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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2023

Exploring for Superhot Geothermal Targets in Magmatic Settings: 2022 Field Campaign at Newberry Volcano

This paper presents preliminary results from a subset of work carried out as part of a multinational research project entitled DErisking Exploration for multiple geothermal Plays in magmatic ENvironments (DEEPEN), supported by the U.S. Department of Energy (DOE) and Geothermica, a joint effort by EU member states and associated countries. The DEEPEN project will develop a customized approach to exploration for supercritical and superhot geothermal plays in magmatic systems, which will be applied to two demonstration sites. This paper summarizes field activities carried out at the U.S. demonstration site, Newberry Volcano in central Oregon. The objective of this work effort is to refine the subsurface model of Newberry Volcano, with special focus on deeper zones including the magmatic plumbing system and other key geologic elements. New data collection included gravity and wideband magnetotelluric (MT) surveys, as well as reinstallation of a seismic network. The National Renewable Energy Laboratory (NREL) and Enthalpion Energy LLC (Enthalpion) worked with the Deschutes National Forest Fort Rock District to use a low ground disturbance method of MT deployment to collect MT data inside the caldera and other restricted areas inside the National Volcanic Monument. This opened these areas to geophysical exploration for the first time in decades. Sites along and adjacent to the south rim of the volcano constituted the primary survey objectives. A team from Lawrence Berkeley National Laboratory (LBNL), the U.S. Geological Survey (USGS), and AltaRock also began the process of reinstalling the seismic network from the AltaRock enhanced geothermal system (EGS) demonstration in anticipation of further development activities at the site. The data ingestion, reduction, and analysis phase of the project is ongoing. We are currently processing the MT and gravity data and are developing a new, highly GPU-accelerated, 3D joint MT and gravity inversion to better localize the south rim/south flank conductive target and better understand its relationship to deep heat, fluid sources, and surface extrusive features. Joint inversions, which have not yet been undertaken at Newberry, will allow us to obtain constraints on the geologic model that cannot be determined from each method in isolation, improving our ability to image key geologic features at depth.

geophysics↗

NASA NTRS1981

Study of gravity and magnetic anomalies using MAGSAT data

The results of modeling satellite-elevation magnetic and gravity data using the constraints imposed by near surface data and seismic evidence shows that the magnetic minimum can be accounted for by either an intracrustal lithologic variation or by an upwarp of the Curie point isotherm. The long wavelength anomalies of the NOO's-vector magnetic survey of the conterminous U.S. were contoured and processed by various frequency filters to enhance particular characteristics. A preliminary inversion of the data was completed and the anomaly field calculated at 450 km from the equivalent magnet sources to compare with the POGO satellite data. Considerable progress was made in studing the satellite magnetic data of South America and adjacent marine areas. Preliminary versions of the 1 deg free-air gravity anomaly map (20 m gal contour interval) and the high cut (lambda approximately 8 deg) filtered anomaly maps are included.

Braile, L. W.↗

NASA NTRS1985

Magnetization models for the source of the 'Kentucky anomaly' observed by Magsat

Both the aeromagnetic data and magnetic anomaly data obtained by Magsat indicate the presence of a very magnetic source region within the crust beneath Kentucky and Tennessee. A source model was previously developed to fit surface gravity and long-wavelength aeromagnetic data, using limited seismic constraint. For the present study the model was further developed, and it is demonstrated that the source region for the satellite anomaly is considerably more extensive than the Kentucky body sensu stricto. The extended source region is modeled using both prismatic model sources and dipole array sources. Magnetization directions for the source region found by inversion of various combinations of scalar and vector data are found to be close to the main field direction, implying the lack of a strong remanent component. It is shown by simulation that in a case (such as this) where the geometry of the source is known, if a strong remanent component is present its direction is determinable, but by scalar data as readily as vector data. Magnetization magnitude for the extended source region is about 3 A/m if the vertical extent of the source includes the whole of the crust.

