SUMMARY We demonstrate the potential of using Deep-Learning (DL) based de-ghosting methods in marine seismic data processing. We use a field-informed synthetic modelling strategy to create a diversified training data set, and account for acquisition uncertainties such as variable depth streamer profiles, wave action and variable surface reflectivity both in 2D and 3D applications. We […]

SUMMARY Image-domain least-squares migration (i-LSM) is a powerful imaging tool that estimates a fullbandwidth reflectivity (or perturbation) model from a seismic image by means of a spatially nonstationary deconvolution process with so-called point-spread functions. However, neither a reflectivity nor a perturbation model are a direct representation of any subsurface reservoir property. In this work, we […]

SUMMARY MEMS (Micro-Electro-Mechanical Systems) -based sensors, initially introduced in seismic streamers, are now also used in Ocean Bottom Nodes (OBN) for benefits such as flat amplitude and phase spectra across the seismic bandwidth, high vector fidelity, low power consumption and more. MEMS measure acceleration and are by design without gain settings making the topic of […]

SUMMARY Site investigations, for instance, in the emerging field of CCS (Carbon Capture and Storage), benefit from improved resolution in the overburden compared to traditional seismic surveys. This is typically achieved by recording data at a 1ms sample rate. However, the resolution of the imaged data does not only depend on the resolution in time […]

SUMMARY This study introduces and evaluates two seismic time-strain inversion methods: total variation (TV) regularised inversion, and joint inversion with segmentation (JIS). Both methods effectively recover seismic time-strain while suppressing noise. Synthetic data experiments highlight the superior performance of JIS, which provides cleaner, more accurate estimates and segments strain into userdefined classes, enhancing interpretability and […]

ABSTRACT Multi-dimensional deconvolution (MDD) is a technique used at different stages of the seismic processing and imaging value chain to suppress overburden effects by deconvolving the up- and downgoing components of a given wavefield at a target of interest. Whilst the time-domain implementation has recently been identified as the de-facto solution for 2D applications, owing […]

SUMMARY Marine vibrators (MVs) have emerged as a promising alternative to traditional airgun arrays, offering precise signal control, reduced environmental impact, and a broad frequency range. However, seismic interference (SI) from nearby airgun surveys poses challenges during MV acquisition. This study introduces a tailored SI mitigation strategy using an adaptive inversion-based de-blending algorithm designed to […]

SUMMARY Full Waveform Inversion (FWI) is a technique that leverages the discrepancy between modelled and observed seismic data to estimate a potentially high-resolution velocity model of the subsurface. However, due to the highly oscillatory nature of seismic waveforms, point-wise discrepancy measures are susceptible to cycle-skipping, particularly when starting from a poor initial velocity model. Over […]

SUMMARY Ocean bottom seismics (OBS) is crucial for seismic imaging and reservoir monitoring but faces challenges due to positioning errors, water velocity variations, and clock drift. These issues impact data accuracy and require correction methods very early in the data processing sequence, when de-signatured seismic traces are not available. This study introduces a method to […]

SUMMARY Across many applications in seismic processing and imaging, multi-dimensional deconvolution (MDD) appears in many forms, and over recent years, has gained much traction, particularly in the context of processing ocean bottom (OB) data. Leveraging a computational implementation that combines matrix-free time-domain multi-dimensional convolutional operators with efficient numerical optimisation schemes, we approach MDD as a […]