Time-Varying Elevation Change at the Centralia Coal Mine in Centralia, Washington (USA), Constrained with InSAR, ASTER, and Optical Imagery
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The Subsurface Tunnel Imaging LeveraGed by Analysis of Rayleigh wave ellipticity (STILGAR) project demonstrated an integrated geophysical approach for detecting, locating, and characterizing underground structural changes using dense seismic arrays and advanced inversion techniques. Field campaigns were conducted at two operational mines—the Redmond salt mine (Utah) and Graymont Pleasant Gap limestone mine (Pennsylvania)—providing real-world testbeds for monitoring anthropogenic subsurface activity. At the Redmond salt mine, seismic interferometry combined with back-projection inversion successfully identified continuous, low-amplitude signals from mining operations. The approach differentiated stationary from migrating anthropogenic sources, captured daily operational cycles, and validated the potential of passive seismic monitoring for remote detection of underground activity. At the Graymont Pleasant Gap mine, two dense seismic deployments in the spring and fall of 2023 generated over 4 TB of high-resolution data. Key outcomes included the relocation of 199 underground and 8 surface explosions with accuracies within tens of meters and the development of a 3D P-wave velocity model using the triple-difference tomography algorithm (tomoTD) that resolved major structural features such as the mine entrance, low-velocity tunnels, and roof-collapse areas. Ambient noise cross-correlation and back-projection analyses revealed persistent sources linked to ongoing mining activity, whereas horizontal-to-vertical spectral ratio (HVSR) and ellipticity studies confirmed stable site responses across seasons and identified soil thickness trends consistent with regional erosional and depositional processes. Checkerboard and sensitivity tests further validated the robustness of the tomographic results. Overall, the findings emphasize that although significant progress has been made in subsurface imaging, further work is needed to enhance the detection and localization of underground structures. Accurate imaging requires higher frequencies, yet anthropogenic sources tend to dominate the seismic record at those frequencies, and high-frequency surface waves are affected by higher modes that complicate interpretation. The improved detection and localization of human-induced signals enabled detailed temporal and spatial mapping of daily mine operations, demonstrating the feasibility of continuous anthropogenic source monitoring. Sensitivity to signals from nontraditional sources, such as fan operations, highlights the broader applicability of this approach to other industrial environments in which continuous and impulsive signals are present. The field campaigns produced a substantial volume of high-quality seismic data, supporting the development and testing of new methods for seismic source characterization and subsurface imaging. Future deployments should include sensors capable of recording lower frequencies to probe deeper structures, increase bandwidth to enhance resolution and sensitivity to both shallow and deep targets, and collect additional large-scale datasets to refine imaging and source characterization techniques. Moreover, conducting 3D modeling studies of seismic wavefields at higher frequencies will provide a better understanding of wave scattering and cavity–wavefield interactions in complex underground environments. In conclusion, the STILGAR project demonstrated that integrated seismic monitoring can effectively characterize underground operations, capturing both natural and anthropogenic signals. The approaches developed provide a foundation for improved detection, localization, and imaging of subsurface structures and are directly transferable to broader industrial monitoring applications.
As of May 1, 2026 Landmine contamination affects 58 countries and many hundreds of thousands of km² of land. For example, the National Mine Action Program Demining Ukraine reporting that up to 144,000 km² of territory are potentially contaminated and require survey and clearance (National Mine Action Program “Demining Ukraine,” n.d.) alone. This contamination includes mines and other explosive remnants of war and continues to constrain civilian access, agricultural use, infrastructure recovery, and broader socioeconomic activity (International Campaign to Ban Landmines–Cluster Munition Coalition [ICBL-CMC], 2024; Mine Action Review, 2024). Current response activities rely on established mine-action approaches including non-technical survey, technical survey, clearance, and explosive ordnance disposal, consistent with international mine-action terminology and operational practice (United Nations Mine Action Service [UNMAS], 2024; Geneva International Centre for Humanitarian Demining [GICHD], 2023). In this context, UAV-based sensing, including UAV-mounted thermal imaging, may provide a useful supplementary capability by supporting faster, safer detection and mapping of suspect hazards prior to ground intervention (Smiljanic, 2022).
The last decade's influx of raw data and derived geophysical parameters from several Earth observing satellites to NASA data centers has created a data-rich environment for Earth science research and applications. While advances in hardware and information management have made it possible to archive petabytes of data and distribute terabytes of data daily to a broad community of users, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications in order to realize the full potential of these valuable datasets. In examining what is needed to enable this progress in the data provider environment that exists today and is expected to evolve in the next several years, we arrived at the concept of an Intelligent Archive in context of a Knowledge Building System (IA/KBS). Our prior work and associated papers investigated usage scenarios, required capabilities, system architecture, data volume issues, and supporting technologies. We identified six key capabilities of an IA/KBS: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting. Among these capabilities, large-scale data mining is perceived by many in the community to be an area of technical risk. One of the main reasons for this is that standard data mining research and algorithms operate on datasets that are several orders of magnitude smaller than the actual sizes of datasets maintained by realistic earth science data archives. Therefore, we defined a test-bed activity to implement a large-scale data mining algorithm in a pseudo-operational scale environment and to examine any issues involved. The application chosen for applying the data mining algorithm is wildfire prediction over the continental U.S. This paper reports a number of observations based on our experience with this test-bed. While proof-of-concept for data mining scalability and utility has been a major goal for the research reported here, it was not the only one. The other five capabilities of an WKBS named above have been considered as well, and an assessment of the implications of our experience for these other areas will also be presented. The lessons learned through the testbed effort and presented in this paper will benefit technologists, scientists, and system operators as they consider introducing IA/KBS capabilities into production systems.
