Adaptive Marine Seismic Imaging for High-Resolution Subsurface Inversion in Complex Geologies

Project Abstract

Adaptive marine seismic imaging has emerged as a pivotal tool for unraveling complex subsurface structures where variable lithology, steep faults, and heterogeneous fluid distributions challenge conventional methods. This study develops an integrated workflow that combines adaptive acquisition strategies, robust preprocessing, and advanced inversion algorithms to achieve high-resolution subsurface inversion in geologically intricate marine settings. We introduce a data-driven, parameterized wavefield reconstruction approach that leverages sparsity-promoting regularization and multi-scale, anisotropic modelling to accommodate strong velocity contrasts and lateral heterogeneity common in continental shelves, transform margins, and sub-salt environments. The methodology starts with an optimized marine nodal acquisition design that dynamically adjusts shot and receiver spacing to maximize illumination of critical targets while minimizing source-side and receiver-side interference. Preprocessing steps implement robust de-noising, coherent noise suppression, and dynamic statics correction to preserve subtle amplitude and phase information essential for accurate inversion. A hierarchical inversion framework is then employed, beginning with root-mean-square (RMS) velocity models refined through full-waveform inversion (FWI) on a multi-parameter basis that includes anisotropy, attenuation, and density alongside velocity. To handle complex geometries and high-contrast interfaces, we integrate adaptive regularization pathways that modify smoothness constraints in response to local data fit and model plausibility, enabling sharp delineation of interfaces such as salt bodies, carbonates, and water layers. The inversion is augmented with multi-parameter, multi-physics data assimilation that fuses controlled-source seismic data with complementary constraints from gravity, magnetotellurics, and well logs, where available, to reduce non-uniqueness and improve confidence in lithology and fluid predictions. We implement a patch-based, parallelizable framework that accelerates convergence through localized model updates and adaptive learning rates, ensuring scalability to large marine datasets. The framework also includes uncertainty quantification via Bayesian-inspired sampling and ensemble methods to provide probabilistic subsurface images and credible intervals for key features. Case studies from diverse marine environments demonstrate enhanced resolution of complex features such as sub-salt channels, reefal complexes, and fault networks, with validation against synthetic models and borehole data. The results show measurable improvements in lateral resolution, reduced migration artifacts, and more accurate velocity and anisotropy distributions, translating into more reliable depth conversion and reservoir characterization. Sensitivity analyses reveal the robustness of the adaptive workflow to noise, incomplete data, and model misspecification, while computational performance assessments highlight near-linear scalability with optimized GPU-accelerated solvers. The study contributes a practical, adaptable blueprint for high-fidelity marine seismic inversion in complex geologies, enabling more accurate delineation of subsurface storage, hydrocarbon prospects, and geotechnical hazards, ultimately supporting safer, more efficient exploration and development in challenging offshore environments.

Project Overview

What This Project Is About

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A straightforward study of how to image underground rock and water layers beneath the ocean floor using offshore seismic data. The project focuses on improving how we turn raw sound waves collected at sea into clear pictures of subsurface structures, especially in areas where geologic layers are complex or varied.

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The Problem It Addresses

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Seismic images can be blurry or distorted when rocks have complicated features like varying rock types, cracks, or irregular layering. This makes it hard to locate reservoirs or understand risks. The project tackles these challenges by using smarter data processing to produce sharper, more reliable subsurface images.

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Objectives of the Project

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1. Learn basic marine seismic data collection concepts.
2. Explore methods to improve image clarity in complex geology.
3. Develop a simple workflow to process and invert seismic data.
4. Compare traditional and improved imaging results on sample data.
5. Evaluate how well the method resolves important subsurface features.

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What You Will Do Step by Step

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1. Review basic seismic theory and marine data collection basics. 2. Acquire or use provided seismic datasets from marine surveys. 3. Apply straightforward preprocessing to clean data (noise removal, alignment). 4. Implement a simple inversion approach to convert data into subsurface images. 5. Compare results with standard methods. 6. Interpret features relevant to geology and potential resources. 7. Document methods and create visual results. 8. Reflect on limitations and possible improvements.

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Expected Outcome

A clear, easy-to-understand method for producing better marine seismic images in complex geology, with example visuals and a short assessment of where it works best and where it may need refinement.