Uncertainty quantification for regularized inversion of electromagnetic geophysical data – Part II: application in 1-D and 2-D problems. Issue 2 (27th June 2022)

• Record Type: Journal Article
• Authors: Blatter, Daniel; Morzfeld, Matthias; Key, Kerry; Constable, Steven
• Is Part Of: Geophysical journal international. Volume 231:Issue 2(2022)
• Page Start: 1075
• Page End: 1095
• Publication Date: 2022-06-27
• Subjects: Inverse theory -- Electromagnetic methods -- Statistical methods -- Inversion -- Electrical resistivity; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggac242
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: This paper is Part II of a two-part series on a mathematical and computational framework for computing a meaningful uncertainty quantification (UQ) for regularized inversions of electromagnetic data. In Part I, we explained the theory behind a sampling algorithm, which we call RTO-TKO, and in Part II, we showcase RTO-TKO in practice. We individually and jointly invert seafloor magnetotelluric (MT) and surface-towed controlled source electromagnetic field data, collected for imaging offshore freshened groundwater beneath the U.S. Atlantic margin. We also invert seafloor MT data collected for subsalt imaging to produce 2-D resistivity models and uncertainty estimates that characterize the salt body geometry and surrounding sediments. We compare the UQ of the RTO-TKO with results from trans-dimensional sampling, and explain the differences arising from different underlying (prior) assumptions of the two algorithms. We also discuss the practical implications of these findings. Most importantly, however, the 2-D case study unambiguously demonstrates the computational advantages of RTO-TKO and its ability to make use of massive parallelism.