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At least 19 records

Anomaly forecasting and early warning generation

The example embodiments are directed to a system and method for forecasting anomalies in feature detection. In one example, the method includes storing feature behavior information of at least one monitoring node of an asset, including a normalcy boundary identifying normal feature behavior and abnormal feature behavior for the at least one monitoring node in feature space, receiving input signals from the at least one monitoring node of the asset and transforming the input signals into feature values in the feature space, wherein the feature values are located within the normalcy boundary, forecasting that a future feature value corresponding to a future input signal from the at least one monitoring node is going to be positioned outside the normalcy boundary based on the feature values within the normalcy boundary, and outputting information concerning the forecasted future feature value being outside the normalcy boundary for display.

Abbaszadeh, Masoud↗

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↗

Prestack reverse time migration for 3D marine reflection seismic data

Prestack reverse time migration (RTM) is a method for imaging the subsurface using the inner product of wavefield extrapolation in shot domain and in receiver domain. It is well known that RTM is better for preserving amplitudes and phases than other prestack migrations. Since 3D seismic data is huge data volume and it needs heavy computing works, it requires parallel computing in order to have a meaningful depth image of the 3D subsurface. We implemented a parallelized version of 3D RTM for prestack depth migration. The results of numerical example for 3D SEG/EAGE salt model showed good agreement with the original geological model. We applied RTM to offshore 3D seismic reflection data. The study area is 12 × 25 km with 120 survey lines. Shot and receiver spacing is 25 m and 12.5 m. The line spacing is 100 m. Shot gathers were preprocessed to enhance signal to noise ratio and velocity model was calculated from conventional stack velocity. Both of them were used to obtain 3D image using RTM. The results show reasonable subsurface image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A New Approach to Predict Hydrogeological Parameters Using Shear Waves from the Multichannel Analysis of Surface Waves Method

For near-surface contaminant characterization, the accurate prediction of hydrogeological parameters in anisotropic and heterogeneous environments has been a challenge since the last decades. However, recent advances in near-surface geophysics have facilitated the use of geophysical data for hydrogeological characterization in the last few years. A pseudo 3-D high resolution P-wave shallow seismic reflection survey was performed at the P Reactor Area, Savannah River Site, South Carolina in order to delineate and predict migration pathways of a large contaminant plume including trichloroethylene. This contaminant plume originates from the northwest section of the reactor facility that is located within the Upper Atlantic Coastal Plain. The data were collected with 40 Hz geophones, an accelerated weight-drop as seismic source and 1 m receiver spacing with near- and far-offsets of 0.5 and 119.5 m, respectively. In such areas with near-surface contaminants, a detailed subsurface characterization of the vadose zone hydraulic parameters is very important. Indeed, an inexpensive method of deriving such parameters by the use of seismic reflection surveys is beneficial, and our approach uses the relationship between seismic velocity and hydrogeological parameters together with empirical observations relating porosity to permeability and hydraulic conductivity. Shear wave velocity ( V s ) profiles were estimated from surface wave dispersion analysis of the seismic reflection data and were subsequently used to derive hydraulic parameters such as porosity, permeability, and hydraulic conductivity. Additional geophysical data including core samples, vertical seismic profiling, surface electrical resistivity tomography, natural gamma and electrical resistivity logs allowed for a robust assessment of the validity and geological significance of the estimated V s and hydrogeological models. The results demonstrate the usefulness of this approach for the upper 15 m of shallow unconsolidated sediments even though the survey design parameters were not optimal for surface wave analysis due to the higher than desired frequency geophones.

