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Least-squares migration (LSM) can potentially provide better amplitude fidelity, higher image resolution, and fewer migration artifacts than standard migration. Conventional LSM is often solved iteratively through linearized inversion, and therefore is often referred to as iterative LSM. In recent years, various single-iteration LSM approaches have been proposed as a cost-effective approximation of iterative LSM and have produced promising results. To exploit the full potential of LSM, we propose to employ the curvelet-domain Hessian filter (CHF), useful in single-iteration LSM, as a preconditioner for conventional iterative LSM. We call this approach CHF-preconditioned LSM (CPLSM). We first validate our CPLSM approach using SEAM I synthetic data and show that it produces better amplitude fidelity over the single-iteration CHF approach and converges faster than conventional iterative LSM. Furthermore, we demonstrate with an application to field data that CPLSM produces fewer migration artifacts and less noise than conventional iterative LSM. This addresses a known problem of iterative LSM that is caused by the use of inaccurate modeling algorithms followed by overfitting the modeled synthetic data to the recorded data.

A machine learning workflow can quickly, and accurately, predict mineralogy, porosity and saturation in multiple wells to better understand productive layers in unconventional oil reservoirs.

Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples, has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward modeling process to generate full-wavefield data, an inversion scheme to minimize the difference between modeled and recorded data, and more importantly, an accurate velocity model to correctly propagate and collapse energy of different wave modes.

Data from all sources, at all scales, has helped to assess petroleum potential offshore Mozambique beyond the relatively small play fairway proven to date. New insights into proven and potential reservoirs, plus improved predictions of the presence, type and maturity of source rocks, have enabled the development of new play concepts.

3500 km2 of marine data from West of Shetlands have been reprocessed using latest processing technologies available (including deghosting). This case study demonstrates the benefit to re-process conventionnal data (acquired in the 90's) to get a broadband result (better definition, larger spectrum).

The Intergovernmental Panel on Climate Change (IPCC) has just concluded its Sixth Assessment Report Cycle (AR6). The reports making up the AR6 provide the most comprehensive overview of the current state of climate change, its impacts and the adaptation and mitigation options that are available. Each word of the Summary for Policy Makers (SPMs) of these reports is approved by the world’s governments. The IPCC has been clear on the urgency that is required to meet the targets of the Paris Agreement. We have, as a global society, no more than seven years and probably only three years to start drastically reducing emissions of greenhouse gases. If we do not, global temperature change will reach the 1.5⁰C threshold after which climate change impacts will become increasingly more dangerous and difficult to adapt to. As well as the three major AR6 reports, three Special Reports were produced, including the Special Report on Climate Change and Land (SRCCL) which emphasised the use of the land as a key resource.

Learn about CGG's latest advances in industry leading geoscience, data science and Earth data that help efficiently and responsibly solve complex digital, energy transition and natural resource challenges.

Here we use a multi-client magnetic airborne survey to interpret and estimate the magnetic basement relief of the Jequitinhonha Basin. Satellite Gravity, seismic lines and well logs are integrated in the interpretation. Exudation points are also analyzed constructing a relationship between structural features and petroleum occurances. A regional structural map for the Basin is also proposed.

Seismic imaging is particularly challenging in the Middle East. The shallow geology is often characterized by strong velocity contrasts from layered sands and carbonates that create multiples and mode conversions. Capturing these velocity variations is essential for accurate imaging. While re-cent developments in full-waveform inversion (FWI) make it a powerful velocity model building tool, its application to land data, with poorer data quality and stronger elastic effects, remains challenging. Elastic FWI further advances velocity model building in these complex geological settings, allowing for a more physically accurate description of the subsurface. We present a workflow based on a combination of diving-wave anisotropic elastic FWI and joint refraction-reflection tomography. With synthetic and real data, we show that it permits inverting a larger part of the diving-wave train compared to acoustic FWI. With the real data ex-ample, we demonstrate the Vp model inverted from elastic FWI gives better focused stack image with improved event continuity and flatter gathers with enhanced coherence.