Autoencoded Elastic Wave-Equation Traveltime Inversion: Toward Reliable Near-Surface Tomogram

Zhang, Chang; Li, Jing; Han, Yu; Liu, Bin

doi:10.1109/tgrs.2023.3243140

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Machine learning (ML) applied to geophysical problems is a cutting-edge direction [33]. In recent years, ML has been utilized in many geophysics application areas, including pattern recognition in seismic attributes [34], noise removal [35,36], and inversion tasks [8,[37][38][39][40]. As a representative of the ML, the deep learning (DL) network comes with strong learning and generalization capabilities, enabling excellent performance in electrical resistivity inversion problems [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

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

Tao,

Han,

Yang

et al. 2024

Minerals

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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.

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