Interpolation of extremely sparse geo-data by data fusion and collaborative Bayesian compressive sampling

• Published in: Computers and geotechnics Vol. 134; p. 104098
• Main Authors: Xu, Jiabao, Wang, Yu, Zhang, Lulu
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.06.2021; Elsevier BV
• Subjects: Bayesian analysis; Bayesian compressive sampling; Bayesian inference; Collaboration; Cone penetration tests; Data; Data fusion; Data integration; Data points; Data sources; Design engineering; Interpolation; Mathematical models; Maximum likelihood method; Penetration; Probability theory; Sampling; Spatial data; Spatial variations; Statistical methods; Tests; Data fusion; Bayesian compressive sampling; Bayesian inference; Maximum likelihood method

In geotechnical or geological engineering, geo-data interpolation based on measurements is often needed for engineering design and analysis. However, measurements are sometimes extremely sparse (e.g., several, or even just a few, data points) because of limited access to the subsurface and the cost...