We apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian pos-
terior mod... more We apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian pos- terior model probability density function of 2-D seafloor resistivity as constrained by marine controlled source electromagnetic data. This density function of earth models conveys infor- mation on which parts of the model space are illuminated by the data. Whereas conventional gradient-based inversion approaches require subjective regularization choices to stabilize this highly non-linear and non-unique inverse problem and provide only a single solution with no model uncertainty information, the method we use entirely avoids model regularization. The result of our approach is an ensemble of models that can be visualized and queried to provide meaningful information about the sensitivity of the data to the subsurface, and the level of res- olution of model parameters. We represent models in 2-D using a Voronoi cell parametrization. To make the 2-D problem practical, we use a source–receiver common midpoint approximation with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where the number of resistivity cells within a 2-D depth section is variable, as are their positions and geometries. Two synthetic studies demonstrate the algorithm’s use in the appraisal of a thin, segmented, resistive reservoir which makes for a challenging exploration target. As a demonstration example, we apply our method to survey data collected over the Scarborough gas field on the Northwest Australian shelf.
SUMMARY Marine magnetotelluric (MT) and marine controlled-source electromagnetic (CSEM) sound- in... more SUMMARY Marine magnetotelluric (MT) and marine controlled-source electromagnetic (CSEM) sound- ings can be used to study sedimentary structure offshore. In an example of this applica- tion, we collected MT and CSEM data in the 1-km deep water of the San Diego Trough, California. The Trough is a pull-apart basin and part of the complex Pacific/North Amer- ican tectonic plate boundary,
We apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian pos-
terior mod... more We apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian pos- terior model probability density function of 2-D seafloor resistivity as constrained by marine controlled source electromagnetic data. This density function of earth models conveys infor- mation on which parts of the model space are illuminated by the data. Whereas conventional gradient-based inversion approaches require subjective regularization choices to stabilize this highly non-linear and non-unique inverse problem and provide only a single solution with no model uncertainty information, the method we use entirely avoids model regularization. The result of our approach is an ensemble of models that can be visualized and queried to provide meaningful information about the sensitivity of the data to the subsurface, and the level of res- olution of model parameters. We represent models in 2-D using a Voronoi cell parametrization. To make the 2-D problem practical, we use a source–receiver common midpoint approximation with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where the number of resistivity cells within a 2-D depth section is variable, as are their positions and geometries. Two synthetic studies demonstrate the algorithm’s use in the appraisal of a thin, segmented, resistive reservoir which makes for a challenging exploration target. As a demonstration example, we apply our method to survey data collected over the Scarborough gas field on the Northwest Australian shelf.
SUMMARY Marine magnetotelluric (MT) and marine controlled-source electromagnetic (CSEM) sound- in... more SUMMARY Marine magnetotelluric (MT) and marine controlled-source electromagnetic (CSEM) sound- ings can be used to study sedimentary structure offshore. In an example of this applica- tion, we collected MT and CSEM data in the 1-km deep water of the San Diego Trough, California. The Trough is a pull-apart basin and part of the complex Pacific/North Amer- ican tectonic plate boundary,
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Papers by Kerry Key
terior model probability density function of 2-D seafloor resistivity as constrained by marine
controlled source electromagnetic data. This density function of earth models conveys infor-
mation on which parts of the model space are illuminated by the data. Whereas conventional
gradient-based inversion approaches require subjective regularization choices to stabilize this
highly non-linear and non-unique inverse problem and provide only a single solution with no
model uncertainty information, the method we use entirely avoids model regularization. The
result of our approach is an ensemble of models that can be visualized and queried to provide
meaningful information about the sensitivity of the data to the subsurface, and the level of res-
olution of model parameters. We represent models in 2-D using a Voronoi cell parametrization.
To make the 2-D problem practical, we use a source–receiver common midpoint approximation
with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where
the number of resistivity cells within a 2-D depth section is variable, as are their positions
and geometries. Two synthetic studies demonstrate the algorithm’s use in the appraisal of a
thin, segmented, resistive reservoir which makes for a challenging exploration target. As a
demonstration example, we apply our method to survey data collected over the Scarborough
gas field on the Northwest Australian shelf.
terior model probability density function of 2-D seafloor resistivity as constrained by marine
controlled source electromagnetic data. This density function of earth models conveys infor-
mation on which parts of the model space are illuminated by the data. Whereas conventional
gradient-based inversion approaches require subjective regularization choices to stabilize this
highly non-linear and non-unique inverse problem and provide only a single solution with no
model uncertainty information, the method we use entirely avoids model regularization. The
result of our approach is an ensemble of models that can be visualized and queried to provide
meaningful information about the sensitivity of the data to the subsurface, and the level of res-
olution of model parameters. We represent models in 2-D using a Voronoi cell parametrization.
To make the 2-D problem practical, we use a source–receiver common midpoint approximation
with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where
the number of resistivity cells within a 2-D depth section is variable, as are their positions
and geometries. Two synthetic studies demonstrate the algorithm’s use in the appraisal of a
thin, segmented, resistive reservoir which makes for a challenging exploration target. As a
demonstration example, we apply our method to survey data collected over the Scarborough
gas field on the Northwest Australian shelf.