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A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data
- Source :
- Asif, M R, Bording, T S, Maurya, P K, Zhang, B, Fiandaca, G, Grombacher, D J, Christiansen, A V, Auken, E & Larsen, J J 2022, ' A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data ', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 4503610 . https://doi.org/10.1109/TGRS.2021.3076121
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Inversion of large-scale time-domain transient electromagnetic (TEM) surveys is computationally expensive and time-consuming. The calculation of partial derivatives for the Jacobian matrix is by far the most computationally intensive task, as this requires calculation of a significant number of forward responses. We propose to accelerate the inversion process by predicting partial derivatives using an artificial neural network. Network training data for resistivity models for a broad range of geological settings are generated by computing partial derivatives as symmetric differences between two forward responses. Given that certain applications have larger tolerances for modeling inaccuracy and varying degrees of flexibility throughout the different phases of interpretation, we present four inversion schemes that provide a tunable balance between computational time and inversion accuracy when modeling TEM datasets. We improve speed and maintain accuracy with a hybrid framework, where the neural network derivatives are used initially and switched to full numerical derivatives in the final iterations. We also present a full neural network solution where neural network forward and derivatives are used throughout the inversion. In a least-squares inversion framework, a speedup factor exceeding 70 is obtained on the calculation of derivatives, and the inversion process is expedited ~36 times when the full neural network solution is used. Field examples show that the full nonlinear inversion and the hybrid approach gives identical results, whereas the full neural network inversion results in higher deviation but provides a reasonable indication about the overall subsurface geology.
- Subjects :
- Speedup
Computer science
inverse modeling
Inversion (discrete mathematics)
Least squares
symbols.namesake
Mathematical model
Range (statistics)
Jacobian matrices
Electrical and Electronic Engineering
Neurons
Conductivity
Artificial neural network
Data models
Computational modeling
Jacobian matrix
Logic gates
neural networks
transient electromagnetics (TEM)
Jacobian matrix and determinant
symbols
General Earth and Planetary Sciences
Partial derivative
Forward modeling
Transient (oscillation)
Algorithm
Subjects
Details
- ISSN :
- 15580644 and 01962892
- Volume :
- 60
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Geoscience and Remote Sensing
- Accession number :
- edsair.doi.dedup.....c5e3f23c74640024090a885b58dd2df1