A Framework for Integrating Seismic and Geoelectric Data in Subsurface Characterization

Thesis Abstract

This study addresses the persistent challenge of accurately characterizing subsurface geological structures by integrating seismic and geoelectric data, which are often collected separately, leading to ambiguities and limited interpretative clarity in geophysical exploration. The primary aim is to develop a comprehensive framework that synergistically combines seismic and electrical resistivity tomography (ERT) data to enhance subsurface imaging accuracy and reliability. Specific objectives include evaluating the individual capabilities and limitations of seismic and geoelectric methods, designing an integrated data processing and interpretation methodology, and validating the proposed framework through field application in a structurally complex sedimentary basin. Employing a mixed-methods research design, the study integrates quantitative data analysis with qualitative insights derived from geophysical interpretation. The population comprises subsurface data collected from the Abadaba Basin, a geologically diverse area covering approximately 150 square kilometers, with a sample of 50 seismic surveys and 50 ERT profiles strategically selected to encompass various lithologies and structural features. Data collection involves deploying a 2D seismic reflection system with a source-receiver array spanning 10 kilometers per profile, along with high-density multichannel resistivity surveys utilizing multi-electrode arrays and a Wenner-Schlumberger configuration. The instruments' validity is ensured through calibration against known test sites, while data reliability is maintained via repeated measurements and cross-validation. The analytical methodology involves several sequential stages. First, seismic data are processed through velocity model building using tomography and migrated to generate subsurface reflection images. Parallelly, ERT data are inverted employing advanced algorithms such as the Smoothness-Constrained Gauss-Newton inversion to produce resistivity models. Subsequently, integrated data sets are aligned geographically and temporally within a Geographic Information System (GIS) platform. Multi-criteria data fusion techniques—including weighted overlay and principal component analysis (PCA)—are applied to produce a combined interpretative model. For statistical validation, regression analysis and analysis of variance (ANOVA) are conducted to assess the significance of correlations between geophysical parameters and known lithological units. The framework’s conceptual foundation draws on the Theory of Geophysical Data Integration and the Litho-Electrical-Structural Model, providing a structured basis for multi-method data synthesis. Expected findings suggest that the integrated framework significantly improves the resolution of subsurface features, enabling more precise delineation of lithological boundaries, fault zones, and aquifer extents. The combined models are anticipated to outperform individual methods in terms of accuracy and interpretative confidence, validated through cross-validation against borehole data and geological maps. The study further identifies key factors affecting data integration efficacy, such as data resolution, depth penetration disparities, and the inherent contrast in geophysical parameters. This research contributes substantially to the body of knowledge by advancing a practical, replicable framework for multi-method geophysical data integration, addressing a critical gap in subsurface characterization techniques. The findings are expected to influence exploration strategies for natural resources, groundwater management, and geotechnical investigations by demonstrating improved subsurface imaging accuracy. The main conclusion underscores the importance of a systematic integration approach in overcoming the limitations of single-method interpretations. Consequently, the study recommends adopting the proposed framework for regional geophysical surveys and encourages future research to incorporate additional geophysical methods such as magnetic and gravity data to further refine subsurface models.