Bayesian Linear Seismic Inversion Integrating Uncertainty of Noise Level Estimation and Wavelet Extraction
نویسندگان
چکیده
Seismic impedance inversion is an important method to identify the spatial characteristics of underground rock physical properties. results and uncertainty evaluation are scientific basis for risk decision-making in oil gas development. Under assumption that error observed seismic data meet Gaussian distribution or log–Gaussian distribution, Bayesian linear can analytically obtain posterior probability impedance. However, errors from observation, calculation, model other factors lead inaccurate incomplete evaluation. In this paper, noise variance used represent level uncertainties wavelet extraction estimation considered inversion. Assuming meets inverse gamma we could conditional one variable given another using well-log data. order integrate into inversion, Gibbs sampler algorithm was applied draw a set realizations wavelet. For each realization, corresponding achieved by final obtained integrating all single probabilities pair variance. Synthetic real experiments showed have influence on their uncertainties. The proposed effectively more accurate comprehensive relationship parameters some specified has high calculation efficiency. it also loses accuracy when assumptions not completely consistent with actual situation.
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ژورنال
عنوان ژورنال: Minerals
سال: 2022
ISSN: ['2075-163X']
DOI: https://doi.org/10.3390/min13010016