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All Outputs (20)

Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks (2015)
Presentation / Conference Contribution
Oughton, R., Wooff, D., Swarbrick, R., & Hobbs, R. (2015). Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks. . https://doi.org/10.3997/2214-4609.201413296

Pore pressure prediction is vital when drilling a well, as unexpected overpressure can cause drilling challenges and uncontrolled hydrocarbon leakage. Predictions often use porosity-based techniques, relying on an idealised compaction trend and using... Read More about Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks.

Joint Inversion. (2007)
Presentation / Conference Contribution
Hobbs, R., Jegen, M., Chen, J., & Heincke, B. (2007). Joint Inversion.

The Metropolis Algorithm (2006)
Presentation / Conference Contribution
Hobbs, R., Flecha, I., Rasmussen, T., Carbonell, R., & Danielsen, M. (2006). The Metropolis Algorithm.

Joint Inversion of marine MT, Gravity and Seismic Data. (2005)
Presentation / Conference Contribution
Jegen-Kulcsar, M., & Hobbs, R. (2005). Joint Inversion of marine MT, Gravity and Seismic Data. In H. Ziska, T. Varming, & D. Bloch (Eds.), proceedings of the 1st conference (163-167)

Exploration of sub-basalt targets is difficult because the basalt units reflect and scatter seismic energy, masking the characteristics of the underlying structure. Electromagnetic soundings are less sensitive to the highly resistive basalt units but... Read More about Joint Inversion of marine MT, Gravity and Seismic Data..