Land seismic low frequencies: acquisition, processing and full wave inversion of 1.5 - 86 Hz

Mahrooqi, S.; Rawahi, S.; Yarubi, S.; Abri, S.; Yahyai, A.; Jahdhami, M.; Hunt, Keith; Shorter, J.

doi:10.1190/segam2012-0961.1

Cited by 20 publications

(3 citation statements)

References 2 publications

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“…8). In the Middle East, most of the sweeps starts now at 1.5 Hz (Mahrooqi et al 2012), and frequencies down to 2-2.5 Hz are preserved in the final image assuming a specific processing by octave is applied (Retailleau, El Asrag and Shorter 2014).…”

Section: Discussion a N D C O N C L U S I O Nmentioning

confidence: 99%

Marine, seabed, and land seismic equipment for broadband acquisition: a review

Mougenot¹

2017

Geophysical Prospecting

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The broadband capabilities of marine, seabed, and land seismic equipment are reviewed with respect to both the source and the receiver sides. In marine acquisition, the main issue at both ends of the spectrum relates to ghosts occurring at the sea surface. Broadband deghosting requires towing at variable depth to introduce notch diversity or using new equipment like multi‐component and/or low‐noise streamers. As a result, a doubling of the bandwidth from about three to six octaves (2.5–200 Hz) has been achieved. Such improvement is not yet observed for seabed surveys in spite of deghosting being a standard process on the receiver side. One issue may be related to the coupling of the particle motion sensor, particularly at high frequencies. For land acquisition, progress came from the vibrators. New shakers and control electronics using broadband sweeps made it possible to add two more octaves to the low‐frequency signal (from 8 to 2 Hz). Whereas conventional 10 Hz geophones are still able to record such low frequencies, 5 Hz high gain geophones or digital accelerometers enhance them to keep the signal above the noise floor. On the high end of the bandwidth, progress is not limited by equipment specifications. Here, the issue is related to a low signal‐to‐noise ratio due to the strong absorption that occurs during signal propagation. To succeed in enlarging the bandwidth, these improved equipment and sweeps must be complemented by a denser spatial sampling of the wavefield by point–source and point–receiver acquisition.

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Section: Discussion a N D C O N C L U S I O Nmentioning

confidence: 99%

Marine, seabed, and land seismic equipment for broadband acquisition: a review

Mougenot¹

2017

Geophysical Prospecting

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“…New developments in acquisition enhance the recent success of FWI by measuring data with lower frequencies and longer offsets (Mahrooqi et al, 2012;Brenders et al, 2018). However, only acoustic FWI was applied to the land data set with low frequencies down to 1.5 Hz (Plessix et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for low frequency extrapolation of multicomponent data in elastic full waveform inversion

Sun,

Demanet

2021

Preprint

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Full waveform inversion (FWI) strongly depends on an accurate starting model to succeed. This is particularly true in the elastic regime: The cycle-skipping phenomenon is more severe in elastic FWI compared to acoustic FWI, due to the short S-wave wavelength. In this paper, we extend our work on extrapolated FWI (EFWI) by proposing to synthesize the low frequencies of multi-component elastic seismic records, and use those "artificial" low frequencies to seed the frequency sweep of elastic FWI. Our solution involves deep learning: we separately train the same convolutional neural network (CNN) on two training datasets, one with vertical components and one with horizontal components of particle velocities, to extrapolate the low frequencies of elastic data. The architecture of this CNN is designed with a large receptive field, by either large convolutional kernels or dilated convolution. Numerical examples on the Marmousi2 model show that the 2-4Hz low frequency data extrapolated from band-limited data above 4Hz provide good starting models for elastic FWI of P-wave and S-wave velocities. Additionally, we study the generalization ability of the proposed neural network over different physical models. For elastic test data, collecting the training dataset by elastic simulation shows better extrapolation accuracy than acoustic simulation, i.e., a smaller generalization gap.

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“…This is achieved (Seeni et al, 2010;Mougenot and Liu, 2012) by using single-sensor single-source (S 4 ) high-density (HD) wide-azimuth acquisition (WAZ). In parallel, definition of an accurate velocity model using techniques such as full-waveform inversion is facilitated by preservation of an adequate signal-to-noise ratio at low frequencies (Mahrooqi et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A high-sensitivity MEMS-based accelerometer

Laine¹,

Mougenot²

2014

The Leading Edge

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A new generation of accelerometers based on a microelectromechanical system (MEMS) can deliver broadband (0 to 800 Hz) and high-fidelity measurements of ground motion even at a low level. Such performance has been obtained by using a closed-loop configuration and a careful design which greatly mitigates all internal mechanical and electronic noise sources. To improve the signal-to-noise ratio toward the low frequencies at which instrument noise increases, a new MEMS has been developed. It provides a significantly lower noise floor (at least -10 dB) and thus a higher dynamic range (+10 dB). This performance has been tested in an underground facility where conditions approach the minimum terrestrial noise level. This new MEMS sensor will even further improve the detection of low frequencies and of weak signals such as those that come from deep targets or from microseismic events.

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Land seismic low frequencies: acquisition, processing and full wave inversion of 1.5 - 86 Hz

Cited by 20 publications

References 2 publications

Marine, seabed, and land seismic equipment for broadband acquisition: a review

Marine, seabed, and land seismic equipment for broadband acquisition: a review

Deep learning for low frequency extrapolation of multicomponent data in elastic full waveform inversion

A high-sensitivity MEMS-based accelerometer

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