3 citations · 6 across the 7 of their papers we have counts for
7 papers
Efficient Geothermal Well-Control Optimization via Diffusion-Surrogate Reinforcement Learning
Ruimin Dai, Guodong Chen, Randy Harsuko +2
Real-time decision-making for enhanced geothermal systems (EGS) is challenging because long-term production periods involve high-dimensional control spaces and a large number of ti…
Subsurface Property Mapping using Google AlphaEarth Foundations
Nori Nakata, Jingxiao Liu, Guodong Chen +2
Subsurface properties are essential for hazard assessment, energy and environmental management, and infrastructure resilience, but direct observations are sparse and uneven, motiva…
Modeling Non-Ergodic Path Effects Using Conditional Generative Model for Fourier Amplitude Spectra
Maxime Lacour, Pu Ren, Rie Nakata +2
Recent developments in non-ergodic ground-motion models (GMMs) explicitly model systematic spatial variations in source, site, and path effects, reducing standard deviation to 30-4…
Advancing Subsurface Discovery and Geothermal Monitoring with an Agentic Artificial Intelligence Framework
Randy Harsuko, Zhengfa Bi, Guodong Chen +1
Geothermal field development typically involves complex processes that require multi-disciplinary expertise in each process. Thus, decision-making often demands the integration of…
Advancing data-driven broadband seismic wavefield simulation with multi-conditional diffusion model
Zhengfa Bi, Nori Nakata, Rie Nakata +3
Sparse distributions of seismic sensors and sources pose challenges for subsurface imaging, source characterization, and ground motion modeling. While large-N arrays have shown the…
Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
Pu Ren, Rie Nakata, Maxime Lacour +9
Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer fro…
WaveCastNet: Rapid Wavefield Forecasting for Earthquake Early Warning via Deep Sequence to Sequence Learning
Dongwei Lyu, Rie Nakata, Pu Ren +4
We propose a new deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequen…