distribution in Green Canyon (GC) 955 and Walker Ridge (WR) 313 blocks using seismic and well data. Gas hydrate was discovered in these blocks in coarse- and fine-grained sediments during the 2009 Joint Industrial project (JIP) Leg 11 drilling expedition. Although the immediate interest of the exploration community is exclusively hydrate which is present in coarse–grained sediments, factors that control hydrate and free gas distribution in the two blocks and whether coarse and fine-grained hydrate-bearing units are related in any manner, formed the core of this research. The project spanned from 10/01/2012 to 07/31/2016. In the project, in both the leased blocks, the interval spanning the gas hydrate stability zone (GHSZ) was characterized using a joint analysis of sparse Ocean Bottom Seismic (OBS) and dense, surface–towed multichannel seismic (MCS) data. The project team had the luxury of calibrating their results with two well logs. Advance processing methods such as depth migration and full-waveform inversion (FWI) were used for seismic data analysis. Hydrate quantification was achieved through interpretation of the FWI velocity field using appropriate rock physics models at both blocks. The seismic modeling/inversion methodology (common to both GC955 and WR313 blocks) was as follows. First, the MCS data were depth migrated using a P-wave velocity (VP) model constructed using inversion of reflection arrival times of a few (four in both cases) key horizons carefully picked in the OBS data to farthest possible offsets. Then, the resolution of the traveltime VP model was improved to wavelength scale by inverting OBS gathers up to the highest frequency possible (21.75 Hz for GC955 and 17.5 for WR313) using FWI. Finally, the hydrate saturation (or the volume fraction) was estimated at the well location assuming one of the other hydrate morphology (filling the primary or the secondary porosity) was extrapolated out from the wells using the FWI VP as a guide. General outcomes were as follows. First and foremost, an imaging methodology using sparse seismic data, which is easily replicable at other sites with similar datasets, has been demonstrated. The end product of this methodology at both the leased blocks is quantitative estimates of hydrate distribution. Second, at both locations there is strong evidence that the base of the GHSZ, which does not appear as a clear Bottom Simulating Reflection (BSR), manifests in the VP perturbations created by FWI, suggesting that FWI is sensitive to subtle compositional changes in shallow sediments and establishes it as a valuable tool for investigations of hydrate-bearing basins. Third, through joint interpretation of the depth migrated image and the FWI VP model, how structure and stratigraphy jointly determine hydrate and free gas distribution in both blocks could be clearly visualized. The joint interpretation also suggests that the coarse and fine grained hydrate-bearing sediments at both leased are connected. Site specific results, in addition to general results, are as follows. At GC955 the overlying fine-grained hydrate-bearing unit could have been sourced from the underlying hydrate coarse-grained channel-levee complex through a chimney feature. The channel-levee system at GC955 is compartmentalized by faults, of which only a few may be impermeable. Although compartmentalized, the channel-levee system in the GC955 as a whole might be in communication except selected zones. At WR313 the overlying fine-grained fracture-filled hydrate unit appears to be sourced from below the GHSZ. The reason that only a particular fine-grained unit has hydrate, despite having lower porosity that the bounding units, could be the presence of secondary porosity (such as those formed from clay dewatering under compaction). In conclusion, the project was a pioneering effort in in joint analysis of OBS and MCS datasets for advancing the knowledge about a hydrate and free–gas system dynamics using advanced processing methods such as FWI and depth migration. Results obtained in this project can greatly advance the tools and techniques used for delineating specific hydrate prospects. Results obtained in this project can also be seamlessly incorporated into other DOE funded project on modeling the potential productivity and commercial viability of hydrate from sand-dominated reservoirs. The OBS and MCS data in this project were acquired in 2012 (after the JIP II drilling) by the USGS and therefore the results are a posteriori. Nonetheless, the seismic inversion workflow established through this project can be used to generate various what-if quantification scenarios even in absence of logs and serve as a valuable tool for guiding drilling operations. Results from this project can augment other DOE sponsored projects on determining the commercial viability of methane production from the Gulf of Mexico.
Here, we apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters—total energy and position—directly from raw digitized waveforms, with minimal exceptions. For the first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo.
This project was focused on the development of tools for the automatic configuration of signal processing systems. The goal is to develop tools that will be useful in a variety of Government and commercial areas and useable by people who are not signal processing experts. In order to get the most benefit from signal processing techniques, deep technical expertise is often required in order to select appropriate algorithms, combine them into a processing chain, and tune algorithm parameters for best performance on a specific problem. Therefore a significant benefit would result from the assembly of a toolbox of processing algorithms that has been selected for their effectiveness in a group of related problem areas, along with the means to allow people who are not signal processing experts to reliably select, combine, and tune these algorithms to solve specific problems. Defining a vocabulary for problem domain experts that is sufficiently expressive to drive the configuration of signal processing functions will allow the expertise of signal processing experts to be captured in rules for automated configuration. In order to test the feasibility of this approach, we addressed a lightning classification problem, which was proposed by DOE as a surrogate for problems encountered in nuclear nonproliferation data processing. We coded a toolbox of low-level signal processing algorithms for extracting features of RF waveforms, and demonstrated a prototype tool for screening data. We showed examples of using the tool for expediting the generation of ground-truth metadata, for training a signal recognizer, and for searching for signals with particular characteristics. The public benefits of this approach, if successful, will accrue to Government and commercial activities that face the same general problem - the development of sensor systems for complex environments. It will enable problem domain experts (e.g. analysts) to construct signal and image processing chains without the aid of signal processing experts. Thus enabled, problem domain experts will be able to work more quickly and produce better quality work.