Engineering↗

Protons Sensitize Epithelial Cells to Mesenchymal Transition

Proton radiotherapy has gained more favor among oncologists as a treatment option for localized and deep-seated tumors. In addition, protons are a major constituent of the space radiation astronauts receive during space flights. The potential for these exposures to lead to, or enhance cancer risk has not been well studied. Our objective is to study the biological effects of low energy protons on epithelial cells and its propensity to enhance transforming growth factor beta 1 (TGFβ1)-mediated epithelial-mesenchymal transition (EMT), a process occurring during tumor progression and critical for invasion and metastasis. Non-transformed mink lung epithelial cells (Mv1Lu) and hTERT- immortalized human esophageal epithelial cells (EPC) were used in this study. EMT was identified by alterations in cell morphology, EMT-related gene expression changes determined using real-time PCR, and EMT changes in specific cellular markers detected by immunostaining and western blotting. Although TGFβ1 treatment alone is able to induce EMT in both Mv1Lu and EPC cells, low energy protons (5 MeV) at doses as low as 0.1 Gy can enhance TGFβ1 induced EMT. Protons alone can also induce a mild induction of EMT. SD208, a potent TGFb Receptor 1 (TGFβR1) kinase inhibitor, can efficiently block TGFβ1/Smad signaling and attenuate EMT induction. We suggest a model for EMT after proton irradiation in normal and cancerous tissue based on our results that showed that low and high doses of protons can sensitize normal human epithelial cells to mesenchymal transition, more prominently in the presence of TGFβ1, but also in the absence of TGFβ1.

59 BASIC BIOLOGICAL SCIENCES↗

Apparatus for synthetic imaging of an object

An apparatus for synthetic imaging of an object is disclosed. The apparatus includes a plurality of transmitter elements spaced apart by a first distance in a first column and a plurality of receiver elements spaced apart by a second distance in a second column. The first distance and the second distance are different. The plurality of transmitter elements is a non-integer multiple of the plurality of receiver elements, and the plurality of receiver elements is a non-integer multiple of the plurality of transmitter elements.

47 OTHER INSTRUMENTATION↗

Detecting dark matter substructures on small scales with fast radio bursts

The matter power spectrum is only weakly constrained on subgalactic scales, while physics beyond the Standard Model can leave unique imprints, especially on sub-parsec scales. We propose measuring the arrival-time difference of fast radio bursts (FRBs) along two adjacent sightlines as a new probe to dark matter substructures on scales down to ∼ 1 AU . We discuss two observational scenarios in which it may be possible to place interesting constraints on such models through the monitoring of repeating FRB sources: (i) By sending radio receivers to space to form a baseline of tens of AU or more and measuring the temporal variation of the arrival-time difference between receivers. (ii) By measuring the temporal variation of the arrival-time difference between two lensed images of one strongly lensed repeater. In both scenarios, obtaining interesting constraints requires correlating the voltage time series to measure the radio signal arrival time to sub-nanosecond precision. We find that two radio dishes separated by 20 AU may be sensitive to the enhancement of small-scale structures at ∼ 10 - 8 M ⊙ masses in the QCD axion dark matter scenario, or from an early epoch of matter domination with a reheating temperature up to 60 MeV. Other dark matter models, such as those composed of ∼ 10 - 13 M ⊙ primordial black holes produced during inflation, would also be probed by this method. We further show that a strong lensing situation of multiple images provides an equivalent ∼ 2000 AU ( σ v / 10 3 km s - 1 ) ( δ t / 10 yr ) baseline, for a typical velocity of dark matter substructures σ v and an observational time span δ . This is much more sensitive, but with the uncertainty that intervening decoherence from the interstellar medium may degrade the timing precision, and that spatial variation in the FRB emission spot may result in confounding signals. We show that the lensing magnifications of Type Ia supernovae constrain a similar quantity to such FRB timing, with present limits being equivalent to ruling out the same parameter space that would be probed by a 0.14 AU baseline.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Automated calibration of multistatic arrays

A method is disclosed for calibrating a multistatic array having a plurality of transmitter and receiver pairs spaced from one another along a predetermined path and relative to a plurality of bin locations, and further being spaced at a fixed distance from a stationary calibration implement. A clock reference pulse may be generated, and each of the transmitters and receivers of each said transmitter/receiver pair turned on at a monotonically increasing time delay interval relative to the clock reference pulse. Ones of the transmitters and receivers may be used such that a previously calibrated transmitter or receiver of a given one of the transmitter/receiver pairs is paired with a subsequently un-calibrated one of the transmitters or receivers of an immediately subsequently positioned transmitter/receiver pair, to calibrate the transmitter or receiver of the immediately subsequent transmitter/receiver pair.

42 ENGINEERING↗

Stringy stability of charged dilaton black holes with flat event horizon

Electrically charged black holes with flat event horizon in anti-de Sitter space have received much attention due to various applications in Anti-de Sitter/Conformal Field Theory (AdS/CFT) correspondence, from modeling the behavior of quark-gluon plasma to superconductor. Critical to the physics on the dual field theory is the fact that when embedded in string theory, black holes in the bulk may become vulnerable to instability caused by brane pair-production. Since dilation arises naturally in the context of string theory, we study the effect of coupling dilation to Maxwell field on the stability of flat charged AdS black holes.

