TY - CHAP
T1 - Bayes Meets Tikhonov: Understanding Uncertainty Within Gaussian Framework for Seismic Inversion
AU - Izzatullah, Muhammad
AU - Peter, Daniel
AU - Kabanikhin, Sergey
AU - Shishlenin, Maxim
N1 - KAUST Repository Item: Exported on 2020-11-12
PY - 2020/10/21
Y1 - 2020/10/21
N2 - In this chapter, we demonstrate the sound connection between the Bayesian approach and the Tikhonov regularisation within Gaussian framework. We provide a thorough uncertainty analysis to answer the following two fundamental questions: (1) How well is the estimate determined by a posteriori PDF, i.e. by the combination of observed data and a priori information? (2) What are the respective contributions of observed data and a priori information? To support the proposed methodology, we demonstrate it through numerical applications in seismic inversions.
AB - In this chapter, we demonstrate the sound connection between the Bayesian approach and the Tikhonov regularisation within Gaussian framework. We provide a thorough uncertainty analysis to answer the following two fundamental questions: (1) How well is the estimate determined by a posteriori PDF, i.e. by the combination of observed data and a priori information? (2) What are the respective contributions of observed data and a priori information? To support the proposed methodology, we demonstrate it through numerical applications in seismic inversions.
UR - http://hdl.handle.net/10754/665903
UR - http://link.springer.com/10.1007/978-981-15-8606-4_8
UR - http://www.scopus.com/inward/record.url?scp=85093868140&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-8606-4_8
DO - 10.1007/978-981-15-8606-4_8
M3 - Chapter
SN - 9789811586057
SP - 121
EP - 145
BT - Advanced Methods for Processing and Visualizing the Renewable Energy
PB - Springer Singapore
ER -