Mayhew, M. A.↗

DOE OSTI2021

Characterizing Stress Orientations in Southern Kansas

We repot that Induced seismicity predominantly occurs along faults that are optimally oriented to the local principal compressive stress direction, and the characterization of these stress orientations is an important component of understanding seismic hazards. The seismicity rate in southern Kansas rapidly increased in 2013 primarily due to the disposal of large volumes of wastewater into the Arbuckle Group. Previously, local stress orientations in this area were poorly constrained, which limited our understanding of the complex faulting and diverse earthquake mechanisms in this region. We use shear-wave splitting and focal mechanism inversion techniques to create multiple, independent estimates of maximum horizontal stress directions (S Hmax ) and their spatial variation across the study area. We then create an integrated model of stress orientations for southern Kansas and northern Oklahoma using our local results in conjunction with previous, regional stress orientation estimates. We find that S Hmax in both southern Kansas and central Oklahoma exhibits an east-northeast (~N78°E) orientation, and these regions bound a northeast (~N59°E) rotation within a ~20km area in northern Oklahoma near the Nemaha ridge.

58 GEOSCIENCES↗

NASA NTRS1985

Small-scale convection beneath the transverse ranges, California: Implications for interpretation of gravity anomalies

Tomographic inversion of upper mantle P wave velocity heterogeneities beneath southern California shows two prominent features: an east-west trending curtain of high velocity material (up to 3% fast) in the upper 250 km beneath the Transverse Ranges and a region of low velocity material (up to 4% slow) in the 100 km beneath the Salton Trough. These seismic velocity anomalies were interpreted as due to small scale convection in the mantle. Using this hypothesis and assuming that temperature and density anomalies are linearly related to seismic velocity anomalies through standard coefficients of proportionality, leads to inferred variations of approx. + or - 300 C and approx. + or - 0.03 g/cc.

Humphreys, E. D.↗

NASA NTRS1988

Oceanic earthquakes and the tectonic evolution of oceanic lithosphere

The body waveform inversion method of Nabelek (1984) is used to study the centroid depths and source properties of oceanic earthquakes. The source parameters for 50 earthquakes which occurred along slowly spreading midocean ridges between 1962 and 1983 are used to examine the mechanical characteristics of the median valley, including the water depth in the epicentral region, the depth range of seismic faulting, the centroid depth and seismic moment versus spreading rate, and the seismic moment budget. The locations and source characteristics of oceanic intraplate earthquakes are discussed, including near-ridge earthquakes, lithospheric stress, and earthquakes in older oceanic lithosphere. The results suggest that the median valley form by the necking of a strong layer. The properties of near-ridge earthquakes support the hypothesis that thermal stress generated by diferential cooling of the plate can be stored and accumulated over millions of years. Earthquakes in older oceanic lithosphere are most likely to reflect stresses generated by plate driving forces.

Solomon, Sean C.↗

DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗

NASA NTRS1984

Analysis of upper mantle structure using wave field continuation of P waves

Wave field continuation theory, which allows transformation of the seismic record section data directly into velocity-depth space, is tested for upper mantle analysis using a large array-recorded data set obtained at the 200-station Caltech-USGS Southern California Seismic Network that is representative of the structure beneath the gulf of California. The method's resolution capability is illustrated by the comparison of the slant stacks and downward continuation of both synthetic and data record sections. It is stressed that when high-quality, densely sampled digital data are available, the technique is easy to implement, provides an inversion which contains all the data in the global format, and produces an objective estimate of depth resolution as a function of ray parameter.

Walck, M. C.↗

DOE OSTI2023

Thermally Significant Fluid Seepage Through Thick Sediment on the Juan de Fuca Plate Entering the Cascadia Subduction Zone

We use heat flux measurements colocated with seismic reflection profiles over a buried basement high on the Juan de Fuca plate ∼25 km seaward of the deformation front offshore Oregon to test for the presence of hydrothermal circulation in the oceanic crust. We also revisit heat flux data crossing a buried basement high ∼25 km seaward of the deformation front ∼150 km north, offshore Washington. Seafloor heat flux is inversely correlated with sediment thickness, consistent with vigorous hydrothermal circulation in the basement aquifer homogenizing temperatures at the top of the basement. Heat flux immediately above the summit of the basement highs is greater than expected solely from conduction. Fluid seepage at rates of ∼2.6–5.4 cm yr −1 in a 1–1.5 km-wide conduit through ∼800–1,300 m thick sediment sections above these basement highs can explain these observations. Observations of thermally significant fluid seepage through sediment >225 m thick on oceanic crust are unprecedented. High sediment permeability, high fluid overpressure in the basement, or a combination of both is required to drive fluid seepage at the observed rates. We infer that rapid seepage occurs because the basement highs rise above the low permeability basal sediment with their tops protruding into the base of high permeability Nitinat or Astoria Fan sediment. Seepage from basement highs penetrating into the submarine fans can affect the thermal state of crust entering the subduction zone.

