Prediction of interface of geological formations using generalized additive model

钻孔 区间(图论) 地质学 置信区间 特征(语言学) 数据挖掘 预测建模 广义加性模型 计算机科学 统计 岩土工程 数学 机器学习 语言学 组合数学 哲学
作者
Xinxin Qi,Zhiyong Yang,Jian Chu
出处
期刊:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards [Taylor & Francis]
卷期号:16 (1): 127-139 被引量:1
标识
DOI:10.1080/17499518.2022.2028847
摘要

Geological information such as geological interfaces is important for the design of underground excavation and supporting measures. This in turn requires a method to predict accurately the locations of geological interfaces for the gap areas between boreholes. This study presents a generalized additive model (GAM) to predict the location of the geological interfaces. The performance of the GAM method is evaluated using both simulated data and borehole data for the determination of rockhead in two different geological formations in Singapore. The results show that the GAM method can provide a reasonable confidence interval (CI) of the mean trend and the prediction interval (PI) in the sense that the 95% CI covers about 95% of the actual mean curve while the 95% PI covers around 95% of testing data. Furthermore, the geological complexity can be well reflected as the prediction uncertainty in the geologically complex area is larger than that in the geologically regular area. More importantly, the users can impose prior information or personal judgment regarding the shape of the geological profile on the model. This is an important feature to enable further improvement in the accuracy of the prediction.Highlights A generalized additive model is used to predict the location of the geological interfacesThe performance of the GAM method is evaluated using both simulated data and actual borehole data from SingaporeThe results show that the GAM method can provide a reasonable confidence interval of the mean trend and the prediction interval of a predictionGeological complexity can be well reflected in the sense that the prediction uncertainty in the geologically complex area is relatively largeExpert judgment or knowledge on the geological profile can be applied to the model, which improves the prediction accuracy

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