What is noise?

Geophysics ◽  
1998 ◽  
Vol 63 (4) ◽  
pp. 1122-1124 ◽  
Author(s):  
John A. Scales ◽  
Roel Snieder
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Ground Motion ◽  
Crucial Role ◽  
Recording Station ◽  
Exploration Seismology ◽  
Population Center ◽  
Earthquake Seismology

The concept of “noise” plays a crucial role in the statistical analysis of data. As an example of a noisy record consider Figure 1 that shows the ground motion of the seismological station NE51 in St. Petersburg after an earthquake in Egypt. (In earthquake seismology, periods may be orders of magnitude larger than in exploration seismology, but the principles are the same.) This time series shows no distinct arrivals or other apparent signatures of an organized nature. Given the proximity of the recording station to a major population center and to the coast, such a noisy record does not seem to be very surprising.

A subset of CyberShake ground-motion time series for response-history analysis

2021 ◽  
pp. 875529302098197
Author(s):  
Jack W Baker ◽  
Sanaz Rezaeian ◽  
Christine A Goulet ◽  
Nicolas Luco ◽  
Ganyu Teng
Keyword(s):  
Time Series ◽  
Los Angeles ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Ground Motions ◽  
Response Spectra ◽  
Motion Time ◽  
History Analysis ◽  
Response History Analysis ◽  
Simulated Ground Motions

This manuscript describes a subset of CyberShake numerically simulated ground motions that were selected and vetted for use in engineering response-history analyses. Ground motions were selected that have seismological properties and response spectra representative of conditions in the Los Angeles area, based on disaggregation of seismic hazard. Ground motions were selected from millions of available time series and were reviewed to confirm their suitability for response-history analysis. The processes used to select the time series, the characteristics of the resulting data, and the provided documentation are described in this article. The resulting data and documentation are available electronically.

scholarly journals Proposal for Tower Crane Productivity Indicators Based on Data Analysis in the Era of Construction 4.0

Buildings ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 21
Author(s):  
Thomas Danel ◽  
Zoubeir Lafhaj ◽  
Anand Puppala ◽  
Sophie Lienard ◽  
Philippe Richard
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Collection ◽  
Structural Phase ◽  
Sampling Technique ◽  
Construction Site ◽  
Data Logger ◽  
Tower Crane ◽  
Productivity Indicators

This article proposes a methodology to measure the productivity of a construction site through the analysis of tower crane data. These data were obtained from a data logger that records a time series of spatial and load data from the lifting machine during the structural phase of a construction project. The first step was data collection, followed by preparation, which consisted of formatting and cleaning the dataset. Then, a visualization step identified which data was the most meaningful for the practitioners. From that, the activity of the tower crane was measured by extracting effective lifting operations using the load signal essentially. Having used such a sampling technique allows statistical analysis on the duration, load, and curvilinear distance of every extracted lifting operation. The build statistical distribution and indicators were finally used to compare construction site productivity.

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

2021 ◽  
Author(s):  
Aidin Tamhidi ◽  
Nicolas Kuehn ◽  
S. Farid Ghahari ◽  
Arthur J. Rodgers ◽  
Monica D. Kohler ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Ground Motion ◽  
San Francisco ◽  
Earthquake Engineering ◽  
Seismic Analysis ◽  
Ground Motions ◽  
Free Field ◽  
Gaussian Process Regression ◽  
Motion Time

ABSTRACT Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

Comparison of Manual and Automated Ground Motion Processing for Small-to-Moderate-Magnitude Earthquakes in Japan

2017 ◽  
Vol 33 (3) ◽  
pp. 875-894 ◽  
Author(s):  
Tadahiro Kishida ◽  
Danilo Di Giacinto ◽  
Giuseppe Iaccarino
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Earthquake Engineering ◽  
Corner Frequency ◽  
Motion Processing ◽  
Ground Motion Parameters ◽  
Acceleration Time ◽  
Motion Parameters ◽  
Filter Noise ◽  
Moderate Magnitude

Numerous time series for small-to-moderate-magnitude (SMM) earthquakes have been recorded in many regions. A uniformly-processed ground-motion database is essential in the development of regional ground-motion models. An automated processing protocol is useful in developing the database for these earthquakes especially when the number of recordings is substantial. This study compares a manual and an automated ground-motion processing methods using SMM earthquakes. The manual method was developed by the Pacific Earthquake Engineering Research Center to build the database of time series and associated ground-motion parameters. The automated protocol was developed to build a database of pseudo-spectral acceleration for the Kiban-Kyoshin network recordings. Two significant differences were observed when the two methods were applied to identical acceleration time series. First, the two methods differed in the criteria for the acceptance or rejection of the time series in the database. Second, they differed in the high-pass corner frequency used to filter noise from the acceleration time series. The influences of these differences were investigated on ground-motion parameters to elucidate the quality of ground-motion database for SMM earthquakes.

scholarly journals Peramalan Data Inflow dan Outflow Uang Kartal Bank Indonesia Provinsi DKI Jakarta Menggunakan Model ARIMAX dan SARIMAX

2021 ◽  
Vol 4 (2) ◽  
pp. 87
Author(s):  
Atika Amalia ◽  
Etik Zukhronah ◽  
Sri Subanti
Keyword(s):  
Time Series ◽  
Crucial Role ◽  
Forecasting Model ◽  
Gregorian Calendar ◽  
Monthly Data ◽  
Sample Data ◽  
The Difference

