Wind and Rain Induced Noise on Reflection Seismic Data

Nørmark, Egon

doi:10.3997/2214-4609.20144485

Cited by 8 publications

(6 citation statements)

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“…In a similar manner a significant drop in noise levels is observed with increasing distance from an active injection platform (Schilke et al, 2014). For non-cultural noise, Nørmark (2011b) identified varying noise levels across the array from meteorological sources dependent on vegetation, wind speed and precipitation levels. These noise studies prove that noise is correlated in space and/or time therefore an efficient noise removal procedure must account for spatio-temporal variations of the noise field.…”

Section: Theorymentioning

confidence: 55%

Seismic arrival enhancement through the use of noise whitening

Birnie

Chambers²,

Angus

2017

Physics of the Earth and Planetary Interiors

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A constant feature in seismic data, noise is particularly troublesome for passive seismic monitoring where noise commonly masks microseismic events. We propose a statistics-driven noise suppression technique that whitens the noise through the calculation and removal of the noise's covariance. Noise whitening is shown to reduce the noise energy by a factor of 3.5 resulting in microseismic events being observed and imaged at lower signal to noise ratios than originally possible -whilst having negligible effect on the seismic wavelet. The procedure is shown to be highly resistant to most changes in the noise properties and has the flexibility of being used as a stand-alone technique or as a first step before standard random noise attenuation methods.

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Section: Theorymentioning

confidence: 55%

Seismic arrival enhancement through the use of noise whitening

Birnie

Chambers²,

Angus

2017

Physics of the Earth and Planetary Interiors

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“…Afegbua and Ezomo include instrument noise, plant-leaves rustling, debris from shot falling to the ground, movement of the streamer through the water, distant storms, rain drops, atmospheric phenomena, ocean waves as well as installed seismic equipment [6][7][8][9][10]. The air blast noise resulting from source energy is also ambient noise.…”

Section: Introductionmentioning

confidence: 99%

“…The air blast noise resulting from source energy is also ambient noise. Other sources of ambient noise are aircraft, earthquake, built-up areas, power line, vehicular traffic including trekking and animal movement, water pumps, quarries and factory [7,8]. Ambient noise obscures the signals and degrade the overall signal quality obtained.…”

Section: Introductionmentioning

confidence: 99%

Ambient Seismic Noise Footprints and Spectra in the Middle Benue Trough, Nigeria

Mbachi¹,

Uko²,

Eze³

et al. 2019

Earth sci. Malays.

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Ambient noise was analysed from a two-dimensional (2D) seismic data acquired in the Middle Benue Trough, Nigeria for the purpose of characterizing the ambient seismic noise. Sercel 428XL recording instrument was deployed on 3 traverse lines where dynamite explosive sources and geophone detectors were used. The acquired data was processed using frequency wavenumber (FK) and wild amplitude attenuation (WAA) algorithms. The dominant amplitude of the primary reflection ranges between -20dB and -10dB, while those of the ambient seismic noise varies between -42dB and -3dB. The primary reflections have dominant frequency varying from 6Hz to 75Hz while that of ambient seismic noise varies between 4Hz and 70Hz. Analysis of the noise shows two distinct ground roll modes with velocities between 400 ms -1 and 810 ms -1 both of which are dispersive with wavelength (λ) of 61.5m and peak frequency at 6.5Hz. Analysis of passive noise records acquired showed that ambient seismic (background) noise level excluding source-generated noise average of 91.56% are below 25µV, which is the tolerance noise level limit. The combination of frequency wavenumber FK and WAA filters effectively attenuated the surface waves especially ground rolls and other high amplitude noise making the primary reflection very visible and better enhanced. The filtered amplitude values estimated from signal-to-noise (SNR) analysis using cross correlation (XC) method are much higher than the values of the unfiltered amplitudes indicating that SNR are highest when noises are attenuated from the data than when noise algorithm is not applied to the data. The attributes of these seismic noises will provide further information and solution for their suppression during seismic data acquisition and processing.

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“…Between 1 and 5 Hz noise sources are likely to be local meteorological events or urban activity and sources above 5 Hz sources are likely to be urban in origin [Bonnefoy-Claudet et al, 2006]. Studies of meteorological noise suggest that wind and rain can have a distinct effect on the noise signature of seismic data [e.g., Nørmark, 2011;Barajas-Olalde and Jeffreys, 2014]. In terms of urban noise, Riahi and Gerstoft [2015] characterised the seismic footprint of traffic and were able to distinguish sources such as trains, aircraft and road traffic.…”

Section: Introductionmentioning

confidence: 99%

Analysis and models of pre-injection surface seismic array noise recorded at the Aquistore carbon storage site

Birnie

Chambers²,

Angus

et al. 2016

Geophys. J. Int.

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Noise is a persistent feature in seismic data and so poses challenges in extracting increased accuracy in seismic images and physical interpretation of the subsurface. In this paper, we analyse passive seismic data from the Aquistore carbon capture and storage pilot project permanent seismic array to characterise, classify and model seismic noise. We perform noise analysis for a three month subset of passive seismic data from the array and provide conclusive evidence that the noise field is not white, stationary, or Gaussian;characteristics commonly yet erroneously assumed in most conventional noise models. We introduce a novel noise modelling method that provides a significantly more accurate characterisation of real seismic noise compared to conventional methods, which is quantified using the Mann-Whitney-White statistical test. This method is based on a statistical covariance modelling approach created through the modelling of individual noise signals. The identification of individual noise signals, broadly classified as stationary, pseudo-stationary and non-stationary, provides a basis on which to build an appropriate spatial and temporal noise field model. Furthermore, we have developed a workflow to incorporate realistic noise models within synthetic seismic datasets providing an opportunity to test and analyse detection and imaging algorithms under realistic noise conditions.

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Wind and Rain Induced Noise on Reflection Seismic Data

Cited by 8 publications

References 0 publications

Seismic arrival enhancement through the use of noise whitening

Seismic arrival enhancement through the use of noise whitening

Ambient Seismic Noise Footprints and Spectra in the Middle Benue Trough, Nigeria

Analysis and models of pre-injection surface seismic array noise recorded at the Aquistore carbon storage site

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