AdS/CFT↗

Superconducting Multilayer High-Density Flexible Printed Circuit Board for Very High Thermal Resistance Interconnections

We have successively developed two superconducting flexible PCBs for cryogenic applications. The first one is monolayer, includes 552 tracks (10 µm wide, 20 µm spacing), and receives 24 wire-bonded integrated circuits. The second one is multilayer, with one track layer between two shielding layers interconnected by microvias, includes 37 tracks, and can be interconnected at both ends by wire bonding or by connectors. The first cold measurements have been performed and show good performances. The novelty of these products is, for the first one, the association of superconducting materials with very narrow pitch and bonded integrated circuits and, for the second one, the introduction of a superconducting multilayer structure interconnected by vias which is, to our knowledge, a world-first.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

First-Principles Investigation of Electrides Derived from Sodalites

Recently, electride materials, with excess anionic electrons confined in their empty space, have received growing attention due to their promising applications in catalysis, nonlinear optics, and spin electronics. However, the utilization of electride materials is limited by their thermal instability. Here, we introduce an alternative way to achieve the localized anionic electron states via the removal of highly symmetric Wyckoff sites of anions from the existing sodalite compounds. Using four halide sodalites as the parental structures, our simulation reveals that the materials after the removal of anionic halide sites exhibit typical electride behaviors that are characterized by the existence of localized electronic states near the Fermi level. Compared to most previously studied electrides, these materials are expected to be more thermally stable due to the complex structural framework and thus promising for practical applications. Among them, Na 4 (AlSiO 4 ) 3 manifests magnetic electronic structure. We demonstrate that this magnetism originates from a highly localized excess electron state surrounded by electropositive alkaline cations. Here, our results suggest Na 4 (AlSiO 4 ) 3 could be a promising spintronics component, thus encouraging further experimental study.

36 MATERIALS SCIENCE↗

Analyzing the subsurface structure using seismic refraction method: Case study STMKG campus

A geophysic survey is performed to detect subsurface structure under STMKG Campus in Pondok Betung, South Tangerang, Indonesia, using seismic refraction method. The survey used PASI 16S24-U24. The waveform data is acquired from 3 different tracks on the research location with a close range from each track. On each track we expanded 24 geofons with spacing between receiver 2 meters and the total length of each track about 48 meters. The waveform data analysed using 2 different ways. First, used a seismic refractionapplication WINSISIM 12 and second, used a Hagiwara Method. From both analysis, we known the velocity of P-wave in the first and second layer and the thickness of the first layer. From the velocity and the thickness informations we made 2-D vertical subsurface profiles. In this research, we only detect 2 layers in each tracks. The P-wave velocity of first layer is about 200-500 m/s with the thickness of this layer about 3-6 m/s. The P-wave velocity of second layer is about 400-900 m/s. From the P-wave velocity data we interpreted that both layer consisted by similar materials such as top soil, soil, sand, unsaturated gravel, alluvium and clay. But, the P-wave velocity difference between those 2 layers assumed happening because the first layer is soil embankment layer, having younger age than the layer below.

58 GEOSCIENCES↗

Scalable free-space photonic antennas in foundry SOI silicon photonic platforms

We present a flexible, scalable, and low-noise design scheme for coupling free-space light into a silicon-on-insulator (SOI) electronic-photonic integrated circuit. The proposed scheme utilizes arrays of grating couplers with compact, inverse-designed power combining networks to couple a distributed optical collection area to a single output waveguide, forming a photonic antenna. Fabrication density compliance is maintained regardless of the antenna size, and the collection area can be scaled while maintaining a fixed noise floor. Using experimental grating array antennas fabricated in the GF45CLO platform, we demonstrate up to a 6.7× increase in the signal-to-noise ratio (SNR) of a lens-less monolithic free-space photonic receiver using a 4×4 grating array.

Rollinson, John (ORCID:0000000157130832)↗

Screening portal, system and method of using same

A portal, system and method for screening an object for a target substance is provided. The portal includes an inflatable bladder expandable to form a test space for receiving the object and a plurality of nozzles positioned about the inflatable bladder. The nozzles are in fluid communication with a fluid source for directing air over the object whereby samples are removed from the object for examination. A collector is operatively connected to the inflatable bladder for collecting the samples removed from the object. A detector is operatively connected to the collector for examining the removed samples for the presence of the target substance. At least one preconcentrator may be operatively connected to the collector for concentrating the samples collected thereby.

