Undergraduate Research Intern
Supervisor: Prof. Tapan Mukerji
AI for Exploration Geophysics
I am an undergraduate student majoring in Geophysics at the University of Science and Technology of China (USTC), expecting to graduate in June 2027.
At USTC, I conduct research in the Computational Interpretation Group under the supervision of Prof. Xinming Wu. My work includes Geo-VAE, a unified latent model for 3D geophysical data.
From July to October 2026, I was an undergraduate research intern at the Stanford Center for Earth Resources Forecasting (SCERF), where I worked with Prof. Tapan Mukerji on CO₂ storage modeling, rock physics, seismic forward modeling, and AI agents.
I plan to pursue a Ph.D. in AI for exploration geophysics, with interests in seismic interpretation, geomodeling, rock physics, and forward modeling.
At Stanford SCERF, I compared five grid resolutions in ECLIPSE 300 CO₂STORE simulations using fixed-time plume geometry, fixed-location saturation histories, space-time plume metrics, and computational cost. I selected a 0.12 ft vertical grid for subsequent modeling.
I mapped geology and CO₂ saturation to elastic properties with a facies-based rock physics workflow, then generated baseline and time-lapse seismic responses for 20 years of injection and 80 years after shut-in.
I also designed an agent workflow using the Stanford Rock Physics and Borehole Geophysics (SRB) model library. Across 45 independent runs, I compared agents with no references, textbook guidance, and textbook guidance plus SRB tools. Without references, agents made unsupported model assumptions. Textbook guidance reproduced reference calculations. SRB MCP tools enabled direct model calls and matched the references when used correctly, but some runs misinterpreted tool outputs and produced incorrect predictions.
Geo‑VAE adapts the pretrained Wan‑VAE to map seismic, impedance, RGT, fault masks, and field seismic into a compact latent domain. Cached causal resampling treats crossline as a pseudo-temporal sequence, yielding isotropic 8×8×8 downsampling and 31:1 scalar compression for 256³ cubes.
Across five 3D geophysical modalities, it achieves strong reconstruction fidelity and a favorable balance of GPU memory use and inference time among tested state-of-the-art video VAEs on 128³–512³ cubes. Its unified compact latent space supports RGT-conditioned seismic generation and complete-volume 768³ denoising.
[Code] [IMAGE 2026 paper (PDF)]
Degradation-representation learning for robust suppression of random noise, acquisition footprints, and migration artifacts in 3D seismic data. [Code] [Download noise addition slides (PDF)]
A 2.5D full-image U-Net emulator mapping global physical ocean fields to coupled biogeochemical variables, with evaluation under anomalous ocean-climate conditions. Manuscript in preparation.
Aligned GNSS TEC-derived phase fields with processed NISAR interferometric phase and trained a residual U-Net to test cross-modal reconstruction. The model followed broad GNSS trends but did not recover NISAR-scale structure from the available sparse observations.
Delivered the final presentation of my Stanford SCERF summer research on CO₂ storage modeling and rock physics agents.
Gave an oral presentation on Geo‑VAE at IMAGE 2026 in Houston.
Joined Stanford SCERF as an Undergraduate Research Intern.
Joined the Computational Interpretation Group at USTC.
Undergraduate Research Intern
Supervisor: Prof. Tapan Mukerji
Undergraduate Researcher
Supervisor: Prof. Xinming Wu
B.S. Candidate in Geophysics, School of the Gifted Young