1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error
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 computationa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/19/3881 |
| _version_ | 1797515805312679936 |
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| author | Peng Bai Giulio Vignoli Thomas Mejer Hansen |
| author_facet | Peng Bai Giulio Vignoli Thomas Mejer Hansen |
| author_sort | Peng Bai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:53:24Z |
| format | Article |
| id | doaj.art-c46e6263733441b79679b129dc979d58 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:53:24Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c46e6263733441b79679b129dc979d582023-11-22T16:42:13ZengMDPI AGRemote Sensing2072-42922021-09-011319388110.3390/rs131938811D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling ErrorPeng Bai0Giulio Vignoli1Thomas Mejer Hansen2Department of Civil and Environmental Engineering and Architecture (DICAAR), University of Cagliari, 09123 Cagliari, ItalyDepartment of Civil and Environmental Engineering and Architecture (DICAAR), University of Cagliari, 09123 Cagliari, ItalyDepartment of Geoscience, Aarhus University, 8000 Aarhus, DenmarkAirborne 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.https://www.mdpi.com/2072-4292/13/19/3881airborne time-domain electromagnetics (ATEM)stochastic inversionmodelling error3D forward modelingrealistic prior |
| spellingShingle | Peng Bai Giulio Vignoli Thomas Mejer Hansen 1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error Remote Sensing airborne time-domain electromagnetics (ATEM) stochastic inversion modelling error 3D forward modeling realistic prior |
| title | 1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error |
| title_full | 1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error |
| title_fullStr | 1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error |
| title_full_unstemmed | 1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error |
| title_short | 1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error |
| title_sort | 1d stochastic inversion of airborne time domain electromagnetic data with realistic prior and accounting for the forward modeling error |
| topic | airborne time-domain electromagnetics (ATEM) stochastic inversion modelling error 3D forward modeling realistic prior |
| url | https://www.mdpi.com/2072-4292/13/19/3881 |
| work_keys_str_mv | AT pengbai 1dstochasticinversionofairbornetimedomainelectromagneticdatawithrealisticpriorandaccountingfortheforwardmodelingerror AT giuliovignoli 1dstochasticinversionofairbornetimedomainelectromagneticdatawithrealisticpriorandaccountingfortheforwardmodelingerror AT thomasmejerhansen 1dstochasticinversionofairbornetimedomainelectromagneticdatawithrealisticpriorandaccountingfortheforwardmodelingerror |