58 GEOSCIENCES↗

NASA NTRS2003

Seismic Study of The Solar Interior: Inferences from SOI/MDI Observations during Solar Activity

The principal investigator describes several types of solar research conducted during the reporting period and gives a statement of work to be performed in the following year. Research conducted during the reporting period includes: exhaustive analysis of observational and instrumental effects that might cause systematic errors in the characterization of high-degree p-modes; study of the structure, asphericity and dynamics of the solar interior from p-mode frequencies and frequency splittings; characterizing the solar rotation; Time-Distance inversion; and developing and using a new peak-fitting method for very long MDI time series at low degrees.

Korzennik, Sylvain G.↗

NASA NTRS1972

The lunar interior

The compressional velocities are estimated for materials in the lunar interior and compared with lunar seismic results. The lower crust has velocities appropriate for basalts or anorthosites. The high velocities associated with the uppermost mantle imply high densities and a change in composition to a lighter assemblage at depths of the order of 120 km. Calcium and aluminum are probably important components of the upper mantle and are deficient in the lower mantle. Much of the moon may have accreted from material similar in composition to eucrites. The important mineral of the upper mantle is garnet; possible accessory minerals are kyanite, spinel, and rutile. If the seismic results stand up, the high velocity layer in the moon is more likely to be a high pressure form of anorthosite than eclogite, pyroxenite, or dunite. The thickness of the layer is of the order of 50 km. Cosmic abundances can be maintained if the lower mantle is ferromagnesium silicate with minimal amounts of calcium and aluminum. Achondrites such as eucrites and howardites have more of the required characteristics of the lunar interior than carbonaceous chondrites. A density inversion in the moon is a strong possibility.

Anderson, D. L.↗

DOE OSTI2020

Characterizing Baselines and Change in Gas Hydrate Systems using EM Methods

The objective of this project was to advance our understanding of gas hydrate systems in nature by characterizing their electrical properties in the field and in the laboratory. In the laboratory measurements, methane hydrate was synthesized from pure water ice and flash frozen seawater, with varying amounts of sand or silt added. Electrical conductivity was determined by impedance spectroscopy, using equivalent circuit modeling to separate the effects of electrodes and to gain insight into conduction mechanisms. Silt and sand increase the conductivity of pure hydrate, inferred to be contaminant NaCl contributing to conduction in hydrate, to a peak conductivity in agreement with peak resistivities observed in well logs through massive hydrate (3,000--10,000 Ωm). The addition of silt and sand lowers the conductivity of hydrate synthesized from seawater, by an amount consistent with Archie's Law. All samples were characterized using cryogenic scanning electron microscopy and energy dispersive spectroscopy, which shows good connectivity of salt and brine phases. Electrical conductivity measurements of pure hydrate and hydrate mixed with silt during pressure-induced dissociation supports previous conclusions that sediment increases dissociation rate. In order to characterize gas hydrate systems in the field, we collected 360 line kilometers of controlled-source electromagnetic data on Walker Ridge 313, Orca Basin (WR100), Mad Dog (GC781), and Green Canyon 955 in the Gulf of Mexico, all areas with known or seismically inferred gas hydrate deposits and which have be drilled or targeted for future drilling. We deep-towed an EM transmitter that generates an alternating electric field which propagates through the seafloor geology. Data were recorded on 6 receivers towed behind the transmitter at distances between 550 and 1550 m. In the presence of conductive geology, the electric fields will be attenuated, and conversely, in resistive geology the fields will be preserved. Our data were inverted using a 2D inversion method that first optimizes the model-data misfit, then finds the smoothest model fitting the data. This ensures that resistivity structures present in the final model are likely necessary. At each of the proposed drilling sites we found increased resistivity, interpreted as increased hydrate concentrations. However, not only were the primary drilling sites not always more resistive than the alternate sites, at WR313 the strongest resistors were not at the locations targeted for drilling.

