Yanjun Li

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

Journal of Geophysical Research Solid Earth · 2023 · 43 citations

Abstract After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Chal…

Quantifying tunneling risks ahead of TBM using Bayesian inference on continuous seismic data

Tunnelling and Underground Space Technology · 2024-03-19 · 21 citations

Inverting continuous in-tunnel passive seismic data for velocity structure mapping ahead of the TBM cutterhead

Tunnelling and Underground Space Technology · 2024-06-04 · 12 citations

Enhancing the automatic facies classification of Brazilian presalt acoustic image logs with SwinV2-Unet: Leveraging transfer learning and confident learning

Geophysics · 2024-01-07 · 8 citations

ABSTRACT Facies classification of image logs plays a vital role in reservoir characterization, especially in the heterogeneous and anisotropic carbonate formations of the Brazilian presalt region. Although manual classification remains the industry standard for handling the complexity and diversity of image logs, it has the notable disadvantages of being time consuming, labor intensive, subjective, and nonrepeatable. Recent advancements in machine learning offer promising solutions for automatio…

Deep-learning optimization using the gradient of a custom objective function: A full-waveform inversion example study on the convolutional objective function

Geophysics · 2024-01-09 · 5 citations

ABSTRACT The integration of conventional high-performance full-waveform inversion (FWI) algorithms with deep-learning frameworks is an innovative and promising research direction with the potential to enhance and broaden the application prospects of this field significantly. Automatic differentiation with backpropagation techniques can derive the gradients of model parameters in an equivalent manner to that of adjoint methods; however, it requires a substantial amount of computer memory, particu…