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> DKI Jakarta Province plays a crucial role as the center of government and economy in Indonesia. The description of currency inflows and outflows is highly required before Bank Indonesia formulates the appropriate policies to control the circulation of money. The monthly data of currency inflow and outflow of Bank Indonesia of DKI Jakarta show a significant increase in each year particularly before, during, and after Eid al-Fitr. The determination of Eid al-Fitr does not follow the Gregorian calendar but based on the Islamic calendar. The difference in the use of the Gregorian and Islamic calendars in a time series causes a calendar variation. Thus, the determination of Eid al-Fitr in the Gregorian calendar changes as it goes forward eleven days each year or one month every three years. This study aims to obtain the best model and forecast currency inflows and outflows of Bank Indonesia DKI Jakarta using the ARIMAX and SARIMAX models. The study used in-sample data from January 2009 to December 2018 and out-sample data from January to October 2019. The best model was selected based on the smallest out-sample MAPE value. The result showed that the best forecasting model of inflow was ARIMAX (1,0,1). Meanwhile, the best forecasting model for outflow was SARIMAX (2,0,1)(0,0,1)<sup>12</sup>.</p><p><strong>Keywords: </strong>ARIMAX, calendar variation, forecasting, SARIMAX</p>

scholarly journals Ground motion areas detection (GMA-D): an innovative approach to identify ground deformation areas using the SAR-based displacement time series

2020 ◽  
Vol 382 ◽  
pp. 277-284
Author(s):  
Roberta Bonì ◽  
Claudia Meisina ◽  
Linda Poggio ◽  
Alessandro Fontana ◽  
Giulia Tessari ◽  
...  
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Ground Motion ◽  
Coastal Plain ◽  
Ground Deformation ◽  
Innovative Approach ◽  
Synthetic Aperture ◽  
Ne Italy ◽  
Innovative Methodology

Abstract. In this work, an innovative methodology to generate the automatic ground motion areas mapping is presented. The methodology is based on the analysis of the Synthetic Aperture Radar (SAR)-based displacement time series. The procedure includes two modules developed using the ModelBuilder tool (ArcGis). These modules allow to identify the ground motion areas (GMA) using only one dataset and the persistent GMA (PGMA) considering the different monitored periods and datasets. These areas represent clusters of targets characterized by the same displacement time series trend. The procedure was tested using different sensors such as ERS-1/2, ENVISAT, COSMO-SkyMed and Sentinel-1 covering the periods, 1992–2000, 2003–2010, 2012–2016 and 2014–2017, respectively, over an area of about 500 km2 in the Venetian-Friulian coastal Plain (NE Italy). The resulting mapping allows to detect priority areas where to address further in situ investigations such as to verify the presence of localized buried landforms.

scholarly journals Monitoring High Throughput Distributed System using Statistical Data Analysis

2020 ◽  
Vol 8 (6) ◽  
pp. 4590-4596
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Distributed System ◽  
High Throughput ◽  
Statistical Data ◽  
Time Interval ◽  
Statistical Data Analysis ◽  
Cloud Data ◽  
Data Pipeline ◽  
Cloud Data Centers

Monitoring high throughput distributed system by using a statistical analysis of the “historical time series” of an Instrumentation Data”. “The Pipeline has been made to process the information which can be otherwise called data pipeline, is a lot of information handling components associated in arrangement, where yield of one component is the contribution of the next one”. Several codes are giving different visualization for statistical analysis of data. “Network and Cloud Data Centers” generate a lot of data every second; this data can be gathered as period arrangement information. A timeseries is a grouping taken at progressive similarly dispersed focuses in time that implies at a particular time interval to a particular time, the estimations of explicit information that was taken is known as information of a time-series. “This time-series information can be gathered utilizing framework measurements like CPU, Memory, and Disk utilization”. The TICK and ELK Stack is abbreviation for a foundation of open source instruments worked “to make collection, storage, graphing, and alerting” on time arrangement data incredibly easy. As an information collector, using Telegraf, “for storing and analyzing” information and the time-series database InfluxDB and Elasticsearch. For plotting and visualizing used Grafana and Kibana. Watchman is utilized for alert refinement and once system metrics usage exceeds the specified threshold, the alert is generated and sends it to the Telegram.

LONG RANGE CORRELATION PROPERTIES OF AFTERSHOCK RELAXATION SIGNALS

Fractals ◽  
1995 ◽  
Vol 03 (04) ◽  
pp. 839-847 ◽  
Author(s):  
A. VESPIGNANI ◽  
A. PETRI ◽  
A. ALIPPI ◽  
G. PAPARO ◽  
M. COSTANTINI
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Statistical Analysis ◽  
Power Spectrum ◽  
Power Law ◽  
Amplitude Distribution ◽  
Relaxation Processes ◽  
Critical Dynamics ◽  
Range Correlation ◽  
Correlation Properties

Relaxation processes taking place after microfracturing of laboratory samples give rise to ultrasonic acoustic emission signals. Statistical analysis of the resulting time series has revealed many features which are characteristic of critical phenomena. In particular, the autocorrelation functions obey a power-law behavior, implying a power spectrum of the kind 1/f. Also the amplitude distribution N(V) of such signals follows a power law, and the obtained exponents are consistent with those found in other experiments: N(V) dV≃V–γ dV, with γ=1.7±0.2. We also analyzed the distribution N(τ) of the delay time τ between two consecutive acoustic emission events. We found that a N(τ) distribution rather close to a power law constitutes a common feature of all the recorded signals. These experimental results can be considered as a striking evidence for a critical dynamics underlying the microfracturing processes.

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