A Framework for Integrating Seismic and Electromagnetic Data for Subsurface Characterization

Thesis Abstract

The integration of seismic and electromagnetic (EM) geophysical data has emerged as a pivotal approach to enhancing subsurface characterization, addressing the limitations inherent in single-method investigations, such as poor resolution and non-uniqueness in interpretation. This study aims to develop a comprehensive framework that systematically combines seismic and EM datasets to improve the accuracy and reliability of subsurface models, particularly in complex geological settings. The specific objectives include (1) to evaluate the individual effectiveness of seismic and EM methods in identifying subsurface lithologies; (2) to design an integrated data processing and interpretation framework utilizing advanced data fusion techniques; (3) to validate the proposed framework through application to a case study in the Southern Basin of Nigeria; and (4) to assess the implications of integrated data analysis for resource exploration and geohazard assessment. The research adopts a mixed-methods design, combining quantitative geophysical data analysis with qualitative interpretation validation. The primary population comprises seismic and electromagnetic survey data collected from 150 boreholes and 300 surface survey points, covering a geologically complex sedimentary basin. Data sources include multi-channel seismic reflection profiles and electromagnetic induction measurements acquired using the EM-31 and EM-38 systems. Data collection instruments are calibrated for noise reduction and signal enhancement, ensuring high-quality datasets suitable for fusion. The analytical approach integrates statistical and geophysical modeling techniques seismic data are processed through amplitude variation with offset (AVO) analysis and Full Waveform Inversion (FWI), while electromagnetic data undergo qualitative inversion and 3D resistivity modeling. The fused datasets are analyzed using joint inversion algorithms grounded in the Bayesian framework, informed by the Structural Models Theory and the Geophysical Inversion Theory, which provide the conceptual basis for interpreting the joint data. Secondary data analysis involves the application of multivariate regression and machine learning algorithms, like Random Forest classifiers, to delineate lithological boundaries with high spatial resolution. Model validation employs cross-validation and Monte Carlo simulations to quantify uncertainty and improve robustness. Ethical considerations include securing permits for field measurements, ensuring data confidentiality, and maintaining transparency in data handling and reporting. Expected findings suggest that the integrated framework will significantly outperform individual seismic or electromagnetic approaches in resolving subsurface heterogeneities, particularly in distinguishing between aquifers, hydrocarbon reservoirs, and clay-rich zones. The joint inversion approach is anticipated to reduce model ambiguity and enhance the spatial accuracy of subsurface delineation, as evidenced by comprehensive error analysis and comparison with borehole logs. This research contributes to the body of geophysical method development by providing an innovative, computationally efficient framework applicable to various geological contexts, thereby advancing the theoretical understanding of data fusion in geophysics. It offers a practical tool for resource exploration agencies, environmental consultants, and hazard mitigation authorities, aligning with the Structural Models Theory, which emphasizes the interconnectedness of surface and subsurface processes. Ultimately, the study recommends implementing the framework in different geological settings to evaluate its adaptability and scalability, suggesting possibilities for developing real-time combined seismic-electromagnetic monitoring systems for dynamic subsurface processes. In conclusion, this research underscores the potential of integrated geophysical approaches to transform subsurface investigations, advocating for the adoption of joint data interpretation frameworks that capitalize on the complementary strengths of seismic and electromagnetic methods. Future work should focus on refining data fusion algorithms, incorporating additional geophysical datasets (such as gravity and magnetic data), and exploring machine learning techniques for automated interpretation, thereby fostering a new paradigm in geoscientific exploration and hazard assessment.