03 NATURAL GAS↗

DOE OSTI2024

Seismic Monitoring near Ithaca, New York, Reveals Nonuniform Distribution of Microseismicity in an Intraplate Region

Abstract Cornell University intends to use a deep direct-use geothermal system to heat its Ithaca, New York, campus. In preparation for this project, the Cornell Seismic Network has been monitoring the background seismicity in this intraplate region since 2019. From January 2020 to June 2023, 95 events were detected within 20 km of the proposed geothermal well site, with local magnitudes ranging from −1.02 to 0.56. None of these events appear in regional or national catalogs. Events locate in a narrow geographic band, with one-fourth exhibiting multimodal hypocentral probability peaks both near the surface and at 1–4 km depth. We relocate events with a joint hypocenter and 1D velocity model inversion, in addition to a fully nonlinear method, and then compare observations with synthetic waveforms. Together, these approaches provide strong evidence for >95% of events locating at the surface or within the 3-km-thick sedimentary sequence. We explore how anthropogenic activity and regional topographic stress may contribute to frequent surficial events. This information is critical for characterizing the background microseismicity for comparison during future geothermal operations. Ithaca’s geology of Paleozoic sediments overlying Precambrian crystalline basement is typical of many continental interiors, so these results also provide insight into intraplate microseismicity patterns.

Geochemistry & Geophysics↗

DOE OSTI2024

Inferring the Focal Depths of Small Earthquakes in Southern California Using Physics-Based Waveform Features

Determining the depths of small crustal earthquakes is challenging in many regions of the world, because most seismic networks are too sparse to resolve trade-offs between depth and origin time with conventional arrival-time methods. Precise and accurate depth estimation is important, because it can help seismologists discriminate between earthquakes and explosions, which is relevant to monitoring nuclear test ban treaties and producing earthquake catalogs that are uncontaminated by mining blasts. Here, we examine the depth sensitivity of several physics-based waveform features for ~8000 earthquakes in southern California that have well-resolved depths from arrival-time inversion. We focus on small earthquakes (2 < M L < 4) recorded at local distances (<150 km), for which depth estimation is especially challenging. We find that differential magnitudes (M w /M L –M c ) are positively correlated with focal depth, implying that coda wave excitation decreases with focal depth. We analyze a simple proxy for relative frequency content, Φ≡log 10 (M 0 )+3log 10 (f c ), and find that source spectra are preferentially enriched in high frequencies, or “blue-shifted,” as focal depth increases. Here, we also find that two spectral amplitude ratios Rg 0.5–2 Hz/Sg 0.5–8 Hz and Pg/Sg at 3–8 Hz decrease as focal depth increases. Using multilinear regression with these features as predictor variables, we develop models that can explain 11%–59% of the variance in depths within 10 subregions and 25% of the depth variance across southern California as a whole. We suggest that incorporating these features into a machine learning workflow could help resolve focal depths in regions that are poorly instrumented and lack large databases of well-located events. Some of the waveform features we evaluate in this study have previously been used as source discriminants, and our results imply that their effectiveness in discrimination is partially because explosions generally occur at shallower depths than earthquakes.

58 GEOSCIENCES↗

NASA NTRS2012

Simplex GPS and InSAR Inversion Software

Changes in the shape of the Earth's surface can be routinely measured with precisions better than centimeters. Processes below the surface often drive these changes and as a result, investigators require models with inversion methods to characterize the sources. Simplex inverts any combination of GPS (global positioning system), UAVSAR (uninhabited aerial vehicle synthetic aperture radar), and InSAR (interferometric synthetic aperture radar) data simultaneously for elastic response from fault and fluid motions. It can be used to solve for multiple faults and parameters, all of which can be specified or allowed to vary. The software can be used to study long-term tectonic motions and the faults responsible for those motions, or can be used to invert for co-seismic slip from earthquakes. Solutions involving estimation of fault motion and changes in fluid reservoirs such as magma or water are possible. Any arbitrary number of faults or parameters can be considered. Simplex specifically solves for any of location, geometry, fault slip, and expansion/contraction of a single or multiple faults. It inverts GPS and InSAR data for elastic dislocations in a half-space. Slip parameters include strike slip, dip slip, and tensile dislocations. It includes a map interface for both setting up the models and viewing the results. Results, including faults, and observed, computed, and residual displacements, are output in text format, a map interface, and can be exported to KML. The software interfaces with the QuakeTables database allowing a user to select existing fault parameters or data. Simplex can be accessed through the QuakeSim portal graphical user interface or run from a UNIX command line.

