Conference Papers

Geothermal systems rely on the presence of long-lived and high-volume, permeable fracture systems. The creation, reactivation, and sustainability of these systems depend on complex coupling among thermal, hydraulic, mechanical, and chemical (THMC) processes occurring in geothermal reservoirs. In part due to a paucity of experimental data, the evolution of fractures at geothermal conditions in resp

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and a

With the success of machine learning, integrating learned models into real-world systems has become a critical chal- lenge. Naively applying predictions to combinatorial opti- mization problems can incur high costs, which has motivated researchers to consider learning augmented algorithms that can make use of faulty or incomplete predictions. Inspired by two matching problems in computational sust

Seismic structural health monitoring (S2HM) has advanced significantly in the last three decades. However, currently there is no consensus on the need for real-time processing of data acquired during an earthquake. Numerous applications exist whereby S2HM-equipped systems record valuable seismic response data. A delayed use of the seismic data prohibits timely discovery of hidden damages in a stru

No abstract available.

The Mariana Islands region hosts interesting geological features as well as abundant biodiversity. The subduction zone and back-arc spreading center have led to active volcanism that can have impacts on local islanders, aircraft flying in the region, and military activities. We deployed a small aperture hydrophone array from June 2017 to June 2018 in the Marianas back-arc to better characterize su

Airborne electromagnetic (AEM) dataare usedto estimate large-scale model structural geometry, i.e. the spatial distribution of different lithological units based on assumed or estimated resistivity-lithology relationships, and the uncertainty in those structures given imperfect measurements. Geophysically derived estimates of model structural uncertainty are then combined with hydrologic observati

This paper presents a physics-guided machine learning approach that incorporates partial differential equations (PDEs) in a graph neural network model to improve the prediction of water temperature in river networks. The standard graph neural network model often uses pre-defined edge weights based on distance or similarity measures. Such static graph structure can be limited in capturing multiple

Puget Sound salmon and estuary recovery strategies identify tens of thousands of acres of floodplain and estuary habitat restoration needed to re-establish ecosystem functions lost or degraded from western land use (Simenstad et al., 2011); the extent for nearshore habitat remains uncertain. Sediment is critical for shaping the structure and functions of these ecosystems and the success of many ha

The long-standing ore-genesis model for world-class deposits of the Butte mining district, Montana, is of deep pre-Main Stage porphyry Cu-Mo and overlying Main Stage Ag-Zn-Cu-zoned lode veinsformed from discrete hydrothermal systems related to rhyolite dikes. The lode-specific model describes metals zones that formed in the lode veins as hydrothermal processes diminished in intensity (changing tem

The 73-story Wilshire Grand in downtown Los Angeles is the recently constructed tallest building in California. It is designed in conformance with performance-based design procedures. The lateral load resisting system of the building is designed with concrete core shear walls, three outriggers with buckling restrained braces (BRBs) located along the height and two three-story truss-belt structural

Previous research has shown that online social networks can provide valuable insights regarding collective human responses to extreme natural events, such as earthquakes. Most previous studies focused on one large earthquake, while the 2019 Ridgecrest earthquakes involved two significant earthquakes occurring within a short period of time (a M6.4 foreshock on July 4 and a M7.1 mainshock on July 5