47 OTHER INSTRUMENTATION↗

System for kick detection during a drilling operation

The disclosure provides a kick detection system for use during a drilling operation where wellbore kick warnings are provided based on indications of standoff conditions in a compensated instrument system. The system provides a warning of a potential kick condition by analyzing a current instrument compensation against a time series of past compensations, in order to monitor whether conditions within the standoff region of the wellbore are unexpectedly changing. The system comprises a source, a short-spaced detector, and a long-spaced detector, and a processor receives the short-spaced signal and the long-spaced signal, compensates the long-spaced signal, and generates standoff data reflecting the corrections applied to the long-range signal. The processor determines and maintains the standoff data as a time series and periodically compares a recent data point to a moving average in order to evaluate indications of a potential well kick.

Rose, Kelly K.↗

Field Validation of MVA Technology for Offshore CCS: Novel Ultra-High-Resolution 3D Marine Seismic Technology (P-Cable) (Final Report)

The objectives of the proposed study were to deploy and validate a specific monitoring technology, high-resolution 3D marine seismic (HR3D), appropriate for large-demonstration and commercial-scale offshore CCS sites. The project accomplished successful acquisition two HR3D seismic surveys. The first HR3D dataset was over the offshore injection site of the Tomakomai, Japan integrated pilot CCS project, which at the time of survey acquisition was actively injecting CO 2 . The first survey also represented a successful international collaboration between the DOE NETL program and Japan’s national CCS program and was the first successful acquisition and use of HR3D over an active CO 2 injection site (Meckel, Feng et al. 2019). The Tomakomai HR3D survey successfully tested a novel 4-streamer HR3D system array in which, for the first time, no cross-cable (aka “P-Cable”) was utilized and only four GeoEel streamers were used instead of the standard 12-streamer configuration. Consequently, this was not, strictly speaking, a deployment of the “P-Cable” system of (Planke and Berndt 2004) but rather a modified version, thereof, and it is the first known demonstration of the modified system configuration. One very positive outcome from the Japanese collaboration earlier in the project was the ability to learn from the Japanese how they used tail buoys with GPS to determine the position of the seismic source and receivers in time and space. Based on that experience, GCCC designed and built six GPS receivers that could be used to position the streamer receivers and the seismic source via tail buoys. A fundamental advance that was made on the original design, was the ability to directly power the tail buoy GPS units and transfer data through the streamers (i.e., vs. the batteries used at Tomakomai). The bulkiness of the GPS batteries caused drag and episodic surging of the buoys, which affected data quality by lifting up the tail end of the streamers so the receivers were not at the same depth. The units were tested onshore for accuracy and functionality, and the design was subsequently and successfully tested in marine acquisition mode during the SLP survey acquisition. The marine acquisition test and survey satisfied Subtasks 2.2.2, Novel Positioning Technology Selection and Subtask 2.2.3, Novel Positioning Technology Deployment. Results of the novel positioning technology selection (Subtask 2.2.2) were considered successful and will be incorporated in future HR3D seismic acquisition projects to reduce costs, improve deployment safety at sea, and integrate both seismic and data recording via a single data transfer through the streamers to the recording system. The project also established a permitting process through NETL NEPA compliance, which included an Environmental Assessment in a marine setting and is required for conducting these types of surveys using Federal funding. The permitting process charted a “boilerplate,” which can allow future surveys related to other funded projects to move forward more expeditiously. Future improvements that could be considered are more robust seals on the GPS module and stronger materials (especially joints) on tail buoy fabrication. These would increase fixed costs, but would be advisable and probably more economic long-term if multiple HR3D surveys are planned. Project Accomplishments include: • Pre-survey Sensitivity Study • Marine geochemistry methods and data analysis • Successful HR3D seismic dataset acquired @ Tomakomai active CO 2 injection marine site • Developed advanced seismic processing techniques • No NRMS anomalies detected in overburden; Demonstration of containment • Repeatability study • Second survey collected @ San Luis Pass, TX • 4D application using positioning techniques developed in the project for monitoring were successful

3D seismic GPS positioning↗