A satellite-based digital data system for low-frequency geophysical data

1989 ◽  
Vol 79 (1) ◽  
pp. 189-198
Author(s):  
Stan Silverman ◽  
Carl Mortensen ◽  
Malcolm Johnston
Keyword(s):  
Real Time ◽  
Crustal Deformation ◽  
Previous History ◽  
Low Frequency ◽  
Deformation Monitoring ◽  
Water Levels ◽  
Geophysical Data ◽  
Physical Models ◽  
Digital Data ◽  
Local Magnetic Field

Abstract A reliable method for collection, display, and analysis of low-frequency geophysical data from isolated sites, which can be throughout North and South America and the Pacific Rim, has been developed for use with the Geostationary Operational Environmental Satellite (GOES) system. Geophysical data primarily intended for earthquake hazard and crustal deformation monitoring are digitized with either 12-bit or 16-bit resolution and transmitted every 10 min through a satellite link to a bank of UNIX-based computers in Menlo Park, California. There the data are available for analysis and display within a few seconds of their transmit time. This system provides real-time monitoring of crustal deformation parameters such as tilt, strain, fault displacement, local magnetic field, crustal geochemistry, and water levels, as well as meteorological and other parameters, along faults in California and Alaska, and in volcanic regions in the western United States, Rabaul, and other locations in the New Britain region of the South Pacific. Various mathematical, statistical, and graphical algorithms process the incoming data to detect changes in crustal deformation and fault slip that may indicate the first stages of catastrophic fault failure. Alert trigger levels based on physical models, signal resolution, and previous history have been defined for particular instrument types. Computer-driven remote paging and mail systems are used to notify appropriate personnel when alarm status is reached. The system supports continuous historical records of low-frequency geophysical data, software for extensive analysis of these data, and programs for modeling fault rupture with and without seismic radiation, as well as providing an environment for real-time attempts at earthquake prediction.

Detecting Pressure Anomalies While Drilling Using a Machine Learning Hybrid Approach

2021 ◽  
Author(s):  
Aurore Lafond ◽  
Maurice Ringer ◽  
Florian Le Blay ◽  
Jiaxu Liu ◽  
Ekaterina Millan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Large Scale ◽  
Hybrid Approach ◽  
Physical Models ◽  
Training Data ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
New System

Abstract Abnormal surface pressure is typically the first indicator of a number of problematic events, including kicks, losses, washouts and stuck pipe. These events account for 60–70% of all drilling-related nonproductive time, so their early and accurate detection has the potential to save the industry billions of dollars. Detecting these events today requires an expert user watching multiple curves, which can be costly, and subject to human errors. The solution presented in this paper is aiming at augmenting traditional models with new machine learning techniques, which enable to detect these events automatically and help the monitoring of the drilling well. Today’s real-time monitoring systems employ complex physical models to estimate surface standpipe pressure while drilling. These require many inputs and are difficult to calibrate. Machine learning is an alternative method to predict pump pressure, but this alone needs significant labelled training data, which is often lacking in the drilling world. The new system combines these approaches: a machine learning framework is used to enable automated learning while the physical models work to compensate any gaps in the training data. The system uses only standard surface measurements, is fully automated, and is continuously retrained while drilling to ensure the most accurate pressure prediction. In addition, a stochastic (Bayesian) machine learning technique is used, which enables not only a prediction of the pressure, but also the uncertainty and confidence of this prediction. Last, the new system includes a data quality control workflow. It discards periods of low data quality for the pressure anomaly detection and enables to have a smarter real-time events analysis. The new system has been tested on historical wells using a new test and validation framework. The framework runs the system automatically on large volumes of both historical and simulated data, to enable cross-referencing the results with observations. In this paper, we show the results of the automated test framework as well as the capabilities of the new system in two specific case studies, one on land and another offshore. Moreover, large scale statistics enlighten the reliability and the efficiency of this new detection workflow. The new system builds on the trend in our industry to better capture and utilize digital data for optimizing drilling.

scholarly journals Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 694 ◽  
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Shuguo Pan ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
High Precision ◽  
High Frequency ◽  
Spectral Response ◽  
Low Frequency ◽  
Deformation Monitoring ◽  
Full Time ◽  
Dynamic Displacement ◽  
Mode Decomposition ◽  
Acceleration Sensors

Real-time dynamic displacement and spectral response on the midspan of Jiangyin Bridge were calculated using Global Navigation Satellite System (GNSS) and a speedometer for the purpose of understanding the dynamic behavior and the temporal evolution of the bridge structure. Considering that the GNSS measurement noise is large and the velocity/acceleration sensors cannot measure the low-frequency displacement, the Variational Mode Decomposition (VMD) algorithm was used to extract the low-frequency displacement of GNSS. Then, the low-frequency displacement extracted from the GNSS time series and the high-frequency vibration calculated by speedometer were combined in this paper in order to obtain the high precision three-dimensional dynamic displacement of the bridge in real time. Simulation experiment and measured data show that the VMD algorithm could effectively resist the modal aliasing caused by noise and discontinuous signals compared with the commonly used Empirical Mode Decomposition (EMD) algorithm, which is guaranteed to get high-precision fusion data. Finally, the fused displacement results can identify high-frequency vibrations and low-frequency displacements of a mm level, which can be used to calculate the spectral characteristics of the bridge and provide reference to evaluate the dynamic and static loads, and the health status of the bridge in the full frequency domain and the full time domain.

