Ultra-Long Offset Sparse Node Project in Deep Water GoM for FWI and Imaging

Roende, H.; Bate, D.; Udengaard, C.; Malik, Raheel; Huang, Yi

doi:10.3997/2214-4609.202011121

Cited by 3 publications

(1 citation statement)

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“…For example, ultra-long offset seabed acquisitions employing an array of ocean-bottom nodes are capable of registering frequencies as low as 1.5 Hz [27,28]. A similar range of ultra-low frequencies might be detected in the marine buoy survey setup [29] where hydrophones are floating under the water surface attached to a buoy. The advanced marine streamer survey [30] might record robust signals with frequency content down to 2.5 Hz.…”

Section: A Low-frequency Seismic Datamentioning

confidence: 94%

Multi-Task Learning for Low-Frequency Extrapolation and Elastic Model Building From Seismic Data

Ovcharenko

Kazei

Alkhalifah

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Low-frequency signal content in seismic data as well as a realistic initial model are key ingredients for robust and efficient full-waveform inversions. However, acquiring low-frequency data is challenging in practice for active seismic surveys. Data-driven solutions show promise to extrapolate low-frequency data given a high-frequency counterpart. While being established for synthetic acoustic examples, the application of bandwidth extrapolation to field datasets remains non-trivial. Rather than aiming to reach superior accuracy in bandwidth extrapolation, we propose to jointly reconstruct low-frequency data and a smooth background subsurface model within a multi-task deep learning framework. We automatically balance data, model and trace-wise correlation loss terms in the objective functional and show that this approach improves the extrapolation capability of the network. We also design a pipeline for generating synthetic data suitable for field data applications. Finally, we apply the same trained network to synthetic and real marine streamer datasets and run an elastic full-waveform inversion from the extrapolated dataset.

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Section: A Low-frequency Seismic Datamentioning

confidence: 94%

Multi-Task Learning for Low-Frequency Extrapolation and Elastic Model Building From Seismic Data

Ovcharenko

Kazei

Alkhalifah

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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New wiggles from old cables: Acquisition, cable, and data aspects

Raknes,

Foseide,

Landrø

et al. 2023

GEOPHYSICS

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Seismic imaging is essential for detailed characterization and understanding of the subsurface. Traditionally, marine seismic data have been acquired either using towed streamers or ocean-bottom receivers. Distributed acoustic sensing (DAS) is a novel and fast-emerging technology that makes it possible to use fiber-optic cables for acoustic measurements. The technology turns the fiber-optic cable into a densely sampled receiver array. A controlled source experiment comparing DAS and conventional towed streamer data for seismic imaging purposes is described. An existing 130 km telecommunication fiber-optic cable was interrogated to acquire DAS data to analyze the signal at long distances and for subsurface imaging. A seismic source vessel was used to create seismic data along the full cable length, while at the same time acquiring conventional towed streamer data for comparison. In addition, the telecommunication fiber-optic cable was used to acquire passive DAS data. The results show that seismic signals in the telecommunication fiber-optic cable are detectable at distances above 100 km from the source point. Furthermore, comparisons of the DAS data and towed streamer data show that the DAS technology allows for the acquisition of large offset seismic data that are comparable with conventional seismic data. Analysis of the passive DAS data shows that the infragravity waves correlate with the observed weather conditions during the acquisition.

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