Surface Wave Attributes for Enhanced Near Surface Anomaly Detection

The shallow subsurface is usually highly complex and characterized by significant heterogeneities, which are crucial to depict for successful subsurface imaging in various applications. These lateral variations, specifically low-velocity zones, can either be a target of investigation or an important constraint for accurate reconstruction of the shear wave velocity model using surface wave analysis. Three surface wave attributes that are energy, energy decay exponent and autospectrum, have been shown to be highly sensitive to lateral variations, specifically low velocity anomalies. The computations for these attributes are based on multifold data, which are stacked to improve the interpretation and readability. In this thesis, we propose a workflow for estimating depth of the anomalies using these surface wave attributes and spectral properties of 3 component data. The proposed method is evaluated using two 3D synthetic data, and a field data set from a test site with known properties of heterogeneities. All surface wave attributes and H/V spectral ratio show the ability to detect and estimate the depth of near-surface sharp lateral variations.