Donnellan, Andrea↗

DOE OSTI2024

Mapping Permafrost Variability and Degradation Using Seismic Surface Waves, Electrical Resistivity, and Temperature Sensing: A Case Study in Arctic Alaska

Subsurface processes significantly influence surface dynamics in permafrost regions, necessitating utilizing diverse geophysical methods to reliably constrain permafrost characteristics. This research uses multiple geophysical techniques to explore the spatial variability of permafrost in undisturbed tundra and its degradation in disturbed tundra in Utqiagvik, Alaska. Here, we integrate multiple quantitative techniques, including multichannel analysis of surface waves (MASW), electrical resistivity tomography (ERT), and ground temperature sensing, to study heterogeneity in permafrost’s geophysical characteristics. MASW results reveal active layer shear wave velocities (V s ) between 240 and 370 m/s, and permafrost V s between 450 and 1,700 m/s, typically showing a low-high-low velocity pattern. Additionally, we find an inverse relationship between in situ V s and ground temperature measurements. The V s profiles along with electrical resistivity profiles reveal cryostructures such as cryopeg and ice-rich zones in the permafrost layer. The integrated results of MASW and ERT provide valuable information for characterizing permafrost heterogeneity and cryostructure. Corroboration of these geophysical observations with permafrost core samples’ stratigraphies and salinity measurements further validates these findings. This combination of geophysical and temperature sensing methods along with permafrost core sampling confirms a robust approach for assessing permafrost’s spatial variability in coastal environments. Our results also indicate that civil infrastructure systems such as gravel roads and pile foundations affect permafrost by thickening the active layer, lowering the V s , and reducing heterogeneity. We show how the resulting V s profiles can be used to estimate key parameters for designing buildings in permafrost regions and maintaining existing infrastructure in polar regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Deformation and Transformation Textures in the NaMgF3 Neighborite—Post-Perovskite System

The D″ region of the lower mantle, which lies just above the core–mantle boundary, is distinct from the bulk of the lower mantle in that it exhibits complex seismic heterogeneity and seismic anisotropy. Seismic anisotropy in this region is likely to be largely due to the deformation-induced texture (crystallographic preferred orientation) development of the constituent mineral phases. Thus, seismic anisotropy can provide a marker for deformation processes occurring in this dynamic region of the Earth. Post-perovskite-structured (Mg,Fe)SiO3 is believed to be the dominant mineral phase in many regions of the D”. As such, understanding deformation mechanisms and texture development in post-perovskite is important for the interpretation of observed seismic anisotropy. Here, we report on high-pressure diamond anvil cell deformation experiments on NaMgF3 neighborite (perovskite structure) and post-perovskite. During deformation, neighborite develops a 100 texture, as has been previously observed, both in NaMgF3 and MgSiO3 perovskite. Upon transformation to the post-perovskite phase, an initial texture of {130} at high angles to compression is observed, indicating that the {100} planes of perovskite become the ~{130} planes of post-perovskite. Further compression results in the development of a shoulder towards (001) in the inverse pole figure. Plasticity modeling using the elasto-viscoplastic self-consistent code shows this texture evolution to be most consistent with deformation on (001)[100] with some contribution of glide on (100)[010] and (001)<110> in NaMgF3 post-perovskite. The transformation and deformation mechanisms observed in this study in the NaMgF3 system are consistent with the behavior generally observed in other perovskite–post-perovskite systems, including the MgSiO3 system. This shows that NaMgF3 is a good analog for the mantle bridgmanite and MgSiO3 post-perovskite.

Geochemistry & Geophysics↗

NASA NTRS2006

Computing Fault Displacements from Surface Deformations

Simplex is a computer program that calculates locations and displacements of subterranean faults from data on Earth-surface deformations. The calculation involves inversion of a forward model (given a point source representing a fault, a forward model calculates the surface deformations) for displacements, and strains caused by a fault located in isotropic, elastic half-space. The inversion involves the use of nonlinear, multiparameter estimation techniques. The input surface-deformation data can be in multiple formats, with absolute or differential positioning. The input data can be derived from multiple sources, including interferometric synthetic-aperture radar, the Global Positioning System, and strain meters. Parameters can be constrained or free. Estimates can be calculated for single or multiple faults. Estimates of parameters are accompanied by reports of their covariances and uncertainties. Simplex has been tested extensively against forward models and against other means of inverting geodetic data and seismic observations. This work

Lyzenga, Gregory↗