Reflection seismic imaging and physical properties of base-metal and associated iron deposits in the Bathurst Mining Camp, New Brunswick, Canada

Delineating fracture zones using surface‐tunnel‐surface seismic data, P‐S, and S‐P mode conversions

“…Some surface seismic data and, in particular, borehole seismic surveys have shown that zones of high impedance contrast to the host rocks, such as fluid‐filled fracture systems, ductile shear zones, and massive sulfide bodies, can increase reflectivity and generate significant amounts of P ‐ S and S ‐ P converted energy [ Ayarza et al ., ; Bellefleur et al ., ; Malehmir and Bellefleur , ; Bellefleur et al ., ; Lundberg et al ., ; Melanson et al ., ]. To provide a possible explanation of the origin of the events seen in Figures and , two modeling approaches were used: 3‐D constant‐velocity ray tracing travel time modeling [e.g., Ayarza et al ., ] and 2‐D finite difference elastic modeling [e.g., Juhlin , ]. …”

Constraints on the geometry of the Suasselkä post-glacial fault, northern Finland, based on reflection seismic imaging

“…In some locations faults have juxtaposed rock units with drastically different acoustic impedances next to each other and in these locations the fault plane can be identified. Sometimes highly and weakly reflective domains alongside each other manifest the existence of a major fault zone (Malehmir and Bellefleur, 2010). Locally, the fault plane itself can be reflective due to fault gouge or strong fracturing adjacent to the displacement surface.…”

Neuro-Bayesian facies inversion of prestack seismic data from a carbonate reservoir in Iran

“…Before the inversion, time-to-depth conversion was carried out and three wavelets were extracted for each angle using the existing well log and seismic data of well A (e.g. Bellefleur, 2010 andMalehmir et al, 2012). We assumed P-and S-wave impedances as elastic parameters and inverted for them from the seismic data using the Bayesian linearized AVO inversion method introduced by Buland and Omre (2003).…”