Noise reduction in seismic data using Fourier correction coefficient filtering

Geophysics ◽  
1997 ◽  
Vol 62 (5) ◽  
pp. 1617-1627 ◽  
Author(s):  
Douglas Alsdorf
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Seismic Data ◽  
Wave Number ◽  
Correction Coefficient ◽  
Band Pass Filter ◽  
Data Set ◽  
Pass Filter ◽  
Phase Angle Difference ◽  
Reduction Methods

The correlation coefficient between two frequency (or two wave number) componets equals the cosine of their phase‐angle difference. This relation can be exploited to build a filter that separates noise from signal in seismic data in either the F‐X or F-K domain (termed “correlation coefficient filtering”). To implement this filter, seismic data are first divided to form two subsets that are then compared using the cosine function. Signal is defined as the correlative frequencies (or wavelengths) while noncorrelative energy is attributed to noise. Depending on the application, appropriate subsets may consist of (1) groups of adjacent traces or (2) low‐fold stacks created from differing shot gathers. When comparing adjacent traces [i.e., (1)], the correlation coefficient filter combines both phase and dip information and assumes that reflections advance relatively little in time across traces and less than the noise. Correlation coefficient filtering of low‐fold stacks [i.e., (2)] does not depend on dip. Reflections are assumed to be present in both subsets whereas the noise is found only in one data set. Hence, the reflections are correlative and the noise is noncorrelative. In either case, the filter reduces linearly dipping coherent energy, ground roll, and randomly occurring noise bursts while generally maintaining signal integrity. A primary advantage of this filter is its simplicity. It is implemented much like a simple band‐pass filter, thus requiring much less parameterization than alternative noise‐reduction methods.

scholarly journals Model-based subtraction of spikes from surface nuclear magnetic resonance data

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. WB1-WB8 ◽  
Author(s):  
Jakob Juul Larsen
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Noise Reduction ◽  
Band Pass Filter ◽  
Data Set ◽  
Pass Filter ◽  
Model Based ◽  
Nmr Data ◽  
Electric Fence ◽  
Nuclear Magnetic

Surface nuclear magnetic resonance (surface NMR) has progressed significantly in recent years due to advances in instrumentation. In particular, the introduction of multichannel surface NMR instruments has been effective in improving the signal-to-noise ratio. The current methodology for noise reduction with multichannel instruments is, however, inadequate in complex noise environments, and there is a need for improved signal processing. We have evaluated a study of impulsive noise (spikes) in surface NMR data acquired with a Numis Poly instrument. We have determined how the spectral content can be used to classify spikes as originating from electric fences or sferics. Measurements of spikes from two electric fences were evaluated. The spikes were highly deterministic and can be modeled as impulsive excitations of the band-pass filter in the surface NMR receiver system. We investigated the feasibility of a model-based approach for subtraction of electric fence spikes. Model-based subtraction was shown to be possible, but it is limited by accidental fitting of the NMR signal in its current embodiment. We evaluated an example of a surface NMR data set in which subtraction of powerline harmonic noise and electric fence spike noise removed all coherence in the multichannel data, and the consequences for further noise reduction using multichannel methods were developed.

A 1D time-varying median filter for seismic random, spike-like noise elimination

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. V17-V24 ◽  
Author(s):  
Yang Liu ◽  
Cai Liu ◽  
Dian Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Random Noise ◽  
Threshold Value ◽  
Band Pass Filter ◽  
Time Varying ◽  
Noise Elimination ◽  
Pass Filter ◽  
Median Filters

Random noise in seismic data affects the signal-to-noise ratio, obscures details, and complicates identification of useful information. We have developed a new method for reducing random, spike-like noise in seismic data. The method is based on a 1D stationary median filter (MF) — the 1D time-varying median filter (TVMF). We design a threshold value that controls the filter window according to characteristics of signal and random, spike-like noise. In view of the relationship between seismic data and the threshold value, we chose median filters with different time-varying filter windows to eliminate random, spike-like noise. When comparing our method with other common methods, e.g., the band-pass filter and stationary MF, we found that the TVMF strikes a balance between eliminating random noise and protecting useful information. We tested the feasibility of our method in reducing seismic random, spike-like noise, on a synthetic dataset. Results of applying the method to seismic land data from Texas demonstrated that the TVMF method is effective in practice.

scholarly journals Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Soojun Kim ◽  
Huiseong Noh ◽  
Narae Kang ◽  
Keonhaeng Lee ◽  
Yonsoo Kim ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Standard Error ◽  
Rainfall Series ◽  
Radar Rainfall ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Filtering Technique ◽  
Filtering Techniques

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.

