Engineering topics
Feng, Shihang
Publications and source records attributed to Feng, Shihang.
HOSSNet: An efficient physics-guided neural network for simulating micro-crack propagation
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Solving Seismic Wave Equations on Variable Velocity Models With Fourier Neural Operator
Here, in the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning (DL) enables solving partial differential equations (PDEs), including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous instances are to be solved. Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice. Instead, inspired by the idea of operator learning, this work leverages the Fourier neural operator (FNO) to effectively learn the frequency domain seismic wavefields under the context of variable velocity models. We also propose a new framework paralleled FNO (PFNO) for efficiently training the FNO-based solver given multiple source locations and frequencies. Numerical experiments demonstrate the high accuracy of both FNO and PFNO with complicated velocity models in the OpenFWI datasets. Furthermore, the cross-dataset generalization test verifies that PFNO adapts to out-of-distribution velocity models. Finally, PFNO admits higher computational efficiency on large-scale testing datasets than the traditional finite-difference method. The aforementioned advantages endow the FNO-based solver with the potential to build powerful models for research on seismic waves.
EFWI: Multiparameter benchmark datasets for elastic full waveform inversion of geophysical properties
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OpenFWI 2.0: Benchmark datasets for elastic full-waveform inversion
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Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
In this article, full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasingly studied with various neural network architectures to improve accuracy and computational efficiency. Nevertheless, the applicability of pre-trained neural networks is severely restricted by potential discrepancies between the source function used in the field survey and the one utilized during training. Here, we develop a Fourier-enhanced deep operator network (Fourier-DeepONet) for FWI with the generalization of seismic sources, including the frequencies and locations of sources. Specifically, we employ the Fourier neural operator as the decoder of DeepONet, and we utilize source parameters as one input of Fourier-DeepONet, facilitating the resolution of FWI with variable sources. To test Fourier-DeepONet, we develop three new and realistic FWI benchmark datasets (FWI-F, FWI-L, and FWI-FL) with varying source frequencies, locations, or both. Our experiments demonstrate that compared with existing data-driven FWI methods, Fourier-DeepONet obtains more accurate predictions of subsurface structures in a wide range of source parameters. Moreover, the proposed Fourier-DeepONet exhibits superior robustness when handling data with Gaussian noise or missing traces and sources with Gaussian noise, paving the way for more reliable and accurate subsurface imaging across diverse real conditions.
Supervised vs. unsupervised deep learning full waveform inversion: a case study at CCUS site, San Juan NM
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The Kimberlina synthetic multiphysics dataset for CO 2 monitoring investigations
Abstract We present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO 2 reservoir model based on a potential CO 2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO 2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO 2 , and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO 2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO 2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.