GLASS3: A Standalone Multiscale Seismic Detection Associator

2019 ◽  
Vol 109 (4) ◽  
pp. 1469-1478 ◽  
Author(s):  
William L. Yeck ◽  
John M. Patton ◽  
Caryl E. Johnson ◽  
David Kragness ◽  
Harley M. Benz ◽  
...  
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Multiple Scales ◽  
Seismic Station ◽  
Processing System ◽  
Data Availability ◽  
Large Dataset ◽  
Noise Characteristics ◽  
Seismic Detection ◽  
National Earthquake Information Center

Abstract The automated global real‐time association of phase picks into seismic sources comes with unique challenges when simultaneously monitoring at local, regional, and global scales. High spatial variability in seismic station density, transitory seismic data availability, and time‐varying noise characteristics of individual stations must be considered in the design of an associator that is fast and accurate with a low false association rate. These challenges are particularly apparent at the U.S. Geological Survey National Earthquake Information Center (NEIC), which monitors seismicity in near‐real time on local, regional, and global scales using seismic data from roughly 2100 real‐time seismic stations. To fully leverage this large dataset, NEIC developed a standalone self‐configuring seismic phase associator, GLobal ASSociator 3 (GLASS3) that simultaneously processes variably scaled 3D association webs, each with a unique set of nucleation criteria (e.g., nucleation stack threshold). GLASS3 has many useful features for real‐time monitoring including its computational efficiency, instantaneous pick processing, and on‐the‐fly configurability such as the creation and removal of targeted association webs and updates to supporting station metadata. GLASS3 runs both as part of a real‐time event processing system and as a configurable standalone associator that can be applied to a large variety of seismic problems. Here, we describe the GLASS3 algorithm and demonstrate (including input data and configuration files) its use in associating phase‐ambiguous picks on multiple scales.

Inferring significant wave height from seismic data: A comparison between methods.

2021 ◽  
Author(s):  
Francisco Bolrão ◽  
Co Tran ◽  
Miguel Lima ◽  
Sheroze Sheriffdeen ◽  
Diogo Rodrigues ◽  
...  
Keyword(s):  
Real Time ◽  
Wave Height ◽  
Seismic Data ◽  
Significant Wave Height ◽  
Transfer Functions ◽  
Seismic Station ◽  
Meteorological Data ◽  
Ocean Waves ◽  
The United States ◽  
Significant Wave

<p>The most pervasive seismic signal recorded on our planet – microseismic ambient noise -results from the coupling of energy between atmosphere, oceans and solid Earth. Because it carries information on ocean waves (source), the microseismic wavefield can be advantageously used to image ocean storms. This imaging is of interest both to climate studies – by extending the record of oceanic activity back into the early instrumental seismic record – and to real-time monitoring – where real-time seismic data can potentially be used to complement the spatially dense but temporally sparse satellite meteorological data.<br>In our work, we develop empirical transfer functions between seismic observations and ocean activity observations, in particular, significant wave height. We employ three different approaches: 1) The approach of Ferretti et al (2013), who compute a seismic significant wave height and invert only for the empirical conversion parameters between oceanic and seismic significant wave heights; 2) The classical approach of Bromirski et al (1999), who computed an empirical transfer function between ground-motion recorded at a coastal seismic station and significant wave height measured at a nearby ocean buoy; and 3) A novel recurrent neural-network (RNN) approach to infer significant wave height from seismic data. <br>We apply the three approaches to seismic and ocean buoy data recorded in the east coast of the United States. All three approaches are able to successfully predict ocean significant wave height from the seismic data. We compare the three approaches in terms of accuracy, computational effort and robustness. In addition, we investigate the regimes where each approach works best.  The results show that the RNN approach is able to predict well the significant wave height recorded at the buoy. The prediction is improved if several nearby seismic stations are used rather than just one. <br>This work is supported by FCT through projects UIDB/50019/2020 – IDL and UTAP-EXPL/EAC/0056/2017 - STORM.</p>

scholarly journals A Real Time Processing system for big data in astronomy: Applications to HERA

2021 ◽  
pp. 100489
Author(s):  
Paul La Plante ◽  
P.K.G. Williams ◽  
M. Kolopanis ◽  
J.S. Dillon ◽  
A.P. Beardsley ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Processing System ◽  
Real Time Processing ◽  
Time Processing

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

Design of a real-time video processing system with FPGA

2002 ◽  
Author(s):  
Wei Liu ◽  
Zeying Chi ◽  
Wenjian Chen
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Processing System

scholarly journals Evaluating Latency in Multiprocessing Embedded Systems for the Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3322
Author(s):  
Sara Alonso ◽  
Jesús Lázaro ◽  
Jaime Jiménez ◽  
Unai Bidarte ◽  
Leire Muguira
Keyword(s):  
Operating System ◽  
Smart Grid ◽  
Real Time ◽  
Processing System ◽  
The Other ◽  
Programmable Logic ◽  
Detailed Characterization ◽  
Timing Constraint ◽  
Time Part

