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1. Hydrologic characterization of an alpine valley infill through integration of ERT, active seismic and active/passive surface wave interferometry (Unaweep Canyon, US)

   https://doi.org/10.5194/egusphere-egu2020-6199  

   Behm, M. (April 2020, EGU General Assembly)

   Unaweep Canyon (Western Colorado, US) is an enigmatic alpine landform and hypothesized to represent a partially exhumed paleo valley which was glacially over-deepened in the late Paleozoic. Processing and interpretation of recently acquired 2D seismic reflection and refraction data support the concept of glacial over-deepening and indicate maximum bedrock depths of about 550 meters. Additionally, pronounced reflectors are observed within the sedimentary infill. The seismic data have also been subjected to surface wave analysis revealing a significant increase of the Vp/Vs ratio below a shallow (50 - 150 m depth) intra-sedimentary reflector. A large Vp/Vs ratio can be caused by both saturation and poor consolidation of dry low-porosity materials (e.g. dry sands).To investigate the potential occurrence of an aquifer associated with this interface, a high-density/long-offset electrical resistivity survey was conducted in fall 2019 along the seismic line. The maximum offset is 915 m at an electrode spacing of 5 meters, aiming at reaching depths of investigations between 150 and 200 meters. Inversion of the ERT data was initially conducted by means of smoothness-constrained algorithms. The imaging results revealed consistent structures with those resolved through seismic methods, at least within the required depth of investigation between 150 - 200 m. Furthermore, improvements in the resolution of the ERT imaging results was investigated after the inclusion of seismic interfaces as structural constraints in the inversion of the data. The comparison of the two approaches permitted to improve the interpretation of the ERT imaging results, which indicate low resistivities in the zone of high Vp/Vs ratios and thus strengthen the aquifer hypothesis. We present an integrated interpretation based on seismic structure, resistivity distribution, Vp and Vs velocities, and a distant well core. In a larger context, the results provide new insights on the subsurface hydrology in this arid part of the continental US as well as on the significance of multi-valued datasets for the interpretation and characterization of aquifers. 

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2. Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

   https://doi.org/10.1016/j.jappgeo.2024.105493  

   Jamil, Ahsan; Rucker, Dale F; Lu, Dan; Brooks, Scott C; Tartakovsky, Alexandre M; Cao, Huiping; Carroll, Kenneth C (October 2024, Journal of Applied Geophysics)

   This study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site. 

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3. Permafrost Mapping with Electrical Resistivity Tomography: A Case Study in Two Wetland Systems in Interior Alaska

   https://doi.org/10.2113/JEEG19-091  

   Conaway, Christopher H.; Johnson, Cordell D.; Lorenson, Thomas D.; Turetsky, Merritt; Euskirchen, Eugénie; Waldrop, Mark P.; Swarzenski, Peter W. (June 2020, Journal of Environmental and Engineering Geophysics)

   null (Ed.)

   Surface-based 2D electrical resistivity tomography (ERT) surveys were used to characterize permafrost distribution at wetland sites on the alluvial plain north of the Tanana River, 20 km southwest of Fairbanks, Alaska, in June and September 2014. The sites were part of an ecologically-sensitive research area characterizing biogeochemical response of this region to warming and permafrost thaw, and the site contained landscape features characteristic of interior Alaska, including thermokarst bog, forested permafrost plateau, and a rich fen. The results show how vegetation reflects shallow (0–10 m depth) permafrost distribution. Additionally, we saw shallow (0–3 m depth) low resistivity areas in forested permafrost plateau potentially indicating the presence of increased unfrozen water content as a precursor to ground instability and thaw. Time-lapse study from June to September suggested a depth of seasonal influence extending several meters below the active layer, potentially as a result of changes in unfrozen water content. A comparison of several electrode geometries (dipole-dipole, extended dipole-dipole, Wenner-Schlumberger) showed that for depths of interest to our study (0–10 m) results were similar, but data acquisition time with dipole-dipole was the shortest, making it our preferred geometry. The results show the utility of ERT surveys to characterize permafrost distribution at these sites, and how vegetation reflects shallow permafrost distribution. These results are valuable information for ecologically sensitive areas where ground-truthing can cause excessive disturbance. ERT data can be used to characterize the exact subsurface geometry of permafrost such that over time an understanding of changing permafrost conditions can be made in great detail. Characterizing the depth of thaw and thermal influence from the surface in these areas also provides important information as an indication of the depth to which carbon storage and microbially-mediated carbon processing may be affected. 

