Appraisal of Resistivity Inversion Models With Convolutional Variational Encoder–Decoder Network

Wilson, Bibin; Singh, Anand; Sethi, Amit

doi:10.1109/tgrs.2022.3217580

Cited by 3 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the practical operation, deterministic inversion faces the challenge of selecting a suitable initial model for the inversion task [8,23]. Using an initial model that better represents the true underground electrical structures reduces the probability that inversion results fall into the local minima [24].…”

Section: Introductionmentioning

confidence: 99%

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Tao,

Han,

Yang

et al. 2024

Minerals

View full text Add to dashboard Cite

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.

show abstract

Section: Introductionmentioning

confidence: 99%

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Tao,

Han,

Yang

et al. 2024

Minerals

View full text Add to dashboard Cite

show abstract

“…Similarly, Vu and Jardani [22] proposed a deep-learning algorithm for the three-dimensional (3D) reconstruction of ERT, using SegNet architecture for the inversion network and training it with subsurface resistivity models generated by a geostatistical anisotropic Gaussian generator and corresponding apparent resistivity. Wilson et al [23] also developed a ERT inversion using deep learning, proposing a variational encoder-decoder network for the inversion network and constructing realistic resistivity synthetic models with complex layers. Furthermore, various studies have applied similar approaches to electromagnetic (EM) inversion, with Oh et al [24] developing a CNN model to delineate salt dome structure from marine controlled-source EM (CSEM) data and other studies utilizing deep-learning technology for the inversion of airborne EM data [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys

Kong

Yoon

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Clays in fault zones have low electrical resistivity, making electrical resistivity tomography (ERT) effective for fault investigations. However, traditional ERT inversion methods struggle to find a unique solution and produce unstable results owing to the ill-posed nature of the problem. To address this, a workflow integrating deep-learning (DL) technology with traditional ERT inversion is proposed. First, a deep-learning model named DL-ERT inversion that maps apparent resistivity data to subsurface resistivity models is developed. To create target-oriented training data, we use approximately 150 field borehole data acquired from various survey areas in South Korea. The DL-ERT inversion algorithm is based on a U-Net structure and includes an additional network called the borehole mixer to incorporate borehole information when available. The DL-ERT inversion model is trained in three stages: base model training, borehole mixer training, and fine-tuning. Results showed that the fine-tuning model provided the highest prediction accuracy for all test datasets. Next, the prediction of the trained model is used as the initial model for the deterministic inversion method to predict the final subsurface model. The efficiency and accuracy of the proposed workflow are demonstrated in fault detection using a field data example compared with traditional deterministic inversion.

show abstract

Deep Learning in Geophysics: Current Status, Challenges, and Future Directions

Yu,

Oh,

Kong

et al. 2024

J. Korean Soc. Miner. Energy Resour. Eng.

View full text Add to dashboard Cite

With the rapid development of artificial intelligence technology, machine learning and deep learning techniques have been actively applied in the field of exploration geophysics. In this study, we extensively investigated the application of deep learning in geophysical techniques including seismic, gravity, magnetic, electromagnetics, ground penetrating radar, and joint inversion. Recent research has confirmed that deep learning techniques optimized for geophysical problems are being developed that transcend traditional theory-based approaches, suggesting that deep learning is becoming a new paradigm in the field of geophysics. Despite these technological advances, there are still challenges to be overcome, such as a lack of training data, model opacity, and heterogeneity between training data and field data, and this study suggests future research directions to address these aspects.

show abstract

Appraisal of Resistivity Inversion Models With Convolutional Variational Encoder–Decoder Network

Cited by 3 publications

References 37 publications

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys

Deep Learning in Geophysics: Current Status, Challenges, and Future Directions

Contact Info

Product

Resources

About