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Machine learning to discover mineral trapping signatures due to CO 2 injection
Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.
Spectral induced polarization signatures of smoldering remediation enhanced with colloidal activated carbon: An experimental study
Monitoring the remediation of soil and groundwater contaminated by organic compounds remains highly challenging. The spectral induced polarization (SIP) method exhibits significant promise for monitoring changes to the electrical properties of soils that are undergoing remediation. Thermal treatments, such as smoldering combustion, have become established remediation techniques for destroying contaminants. Smoldering combustion is now being supported by colloidal activated carbon (CAC), with CAC able to adsorb contaminants and supplement the fuel source for destroying contaminants. The objective of this study is to investigate the potential of SIP for tracking the smoldering remediation of imitated field soils supplemented with CAC. SIP column experiments were first conducted to assess the response of SIP to varying concentrations of CAC in field soils that contain, or do not contain, organic material (OM). Here these results demonstrate that increasing OM and CAC contents increase both the real and imaginary components of the complex conductivity, with the imaginary conductivity also showing frequency dependence. Next, a suite of smoldering and SIP column experiments was conducted to investigate if SIP can detect changes in imitated field soils of varying OM and CAC contents that have been remediated by smoldering combustion. The SIP results on the examined soils both before and after smoldering show that SIP can detect changes in the real conductivity, and particularly the imaginary conductivity, between different soil compositions and different stages of the remediation process. High resolution scanning electron microscopy (SEM) imaging was performed on all samples to validate the SIP and smoldering experiments, confirming significant reductions in carbon after smoldering. Overall, this study suggests that SIP has potential to detect changes in the electrical properties of field soils due to the addition of remedial fluids like CAC and contaminant destruction by smoldering remediation.
Geophysical signatures of soil AFFF contamination from spectral induced polarization and low field nuclear magnetic resonance methods
Few field methods are available for characterizing source zones contaminated with aqueous film forming foam (AFFF). Non-invasive geophysical characterization of AFFF source zone contamination in situ could assist with the delineation and characterization of these sites, allowing for more informed sampling regimes aimed at quantifying subsurface poly- and perfluoroalkyl substances (PFAS) contamination. In this study, we present initial results from the investigation of the sensitivity of two existing surface and borehole-deployable geophysical technologies, spectral induced polarization (SIP), and low field nuclear magnetic resonance (NMR), to soils impacted with AFFF. To investigate the sensitivity of these methods to AFFF-impacted soil, bench-scale column experiments were conducted on samples consisting of natural and synthetic soils and groundwater. While our findings do not show strong evidence of NMR sensitivity to soil PFAS contamination, we do find evidence that SIP has sufficient sensitivity to detect PFAS sorption to soils. This finding is based on evidence that AFFF constituents associated with the pore surface produce a measurable polarization response in both freshly contaminated synthetic soils and in soils historically impacted with AFFF.
Post-remediation geophysical assessment: Investigating long-term electrical geophysical signatures resulting from bioremediation at a chlorinated solvent contaminated site
There is a growing need to assess long-term impacts of active remediation strategies on treated aquifers. A variety of biogeochemical alterations can result from interactions of the amendment with the aquifer, conceivably leading to a geophysical footprint of the long-term alteration of an aquifer. This concept of post-remediation geophysical assessment was investigated in a shallow, chlorinated solvent-contaminated aquifer six to eight years after amendment delivery. Surface resistivity imaging and cross-borehole resistivity and induced polarization (IP) imaging were performed on a transect that spanned treated and untreated zones of the aquifer. Established relationships between IP parameters and surface electrical conductivity were used to predict vertical profiles of electrolytic conductivity and surface conductivity from the inverted cross-borehole images. Aqueous geochemistry data, along with natural gamma and magnetic susceptibility logs, were used to constrain the interpretation. The electrical conductivity structure determined from surface and borehole imaging was foremost controlled by the electrolytic conductivity of the interconnected pore space, being linearly related to fluid specific conductance. The electrolytic conductivity (and thus the conductivity images alone) did not discriminate between treated and untreated zones of the aquifer. In contrast, inverted phase angles and surface conductivities did discriminate between treated and untreated zones of the aquifer, with the treated zone being up to an order of magnitude more polarizable in places. Supporting aqueous chemistry and borehole logging datasets indicate that this geophysical footprint of the long-term impact of the remediation on the aquifer is most likely associated with the formation of polarizable, dispersed iron sulfide minerals. The study opens the door to the possibility of employing time-lapse electrical geophysical measurements to assist with long-term environmental stewardship of legacy sites undergoing active remediation.
Raman signatures from age-dating PuO 2 since last calcination
The self-irradiation of PuO 2 has been shown to affect the attributes of fifteen vibrational bands in the Raman spectrum. The vibrational bands correspond to the Raman allowed vibrational mode T 2g , overtones, defects, and bands from electronic origin. Defects in the crystal lattice from low-calcination temperature and alpha decay origins are responsible for the changes in the band attributes (band position, full-width half maximum (FWHM), intensity). Crystal lattice defects are also responsible for the growth of the defect bands. The temporal behavior of the vibrational bands can be used to estimate the age of the material since last calcination. Laser annealing was used to reset the material to full crystallinity prior to the aging study. 240 PuO 2 , with an alpha decay rate of 3.67 greater than the decay rate of 239 PuO 2 , was primarily used to assess the alpha decay-induced damage within 3 years. The aging study focused on the time-dependent properties of the T 2g band and the growth of defect bands. The FWHM of the T 2g band and the ratio of the areas of the defect bands/T 2g band were measured through a time period in excess of 3 years. The data from this study were found very close to the data describing the temporal evolution of the lattice constant measured with the X-ray diffraction (XRD) technique. The local physical properties of the material measured with Raman spectroscopy and the lattice parameter constant measured with XRD indicates a strong correlation between the two techniques. Although both techniques provide similar age-dating information, in contrast to the few milligram quantities required for XRD, Raman spectroscopy requires a 10 µm diameter PuO 2 particle to provide an estimate of the material age since last calcination.
Integrating in-situ multi-modal characterizations with signatures to investigate localized deformation
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Similarity analysis for thermal signature comparison in metal additive manufacturing
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Alterations in ECM signature underscore multiple sub-phenotypes of intervertebral disc degeneration
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Tutorial: Extracting entanglement signatures from neutron spectroscopy
This tutorial is a pedagogical introduction to recent methods of computing quantum spin entanglement witnesses from spectroscopy, with a special focus on neutron scattering on quantum spin systems. We offer a brief introduction to the concepts and equations, define a data analysis protocol, and discuss the interpretation of three entanglement witnesses: one-tangle, two-tangle, and Quantum Fisher Information. We also discuss practical experimental considerations, and give three examples of extracting entanglement witnesses from experimental data: Copper Nitrate, KCuF 3 , and NiPS 3 .
Plutonium mass estimation utilizing the (α,n) signature in mixed electrochemical samples
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Gravitational interaction signatures in isolated galaxy triplet systems: A photometric analysis
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Measurement of neutron active interrogation contraband signatures using organic scintillators
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Near-critical supernova outflows and their neutrino signatures
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