Smart grid endpoints need to use two environments within a processing system (PS), one with a Linux-type operating system (OS) using the Arm Cortex-A53 cores for management tasks, and the other with a standalone execution or a real-time OS using the Arm Cortex-R5 cores. The Xen hypervisor and the OpenAMP framework allow this, but they may introduce a delay in the system, and some messages in the smart grid need a latency lower than 3 ms. In this paper, the Linux thread latencies are characterized by the Cyclictest tool. It is shown that when Xen hypervisor is used, this scenario is not suitable for the smart grid as it does not meet the 3 ms timing constraint. Then, standalone execution as the real-time part is evaluated, measuring the delay to handle an interrupt created in programmable logic (PL). The standalone application was run in A53 and R5 cores, with Xen hypervisor and OpenAMP framework. These scenarios all met the 3 ms constraint. The main contribution of the present work is the detailed characterization of each real-time execution, in order to facilitate selecting the most suitable one for each application.

Capturing, Preserving, and Digitizing Legacy Seismic Data from the Montserrat Volcano Observatory Analog Seismic Network, July 1995–December 2004

2020 ◽  
Vol 91 (4) ◽  
pp. 2127-2140 ◽  
Author(s):  
Glenn Thompson ◽  
John A. Power ◽  
Jochen Braunmiller ◽  
Andrew B. Lockhart ◽  
Lloyd Lynch ◽  
...  
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Original Data ◽  
South Florida ◽  
Seismic Network ◽  
Quality Data ◽  
Spectral Amplitude ◽  
Assistance Program ◽  
Waveform Data ◽  
Amplitude Measurement

Abstract An eruption of the Soufrière Hills Volcano (SHV) on the eastern Caribbean island of Montserrat began on 18 July 1995 and continued until February 2010. Within nine days of the eruption onset, an existing four-station analog seismic network (ASN) was expanded to 10 sites. Telemetered data from this network were recorded, processed, and archived locally using a system developed by scientists from the U.S. Geological Survey (USGS) Volcano Disaster Assistance Program (VDAP). In October 1996, a digital seismic network (DSN) was deployed with the ability to capture larger amplitude signals across a broader frequency range. These two networks operated in parallel until December 2004, with separate telemetry and acquisition systems (analysis systems were merged in March 2001). Although the DSN provided better quality data for research, the ASN featured superior real-time monitoring tools and captured valuable data including the only seismic data from the first 15 months of the eruption. These successes of the ASN have been rather overlooked. This article documents the evolution of the ASN, the VDAP system, the original data captured, and the recovery and conversion of more than 230,000 seismic events from legacy SUDS, Hypo71, and Seislog formats into Seisan database with waveform data in miniSEED format. No digital catalog existed for these events, but students at the University of South Florida have classified two-thirds of the 40,000 events that were captured between July 1995 and October 1996. Locations and magnitudes were recovered for ∼10,000 of these events. Real-time seismic amplitude measurement, seismic spectral amplitude measurement, and tiltmeter data were also captured. The result is that the ASN seismic dataset is now more discoverable, accessible, and reusable, in accordance with FAIR data principles. These efforts could catalyze new research on the 1995–2010 SHV eruption. Furthermore, many observatories have data in these same legacy data formats and might benefit from procedures and codes documented here.

A New Flexible Rapid Thermal Processing System

1995 ◽  
Vol 389 ◽  
Author(s):  
K. C. Saraswat ◽  
Y. Chen ◽  
L. Degertekin ◽  
B. T. Khuri-Yakub
Keyword(s):  
Control System ◽  
Real Time ◽  
Processing System ◽  
Process Conditions ◽  
Temperature Uniformity ◽  
Real Time Control ◽  
Wafer Temperature ◽  
Time Control ◽  
Wide Range ◽  
Optical Flux

ABSTRACTA highly flexible Rapid Thermal Multiprocessing (RTM) reactor is described. This flexibility is the result of several new innovations: a lamp system, an acoustic thermometer and a real-time control system. The new lamp has been optimally designed through the use of a “virtual reactor” methodology to obtain the best possible wafer temperature uniformity. It consists of multiple concentric rings composed of light bulbs with horizontal filaments. Each ring is independently and dynamically controlled providing better control over the spatial and temporal optical flux profile resulting in excellent temperature uniformity over a wide range of process conditions. An acoustic thermometer non-invasively allows complete wafer temperature tomography under all process conditions - a critically important measurement never obtained before. For real-time equipment and process control a model based multivariable control system has been developed. Extensive integration of computers and related technology for specification, communication, execution, monitoring, control, and diagnosis demonstrates the programmability of the RTM.

Processing system for real-time holographic video computation

1999 ◽  
Author(s):  
Thomas A. Nwodoh ◽  
V. Michael Bove, Jr. ◽  
John A. Watlington ◽  
Stephen A. Benton
Keyword(s):  
Real Time ◽  
Processing System
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