### Abstract

An iterative coconditional Monte Carlo simulation method is developed to estimate coconditional means and variances of transmissivity (T) and head (φ) distributions based on some T and φ measurements in heterogeneous aquifers under steady state condition. This method uses the classical coconditional Monte Carlo simulation technique, and a successive linear estimator to produce realizations of random T and φ fields that preserve measurements of T and φ at sampling locations. In addition, it ensures these random fields to be consistent with the governing flow equation. As a result, the iterative coconditional Monte Carlo simulation method alleviates problems associated with the classical coconditional Monte Carlo simulation method by incorporating the nonlinear relationship between T and φ. The averages of all the coconditional realizations then yields the coconditional means of T and φ fields, and the variances around the means become coconditional variances of T and φ. These variances can be utilized as a measure of the reduction in estimation uncertainty due to T and φ observations.

Original language | English (US) |
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Title of host publication | International Conference on Computational Methods in Water Resources, CMWR |

Publisher | Computational Mechanics Publ |

Pages | 679-687 |

Number of pages | 9 |

Volume | 1 |

State | Published - 1996 |

Event | Proceedings of the 1996 11th International Conference on Computational Methods in Water Resources, CMWR'96. Part 1 (of 2) - Cancun, Mex Duration: Jul 1 1996 → Jul 1 1996 |

### Other

Other | Proceedings of the 1996 11th International Conference on Computational Methods in Water Resources, CMWR'96. Part 1 (of 2) |
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City | Cancun, Mex |

Period | 7/1/96 → 7/1/96 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*International Conference on Computational Methods in Water Resources, CMWR*(Vol. 1, pp. 679-687). Computational Mechanics Publ.

**Iterative coconditional Monte Carlo simulation method for steady-state flow in aquifers.** / Yeh, Tian-Chyi J; Hanna, S.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*International Conference on Computational Methods in Water Resources, CMWR.*vol. 1, Computational Mechanics Publ, pp. 679-687, Proceedings of the 1996 11th International Conference on Computational Methods in Water Resources, CMWR'96. Part 1 (of 2), Cancun, Mex, 7/1/96.

}

TY - GEN

T1 - Iterative coconditional Monte Carlo simulation method for steady-state flow in aquifers

AU - Yeh, Tian-Chyi J

AU - Hanna, S.

PY - 1996

Y1 - 1996

N2 - An iterative coconditional Monte Carlo simulation method is developed to estimate coconditional means and variances of transmissivity (T) and head (φ) distributions based on some T and φ measurements in heterogeneous aquifers under steady state condition. This method uses the classical coconditional Monte Carlo simulation technique, and a successive linear estimator to produce realizations of random T and φ fields that preserve measurements of T and φ at sampling locations. In addition, it ensures these random fields to be consistent with the governing flow equation. As a result, the iterative coconditional Monte Carlo simulation method alleviates problems associated with the classical coconditional Monte Carlo simulation method by incorporating the nonlinear relationship between T and φ. The averages of all the coconditional realizations then yields the coconditional means of T and φ fields, and the variances around the means become coconditional variances of T and φ. These variances can be utilized as a measure of the reduction in estimation uncertainty due to T and φ observations.

AB - An iterative coconditional Monte Carlo simulation method is developed to estimate coconditional means and variances of transmissivity (T) and head (φ) distributions based on some T and φ measurements in heterogeneous aquifers under steady state condition. This method uses the classical coconditional Monte Carlo simulation technique, and a successive linear estimator to produce realizations of random T and φ fields that preserve measurements of T and φ at sampling locations. In addition, it ensures these random fields to be consistent with the governing flow equation. As a result, the iterative coconditional Monte Carlo simulation method alleviates problems associated with the classical coconditional Monte Carlo simulation method by incorporating the nonlinear relationship between T and φ. The averages of all the coconditional realizations then yields the coconditional means of T and φ fields, and the variances around the means become coconditional variances of T and φ. These variances can be utilized as a measure of the reduction in estimation uncertainty due to T and φ observations.

UR - http://www.scopus.com/inward/record.url?scp=0029728472&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029728472&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0029728472

VL - 1

SP - 679

EP - 687

BT - International Conference on Computational Methods in Water Resources, CMWR

PB - Computational Mechanics Publ

ER -