scholarly journals One Year of Daily Satellite Orbit and Polar Motion Estimation for Near Real Time Crustal Deformation Monitoring

1993 ◽  
Vol 156 ◽  
pp. 279-284
Author(s):  
Yehuda Bock ◽  
Jie Zhang ◽  
Peng Fang ◽  
Joachim Genrich ◽  
Keith Stark ◽  
...  
Keyword(s):  
Real Time ◽  
Crustal Deformation ◽  
Polar Motion ◽  
Southern California ◽  
Regional Scale ◽  
Satellite Orbit ◽  
Pole Position ◽  
Terrestrial Reference Frame ◽  
Deformation Monitoring ◽  
Worst Case

The Permanent GPS Geodetic Array (PGGA) in southern California consists of five continuously operating stations established to monitor crustal deformation in near real time. The near real time requirement has been problematic since GPS satellite ephemerides and predicted earth orientation values (IERS Bulletins A and B) have been found to be neither sufficiently timely nor accurate to achieve horizontal position accuracies of several mm on regional scales. Therefore, we have been estimating precise GPS ephemerides and polar motion since August 1991. An examination of overlapping 24-hour satellite arcs indicates worst-case orbital errors of approximately 0.2 meters in the radial components, 1 meter in the cross-track components and 2–3 meters in the along-track components. A comparison with very long baseline interferometry indicates an accuracy of less than 1 mas in our determination of 24-hour values of pole position. These products are sufficiently timely and accurate to achieve several mm long-term horizontal precision in regional scale measurements of crustal deformation in near real time, as has been demonstrated during the 28 June, 1992 Landers and Big Bear earthquakes in southern California. The PGGA stations were able to detect seismically induced, sub-centimeter-level motions with respect to a terrestrial reference frame defined by the global tracking stations.

scholarly journals Real-time monitoring of seismic data using satellite telemetry

1997 ◽  
Vol 40 (4) ◽  
Author(s):  
G. Calderoni ◽  
B. De Simoni ◽  
F. M. De Simoni ◽  
L. Merucci
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Seismic Data ◽  
Data Exchange ◽  
Low Frequency ◽  
Satellite Communications ◽  
Geophysical Data ◽  
Processing Centre ◽  
Duration Magnitude ◽  
Master Station

This article describes the ARGO Satellite Seismic Network (ARGO SSN) as a reliable system for monitoring, collection, visualisation and analysis of seismic and geophysical low-frequency data, The satellite digital telemetry system is composed of peripheral geophysical stations, a centraI communications node (master sta- tion) located in CentraI Italy, and a data collection and processing centre located at ING (Istituto Nazionale di Geofisica), Rome. The task of the peripheral stations is to digitalise and send via satellite the geophysical data collected by the various sensors to the master station. The master station receives the data and forwards them via satellite to the ING in Rome; it also performs alI the monitoring functions of satellite communications. At the data collection and processing centre of ING, the data are received and analysed in real time, the seismic events are identified and recorded, the low-frequency geophysical data are stored. In addition, the generaI sta- tus of the satellite network and of each peripheral station connected, is monitored. The procedure for analysjs of acquired seismic signals allows the automatic calculation of local magnitude and duration magnitude The communication and data exchange between the seismic networks of Greece, Spain and Italy is the fruit of a recent development in the field of technology of satellite transmission of ARGO SSN (project of European Community "Southern Europe Network for Analysis of Seismic Data" )

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

Airborne geophysical surveys in Greenland in 1998

1999 ◽  
pp. 34-38 ◽  
Author(s):  
Thorkild M. Rasmussen ◽  
Leif Thorning
Keyword(s):  
High Resolution ◽  
Geophysical Data ◽  
Digital Data ◽  
Geological Survey ◽  
Magnetic Data ◽  
Magnetic Survey ◽  
Original Series ◽  
Geophysical Surveys ◽  
The Government

