226 research outputs found

    Finite Difference Elastic Wave Modeling Including Surface Topography

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    Surface topography and the weathered zone (i.e., heterogeneity near the earth’s surface) have great effects on elastic wave propagation. Both surface waves and body waves are contaminated by scattering and conversion by the irregular surface topographic features. In this paper, we present a 2D numerical solver for the elastic wave equation that combines a 4th-order ADER scheme (Arbitrary high-order accuracy using DERivatives) with the characteristic variable method at the free surface boundary. The method is based on the velocity-stress formulation. We demonstrate the method by calculating synthetic seismograms for simple features

    Predicting stress-induced anisotropy around a borehole

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    Formation elastic properties near a borehole may be altered from their original state due to the stress concentration around the borehole. This could result in a biased estimation of formation properties but could provide a means to estimate in situ stress from sonic logging data. In order to properly account for the formation property alteration, we propose an iterative numerical approach to calculate the stress-induced anisotropy around a borehole by combining Mavko’s rock physics model and a finite-element method. We show the validity and accuracy of our approach by comparing numerical results to laboratory measurements of the stress-strain relation of a sample of Berea sandstone, which contains a borehole and is subjected to uniaxial stress loading. Our iterative approach converges very fast and can be applied to calculate the spatially varying stiffness tensor of the formation around a borehole for any given stress state.Massachusetts Institute of Technology. Earth Resources Laboratory (Founding Member Postdoctoral Fellowship

    Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty

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    We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophysical features within the state of Nevada, USA are used to define geothermal potential across a broad region. We have available a relatively small set of positive training sites (known resources or active power plants) and negative training sites (known drill sites with unsuitable geothermal conditions) and use these to constrain and optimize artificial neural networks for this classification task. The main objective is to predict the geothermal resource potential at unknown sites within a large geographic area where the defining features are known. These predictions could be used to target promising areas for further detailed investigations. We describe the evolution of our work from defining a specific neural network architecture to training and optimization trials. Upon analysis we expose the inevitable problems of model variability and resulting prediction uncertainty. Finally, to address these problems we apply the concept of Bayesian neural networks, a heuristic approach to regularization in network training, and make use of the practical interpretation of the formal uncertainty measures they provide.Comment: 27 pages, 12 figure

    Antibodies to Chlamydia trachomatis in patients presenting with ectopic pregnancy at Groote Schuur hospital

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    Objectives. To detennine the prevalence of antibodies to Chlamydia trachomatis in women presenting with ectopic pregnancies to Groote Schuur Hospital.Methods. C. trachomatis antibody titres were measured using a modified micro-immunofluorescence test in women presenting with ectopic pregnancy. Control subjects were drawn from women with term pregnancies and an uneventful reproductive history.Results. Seventy-four patients and controls were studied. Demographic variables were controlled for at time of entry into the study. A significant association between the number of lifetime sexual partners and exposure to C. trachomatis was noted (P = 0.001). Patients with ectopic pregnancies had significantly higher antibody titres than control subjects (P = 0.001), and in both groups the prevalence of background antichlamydial antibody was high (ectopic pregnancies 59%, pregnant controls 32%).Conclusions. While the role of C. trachomatis infection in women who develop ectopic pregnancies needs to be explored further, it seems wise to treat them all with empirical antibiotics at the time of presentation

    In vivo imaging of microenvironmental and anti-PD-L1-mediated dynamics in cancer using S100A8/S100A9 as an imaging biomarker

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    Purpose: As a promotor of tumor invasion and tumor microenvironment (TME) formation, the protein complex S100A8/S100A9 is associated with poor prognosis. Our aim was to further evaluate its origin and regulatory effects, and to establish an imaging biomarker for TME activity. Methods: S100A9−/−cells (ko) were created from syngeneic murine breast cancer 4T1 (high malignancy) and 67NR (low malignancy) wildtype (wt) cell lines and implanted into either female BALB/c wildtype or S100A9−/− mice (n = 10 each). Anti-S100A9-Cy5.5-targeted fluorescence reflectance imaging was performed at 0 h and 24 h after injection. Potential early changes of S100A9-presence under immune checkpoint inhibition (anti-PD-L1, n = 7 vs. rat IgG2b as isotype control, n = 3) were evaluated. Results: In S100A9−/−mice contrast-to-noise-ratios were significantly reduced for wt and S100A9−/−tumors. No significant differences were detected for 4T1 ko and 67NR ko cells as compared to wildtype cells. Under anti-PD-L1 treatment S100A9 presence significantly decreased compared with the control group. Conclusion: Our results confirm a secretion of S100A8/S100A9 by the TME, while tumor cells do not apparently release the protein. Under immune checkpoint inhibition S100A9-imaging reports an early decrease of TME activity. Therefore, S100A9-specific imaging may serve as an imaging biomarker for TME formation and activity
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