scholarly journals The influence of geomagnetic storms on the quality of magnetotelluric impedance

2021 ◽  
Author(s):  
Hao Chen ◽  
Hideki Mizunaga ◽  
Toshiaki Tanaka
Keyword(s):  
Geomagnetic Storm ◽  
Field Data ◽  
Geomagnetic Storms ◽  
Amplitude Ratio ◽  
Signal To Noise Ratio ◽  
Series Data ◽  
Electromagnetic Signal ◽  
Artificial Noise ◽  
Signal To Noise ◽  
Noise Ratio

Abstract Magnetotelluric field data contain natural electromagnetic signals and artificial noise sources (instrumental, anthropogenic, etc.). Not all available time-series data contain usable information of the electrical conductivity distribution at depth with a low signal-to-noise ratio. The variation of the natural electromagnetic signal increases dramatically in a strong geomagnetic storm, and the signal-to-noise ratio increases. A more reliable impedance may be obtained using the storm data in a noisy environment. Three field data observed at mid-latitude were used to investigate the effect of geomagnetic storms on MT impedance quality. We mainly combined the coherence between the electric and magnetic fields and the result of MT impedance to evaluate the MT impedance quality; we also used the polarization direction, linear coherence and amplitude ratio between the local and remote magnetic field to evaluate the data quality in the noisy environments. The case studies showed that the utilization of the data observed during the geomagnetic storm could overcome the local noise and bring a reliable impedance.

scholarly journals The Influence of Geomagnetic Storms on Calculating Magnetotelluric Impedance

2021 ◽  
Author(s):  
Hao Chen ◽  
Hideki Mizunaga ◽  
Toshiaki Tanaka
Keyword(s):  
Geomagnetic Storm ◽  
Field Data ◽  
Time Series Data ◽  
Geomagnetic Storms ◽  
Signal To Noise Ratio ◽  
Series Data ◽  
Electromagnetic Signal ◽  
Artificial Noise ◽  
Noise Sources ◽  
Usable Information

Abstract Magnetotelluric (MT) field data contain natural electromagnetic signals and artificial noise sources (instrumental, anthropogenic, etc.). Not all available time-series data contain usable information about the electrical conductivity distribution at depth, particularly when the signal-to-noise ratio (SNR) is low. Geomagnetic storms represent temporary disturbances of the Earth's magnetosphere caused by solar wind-shock wave interacts with Earth's magnetic field. The variation of the electromagnetic signal increases dramatically in the presence of a strong geomagnetic storm. Using the data observed during a strong geomagnetic storm may overcome the locale noise and bring a reliable MT impedance at contaminated sites. Three case studies are presented to show the positive effect of geomagnetic storms on MT field data. A more reliable and interpretable impedance calculated from a survey line contaminated by strong noise is obtained using the data observed during a strong geomagnetic storm.

scholarly journals The influence of geomagnetic storms on magnetotelluric data analysis

2021 ◽  
Author(s):  
Hao Chen ◽  
Hideki Mizunaga ◽  
Toshiaki Tanaka
Keyword(s):  
Geomagnetic Storm ◽  
Geomagnetic Storms ◽  
Signal To Noise Ratio ◽  
Weather Forecast ◽  
Impedance Tensor ◽  
Signal To Noise ◽  
Magnetotelluric Data ◽  
Usable Information ◽  
Continuous Noise ◽  
Noise Ratio

Abstract The geomagnetic storm is a temporary disturbance of the earth's magnetosphere caused by a solar wind shock wave interacts with the earth's magnetic field. It is rarely acquired in the practical magnetotelluric (MT) survey. The rare MT researcher pays attention to the influence of geomagnetic storms on the MT data. MT data include the natural electromagnetic signals and artificial noises (instrumental, humanmade, and so on). Therefore, not all the time series contain usable information about the electrical conductivity distribution at depth, particularly when the signal-to-noise ratio is low. However, the signal-to-noise ratio will increase when there is a geomagnetic storm. In this paper, we focus on research the influences of the geomagnetic storm on MT data. Three case studies were demonstrated to show the positive effect of the geomagnetic storm on MT data. As a result, we could obtain reliable MT impedances at the noisy site using the geomagnetic storm data. It is difficult to get a reliable impedance tensor under electromagnetic environments contaminated by continuous noise. Therefore, predicting the geomagnetic storm by the space weather forecast before acquiring the MT data is effective. Utilizing the MT data during a geomagnetic storm may get a reliable result at the site contaminated by the continuous noise.

The lattice filter in ground‐roll suppression

Geophysics ◽  
1994 ◽  
Vol 59 (4) ◽  
pp. 623-631 ◽  
Author(s):  
Ruhi Saatçilar ◽  
Nezihi Canitez
Keyword(s):  
Surface Waves ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Filter Technique ◽  
Lattice Filter ◽  
Ground Roll ◽  
Noise Ratio ◽  
Seismic Reflections

Seismic reflections are sometimes masked by Ray‐leigh‐type surface waves that are termed ground roll in seismic literature. An adaptive lattice filter is used to recover reflected signals contaminated by ground roll. Experiments on synthetic and field data showed that the adaptive lattice filter technique is very effective in ground‐roll elimination. In addition, the filter works as a whitening operator, compresses the signal, and increases the signal‐to‐noise ratio.

scholarly journals Noise reduction with reflection supervirtual interferometry

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V249-V256
Author(s):  
Kai Lu ◽  
Zhaolun Liu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Noise Reduction ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Reflection Data ◽  
The Poor ◽  
The Earth ◽  
The Common ◽  
Noise Ratio