This paper presents the implementation and initial test results of an Orthogonal Frequency Division Multiplexing (OFDM) digital modem (modulator and demodulator) with an aggregate information throughput of 622 megabits per second (Mbps). The OFDM waveform is constructed by dividing an incoming data stream into four channels, each channel using either a 16-ary Quadrature Amplitude Modulation (16QAM) scheme or an 8-Phase Shift Keying (8PSK) scheme. The generation and detection of the composite waveform are performed using Discrete Fourier Transform (DFT) and polyphase filtering, to digitally stack and band-limit the individual carriers respectively. The four-channel OFDM approach enables the implementation of a modem that can be both power and bandwidth efficient, with sufficient parallelism to meet higher data rate goals. As a result, the OFDM modem requires only a 240 MHz bandwidth to transmit 622 Mbps. Hardware and simulation results in the form of spectrum diagrams and bit-error-rate (BER) curves are also presented in this paper.
Application of a trapezoidal filter to data from a benchtop measurement and an experiment at DANCE is reported. The measurements were performed with a 252Cf source and sets of two and four Si detectors, respectively, recording the detectors waveforms for offline analysis. In addition, the trapezoidal filter was applied to data from an HPGe detector installed at DANCE. The parameters of the filter were optimized to provide the best energy resolution. The results of the trapezoidal filter are evaluated against the standard waveform-integration method.
An existing technique to use broadband lightning waveforms to probe the D-region ionosphere (60–90 km altitude) is shown to be extendable to a global scale using the Earth Networks Total Lightning Network (ENTLN). This paper demonstrates the technique in detail on a region of the Southeastern United States. This demonstration shows that diurnal D-region height variation and smaller time-scale variations on the order of tens of minutes to hours are evident in the measurement. The technique is then extended to three additional global regions on this same day: Northeastern U.S., India, and Japan. The diurnal behavior between these different regions is compared to a D-region model from the International Reference Ionosphere.
Ignitrons are electrical switching devices that operate at switching times that are on the order of microseconds, can conduct high currents of thousands of amps, and are capable of holding off tens of thousands of volts between pulses. They consist of a liquid metal pool within an evacuated tube that serves both the cathode and the source of atoms and electrons for an arc discharge. Facing the liquid metal pool is an anode suspended above the cathode, with a smaller ignitor electrode tip located just above the surface of the cathode. The ignitron can be charged to significant voltages, with a potential difference of thousands of volts between anode and cathode. When an ignition pulse is delivered from the ignitor electrode to the cathode, a small amount of the liquid metal is vaporized and subsequently ionized, with the high voltage between the anode and cathode causing the gas to bridge the gap between the two electrodes. The electrons and ions move rapidly towards the anode and cathode, respectively, with the ions liberating still more atoms from the liquid metal cathode surface as a high-current plasma arc discharge is rapidly established. This arc continues in a self-sustaining fashion until the potential difference between the anode and cathode drops below some critical value. Ignitrons have been used in a variety of pulsed power applications, including the railroad industry, industrial chemical processing, and high-power arc welding. In addition, they might prove useful in terrestrial power grid applications, serving as high-current fault switches, quickly shunting dangerous high-current or high-voltage spikes safely to ground. The motivation for this work stemmed from the fact that high-power, high-reliability, pulsed power devices like the ignitron have been used for ground testing in-space pulsed electric thruster technologies, and the continued use of ignitrons could prove advantageous to the future development and testing of such thrusters. Previous ignitron designs have used mercury as the liquid metal cathode, owing to its presence as a liquid at room temperatures and a vapor pressure of 10 Pa (75 mtorr) at room temperature. While these are favorable properties, there are obvious environmental and personal safety concerns with the storage, handling, and use of mercury and its compounds. The purpose of the present work was to fabricate and test an ignitron that used as its cathode an alternate liquid metal that was safe to handle and store. To that end, an ignitron test article that used liquid gallium as the cathode material was developed and tested. Gallium is a metal that has a melting temperature of 29.76 C, which is slightly above room temperature, and a boiling point of over 2,300 C at atmospheric pressure. This property makes gallium the element with the largest relative difference between melting and boiling points. Gallium has a limited role in biology, and when ingested, it will be subsequently processed by the body and expelled rather than accumulating to toxic levels. The next section of this Technical Memorandum (TM) provides background information on the development of mercury-based ignitrons, which serves as the starting point for the development of the gallium-based variant. Afterwards, the experimental hardware and setup used in proof-of-concept testing of a basic gallium ignitron are presented. Experimental data, consisting of discharge voltage and current waveforms as well as high-speed imaging of the gallium arc discharge in the gallium ignitron test article, are presented to demonstrate the efficacy of the concept. Discussion of the data and suggestions on improvements for future iterations of the design are presented in the final two sections of this TM.