scholarly journals Correlation of pre-earthquake electromagnetic signals with laboratory and field rock experiments

2010 ◽  
Vol 10 (9) ◽  
pp. 1965-1975 ◽  
Author(s):  
T. Bleier ◽  
C. Dunson ◽  
C. Alvarez ◽  
F. Freund ◽  
R. Dahlgren

Abstract. Analysis of the 2007 M5.4 Alum Rock earthquake near San José California showed that magnetic pulsations were present in large numbers and with significant amplitudes during the 2 week period leading up the event. These pulsations were 1–30 s in duration, had unusual polarities (many with only positive or only negative polarities versus both polarities), and were different than other pulsations observed over 2 years of data in that the pulse sequence was sustained over a 2 week period prior to the quake, and then disappeared shortly after the quake. A search for the underlying physics process that might explain these pulses was was undertaken, and one theory (Freund, 2002) demonstrated that charge carriers were released when various types of rocks were stressed in a laboratory environment. It was also significant that the observed charge carrier generation was transient, and resulted in pulsating current patterns. In an attempt to determine if this phenomenon occurred outside of the laboratory environment, the authors scaled up the physics experiment from a relatively small rock sample in a dry laboratory setting, to a large 7 metric tonne boulder comprised of Yosemite granite. This boulder was located in a natural, humid (above ground) setting at Bass Lake, Ca. The boulder was instrumented with two Zonge Engineering, Model ANT4 induction type magnetometers, two Trifield Air Ion Counters, a surface charge detector, a geophone, a Bruker Model EM27 Fourier Transform Infra Red (FTIR) spectrometer with Sterling cycle cooler, and various temperature sensors. The boulder was stressed over about 8 h using expanding concrete (Bustartm), until it fractured into three major pieces. The recorded data showed surface charge build up, magnetic pulsations, impulsive air conductivity changes, and acoustical cues starting about 5 h before the boulder actually broke. These magnetic and air conductivity pulse signatures resembled both the laboratory rock stressing results and the 30 October 2007 M5.4 Alum Rock earthquake field data. The second part of this paper examined other California earthquakes, prior to the Alum Rock earthquake, to see if magnetic pulsations were also present prior to those events. A search for field examples of medium earthquakes was performed to identify earthquakes where functioning magnetometers were present within 20 km, the expected detection range of the magnetometers. Two earthquakes identified in the search included the 12 August 1998 M5.1 San Juan Bautista (Hollister Ca.) earthquake and the 28 September 2004 M6.0 Parkfield Ca. earthquake. Both of these data sets were recorded using EMI Corp. Model BF4 induction magnetometers, installed in equipment owned and operated by UC Berkeley. Unfortunately, no air conductivity or IR data were available for these earthquake examples. This new analysis of old data used the raw time series data (40 samples per s), and examined the data for short duration pulsations that exceeded the normal background noise levels at each site, similar to the technique used at Alum Rock. Analysis of Hollister magnetometer, positioned 2 km from the epicenter, showed a significant increase in magnetic pulsations above quiescient threshold levels several weeks prior, and especially 2 days prior to the quake. The pattern of positive and negative pulsations observed at Hollister, were similar, but not identical to Alum Rock in that the pattern of pulsations were interspersed with Pc 1 pulsation trains, and did not start 2 weeks prior to the quake, but rather 2 days prior. The Parkfield data (magnetometer positioned 19 km from the epicenter) showed much smaller pre-earthquake pulsations, but the area had significantly higher conductivity (which attenuates the signals). More interesting was the fact that significant pulsations occurred between the aftershock sequences of quakes as the crustal stress patterns were migrating. Comparing laboratory, field experiments with a boulder, and earthquake events, striking similarities were noted in magnetic pulsations and air conductivity changes, as well as IR signals (where instrumented). More earthquake samples, taken with the appropriate detectors and within 10–15 km proximity to large (>M5) earthquakes, are still needed to provide more evidence to understand the variability between earthquakes and various electromagnetic signals detected prior to large earthquakes.

2018 ◽  
Author(s):  
A.A Adnan ◽  
J. Diels ◽  
J.M. Jibrin ◽  
A.Y. Kamara ◽  
P. Craufurd ◽  
...  

AbstractMost crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha−1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.


