arXiv subjects

Guoyi Chen

Publications and source records attributed to Guoyi Chen.

5 recordsLinked to original sources

arXiv2026

pDSurfTomo: A High-Performance Parallel Computing Package for Direct Surface Wave Tomography

Surface wave tomography is essential for investigating the shear-wave velocity structure of the crust and upper mantle. The direct surface wave tomography method, DSurfTomo, has become one of the most widely adopted packages due to its ability to account for ray path bending in complex media to increase subsurface characterization accuracy. However, its inherent serial architecture lacks effective support for multicore CPUs and GPUs. Furthermore, its built-in solver is computationally expensive when solving large-scale linear systems. Consequently, the software struggles to meet current demands for large-scale, high-resolution surface wave tomography. To address these limitations, we propose pDSurfTomo, a highly optimized package utilizing hybrid CPU-GPU acceleration. First, it overcomes the scalability bottleneck in sensitivity kernel computation through a refined parallel design; also, it uses vectorization techniques to accelerate the modeling of surface wave dispersion, achieving efficient computation of the sensitivity kernel. Second, it implements parallelization of the serial fast marching method using OpenMP, significantly reducing computation time for surface wave traveltimes. Finally, it incorporates GPU acceleration to efficiently solve large-scale sparse linear least-squares problems. To streamline the workflow, we provide a cross-platform GUI with remote server connectivity, allowing users to execute and visualize inversion tasks locally while seamlessly utilizing remote computing clusters. Application to an observed dispersion dataset from 229 stations in North China demonstrates that pDSurfTomo reduces computation time by more than an order of magnitude while maintaining a negligible discrepancy compared to the original DSurfTomo. It is expected that pDSurfTomo will provide a highly efficient and accessible solution for large-scale, high-resolution surface wave tomography.

physics.geo-ph↗

arXiv2026

Accurate Interpolation of Ambient Noise Empirical Green's Functions by Denoising Diffusion Probabilistic Model and Implicit Neural Representation

Empirical Green's functions (EGFs) extracted from seismic ambient noise have been widely used to image Earth's interior structures, and the resolution of EGF-based tomography depends on the spatial density of seismic stations. However, due to cost and logistical constraints, it is often difficult to deploy dense seismic networks suitable for high-resolution tomography. While reliable interpolation of EGFs at unsampled locations could enhance tomographic resolution, the task remains inherently challenging and underexplored due to the dispersive nature of EGFs. In this study, we introduce DIER (diffusion-assisted implicit EGF representation), a self-supervised learning framework that integrates implicit neural representation with denoising diffusion probabilistic models to achieve high-fidelity EGF interpolation. In DIER, the diffusion process is conditioned on station coordinates to guide the transformation from random noise into EGF waveforms, which allows flexible reconstruction of five-dimensional EGF fields without labeled data or synthetic waveforms. We demonstrate the effectiveness of DIER through continent-scale EGF interpolation across the United States. The results show that DIER significantly outperforms the conventional radial basis function-based interpolation approach by generating EGFs with markedly improved phase alignment and dispersion characteristics. Surface wave tomography using the phase velocities derived from the interpolated EGFs also closely matches a reference model constructed from data acquired by a much denser seismic network. Our findings suggest that DIER provides a promising and cost-effective approach toward high-resolution ambient noise tomography in regions with sparse station coverage.

physics.geo-ph↗

arXiv2025

Self-Reinforced Deep Priors for Reparameterized Full Waveform Inversion

Full waveform inversion (FWI) has become a widely adopted technique for high-resolution subsurface imaging. However, its inherent strong nonlinearity often results in convergence toward local minima. Recently, deep image prior-based reparameterized FWI (DIP-FWI) has been proposed to alleviate the dependence on massive training data. By exploiting the spectral bias and implicit regularization in the neural network architecture, DIP-FWI can effectively avoid local minima and reconstruct more geologically plausible velocity models. Nevertheless, existing DIP-FWI typically use a fixed random input throughout the inversion process, which fails to utilize the mapping and correlation between the input and output of the network. Moreover, under complex geological conditions, the lack of informative prior in the input can exacerbate the ill-posedness of the inverse problem, leading to artifacts and unstable reconstructions. To address these limitations, we propose a self-reinforced DIP-FWI (SRDIP-FWI) framework, in which a steering algorithm alternately updates both the network parameters and the input at each iteration using feedback from the current network output. This design allows adaptive structural enhancement and improved regularization, thereby effectively mitigating the ill-posedness in FWI. Additionally, we analyze the spectral bias of the network in SRDIP-FWI and quantify its role in multiscale velocity model building. Synthetic tests and field land data application demonstrate that SRDIP-FWI achieves superior resolution, improved accuracy and greater depth penetration compared to multiscale FWI. More importantly, SRDIP-FWI eliminates the need for manual frequency-band selection and time-window picking, substantially simplifying the inversion workflow. Overall, the proposed method provides a novel, adaptive and robust framework for accurate subsurface velocity model reconstruction.

physics.geo-ph↗

arXiv2023

Implementation of Fuzzy Control Algorithm in Two-Wheeled Differential Drive Platform

Designing and developing Artificial Intelligence controllers on separately dedicated chips have many advantages. This report reviews the development of a real-time fuzzy logic controller for optimizing locomotion control of a two-wheeled differential drive platform using an Arduino Uno board. Based on the Raspberry Pi board, fuzzy sets are used to optimize color recognition, enabling the color sensor to correctly recognize color at long distances, across a wide range of light intensity, and with high fault tolerance.

eess.SY↗

arXiv2023

Deep generative model conditioned by phase picks for synthesizing labeled seismic waveforms with limited data

Shortage of labeled seismic field data poses a significant challenge for deep-learning related applications in seismology. One approach to mitigate this issue is to use synthetic waveforms as a complement to field data. However, traditional physics-driven methods for synthesizing data are computationally expensive and often fail to capture complex features in real seismic waveforms. In this study, we develop a deep-learning-based generative model, PhaseGen, for synthesizing realistic seismic waveforms dictated by provided P- and S-wave arrival labels. Contrary to previous generative models which require a large amount of data for training, the proposed model can be trained with only 100 seismic events recorded by a single seismic station. The fidelity, diversity and alignment for waveforms synthesized by PhaseGen with diverse P- and S-wave arrival labels are quantitatively evaluated. Also, PhaseGen is used to augment a labelled seismic dataset used for training a deep neural network for the phase picking task, and it is found that the picking capability trained with the augmented dataset is unambiguously improved. It is expected that PhaseGen can offer a valuable alternative for synthesizing realistic waveforms and provide a promising solution for the lack of labeled seismic data.

physics.geo-ph↗