1D stochastic inversion of airborne time-domain electromagnetic data with realistic prior and accounting for the forward modeling error

TY - JOUR

T1 - 1D stochastic inversion of airborne time-domain electromagnetic data with realistic prior and accounting for the forward modeling error

AU - Bai, Peng

AU - Vignoli, Giulio

AU - Hansen, Thomas Mejer

PY - 2021/10/1

Y1 - 2021/10/1

N2 - Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

AB - Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

KW - 3D forward modeling

KW - Airborne time-domain electromagnetics (ATEM)

KW - Modelling error

KW - Realistic prior

KW - Stochastic inversion

UR - http://www.scopus.com/inward/record.url?scp=85116206129&partnerID=8YFLogxK

U2 - 10.3390/rs13193881

DO - 10.3390/rs13193881

M3 - Journal article

AN - SCOPUS:85116206129

SN - 2072-4292

VL - 13

JO - Remote Sensing

JF - Remote Sensing

IS - 19

M1 - 3881

ER -