scholarly journals Variable structures in M87* from space, time and frequency resolved interferometry

2022 ◽  
Author(s):  
Philipp Arras ◽  
Philipp Frank ◽  
Philipp Haim ◽  
Jakob Knollmüller ◽  
Reimar Leike ◽  
...  
Keyword(s):  
Event Horizon ◽  
Very Long Baseline Interferometry ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Time Variable ◽  
Signal To Noise ◽  
Inference Problem ◽  
Long Baseline ◽  
Astrophysical Objects ◽  
Spectrally Resolved

AbstractThe immediate vicinity of an active supermassive black hole—with its event horizon, photon ring, accretion disk and relativistic jets—is an appropriate place to study physics under extreme conditions, particularly general relativity and magnetohydrodynamics. Observing the dynamics of such compact astrophysical objects provides insights into their inner workings, and the recent observations of M87* by the Event Horizon Telescope1–6 using very-long-baseline interferometry techniques allows us to investigate the dynamical processes of M87* on timescales of days. Compared with most radio interferometers, very-long-baseline interferometry networks typically have fewer antennas and low signal-to-noise ratios. Furthermore, the source is variable, prohibiting integration over time to improve signal-to-noise ratio. Here, we present an imaging algorithm7,8 that copes with the data scarcity and temporal evolution, while providing an uncertainty quantification. Our algorithm views the imaging task as a Bayesian inference problem of a time-varying brightness, exploits the correlation structure in time and reconstructs (2 + 1 + 1)-dimensional time-variable and spectrally resolved images. We apply this method to the Event Horizon Telescope observations of M87*9 and validate our approach on synthetic data. The time- and frequency-resolved reconstruction of M87* confirms variable structures on the emission ring and indicates extended and time-variable emission structures outside the ring itself.

scholarly journals Evaluation of GRACE/GRACE Follow-On Time-Variable Gravity Field Models for Earthquake Detection above Mw8.0s in Spectral Domain

2021 ◽  
Vol 13 (16) ◽  
pp. 3075
Author(s):  
Ming Xu ◽  
Xiaoyun Wan ◽  
Runjing Chen ◽  
Yunlong Wu ◽  
Wenbing Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Time Variable ◽  
Model Errors ◽  
Signal To Noise ◽  
Time Variable Gravity ◽  
Earthquake Detection ◽  
Gravity Fields ◽  
Variable Gravity ◽  
Gravity Recovery ◽  
Gravity Field Models

This study compares the Gravity Recovery And Climate Experiment (GRACE)/GRACE Follow-On (GFO) errors with the coseismic gravity variations generated by earthquakes above Mw8.0s that occurred during April 2002~June 2017 and evaluates the influence of monthly model errors on the coseismic signal detection. The results show that the precision of GFO monthly models is approximately 38% higher than that of the GRACE monthly model and all the detected earthquakes have signal-to-noise ratio (SNR) larger than 1.8. The study concludes that the precision of the time-variable gravity fields should be improved by at least one order in order to detect all the coseismic gravity signals of earthquakes with M ≥ 8.0. By comparing the spectral intensity distribution of the GFO stack errors and the 2019 Mw8.0 Peru earthquake, it is found that the precision of the current GFO monthly model meets the requirement to detect the coseismic signal of the earthquake. However, due to the limited time length of the observations and the interference of the hydrological signal, the coseismic signals are, in practice, difficult to extract currently.

Signal-to-Noise Ratio Enhancement of Spectra using Recursive Integration

1989 ◽  
Vol 43 (8) ◽  
pp. 1409-1413 ◽  
Author(s):  
Ron Williams
Keyword(s):  
Signal To Noise Ratio ◽  
Recursive Algorithm ◽  
Synthetic Data ◽  
Spectral Elements ◽  
Fourier Transform Spectrometer ◽  
Peak Shape ◽  
Signal Averaging ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Recursive Integration

A recursive algorithm independent of any functional peak shape is presented for determining optimal integration limits of spectral data from multiwavelength spectrometers. The resulting areas have significantly higher signal-to-noise ratios than the peak maxima. Signal-to-noise ratios are computed for synthetic data with both shot and white noise limitations. The algorithm is also applied to data from a Fourier transform spectrometer. For these data, integration of 25 adjacent spectral elements improves the signal-to-noise ratio as well as the signal averaging peak maxima from 25 successive spectra.

Computer Averaging of Time-variable Signals by Autocorrelation

1986 ◽  
Vol 41 (10) ◽  
pp. 1219-1221
Author(s):  
Klaus Roth
Keyword(s):  
Autocorrelation Function ◽  
Time Scale ◽  
Signal To Noise Ratio ◽  
Time Variable ◽  
Expected Improvement ◽  
Signal To Noise ◽  
Autocorrelation Functions ◽  
Noise Ratio ◽  
General Method

A general method is given for a computer accumulation of signals with unpredictable and variable drift with respect to the time-scale. By calculating the autocorrelation function of each individual scan via two forward Fourier transformations any time instability can be removed. Coaddition of the autocorrelation functions leads to the expected improvement in signal-to-noise ratio. Some examples for various signal shapes are given.

scholarly journals A powerful notch filter for PLI cancelation

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Arman Kheirati Roonizi ◽  
Reza Boostani
Keyword(s):  
Signal To Noise Ratio ◽  
Effective Means ◽  
Synthetic Data ◽  
Notch Filter ◽  
Least Square ◽  
Noise Levels ◽  
Signal To Noise ◽  
Power Line Interference ◽  
Zero Phase ◽  
Line Interference