2011 ◽  
Vol 121-126 ◽  
pp. 1692-1696
Author(s):  
Xin Quan Jiao ◽  
Yong Xing Yao ◽  
Qing Meng

For the need to test square wave pulse sequence, designed and implemented a recorder. Recorder using FPGA for judging square wave pulse’s edge and precise timing functions, using C8051F MCU to storage the results of time-series data and printing functions, FPGA and MCU using a custom protocol for data exchange and command transfer. After experimental verification, the recorder have 1ms accuracy to record 28 roads square wave pulse signal timing, and fixed-format print the results.


MAUSAM ◽  
2021 ◽  
Vol 42 (1) ◽  
pp. 17-24
Author(s):  
R. R, RAO ◽  
K. V. SANIL K UMAR ◽  
BASIL MA THBW

The observed short term. variability in .the current field of the upper layers of the northern Bay of Bengal IS examined utilizing the available time series data sets of current meter records obtained from mooring lines deployed from USSR stationary ship polygons during MONSOON-77 and MONEX-79 field experiments. Supplementary time series data sets on the vertical profiles of temperature and salinity in addition to surface winds were also made use of to describe the observed variability and structure of the horizontal velocity in the upper 200 m water column. Although the thermal regime appeared to be homogeneous within both the observational arrays considerable differences were noticed in the salinity and current regimes. The strong vertical stratification which is variable in the northern Bay of Bengal appeared to have Influenced the observed upper oceanic flow regime. Evidence for Ekman type of balance was rather weak suggesting the importance of baroclinic and river driven circulation modes. A clockwise eddy type of circulation was evident only during MONEX-79 but not during MONSOON- 77. The vector time series of current meter records were subjected to rotary spectral analysis to identity the periodicities of energetic oscillations and to infer the nature of circulation. Three to five-day oscillations in the flow regime were noticed during MONEX- 79.


2008 ◽  
Vol 25 (7) ◽  
pp. 1136-1148 ◽  
Author(s):  
Yadong Wang ◽  
Tian-You Yu ◽  
Mark Yeary ◽  
Alan Shapiro ◽  
Shamim Nemati ◽  
...  

Abstract Tornado vortices observed from Doppler radars are often associated with strong azimuthal shear and Doppler spectra that are wide and flattened. The current operational tornado detection algorithm (TDA) primarily searches for shear signatures that are larger than the predefined thresholds. In this work, a tornado detection procedure based on a fuzzy logic system is developed to integrate tornadic signatures in both the velocity and spectral domains. A novel feature of the system is that it is further enhanced by a neural network to refine the membership functions through a feedback training process. The hybrid approach herein, termed the neuro–fuzzy tornado detection algorithm (NFTDA), is initially verified using simulations and is subsequently tested on real data. The results demonstrate that NFTDA can detect tornadoes even when the shear signatures are degraded significantly so that they would create difficulties for typical vortex detection schemes. The performance of the NFTDA is assessed with level I time series data collected by the KOUN radar, a research Weather Surveillance Radar-1988 Doppler (WSR-88D) operated by the National Severe Storms Laboratory (NSSL), during two tornado outbreaks in central Oklahoma on 8 and 10 May 2003. In these cases, NFTDA and TDA provide good detections up to a range of 43 km. Moreover, NFTDA extends the detection range out to approximately 55 km, as the results indicate here, to detect a tornado of F0 magnitude on 10 May 2003.


2020 ◽  
Vol 12 (12) ◽  
pp. 2032 ◽  
Author(s):  
Xiaoran Lv ◽  
Falk Amelung ◽  
Yun Shao ◽  
Shu Ye ◽  
Ming Liu ◽  
...  

We use 2018–2020 Sentinel-1 InSAR time series data to study post-seismic deformation processes following the 2017 Mw 7.3 Kermanshah, Iraq earthquake. We remove displacements caused by two large aftershock sequences from the displacement field. We find that for a six month period the response is dominated by afterslip along the up-dip extension of the coseismic rupture zone, producing up to 6 cm of radar line-of-sight displacements. The moment magnitude of afterslip is Mw 5.9 or 12% of the mainshock moment. After that period, the displacement field is best explained by viscoelastic relaxation and a lower crustal viscosity of η l c = 1 − 0.4 + 0.8 × 10 19 Pas . The viscosity of the uppermost mantle is not constrained by the data, except that it is larger than 0.6 × 10 19 Pas . The relatively high lower crustal and uppermost mantle viscosities are consistent with a cold and dry lithosphere of the Zagros region.


2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.


Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.


ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.


2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai

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