NOTE: This article was published in a former series of GEUS Bulletin. Please use the original series name when citing this article, for example: Rasmussen, T. M., & Thorning, L. (1999). Airborne geophysical surveys in Greenland in 1998. Geology of Greenland Survey Bulletin, 183, 34-38. https://doi.org/10.34194/ggub.v183.5202 _______________ Airborne geophysical surveying in Greenland during 1998 consisted of a magnetic project referred to as ‘Aeromag 1998’ and a combined electromagnetic and magnetic project referred to as ‘AEM Greenland 1998’. The Government of Greenland financed both with administration managed by the Geological Survey of Denmark and Greenland (GEUS). With the completion of the two projects, approximately 305 000 line km of regional high-resolution magnetic data and approximately 75 000 line km of detailed multiparameter data (electromagnetic, magnetic and partly radiometric) are now available from government financed projects. Figure 1 shows the location of the surveyed areas with highresolution geophysical data together with the area selected for a magnetic survey in 1999. Completion of the two projects was marked by the release of data on 1 March, 1999. The data are included in the geoscientific databases at the Survey for public use; digital data and maps may be purchased from the Survey.

scholarly journals Real-time automatic uncertainty estimation of coseismic single rectangular fault model using GNSS data

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Keitaro Ohno ◽  
Yusaku Ohta ◽  
Satoshi Kawamoto ◽  
Satoshi Abe ◽  
Ryota Hino ◽  
...  
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
Order Approximation ◽  
Nonlinear Problems ◽  
Large Earthquake ◽  
Fault Model ◽  
Deformation Monitoring ◽  
Model Parameters ◽  
The Sea Of Japan ◽  
Plate Interface

AbstractRapid estimation of the coseismic fault model for medium-to-large-sized earthquakes is key for disaster response. To estimate the coseismic fault model for large earthquakes, the Geospatial Information Authority of Japan and Tohoku University have jointly developed a real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD can estimate the single rectangular fault model and slip distribution along the assumed plate interface. The single rectangular fault model is useful as a first-order approximation of a medium-to-large earthquake. However, in its estimation, it is difficult to obtain accurate results for model parameters due to the strong effect of initial values. To solve this problem, this study proposes a new method to estimate the coseismic fault model and model uncertainties in real time based on the Bayesian inversion approach using the Markov Chain Monte Carlo (MCMC) method. The MCMC approach is computationally expensive and hyperparameters should be defined in advance via trial and error. The sampling efficiency was improved using a parallel tempering method, and an automatic definition method for hyperparameters was developed for real-time use. The calculation time was within 30 s for 1 × 106 samples using a typical single LINUX server, which can implement real-time analysis, similar to REGARD. The reliability of the developed method was evaluated using data from recent earthquakes (2016 Kumamoto and 2019 Yamagata-Oki earthquakes). Simulations of the earthquakes in the Sea of Japan were also conducted exhaustively. The results showed an advantage over the maximum likelihood approach with a priori information, which has initial value dependence in nonlinear problems. In terms of application to data with a small signal-to-noise ratio, the results suggest the possibility of using several conjugate fault models. There is a tradeoff between the fault area and slip amount, especially for offshore earthquakes, which means that quantification of the uncertainty enables us to evaluate the reliability of the fault model estimation results in real time.

Detection by real-time quaking-induced conversion (RT-QuIC), ELISA, and IHC of chronic wasting disease prion in lymph nodes from Pennsylvania white-tailed deer with specific PRNP genotypes

2021 ◽  
pp. 104063872110214
Author(s):  
Deepanker Tewari ◽  
David Steward ◽  
Melinda Fasnacht ◽  
Julia Livengood
Keyword(s):  
Real Time ◽  
Disease Susceptibility ◽  
Chronic Wasting Disease ◽  
Low Frequency ◽  
The United States ◽  
Detection Methods ◽  
Spongiform Encephalopathy ◽  
Wasting Disease ◽  
Diagnostic Laboratory ◽  
Highly Sensitive

Chronic wasting disease (CWD) is a prion-mediated, transmissible disease of cervids, including deer ( Odocoileus spp.), which is characterized by spongiform encephalopathy and death of the prion-infected animals. Official surveillance in the United States using immunohistochemistry (IHC) and ELISA entails the laborious collection of lymphoid and/or brainstem tissue after death. New, highly sensitive prion detection methods, such as real-time quaking-induced conversion (RT-QuIC), have shown promise in detecting abnormal prions from both antemortem and postmortem specimens. We compared RT-QuIC with ELISA and IHC for CWD detection utilizing deer retropharyngeal lymph node (RLN) tissues in a diagnostic laboratory setting. The RLNs were collected postmortem from hunter-harvested animals. RT-QuIC showed 100% sensitivity and specificity for 50 deer RLN (35 positive by both IHC and ELISA, 15 negative) included in our study. All deer were also genotyped for PRNP polymorphism. Most deer were homozygous at codons 95, 96, 116, and 226 (QQ/GG/AA/QQ genotype, with frequency 0.86), which are the codons implicated in disease susceptibility. Heterozygosity was noticed in Pennsylvania deer, albeit at a very low frequency, for codons 95GS (0.06) and 96QH (0.08), but deer with these genotypes were still found to be CWD prion-infected.

Sign in / Sign up

Export Citation Format

Share Document