<div>Abstract—In this work, we present a powerful notch filter for power-line interference (PLI) cancelation from biomedical signals. This filter has a unit gain and a zero-phase response. Moreover, the filter can be implemented adaptively to adjust its bandwidth based on the signal-to-noise ratio. To realize this filter, a dynamic model is defined for PLI based on its sinusoid property. Then, a constrained least square error estimation is used to emerge the PLI based on the observations while the constraint is the PLI dynamic. At last, the estimated PLI is subtracted from recordings. The proposed filter is assessed using synthetic data and real biomedical recordings in different noise levels. The results demonstrate this filter as a very powerful and effective means for canceling the PLI out.</div>

scholarly journals A powerful notch filter for PLI cancelation

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Arman Kheirati Roonizi ◽  
Reza Boostani
Keyword(s):  
Signal To Noise Ratio ◽  
Effective Means ◽  
Synthetic Data ◽  
Notch Filter ◽  
Least Square ◽  
Noise Levels ◽  
Signal To Noise ◽  
Power Line Interference ◽  
Zero Phase ◽  
Line Interference

<div>Abstract—In this work, we present a powerful notch filter for power-line interference (PLI) cancelation from biomedical signals. This filter has a unit gain and a zero-phase response. Moreover, the filter can be implemented adaptively to adjust its bandwidth based on the signal-to-noise ratio. To realize this filter, a dynamic model is defined for PLI based on its sinusoid property. Then, a constrained least square error estimation is used to emerge the PLI based on the observations while the constraint is the PLI dynamic. At last, the estimated PLI is subtracted from recordings. The proposed filter is assessed using synthetic data and real biomedical recordings in different noise levels. The results demonstrate this filter as a very powerful and effective means for canceling the PLI out.</div>

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.

Correcting Fluctuating Baselines and Spectral Overlap with Genetic Regression

1997 ◽  
Vol 51 (1) ◽  
pp. 92-100 ◽  
Author(s):  
Rajesh P. Paradkar ◽  
Ronald R. Williams
Keyword(s):  
Linear Regression ◽  
Signal To Noise Ratio ◽  
Optimization Technique ◽  
Synthetic Data ◽  
Background Correction ◽  
Effective Wavelength ◽  
Signal To Noise ◽  
Spectral Overlap ◽  
Noise Ratio ◽  
Genetic Regression

The application of a new algorithm, known as genetic regression (GR), to calibration problems with spectra containing complex fluctuating baselines is illustrated with the use of synthetic data. The ability of the algorithm to automatically compensate for the presence of linear and polynomial (quadratic and cubic) baselines in the presence of complex spectral overlap is investigated along with the effect of noise. GR is unique in that it provides an effective wavelength optimization technique by sorting through the spectr al data and selecting and appropriately combining wavelengths that compensate for structured baseline and spectral overlap. The results obtained with GR are compared with those obtained with background-corrected linear regression. GR is shown to give much better results and, in constrast to traditional background correction, is much faster and can compensate for the presence of both structured baseline and complex spectral overlap simultaneously. The results of a noise study show that the method works at low signal-to-noise ratio (SNR) and that the error in the final result is a function of the noise.

Improve NMR estimation of water content and distribution in unsaturated bedrocks

2021 ◽  
Author(s):  
Fan Zhang ◽  
Chi Zhang
Keyword(s):  
Relaxation Time ◽  
Water Content ◽  
Water Distribution ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Signal To Noise ◽  
Nmr Data ◽  
Unsaturated Zones ◽  
Peak Decomposition

&lt;p&gt;Nuclear magnetic resonance (NMR) has been widely used in near-surface geophysics due to its direct sensitivity to water. As a field form of NMR, borehole NMR has been applied to in situ hydrological investigations for decades. However, the recent implementations of borehole NMR to unsaturated zones face challenges due to the complex geology. Due to the fast operation speed and unsaturated conditions in critical zones, the raw NMR signals often suffer from limited relaxation time ranges and low signal to noise ratios. Such low quality of raw data can induce artifacts during inversion and following data interpretations. This study investigates the long-overdue evaluations of how the low borehole NMR data quality affects water distribution estimation in unsaturated zones. A synthetic analysis based on lab NMR data was first performed to simulate the inversion errors induced by the low-quality borehole NMR data. Lab NMR measurements were conducted on carbonate and shale samples from a well that has a corresponding borehole NMR profile. In order to match the low signal-to-noise ratio and data size of the low-quality borehole NMR data, lab NMR data points were reduced, deadtime was increased and normally distributed noise was added.&amp;#160; The inversion results of the synthetic data reveal that the low signal to noise ratio leads to an overestimation of signals at lower relaxation time while the limited relaxation time range does not significantly affect the total water estimation. To improve the water estimation from the low-quality borehole data, a peak decomposition and peak fusion method were then applied to the synthetic data. Relaxation time distribution of both lab and synthetic data were decomposed into multiple normally distributed peaks. The first peak with the shortest relaxation time from lab NMR was used to substitute the first peak of the synthetic borehole NMR relaxation time distribution. After peak decomposition and fusion, the predicted water contents were closer to lab NMR than original synthetic data. This study reveals the mispredictions of water distribution due to the low data quality of borehole NMR. The success of improving water content estimation on the synthetic study has clear implications that the peak decomposition and peak fusion method can be applied to actual borehole NMR data to improve water content and distribution estimation in unsaturated zones.&amp;#160;&amp;#160;&amp;#160;&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document