PC‐based acquisition and processing of high‐resolution marine seismic data

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1804-1812 ◽  
Author(s):  
Ho‐Young Lee ◽  
Byung‐Koo Hyun ◽  
Young‐Sae Kong
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Spectral Characteristics ◽  
Processing System ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Optical Disk Drive ◽  
Air Gun ◽  
Marine Seismic

We have improved the quality of high‐resolution marine seismic data using a simple PC‐based acquisition and processing system. The system consists of a PC, an A/D converter, and a magneto‐optical disk drive. The system has been designed to acquire single‐channel data at up to 60,000 samples per second and to perform data processing of seismic data by a simple procedure. Test surveys have been carried out off Pohang, southern East Sea of Korea. The seismic systems used for the test were an air gun and a 3.5 kHz sub‐bottom profiling system. Spectral characteristics of the sources were analyzed. Simple digital signal processes which include gain recovery, deconvolution, band‐pass filter, and swell filter were performed. The quality of seismic sections produced by the system is greatly enhanced in comparison to analog sections. The PC‐based system for acquisition and processing of high‐resolution marine seismic data is economical and versatile.

scholarly journals SYNTHESIS OF SM3+ DOPED WITH TIO2 NANOPARTICLES POWDER AS MID-IR OPTICAL FILTER

2021 ◽  
Vol 03 (01) ◽  
pp. 1-7
Author(s):  
Hanaa M NAJAM ◽  
Hanaa M YASEEN
Keyword(s):  
Tio2 Nanoparticles ◽  
Sol Gel ◽  
Optical Filter ◽  
Infrared Region ◽  
Pure Tio2 ◽  
Wave Number ◽  
Band Pass Filter ◽  
Tio2 Sample ◽  
Pass Filter ◽  
Single Transition

Samarium ions doped TiO2 nanoparticles were prepared successfully via Sol-Gel Technique with varying conditions. Effects of Sm3+ doping concentrations on the optical properties in the mid-infrared region was studied. FTIR spectra for pure and doped samples after annealing process show a single transition peak at wave number around 1109 cm-1 and 1116 cm-1 respectively, the wave number of the single transition peak depends on the Sm+3 doping ratio. By Comparing with wavelength of the high transition ratio of the pure TiO2 sample, slightly decries shift on the peak wavelength occur with an increment of doping concentration ratio. The FTIR spectrum gives a good indication the direction of synthesis of optical band-pass filter at a wavelength around 8.964µm (~1116 Cm-1).

PROCESSING OF MARINE SEISMIC DATA FROM AREAS WITH LARGE AND RAPID VARIATION IN WATER DEPTH

1971 ◽  
Vol 11 (1) ◽  
pp. 95
Author(s):  
Al Sabitay
Keyword(s):  
Seismic Data ◽  
Bottom Topography ◽  
Computer Programs ◽  
Bottom Surface ◽  
Band Pass Filter ◽  
Process Data ◽  
Rapid Variation ◽  
Pass Filter ◽  
Marine Seismic ◽  
Water Bottom

The offshore search for oil and gas is progressively moving further out to sea as near-shore structures are delineated and drilled. Prospects that overlap the edge of the continental shelf and slope will more than likely present problems in the processing of marine seismic data because of large and rapid variation in water depth.Magellan Petroleum encountered such difficulties in the digital computer processing of its East Gippsland Basin Prospect which is located some 50 miles southeast of the Victoria coastline.A series of problems developed when an integrated computer program sequence or "package" was applied to the data. It was found that first break suppression schedules, deconvolution design gates, band-pass filter application gates and velocity functions could not be changed often enough due to program restrictions.Where the water bottom topography was rough, the restriction of submitting only three or four water depths to vary the velocity function and subsequent calculation of normal move-out corrections resulted in questionable accuracy for the corrected results.Sometimes, water bottom variations required individual trace static corrections which were not available in this particular "package" processing.Water bottom multiple periods vary as rapidly as the surface that generates them. A meticulous selection of the parameters of deconvolution programs is necessary to attenuate multiples under such conditions. Also close examination of the purposes and consequently methods of deconvolution computer programs is necessary to maximize the effectiveness of this powerful processing tool.Diffractions are frequently generated at points on an irregular sea bottom surface. Such detractions mask true water bottom reflections in deeper water and thus decrease the geophysicist's ability to process data accurately where computer programs require true water bottom depth.Presentation of record sections which illustrate problems and their probable solutions comprise a major part of this paper.

