Building 3D probabilistic geology differentiation models using mixed Lp norm joint inversion, airborne geophysics and petrophysical information
Abstract
Geology differentiation focuses on identifying geologic units based upon multiple physical property models obtained from joint (or, separate) inversions as well as available prior geologic information. Each geologic unit is characterized by a distinct range of physical properties. However, assessing uncertainty of differentiated geologic units remains largely under explored. To fill the gap, we performed 3D probabilistic geology differentiation by integrating mixed Lp norm joint inversion, airborne geophysics and petrophysical information. We implemented mixed Lp norm joint inversion with different regularization terms controlled by two tuning parameters. These two user-specified parameters, among others, affect the recovered model characteristics. We randomly sampled these two tuning parameters and correspondingly performed mixed Lp norm joint inversions in multiple times. We thus obtained a sequence of jointly recovered models that all fit the observed geophysical data, but exhibit diverse model features. Then, we determined the acceptance or rejection of these jointly recovered models according to the available petrophysical measurements. Specifically, the models whose physical property values are within the expected ranges of sample measurements are accepted, and vice versa. We performed geology differentiation for all accepted models and obtained a suite of 3D quasi-geology models each of which consists of multiple geologic units and displays different spatial extents. statistical analysis was carried to obtain a 3D probabilistic quasi-geology model that provides practical insights into the uncertainty of the differentiated units. We analyzed two types of uncertainties in our work: (1) the uncertainty of spatial distribution for each geologic unit and (2) the probability of lithologic types at specific locations in the research area. We constructed 3D probabilistic quasi-geology models using the field data collected over the Decorah area in the northeast Iowa. Our results show that different geologic units, as shown in Figure 1, display location-dependent probabilities (or, uncertainties). These probabilistic models provide additional information and constraints for interpretation and understanding of the geological features in the study area.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMNG25A0485W