High-Resolution 3D Full-Williamson Seismic Inversion for Subsurface Imaging Using Passive Seismic Data in Indigenous Reservoirs

Project Abstract

This study presents a novel methodology for achieving high-resolution 3D full-Williamson seismic inversion tailored to subsurface imaging in indigenous reservoirs by leveraging passive seismic data. Building on the full-Williamson transform, the approach integrates array-based passive source information with conventional active data to recover accurate impedance contrasts, anisotropy parameters, and complex near-surface effects. We develop a rigorous forward model that couples 3D elastic wave propagation with stochastic representations of heterogeneity and damping, enabling the simultaneous inversion of velocity, density, and Q-factors while accounting for irregular acquisition geometries typical of indigenous settings. A data-driven, multi-stage inversion workflow is introduced (i) robust preprocessing and ambient-noise suppression to enhance signal coherence, (ii) passive source localization and time-reversal-based deconvolution to maximize the usable bandwidth, (iii) construction of a 3D full-Williamson operator that enforces physically consistent scale-limited representations of the subsurface, and (iv) iterative, regularized inversion that balances data fidelity with model smoothness and sparsity in the transformed domain. To handle the limited illumination and high-frequency content inherent in passive datasets, the method employs adaptive dictionary learning within the Williamson transform space to capture intricate reservoir geometries and fracture networks. We implement an efficient, high-performance computational framework leveraging domain decomposition and GPU acceleration to render 3D inversions tractable for field-scale problems. Synthetic experiments demonstrate that the proposed framework reconstructs complex impedance contrasts and anisotropic features with substantially improved resolution over conventional full-waveform inversions under equivalent data budgets. We further validate the approach on field data from indigenous reservoirs where passive seismic events are abundant, showing enhanced delineation of subtle stratigraphic boundaries, fault/fracture zones, and reservoir heterogeneity. Quantitative metrics including peak signal-to-noise ratio, structural similarity, and recovery of true elastic parameters indicate robust performance against noise, limited aperture, and incomplete coverage. Sensitivity analyses reveal the method’s resilience to misestimated background velocities and anisotropy, with guided regularization parameters optimizing convergence. The study also investigates the benefits of joint inversion with partial active-source constraints to anchor large-scale velocity structures while preserving the high-resolution interior features afforded by passive illumination. Practical implications for exploration and development in resource-constrained or environmentally sensitive environments are discussed, including reduced surface disturbance, lower emissions due to fewer active sources, and improved reservoir characterization critical for optimized production planning. The results underscore the potential of 3D full-Williamson seismic inversion as a powerful tool for passive seismic imaging in indigenous reservoirs, delivering high-resolution, physically consistent subsurface reconstructions that inform risk-aware decision-making in geoscience operations.

Project Overview

What This Project Is About

---

The project explores improving how we image underground rock features using passive seismic data, meaning we use naturally occurring earthquakes and ambient ground noise instead of active blasting. It aims to produce a detailed 3D image of subsurface structures by applying a full-Williamson seismic inversion approach, which helps refine estimates of rock properties and geometry in Indigenous reservoirs.

---

---

The Problem It Addresses

---

---

---

Objectives of the Project

---

1. Understand basic principles of seismic imaging and inversion.
2. Implement a 3D full-Williamson inversion workflow on passive seismic data.
3. Evaluate image resolution and accuracy against synthetic benchmarks.
4. Develop practical guidelines for using passive data in Indigenous reservoirs.
5. Assess computational requirements and optimization strategies.

---

---

What You Will Do Step by Step

---

1) Review foundational concepts in seismology and inverse problems. 2) Collect or simulate passive seismic datasets representative of Indigenous reservoirs. 3) Build a 3D inversion pipeline implementing Williamson-type formulations. 4) Run tests on synthetic models to gauge resolution. 5) Apply the method to real data if available and compare results. 6) Analyze sensitivity to noise, sampling, and model assumptions. 7) Document procedures and create a user-friendly workflow. 8) Discuss limitations and potential real-world applications.

---

---

---

---

Expected Outcome

Deliverables include a functional 3D inversion workflow for passive data, a set of resolution assessments, and practical recommendations for deploying the approach in Indigenous reservoirs. The project should produce visual subsurface images and a transparent report explaining the method, its strengths, and its limitations.