Automated Transient Electromagnetic Data Processing for Ground-Based and Airborne Systems by a Deep Learning Expert System

Asif, Muhammad Rizwan; Maurya, Pradip Kumar; Foged, Nikolaj; Larsen, Jakob Juul; Auken, Esben; Christiansen, Anders Vest

doi:10.1109/tgrs.2022.3202304

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…They have gradually emerged as the mainstay of modern machine learning methods. Asif et al developed an expert system based on a deep convolutional autoencoder to eliminate interference from power lines, fences, and other structures in data [15]. Pan et al proposed an encoder architecture that combines one-dimensional convolution and vision transformers, retaining both the local perceptual features of convolutional networks and the global perceptual features of transformers.…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

Xing,

Li,

et al. 2024

Remote Sensing

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Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques.

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Section: Introductionmentioning

confidence: 99%

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

Xing,

Li,

et al. 2024

Remote Sensing

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“…Machine learning has been used to remove noise (X. Wu et al, 2020) and process AEM raw data (Asif et al, 2022), conduct geophysical inversions (S. Wu et al, 2022Wu et al, , 2023a, interpret AEM inversions (Haber et al, 2019), simulate AEM response (S. Wu et al, 2023b), model glacial till using electrical conductivity derived from AEM (Gunnink et al, 2012), map quick clay using AEM (C. W. Christensen et al, 2021), construct field-scale rock-physics transform and simulate AEM (Gottschalk & Knight, 2022), and to cluster AEM (Dumont et al, 2018). Friedel et al (2016) and Friedel (2016) used machine learning to estimate aquifer distributions and hydrostratigraphic units using AEM, borehole and hydrogeological data.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, machine learning techniques, such as supervised and unsupervised classification, have been widely used to process and model the subsurface using AEM. Machine learning has been used to remove noise (X. Wu et al., 2020) and process AEM raw data (Asif et al., 2022), conduct geophysical inversions (S. Wu et al., 2022, 2023a), interpret AEM inversions (Haber et al., 2019), simulate AEM response (S. Wu et al., 2023b), model glacial till using electrical conductivity derived from AEM (Gunnink et al., 2012), map quick clay using AEM (C. W. Christensen et al., 2021), construct field‐scale rock‐physics transform and simulate AEM (Gottschalk & Knight, 2022), and to cluster AEM (Dumont et al., 2018). Friedel et al.…”

Section: Introductionmentioning

confidence: 99%

Three‐Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning

Kawo,

Korus,

Kishawi

et al. 2024

Water Resources Research

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Characterizing the 3D distribution of hydraulic properties in glacial sediments is challenging due to fine‐scale heterogeneity and complexity. Borehole lithological data provide high vertical resolution but low horizontal resolution. Geophysical methods can fill gaps between boreholes, providing improved horizontal resolution but low vertical resolution. Machine learning can combine borehole and geophysical data to overcome these challenges. However, few studies have compared multiple machine learning methods for predicting hydrofacies in glacial aquifer systems. This study uses colocated airborne electromagnetic resistivity and borehole lithology data to train multiple machine learning models and predict the 3D distribution of hydrofacies in glacial deposits of eastern Nebraska, USA. Random Forest, Gradient Boosting Classifier, Extreme Gradient Boosting, Multilayer Perceptron, and Stacking Classifier were used to model 3D probabilistic distributions of hydrofacies (sand and clay) at a grid size of 200 m × 200 m × 3 m. Comparison of the predicted 3D hydrofacies models shows that the probability distributions and the contrasts between hydrofacies vary. The classification metrics show that the Stacking Classifier model performed better than other machine learning models in predicting hydrofacies. Multi‐Layer Perceptron and Stacking Classifier models show sharp vertical transitions between the low and high sand probability while other machine learning models show gradual transitions. K‐means clustering was used to translate the Stacking Classifier model into a 4‐class hydraulic conductivity model. This study shows that machine learning methods advance our understanding of glacial hydrogeology by improving the vertical and horizontal resolution of hydrofacies distribution and resolving aquifer‐aquifer and stream‐aquifer connections.

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Industrial Expert Systems Review: A Comprehensive Analysis of Typical Applications

Yang,

Zhu

2024

IEEE Access

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Automated Transient Electromagnetic Data Processing for Ground-Based and Airborne Systems by a Deep Learning Expert System

Cited by 8 publications

References 33 publications

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

Three‐Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning

Industrial Expert Systems Review: A Comprehensive Analysis of Typical Applications

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