Self-Potential and electromagnetic monitoring during fluid injection into magmatic rocks

Still today, induced seismicity is a major challenge during massive hydraulic stimulation in geothermal energy. Several approaches have been made to deal with it: e.g., adapted traffic light systems for real-time monitoring of hydraulic parameters and induced seismicity or testing new injection protocols such as fatigue hydraulic fracturing. In reducing the magnitudes and number of seismic events, the seismic signal is weakened, affecting the seismic monitoring itself again. Therefore, new monitoring techniques are of major interest. Recent studies showed the successful application of electric and electromagnetic monitoring surveys in the context of hydraulic injection experiments. This thesis includes data sets from two different injection experiments at the reservoir- and the mine-scale to investigate possible electrokinetic or seismoelectric effects and to reproduce the observations from other injection experiments, such as Rittershoffen. The experiments at reservoir-scale were electromagnetically monitored using magnetotelluric (MT) data. The results of this study show a temporal relation between decreasing apparent resistivity in the period ranges of 0.15-1 s and 4-8 s and (i) the geomagnetic field activity, (ii) the fluid losses up to 60 L/s, as well as (iii) mechanic processes occurring before induced seismicity. Against this background, the full physical meaning of the apparent resistivity changes remains a matter of debate. Note that forward modeling of the effect of the injected water volume could not explain the observed resistivity changes. Hydraulic fracturing (HF) experiments at underground lab scale, HF2 (conventional) and HF3 (fatigue hydraulic fracturing), were self-potential (SP) and electromagnetic radiation (EMR) monitored. Changes in SP have been observed with a temporal delay in both experiments in the near-field sensors (about 50 - 70 m distance from the experiment). However, in the far-field sensors (about 150 - 200 m from the experiment), such changes are observed only during the HF2 experiment. Furthermore, the background signal is reached about 45 minutes after the last pressure release. Generally, minima and maxima obtained from different electrode offsets between the individual injection steps are in phase. In contrast, after completing the two experiments, HF2 and HF3, the major minima and maxima are characterized by a significant phase shift. In addition, during shut-in phases, the results show an inverse correlation between pressure drop and increasing EMR amplitudes during HF2 and HF3. In conclusion, the objectives of this thesis have been achieved by (i) providing a new magnetotelluric monitoring data set of fluid injection in a different rock type, i.e., basalt, (ii) filling the scale-gap of SP and electromagnetic (EM) data sets by monitoring underground-lab scale injection tests, and (iii) indicating precursor of seismic events under controllable condition. However, some open questions remain, such as if the precursor observations are verified in other experiments and on underground lab- or lab-scale. And last but not least, is the supposed link between pressure and EM or SP signal linked to fluid flow on micro-fractures or only to pressure

Gene panel testing of 5589 BRCA1/2-negative index patients with breast cancer in a routine diagnostic setting: results of the German Consortium for Hereditary Breast and Ovarian Cancer

The prevalence of germ line mutations in non-BRCA1/2 genes associated with hereditary breast cancer (BC) is low, and the role of some of these genes in BC predisposition and pathogenesis is conflicting. In this study, 5589 consecutive BC index patients negative for pathogenic BRCA1/2 mutations and 2189 female controls were screened for germ line mutations in eight cancer predisposition genes (ATM, CDH1, CHEK2, NBN, PALB2, RAD51C, RAD51D, and TP53). All patients met the inclusion criteria of the German Consortium for Hereditary Breast and Ovarian Cancer for germ line testing. The highest mutation prevalence was observed in the CHEK2 gene (2.5%), followed by ATM (1.5%) and PALB2 (1.2%). The mutation prevalence in each of the remaining genes was 0.3% or lower. Using Exome Aggregation Consortium control data, we confirm significant associations of heterozygous germ line mutations with BC for ATM (OR: 3.63, 95% CI: 2.67-4.94), CDH1 (OR: 17.04, 95% CI: 3.54-82), CHEK2 (OR: 2.93, 95% CI: 2.29-3.75), PALB2 (OR: 9.53, 95% CI: 6.25-14.51), and TP53 (OR: 7.30, 95% CI: 1.22-43.68). NBN germ line mutations were not significantly associated with BC risk (OR: 1.39, 95% CI: 0.73-2.64). Due to their low mutation prevalence, the RAD51C and RAD51D genes require further investigation. Compared with control datasets, predicted damaging rare missense variants were significantly more prevalent in CHEK2 and TP53 in BC index patients. Compared with the overall sample, only TP53 mutation carriers show a significantly younger age at first BC diagnosis. We demonstrate a significant association of deleterious variants in the CHEK2, PALB2, and TP53 genes with bilateral BC. Both, ATM and CHEK2, were negatively associated with triple-negative breast cancer (TNBC) and estrogen receptor (ER)-negative tumor phenotypes. A particularly high CHEK2 mutation prevalence (5.2%) was observed in patients with human epidermal growth factor receptor 2 (HER2)-positive tumors

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

DUNE Offline Computing Conceptual Design Report

This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation