Bayesian Stratigraphy Integration of Geophysical, Geological, and Geotechnical Surveys Data

Z. Medina-Cetina, J. Son, M. Moradi
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引用次数: 3

Abstract

This paper introduces a probabilistic approach to significantly improve offshore site characterization from integrated geophysical, geological and geotechnical survey data, and from different technologies used from within each of these disciplines. The proposed Bayesian stratigraphy integration methodology is based on the sequential integration of available evidence (experimental observations, model predictions and experts’ beliefs), which allows for the reduction of uncertainty and improve the quality of geospatial analysis translated into higher stratigraphy resolution and higher confidence on the determination of sediments’ mechanical characteristics. A synthetic case study for a 2D heterogeneous shallow offshore soil media is presented to illustrate the overall methodology. One application of probabilistic cluster identification based on geological data is discussed (e.g. 1D density upscaling profile), as this is then transferred to a probabilistic geophysical inversion, including the corresponding uncertainty propagation and.
地球物理、地质和岩土工程调查数据的贝叶斯地层学整合
本文介绍了一种概率方法,可以通过综合地球物理、地质和岩土工程调查数据,以及这些学科中使用的不同技术,显著改善海上场地的特征。提出的贝叶斯地层学整合方法是基于现有证据(实验观察、模型预测和专家的信念)的顺序整合,这可以减少不确定性,提高地理空间分析的质量,从而转化为更高的地层学分辨率和沉积物力学特征确定的更高置信度。本文以二维非均质浅海土壤介质为例,阐述了整体方法。讨论了基于地质数据的概率聚类识别的一种应用(例如,一维密度升级剖面),因为它随后被转移到概率地球物理反演,包括相应的不确定性传播和。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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