To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

scholarly journals 3D supervirtual refraction interferometry

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. Q1-Q10 ◽  
Author(s):  
Kai Lu ◽  
Sergio Chávez-Pérez
Keyword(s):  
Field Data ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Theory And Practice ◽  
Signal To Noise ◽  
Stationary Source ◽  
3D Data ◽  
First Arrival ◽  
Noise Ratio

We have developed the theory and practice of 3D supervirtual interferometry (SVI) for enhancing the signal-to-noise ratio (S/N) of refraction arrivals in 3D data. Unlike 2D SVI, 3D SVI requires an extra integration along the inline direction to compute the stationary source-receiver pairs for enhanced stacking of the refraction events. The result is a significant increase in the S/N of first arrivals in the far-offset traces. We have evaluated 3D synthetic and field data examples to demonstrate the effectiveness of the proposed method. For the synthetic data tests, SVI has extended the source-receiver offset range of pickable traces from 11 to 15 km. In the field data example, SVI has extended the source-receiver offset of traces with pickable first-arrival traveltimes from 12 km to a maximum of 18 km, and the total number of reliable traveltime picks has increased by 12%, which contributes to a deeper velocity update in the traveltime tomogram.

Iterative supervirtual refraction interferometry

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. Q21-Q30 ◽  
Author(s):  
Ola Al-Hagan ◽  
Sherif M. Hanafy ◽  
Gerard T. Schuster
Keyword(s):  
Field Data ◽  
Signal To Noise Ratio ◽  
Major Obstacle ◽  
Signal To Noise ◽  
Empirical Results ◽  
Refraction Tomography ◽  
Interferometry Method ◽  
Noise Ratio ◽  
Offset Curve

In refraction tomography, the low signal-to-noise ratio (S/N) can be a major obstacle in picking the first-break arrivals at the far-offset receivers. To increase the S/N, we evaluated iterative supervirtual refraction interferometry (ISVI), which is an extension of the supervirtual refraction interferometry method. In this method, supervirtual traces are computed and then iteratively reused to generate supervirtual traces with a higher S/N. Our empirical results with both synthetic and field data revealed that ISVI can significantly boost up the S/N of far-offset traces. The drawback is that using refraction events from more than one refractor can introduce unacceptable artifacts into the final traveltime versus offset curve. This problem can be avoided by careful windowing of refraction events.

SIGNAL‐TO‐NOISE RATIO IMPROVEMENTS BY FILTERING AND MIXING

Geophysics ◽  
1953 ◽  
Vol 18 (3) ◽  
pp. 587-604 ◽  
Author(s):  
Harold R. Frank ◽  
William E. N. Doty
Keyword(s):  
Amplitude Ratio ◽  
Signal To Noise Ratio ◽  
Frequency Content ◽  
Signal To Noise ◽  
Noise Interference ◽  
Mixing Schemes ◽  
Noise Ratio

This paper is concerned with the signal‐to‐noise (interference) amplitude ratio of seismograms. The problem of improving the signal‐to‐noise ratio by filtering and mixing is approached quantitatively. Records of known signal‐to‐amplitude ratio and known frequency content were made employing multiple‐trace variable area equipment such that identical energy was reproduced and re‐recorded through various filtering and mixing schemes and comparisons made. Equipment effects on wavelet character and stepout times are illustrated.

scholarly journals The Influence of Signal to Noise Ratio on the Pharmacokinetic Analysis in DCE-MRI Studies

2020 ◽  
Author(s):  
Azimeh NV Dehkordi ◽  
Saeideh Koohestani
Keyword(s):  
Model Selection ◽  
Pharmacokinetic Model ◽  
Signal To Noise Ratio ◽  
Series Data ◽  
Mr Images ◽  
Signal To Noise ◽  
Dce Mri ◽  
Estimated Parameters ◽  
Mri Studies ◽  
Noise Ratio

Purpose: Recruiting the Pharmacokinetic (PK) parameters estimated from non-invasive methods such as Dynamic Contrast Enhanced MRI (DCE-MRI) to evaluate or plan treatment procedure is widely investigated in clinical practices. Interpretation of the DCE-MRI data is highly dependent to precision and accuracy of the estimated parameters. One of the most effective factors on the DCE-MR images and on the contrast concentration profile is the Signal to Noise Ratio (SNR). This work focuses on the analytical evaluation of the noise effect on accuracy of the estimated PK parameters in DCE-MRI studies. Materials and Methods: Tofts model as a popular pharmacokinetic model and model selection technique was used to simulate 3470 time curves of contrast concentration. Maximum likelihood estimator as a minimum variance unbiased estimator was recruited to estimate the PK parameters. Eleven levels of signal to noise ratios (SNR= 5, 8, 10, 13, 15, 20, 25, 30, 35, 50, Noiseless) were added to the simulated CA concentration profiles. The PK parameters were estimated for 11 series data and then Mean Percentage Error (MPE) was calculated for estimated parameters. Results: The results indicate that the most sensitive parameter to the SNR of the DCE-MR images is inverse transfer constant. A SNR greater than 25 was found to ensure a reasonable error (MPE <5%) in all models parameters. Conclusion: Clinical decision based on the DCE-MRI data analysis and estimated PK parameters needs a good image quality (SNR>25), an accurate and robust estimator and correct pharmacokinetic model selection

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

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