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Lin, Youzuo

Publications and source records attributed to Lin, Youzuo.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Ultrasound waveform tomography with spatial and edge regularization

Synthetic-aperture ultrasound tomography systems and methods using scanning arrays and algorithms configured to simultaneously acquire ultrasound transmission and reflection data, and process the data for improved ultrasound tomography imaging, wherein the tomography imaging comprises total-variation regularization, or a modified total variation regularization, particularly with edge-guided or spatially variant regularization.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Hydrologic Model Analyses for Decision Support at the Los Alamos National Laboratory Site - 18363

Modeling hydrologic flow and transport in the subsurface presents a number of challenges including large datasets that contain limited information about the properties of the subsurface and models with a large number of parameters characterizing the properties of the subsurface and the ambient hydrologic conditions (e.g., groundwater recharge). This, in turn, leads to substantial uncertainty in predictions about quantities of interest (e.g., the concentration of a contaminant at points of compliance). We present a number of methods and tools that have recently been developed to address these problems. The model calibration tools are capable of assimilating large datasets (for example, on the order of 1,000,000 model calibration targets) into numerical models, calibrating models with a large number of model parameters (on the order of 1,000,000), or both. These tools leverage recently developed methods in numerical linear algebra such as matrix-free methods and randomized matrix sketching. The uncertainty quantification tools are highly-parallel and enable us to robustly quantify the uncertainties in model parameters (and therefore uncertainty in quantities of interest). Having these capabilities in model analysis tools enables more informed decision making related to remediation of groundwater contaminants. The model analyses that we present are related to the Los Alamos National Laboratory (LANL) chromium site. The analyses are performed by MADS (Model Analysis and Decision Support; http://mads.lanl.gov). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Geologic CO 2 sequestration monitoring design: A machine learning and uncertainty quantification based approach

Monitoring is a crucial aspect of geologic carbon dioxide (CO 2 ) sequestration risk management. Effective monitoring is critical to ensure CO 2 is safely and permanently stored throughout the life-cycle of a geologic CO 2 sequestration project. Effective monitoring involves deciding: (i) where is the optimal location to place the monitoring well(s), and (ii) what type of data (pressure, temperature, CO 2 saturation, etc.) should be measured taking into consideration the uncertainties at geologic sequestration sites. We have developed a filtering-based data assimilation procedure to design effective monitoring approaches. To reduce the computational cost of the filtering-based data assimilation process, a machine-learning algorithm: Multivariate Adaptive Regression Splines is used to derive computationally effcient reduced order models from results of full-physics numerical simulations of CO 2 injection in saline aquifer and subsequent multi-phase fluid flow. We use example scenarios of CO 2 leakage through legacy wellbore and demonstrate a monitoring strategy can be selected with the aim of reducing uncertainty in metrics related to CO 2 leakage. We demonstrate the proposed framework with two synthetic examples: a simple validation case and a more complicated case including multiple monitoring wells. The examples demonstrate that the proposed approach can be effective in developing monitoring approaches that take into consideration uncertainties.

58 GEOSCIENCES↗
DOE OSTI2018

Randomization in Characterizing the Subsurface

Current methods for characterizing Earth’s subsurface, such as standard inverse techniques, are not sufficiently accurate to meet the needs of modern applications in the fields of energy exploration, environmental management, and global security. While increasing the quantity of field measurements and robustness of the applied data-/model-analysis methods can improve accuracy, such approaches can be computationally impractical for large data sets and complex site conditions. Thus, there is a need to develop economically-feasible and robust computational methods while maintaining accuracy. For example, in-field drilling for geothermal operations may yield high failure rates, resulting in unacceptably high costs; errors and/or large uncertainties in the estimated subsurface characteristics are the main impediment to the successful siting of an in-field well. This problem is not uniquely geothermal. Accurate characterization of uncertain subsurface properties is also essential for monitoring storage of carbon dioxide, estimating pathways of subsurface contaminant transport, and supervising ground-based nuclear-explosion tests.

58 GEOSCIENCES↗
DOE OSTI2017

Large-scale inverse model analyses employing fast randomized data reduction: INVERSE MODELING WITH DATA REDUCTION

When the number of observations is large, it is computationally challenging to apply classical inverse modeling techniques. We have developed a new computationally efficient technique for solving inverse problems with a large number of observations (e.g., on the order of 10 7 or greater). Our method, which we call the randomized geostatistical approach (RGA), is built upon the principal component geostatistical approach (PCGA). We employ a data reduction technique combined with the PCGA to improve the computational efficiency and reduce the memory usage. Specifically, we employ a randomized numerical linear algebra technique based on a so-called “sketching” matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis. In this way, the computational and memory costs for RGA scale with the information content rather than the size of the calibration data. Our algorithm is coded in Julia and implemented in the MADS open-source high-performance computational framework (http://mads.lanl.gov). We apply our new inverse modeling method to invert for a synthetic transmissivity field. Compared to a standard geostatistical approach (GA), our method is more efficient when the number of observations is large. Moreover, our method is capable of solving larger inverse problems than the standard GA and PCGA approaches. Thus, our new model inversion method is a powerful tool for solving large-scale inverse problems. The method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Final Project Report: Imaging Fault Zones Using a Novel Elastic Reverse-Time Migration Imaging Technique

Imaging fault zones and fractures is crucial for geothermal operators, providing important information for reservoir evaluation and management strategies. However, there are no existing techniques available for directly and clearly imaging fault zones, particularly for steeply dipping faults and fracture zones. In this project, we developed novel acoustic- and elastic-waveform inversion methods for high-resolution velocity model building. In addition, we developed acoustic and elastic reverse-time migration methods for high-resolution subsurface imaging of complex subsurface structures and steeply-dipping fault/fracture zones. We first evaluated and verified the improved capabilities of our newly developed seismic inversion and migration imaging methods using synthetic seismic data. Our numerical tests verified that our new methods directly image subsurface fracture/fault zones using surface seismic reflection data. We then applied our novel seismic inversion and migration imaging methods to a field 3D surface seismic dataset acquired at the Soda Lake geothermal field using Vibroseis sources. Our migration images of the Soda Lake geothermal field obtained using our seismic inversion and migration imaging algorithms revealed several possible fault/fracture zones. AltaRock Energy, Inc. is working with Cyrq Energy, Inc. to refine the geologic interpretation at the Soda Lake geothermal field. Trenton Cladouhos, Senior Vice President R&D of AltaRock, was very interested in our imaging results of 3D surface seismic data from the Soda Lake geothermal field. He planed to perform detailed interpretation of our images in collaboration with James Faulds and Holly McLachlan of University of Nevada at Reno. Using our high-resolution seismic inversion and migration imaging results can help determine the optimal locations to drill wells for geothermal energy production and reduce the risk of geothermal exploration.

47 OTHER INSTRUMENTATION↗