Mafic and ultramafic rocks present an intriguing pathway for CO 2 capture through strategic enhancements to the natural silicate weathering cycle. Simultaneously, these rock types are often hosts to appreciable amounts of metals critical to U.S. energy independence, particularly in the context of alkaline mine tailings from historical metal mining. The research and scientific prerogative, then, is to identify promising mafic and ultramafic feedstocks and conduct exploratory studies to effectively recover these critical minerals while preserving the potential for mineral carbonation. The Keweenaw Peninsula of Michigan hosts the largest native Cu reservoir within basalt in the world and experienced a rich history of Cu production spanning from pre-historic times to the mid-1900s. Today, much of the Cu mining legacy remains as mine waste tailings. In this study, we examined the Cu extractability from Keweenaw Basalt tailings using a sodium glycinate solution, in comparison with acid and sodium hydroxide leaching. Experimental results showed an 85 % Cu extraction rate using sodium glycinate as the extraction solution with negligible release of other cations from the basalt. The kinetic and extraction mechanisms of Cu selective recovery using glycinate solution were discussed using time-resolved experimental data and kinetic geochemical modeling. Theoretical estimation of carbon mineralization potential of all the existing basalt waste tailings (∼500 million tons) can reach 85.5 MMT CO 2 . A total of 0.786 MMT Cu can be recovered with sodium glycinate, with a value of 7.7 billion USD. In conclusion, this novel application of alkaline glycinate for selective Cu recovery from basalt mine tailings demonstrates the viability of selective metal recovery using a non-hazardous chemical while preserving CO 2 capture potential and presents a potential pathway toward reducing energy-related emissions and providing an unconventional domestic source of critical minerals.
In December 2023, the United States pledged at a United Nations conference to triple nuclear energy by 2050. In May of 2025 a new administration announced plans to quadruple nuclear energy by 2025, setting a new target that requires understanding the key infrastructure and resources needed to achieve large-scale deployment. This study attempts to quantify “what it takes” to deploy an additional 200 GWe of nuclear capacity. It projects demand levels for workforce, fuel, uranium mining and enrichment, waste management, supply chain, land, licensing, and water. Demand growth is also contextualized. For example, the peak number of construction workers required is approximately 215,000, a 37 % increase in the current US utility construction workforce. Up to 80,000 MT/year of mined uranium will be needed, necessitating a 60-fold increase in US mining production or a doubling of global mining if sourced externally. Advanced reactor fuels, uranium mining, and nuclear-grade equipment production would need substantial expansion. Operational workforce, enrichment needs, and licensing submissions also face significant increases. Although water consumption may see relatively lower increases, securing water rights poses unique challenges. This study consolidates various demand metrics in a broad context, highlighting the necessity of early preparations, such as workforce training, to support a tripling of nuclear energy. While it outlines the demand-side requirements, it does not assess the difficulties of ramping up or other supply-side issues. The findings also shed light on the impact of reactor size and reactor technology on various demand metrics.
The University of North Dakota Energy & Environmental Research Center (EERC) is leading the Williston Basin Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) Initiative to drive the expansion and transformation of coal and coal-based resource usage within the Williston Basin to produce rare-earth elements (REEs), CMs, and nonfuel carbon-based products (CBPs). This project is the first phase in a long-term program and set the stage for future work by assessing resource, market, technology, and infrastructure knowledge; identifying knowledge gaps; developing a series of plans to be carried out in future work; and initiating stakeholder engagement. Composed of several tasks, the project sought to identify, characterize, and assess several necessary aspects vital to make this future work a reality. The project’s fundamental task was to characterize the Williston Basin CORE-CM resources. Over 2500 samples from multiple sources were utilized to begin the assessment. Several locations were identified in western North Dakota where sample analysis identified the total REE (TREE) concentration as being over 500 parts per million (ppm), which is at a concentration level that would be suitable to consider for mining and extraction. Current operating coal mines have sufficient concentrations of TREEs for consideration. However, the current data across the basin are still not adequate to fully characterize REE and CM content nor give reliable estimates of the total resource potential. Waste stream reuse was also considered, and several streams were identified which ranged from potential energy sources to chemicals to material wastes. This includes streams that result from oil and gas production. These streams are not fully characterized, and further data are needed before they can be accurately assessed. Infrastructure within the Williston Basin is suitable for expansion of a new industry to mine, extract, and concentrate REEs and CMs. The development of this industry will not only preserve many existing jobs in the coal-mining industry but produce many new jobs. The supply chain for REEs and CMs is currently controlled outside of the United States in nations such as China, but the potential to develop the supply chain within the basin is considered possible. Processing of the mined materials for REEs and CMs needs further research. The technology and knowhow exist outside of the United States, and within the country much of the knowledge has been lost and must be regained. To develop the supply chain and regain lost processing technology, the creation of technology innovation centers (TICs) is crucial. The Williston Basin contains several similar centers and entrepreneurial assistance for other industries that can be applied in the development of REE and CM innovation centers. Education to develop the new skill sets required is also needed. Outreach is important for the development of the REE and CM industry within the basin. Understanding throughout federal and state governments, state agencies, industry, and resource end users is vital for the industry to form and grow. Through this project these groups have been contacted through bulletins, presentations, webinars, and annual symposiums. The report is a summary of the work conducted and throughout refers to a series of appendixes which contain more thorough and specific information about each section.