Petrophysical properties prediction from prestack seismic data using convolutional neural networks

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. N41-N55
Author(s):  
Vishal Das ◽  
Tapan Mukerji
Keyword(s):  
Neural Networks ◽  
Correlation Coefficient ◽  
Elastic Properties ◽  
Convolutional Neural Networks ◽  
Seismic Data ◽  
High Porosity ◽  
Petrophysical Properties ◽  
Average Correlation ◽  
Data Set ◽  
End To End

We have built convolutional neural networks (CNNs) to obtain petrophysical properties in the depth domain from prestack seismic data in the time domain. We compare two workflows — end-to-end and cascaded CNNs. An end-to-end CNN, referred to as PetroNet, directly predicts petrophysical properties from prestack seismic data. Cascaded CNNs consist of two CNN architectures. The first network, referred to as ElasticNet, predicts elastic properties from prestack seismic data followed by a second network, referred to as ElasticPetroNet, that predicts petrophysical properties from elastic properties. Cascaded CNNs with more than twice the number of trainable parameters as compared to end-to-end CNN demonstrate similar prediction performance for a synthetic data set. The average correlation coefficient for test data between the true and predicted clay volume (approximately 0.7) is higher than the average correlation coefficient between the true and predicted porosity (approximately 0.6) for both networks. The cascaded workflow depends on the availability of elastic properties and is three times more computationally expensive than the end-to-end workflow for training. Coherence plots between the true and predicted values for both cases show that maximum coherence occurs for values of the inverse wavenumber greater than 15 m, which is approximately equal to 1/4 the source wavelength or λ/4. The network predictions have some coherence with the true values even at a resolution of 10 m, which is half of the variogram range used in simulating the spatial correlation of the petrophysical properties. The Monte Carlo dropout technique is used for approximate quantification of the uncertainty of the network predictions. An application of the end-to-end network for prediction of petrophysical properties is made with the Stybarrow field located in offshore Western Australia. The network makes good predictions of petrophysical properties at the well locations. The network is particularly successful in identifying the reservoir facies of interest with high porosity and low clay volume.

Noise Reduction Using Wavelet Transform in Ultrasonic Flaw Detection of Small-Diameter Steel Pipe with Thick Wall

2011 ◽  
Vol 383-390 ◽  
pp. 4755-4761
Author(s):  
Shao Jiang Wang ◽  
Li Hou ◽  
Yu Lin Wang ◽  
Jian Quan Zhang
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Flaw Detection ◽  
Small Diameter ◽  
Internal Noise ◽  
Thick Wall ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Ultrasonic Signals ◽  
Steel Pipes

In order to ensure that small diameter steel pipes with thick wall have high intensity and high quality, ultrasonic immersion method with focusing probe was used to detect the flaw of the small-diameter steel pipes with thick wall. In practice, the echoes are often corrupted with external noise or internal noise, therefore, it is necessary to reduce the noise and to enhance the SNR of ultrasonic signals. A technique for improving the SNR of ultrasonic signals using wavelet transform is presented. In this method, WT, consider as one band-pass filter, is used to remove the noises. The performance of this technique has been verified by experimental, which is done by using a series of flaw ultrasonic echoes obtained from a specimen of the small-diameter steel pipes with thick wall. In particular we have found the processing of the ultrasonic signals using wavelet transform extremely useful for noise reduction. After processing, the SNR of ultrasonic signals are enhanced substantially. All experimental results show that this technique is effective for removing the white noise from the ultrasonic signals.

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