TY - JOUR

T1 - Automated transient electromagnetic data processing for ground-based and airborne systems by a deep learning expert system

AU - Asif, Muhammad Rizwan

AU - Maurya, Pradip K.

AU - Foged, Nikolaj

AU - Larsen, Jakob Juul

AU - Auken, Esben

AU - Christiansen, Anders V.

PY - 2022

Y1 - 2022

N2 - Modern transient electromagnetic (TEM) surveys, either ground-based or airborne, may yield thousands of line kilometers of data. Parts of these data, especially in areas with dense infrastructure, are often disturbed by electromagnetic couplings due to infrastructure, e.g., power cables and fences. In most cases and in particular when working in a hydro-geological context, such coupled data must be culled before inversion. The process of identifying and culling coupled data is a manual task, requiring specialists to examine and process the data in detail. Manual data processing is subjective, difficult to reproduce, and time-consuming. To automate the complex data processing workflows, we propose an expert system based on a deep convolutional auto-encoder to identify couplings in the data. We configure the auto-encoder to learn an encoded representation of TEM data in a latent space. A reconstruction part that decodes the encoded representation is also trained, aiming to reconstruct input data. If the data unaffected by electromagnetic couplings are observed by the auto-encoder, the reconstructed output will have low error to the input. However, when having couplings in the data, the reconstruction error is elevated, indicating a non-geologic anomaly. The size of the anomaly is based on the relative error between the input data and the reconstructed output normalized by the data standard deviation. We show that the proposed approach displays high quality data processing within a fraction of a second for a ground-based and an airborne system, which is either ready for inversion or requires minimal further quality inspection.

AB - Modern transient electromagnetic (TEM) surveys, either ground-based or airborne, may yield thousands of line kilometers of data. Parts of these data, especially in areas with dense infrastructure, are often disturbed by electromagnetic couplings due to infrastructure, e.g., power cables and fences. In most cases and in particular when working in a hydro-geological context, such coupled data must be culled before inversion. The process of identifying and culling coupled data is a manual task, requiring specialists to examine and process the data in detail. Manual data processing is subjective, difficult to reproduce, and time-consuming. To automate the complex data processing workflows, we propose an expert system based on a deep convolutional auto-encoder to identify couplings in the data. We configure the auto-encoder to learn an encoded representation of TEM data in a latent space. A reconstruction part that decodes the encoded representation is also trained, aiming to reconstruct input data. If the data unaffected by electromagnetic couplings are observed by the auto-encoder, the reconstructed output will have low error to the input. However, when having couplings in the data, the reconstruction error is elevated, indicating a non-geologic anomaly. The size of the anomaly is based on the relative error between the input data and the reconstructed output normalized by the data standard deviation. We show that the proposed approach displays high quality data processing within a fraction of a second for a ground-based and an airborne system, which is either ready for inversion or requires minimal further quality inspection.

KW - Anomaly detection

KW - convolutional neural network

KW - data processing

KW - deep learning

KW - expert system

KW - subsurface information

KW - transient electromagnetics

UR - http://www.scopus.com/inward/record.url?scp=85137610689&partnerID=8YFLogxK

U2 - 10.1109/TGRS.2022.3202304

DO - 10.1109/TGRS.2022.3202304

M3 - Journal article

AN - SCOPUS:85137610689

VL - 60

JO - I E E E Transactions on Geoscience and Remote Sensing

JF - I E E E Transactions on Geoscience and Remote Sensing

SN - 0196-2892

M1 - 5919814

ER -