Abandoned coal mine lands (AMLs) represent one of the most persistent environmental challenges in the United States. Prior to the enactment of the Surface Mining Control and Reclamation Act (SMCRA) in 1977, coal mining operations were not legally required to reclaim disturbed lands, leaving behind approximately 500,000 AML sites nationwide. These sites pose severe environmental and health risks, including acid mine drainage, soil and water contamination, and spontaneous combustion of waste coal piles. Millions of Americans live within one mile of these AMLs, underscoring the urgency of remediation. Traditional reclamation practices, such as planting cool-season grasses, often fail to fully restore ecological function or leverage the economic potential of these lands. This project addressed these challenges by developing integrated strategies for resource recovery, land reclamation, and sustainable energy production. This project evaluated an integrated strategy to convert this liability into an opportunity by recovering waste coal and co-firing it with switchgrass (Panicum virgatum L.) cultivated on reclaimed or marginal AML areas in existing coal-fired power plants. Switchgrass not only provides a renewable feedstock but also aids in land reclamation and carbon sequestration. 1) Remote Sensing and Machine Learning for Waste Coal Identification Using Sentinel-2 satellite imagery and supervised classification, we applied four machine learning models to detect historical waste coal piles. Random Forest achieved the highest accuracy (precision: 86%, recall: 77%). Time-series analysis revealed gradual vegetation recovery since 1986, indicating natural reclamation processes in historical sites, while active mining areas showed ongoing disturbance. This workflow enables scalable monitoring and prioritization of reclamation efforts. 2) UAS-Based Stockpile Volume Estimation To quantify recoverable waste coal, we evaluated Unmanned Aerial Systems (UAS) equipped with Light Detection and Ranging (LiDAR) and multispectral sensors. Structure-from-Motion (SfM) photogrammetry combined with interpolated Digital Terrain Models (DTMs) achieved strong agreement with LiDAR reference volumes (Root Mean Square Error (RMSE) ≈147 m 3 , Mean Absolute Percentage Error (MAPE) ≈2%). Sensitivity analysis confirmed that spatial resolution significantly influences accuracy, emphasizing the need for high-resolution data for precise volume estimation. This approach offers a scalable, cost-effective, and accurate alternative to conventional ground-based surveys. 3) Switchgrass Cultivation for Bioenergy and Water Quality Improvement We assessed the hydrological and water quality impacts of converting AMLs to switchgrass production areas using the Soil and Water Assessment Tool (SWAT). Results showed that converting 10% of the watershed area into the switchgrass production zone reduced streamflow by 3.1%, total suspended solids by 18.1%, total nitrogen by 7.6%, and total phosphorus by 6.2%, while achieving biomass yields of 8.6–9.2 metric tons per hectare. These findings highlight switchgrass as a dual-benefit strategy for land reclamation and bioenergy feedstock production. 4) Integrated Co-Firing and CCS for Carbon-Negative Power Generation We modeled co-firing scenarios using the Power Plant Flexible Model (PPFM) to evaluate plant efficiency, greenhouse gas (GHG) emissions, and levelized cost of electricity (LCOE). Without carbon capture and storage (CCS), increasing switchgrass co-firing ratios reduced LCOE from $\$$150/MWh at 0% biomass to $\$$110/MWh at full substitution. Under CCS, costs remained higher (~$\$$250/MWh at 0% biomass) but decreased to $\$$200/MWh at 100% biomass, while enabling net-zero or carbon-negative electricity due to switchgrass sequestration benefits. Although CCS introduced efficiency penalties, pairing it with biomass co-firing offset these impacts and maximized climate benefits. Overall, optimizing co-firing ratios between 60-100%, supported by reliable logistics and storage strategies, emerged as a practical pathway to balance affordability, sustainability, and net-zero or negative GHG emissions while promoting productive reuse of AMLs.
A lunar strip mining system is presented which is capable of excavating and transporting 3 million metric tons of ore per year to a central processing plant on the moon's surface. The mining system would grow from a single front-end loader in the first year, to a fleet of ten haulers in the 30th year. Lunar personnel requirements would consist of a single individual, whose primary function would be to perform maintenance. All of the mining equipment would either operate automatically or by remote control from earth. The projected cost for the lunar mining system is approximately $12 to $37 per ton of ore over the life of the mine, an important part of the overall economics of exploiting lunar resources.