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.

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.

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.

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.

THE ELUSIVE FIRST ARRIVAL

Geophysics ◽  
1964 ◽  
Vol 29 (5) ◽  
pp. 806-813 ◽  
Author(s):  
J. G. Hagedoorn
Keyword(s):  
Frequency Response ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Recording System ◽  
Transmission Curve ◽  
Frequency Noise ◽  
Signal To Noise ◽  
First Arrival ◽  
Noise Ratio

In first‐arrival refraction work, the initial deflection of the first loop of the signal arriving from the shotpoint must be readily recognised against a background of seismic disturbances due to sources other than the explosion. The accuracy of timing a first arrival is determined by this signal‐to‐noise ratio. It depends primarily on the location of shot and receivers, the size of the charge, and the existing ground unrest at the time of registration. Experiments carried out with these variables kept constant, by recording at the same location from the same shot, show how much the signal‐to‐noise ratio also depends on the characteristics of the recording equipment used. The best signal‐to‐noise ratio is certainly not obtained when the transmission curve of the entire system, comprising geophone, amplifier, and galvanometer, peaks at the apparent dominant frequency of the refraction signal. Practical examples show that the signal‐to‐noise ratio can be improved considerably by using recording systems that transmit a band of frequencies extending many octaves below the observed dominant frequency. The inception of an oscillatory signal was found to be particularly sensitive to the characteristics of a recording system. A seismometer, for example, will transform a starting sine wave with a frequency equal to the natural frequency of the seismometer into a signal with a first loop that is about half as high and half as long as the succeeding loops, the latter moreover being advanced by one‐quarter period. This relative constriction of the initial part of a signal is called the “cramping” effect. Such an effect will weaken a refraction first arrival relative to simultaneously arriving later parts of noise signals. This explains why a cramping effect will impair the signal‐to‐noise ratio. A cramping effect can, of course, be avoided by using a recording system with a flat frequency response. The opposite effect, which can be expected to improve the signal‐to‐noise ratio, could obviously be achieved by using systems with relatively increased low‐frequency response. The practical limit to this improvement would be set by the low‐frequency noise that is enhanced by this procedure.

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.

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.

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.

Prestack seismic data enhancement with partial common-reflection-surface (CRS) stack

Geophysics ◽  
2009 ◽  
Vol 74 (3) ◽  
pp. V49-V58 ◽  
Author(s):  
Mikhail Baykulov ◽  
Dirk Gajewski
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Original Data ◽  
Signal To Noise ◽  
Prestack Depth Migration ◽  
Depth Migration ◽  
Common Reflection Surface ◽  
Noise Ratio

We developed a new partial common-reflection-surface (CRS) stacking method to enhance the quality of sparse low-fold seismic data. For this purpose, we use kinematic wavefield attributes computed during the automatic CRS stack. We apply a multiparameter CRS traveltime formula to compute partial stacked CRS supergathers. Our algorithm allows us to generate NMO-uncorrected gathers without the application of inverse NMO/DMO. Gathers obtained by this approach are regularized and have better signal-to-noise ratio compared with original common-midpoint gathers. Instead of the original data, these improved prestack data can be used in many conventional processing steps, e.g., velocity analysis or prestack depth migration, providing enhanced images and better quality control. We verified the method on 2D synthetic data and applied it to low-fold land data from northern Germany. The synthetic examples show the robustness of the partial CRS stack in the presence of noise. Sparse land data became regularized, and the signal-to-noise ratio of the seismograms increased as a result of the partial CRS stack. Prestack depth migration of the generated partially stacked CRS supergathers produced significantly improved common-image gathers as well as depth-migrated sections.

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.

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