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Uncertainties in 3-D stochastic geological modeling of fictive grain size distributions in detrital systems

Geological 3-D models are very useful tools to predict subsurface properties. However, they are always subject to uncertainties, starting from the primary data. To ensure the reliability of the model outputs and, thus, to support the decision-making process, the incorporation and quantification of uncertainties have to be integrated into the geo-modeling strategies. Among all modeling approaches, the novel Di models method was conceived as a stochastic approach to make predictions of the 3-D lithological composition of detrital systems, based on estimating the fictive grain size distribution of the sediment mixture by using soil observations from drilled materials. Within the present study, we aim to adapt the geo-modeling framework of this method in order to incorporate uncertainties linked to systematic imprecisions in the soil observations used as input data. Following this, uncertainty quantification measures are proposed, based on entropy and joint entropy for the main outcomes of the method, i.e., the partial percentile lithological models, and for the whole sediment mixture. Both the ability of the uncertainty quantification measures and the uncertainty propagation derived from the extension of the method are investigated in the model outcomes in a simulation experiment with real data conducted in a small-scale domain located in Munich (Germany). The results show that this adaptation of the Di models method overcomes potential bias caused by ignoring imprecise input data, thus providing a more realistic assessment of uncertainty. The uncertainty measures provide very useful insight for quantifying local uncertainties, comparing between average uncertainties and for better understanding how the implementation parameters of the geo-modeling process influence the property estimation and the underlying uncertainties. The main findings of the present study have great potential for providing robust uncertainty information about model outputs, which ultimately strengthens the decision-making process for practical applications based on the implementation of the Di models method.

Ketersediaan
161551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
20 hlm PDF, 35.441 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.19, September 2023
Subjek
Uncertainty quantification
Geostatistics
Geological 3-D modeling
Entropy
Sequential indicator simulation
Geomathematics
Info Detail Spesifik
-
Pernyataan Tanggungjawab
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Tidak tersedia versi lain

Lampiran Berkas
  • Uncertainties in 3-D stochastic geological modeling of fictive grain size distributions in detrital systems
    Geological 3-D models are very useful tools to predict subsurface properties. However, they are always subject to uncertainties, starting from the primary data. To ensure the reliability of the model outputs and, thus, to support the decision-making process, the incorporation and quantification of uncertainties have to be integrated into the geo-modeling strategies. Among all modeling approaches, the novel Di models method was conceived as a stochastic approach to make predictions of the 3-D lithological composition of detrital systems, based on estimating the fictive grain size distribution of the sediment mixture by using soil observations from drilled materials. Within the present study, we aim to adapt the geo-modeling framework of this method in order to incorporate uncertainties linked to systematic imprecisions in the soil observations used as input data. Following this, uncertainty quantification measures are proposed, based on entropy and joint entropy for the main outcomes of the method, i.e., the partial percentile lithological models, and for the whole sediment mixture. Both the ability of the uncertainty quantification measures and the uncertainty propagation derived from the extension of the method are investigated in the model outcomes in a simulation experiment with real data conducted in a small-scale domain located in Munich (Germany). The results show that this adaptation of the Di models method overcomes potential bias caused by ignoring imprecise input data, thus providing a more realistic assessment of uncertainty. The uncertainty measures provide very useful insight for quantifying local uncertainties, comparing between average uncertainties and for better understanding how the implementation parameters of the geo-modeling process influence the property estimation and the underlying uncertainties. The main findings of the present study have great potential for providing robust uncertainty information about model outputs, which ultimately strengthens the decision-making process for practical applications based on the implementation of the Di models method.
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