Extended source imaging, a unifying framework for seismic & medical imaging

We present three imaging modalities that live on the crossroads of seismic and medical imaging. Through the lens of extended source imaging, we can draw deep connections among the fields of wave-equation based seismic and medical imaging, despite first appearances. From the seismic perspective, we underline the importance to work with the correct physics and spatially varying velocity fields. Medical imaging, on the other hand, opens the possibility for new imaging modalities where outside stimuli, such as laser or radar pulses, can not only be used to identify endogenous optical or thermal contrasts but that these sources can also be used to insonify the medium so that images of the whole specimen can in principle be created.Comment: Submitted to the Society of Exploration Geophysicists Annual Meeting 202

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO2 remains within the storage complex. Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images. However, these superior features come, unfortunately, at prohibitive costs and time-intensive efforts potentially rendering extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology where time-lapse images are created by inverting non-replicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of noisy time-lapse seismic datasets are simulated that contain imprints of regular CO2 plumes and irregular plumes that leak. These time-lapse datasets are subsequently inverted to produce time-lapse difference images used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO2 leakage automatically on unseen data and with a reasonable accuracy

Analysis of the dynamics of Staphylococcus aureus binding to white blood cells using whole blood assay and geno-to-pheno mapping

Given that binding and internalization of bacteria to host cells promotes infections and invasion, we aimed at characterizing how various S. aureus isolates adhere to and are internalized by different white blood cells. In particular, the role of genetic determinants on the association kinetics should be unveiled. A flow cytometric (FACS) whole blood assay with fluorescently labelled isolates was applied to 56 clinical S. aureus isolates. This phenotypic data was then linked to previously obtained genotyping data (334 genes) with the help of a redescription mining algorithm. Professional phagocytes showed a time-dependent increase of bacterial adhesion and internalization. Isolates showing higher affinity to granulocytes were associated with lower binding to monocytes. In contrast binding activity between S. aureus and lymphocytes could be subdivided into two phases. Preliminary binding (phase 1) was highest directly after co-incubation and was followed by S. aureus detachment or by sustained binding of a small lymphocyte subset (phase 2). Strain-dependent low granulocyte binding was observed for clonal complex 5 (CC5) isolates (MRSA), as compared to CC30 and CC45 (MSSA). S. aureus isolates associated with low granulocyte phagocytosis were characterized by the presence (cap8, can) and the absence (sasG, lukD, isdA, splA, setC) of specific hybridization signals