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4. Artifact propagation in electrocorticography stimulation

   Lim, J.; Wang, P.T.; Shaw, S.J.; Armacost, M.; Gong, H.; Liu, C.Y.; Heydari, P.; Do, A.H.; Nenadic, Z. (May 2018, Seventh International BCI Meeting, Abstract Book)

   Introduction:Current brain-computer interfaces (BCIs) primarily rely on visual feedback. However, visual feedback may not be sufficient for applications such as movement restoration, where somatosensory feedback plays a crucial role. For electrocorticography (ECoG)-based BCIs, somatosensory feedback can be elicited by cortical surface electro-stimulation [1]. However, simultaneous cortical stimulation and recording is challenging due to stimulation artifacts. Depending on the orientation of stimulating electrodes, their distance to the recording site, and the stimulation intensity, these artifacts may overwhelm the neural signals of interest and saturate the recording bioamplifiers, making it impossible to recover the underlying information [2]. To understand how these factors affect artifact propagation, we performed a preliminary characterization of ECoG signals during cortical stimulation.Materials/Methods/ResultsECoG electrodes were implanted in a 39-year old epilepsy patient as shown in Fig. 1. Pairs of adjacent electrodes were stimulated as a part of language cortical mapping. For each stimulating pair, a charge-balanced biphasic square pulse train of current at 50 Hz was delivered for five seconds at 2, 4, 6, 8 and 10 mA. ECoG signals were recorded at 512 Hz. The signals were then high-pass filtered (≥1.5 Hz, zero phase), and the 5-second stimulation epochs were segmented. Within each epoch, artifact-induced peaks were detected for each electrode, except the stimulating pair, where signals were clipped due to amplifier saturation. These peaks were phase-locked across electrodes and were 20 ms apart, thus matching the pulse train frequency. The response was characterized by calculating the median peak within the 5-second epochs. Fig. 1 shows a representative response of the right temporal grid (RTG), with the stimulation channel at RTG electrodes 14 and 15. It also shows a hypothetical amplifier saturation contour of an implantable, bi-directional, ECoG-based BCI prototype [2], assuming the supply voltage of 2.2 V and a gain of 66 dB. Finally, we quantify the worstcase scenario by calculating the largest distance between the saturation contour and the midpoint of each stimulating channel.Discussion:Our results indicate that artifact propagation follows a dipole potential distribution with the extent of the saturation region (the interior of the white contour) proportional to the stimulation amplitude. In general, the artifacts propagated farthest when a 10 mA current was applied with the saturation regions extending from 17 to 32 mm away from the midpoint of the dipole. Consistent with the electric dipole model, this maximum spread happened along the direction of the dipole moment. An exception occurred at stimulation channel RTG11-16, for which an additional saturation contour emerged away from the dipole contour (not shown), extending the saturation region to 41 mm. Also, the worst-case scenario was observed at 6 mA stimulation amplitude. This departure could be a sign of a nonlinear, switch-like behavior, wherein additional conduction pathways could become engaged in response to sufficiently high stimulation.Significance:While ECoG stimulation is routinely performed in the clinical setting, quantitative studies of the resulting signals are lacking. Our preliminary study demonstrates that stimulation artifacts largely obey dipole distributions, suggesting that the dipole model could be used to predict artifact propagation. Further studies are necessary to ascertain whether these results hold across other subjects and combinations of stimulation/recording grids. Once completed, these studies will reveal practical design constraints for future implantable bi-directional ECoG-based BCIs. These include parameters such as the distances between and relative orientations of the stimulating and recording electrodes, the choice of the stimulating electrodes, the optimal placement of the reference electrode, and the maximum stimulation amplitude. These findings would also have important implications for the design of custom, low-power bioamplifiers for implantable bi-directional ECoG-based BCIs.References:[1] Hiremath, S. V., et al. "Human perception of electrical stimulation on the surface of somatosensory cortex." PloS one 12.5 (2017): e0176020.[2] Rouse, A. G., et al. "A chronic generalized bi-directional brain-machine interface." Journal of Neural Engineering 8.3 (2011): 036018 

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5. Electrochemical Battery State Estimation Under Parameter Uncertainty Caused by Aging Using Expansion Measurements

   https://doi.org/10.23919/ACC50511.2021.9482886  

   Pannala, Sravan; Valecha, Puneet; Mohtat, Peyman; Siegel, Jason B.; Stefanopoulou, Anna G. (May 2021, 2021 American Control Conference)

   null (Ed.)

   Accurate tracking of the internal electrochemical states of lithium-ion battery during cycling enables advanced battery management systems to operate the battery safely and maintain high performance while minimizing battery degradation. To this end, techniques based on voltage measurement have shown promise for estimating the lithium surface concentration of active material particles, which is an important state for avoiding aging mechanisms such as lithium plating. However, methods relying on voltage often lead to large estimation errors when the model parameters change during aging. In this paper, we utilize the in-situ measurement of the battery expansion to augment the voltage and develop an observer to estimate the lithium surface concentration distribution in each electrode particle. We demonstrate that the addition of the expansion signal enables us to correct the negative electrode concentration states in addition to the positive electrode. As a result, compared to a voltage only observer, the proposed observer can successfully recover the surface concentration when the electrodes' stoichiometric window changes, which is a common occurrence under aging by loss of lithium inventory. With a 5% shift in the electrodes' stoichiometric window, the results indicate a reduction in state estimation error for the negative electrode surface concentration. Under this simulated aged condition, the voltage based observer had 9.3% error as compared to the proposed voltage and expansion observer which had 0.1% error in negative electrode surface concentration. 

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