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Interpretations of paleomagnetic observations assume that naturally occurring magnetic particles can retain their primary magnetic recording over billions of years. The ability to retain a magnetic recording is inferred from laboratory measurements, where heating causes demagnetization on the order of seconds. The theoretical basis for this inference comes from previous models that assume only the existence of small, uniformly magnetized particles, whereas the carriers of paleomagnetic signals in rocks are usually larger, nonuniformly magnetized particles, for which there is no empirically complete, thermally activated model. This study has developed a thermally activated numerical micromagnetic model that can quantitatively determine the energy barriers between stable states in nonuniform magnetic particles on geological timescales. We examine in detail the thermal stability characteristics of equidimensional cuboctahedral magnetite and find that, contrary to previously published theories, such nonuniformly magnetized particles provide greater magnetic stability than their uniformly magnetized counterparts. Hence, nonuniformly magnetized grains, which are commonly the main remanence carrier in meteorites and rocks, can record and retain high-fidelity magnetic recordings over billions of years.

HPC or super-computing clusters are designed for executing computationally intensive operations that typically involve large scale I/O operations. This most commonly involves using a standard MPI library implemented in C/C++. The MPI-I/O performance in HPC clusters tends to vary significantly over a range of configuration parameters that are generally not taken into account by the algorithm. It is commonly left to individual practitioners to optimise I/O on a case by case basis at code level. This can often lead to a range of unforeseen outcomes. The ExSeisDat utility is built on top of the native MPI-I/O library comprising of Parallel I/O and Workflow Libraries to process seismic data encapsulated in SEG-Y file format. The SEG-Y File data structure is complex in nature, due to the alternative arrangement of trace header and trace data. Its size scales to petabytes and the chances of I/O performance degradation are further increased by ExSeisDat. This research paper presents a novel study of the changing I/O performance in terms of bandwidth, with the use of parallel plots against various MPI-I/O, Lustre (Parallel) File System and SEG-Y File parameters. Another novel aspect of this research is the predictive modelling of MPI-I/O behaviour over SEG-Y File benchmarks using Artificial Neural Networks (ANNs). The accuracy ranges from 62.5% to 96.5% over the set of trained ANN models. The computed Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values further support the generalisation of the prediction models. This paper demonstrates that by using our ANNs prediction technique, the configurations can be tuned beforehand to avoid poor I/O performance.

Dusty olivine (olivine containing multiple sub-micrometer inclusions of metallic iron) in chondritic meteorites is considered an ideal carrier of paleomagnetic remanence, capable of maintaining a faithful record of pre-accretionary magnetization acquired during chondrule formation. Here we show how the magnetic architecture of a single dusty olivine grain from the Semarkona LL3.0 ordinary chondrite meteorite can be fully characterized in three dimensions, using a combination of focused ion beam nanotomography (FIB-nT), electron tomography, and finite-element micromagnetic modeling. We present a three-dimensional (3D) volume reconstruction of a dusty olivine grain, obtained by selective milling through a region of interest in a series of sequential 20 nm slices, which are then imaged using scanning electron microscopy. The data provide a quantitative description of the iron particle ensemble, including the distribution of particle sizes, shapes, interparticle spacings and orientations. Iron particles are predominantly oblate ellipsoids with average radii 242 ± 94 × 199 ± 80 × 123 ± 58 nm. Using analytical TEM we observe that the particles nucleate on sub-grain boundaries and are loosely arranged in a series of sheets parallel to (001) of the olivine host. This is in agreement with the orientation data collected using the FIB-nT and highlights how the underlying texture of the dusty olivine is crystallographically constrained by the olivine host. The shortest dimension of the particles is oriented normal to the sheets and their longest dimension is preferentially aligned within the sheets. Individual particle geometries are converted to a finite-element mesh and used to perform micromagnetic simulations. The majority of particles adopt a single vortex state, with \"bulk\" spins that rotate around a central vortex core. We observed no particles that are in a true single domain state. The results of the micromagnetic simulations challenge some preconceived ideas about the remanence-carrying properties of vortex states. There is often not a simple predictive relationship between the major, intermediate, and minor axes of the particles and the remanence vector imparted in different fields. Although the orientation of the vortex core is determined largely by the ellipsoidal geometry (i.e., parallel to the major axis for prolate ellipsoids and parallel to the minor axis for oblate ellipsoids), the core and remanence vectors can sometimes lie at very large (tens of degrees) angles to the principal axes. The subtle details of the morphology can control the overall remanence state, leading in some cases to a dominant contribution from the bulk spins to the net remanence, with profound implications for predicting the anisotropy of the sample. The particles have very high switching fields (several hundred millitesla), demonstrating their high stability and suitability for paleointensity studies.

Seismic data-sets are extremely large and are broken into data files, ranging in size from 100s of GiBs to 10s of TiBs and larger. The parallel I/O for these files is complex due to the amount of data along with varied and multiple access patterns within individual files. Properties of legacy file formats, such as the de-facto standard SEG-Y, also contribute to the decrease in developer productivity while working with these files. SEG-Y files embed their own internal layout which could lead to conflict with traditional, file-system-level layout optimization schemes. Additionally, as seismic files continue to increase in size, memory bottlenecks will be exacerbated, resulting in the need for smart I/O optimization not only to increase the efficiency of read/writes, but to manage memory usage as well. The ExSeisDat (Extreme-Scale Seismic Data) set of libraries addresses these problems through the development and implementation of easy to use, object oriented libraries that are portable and open source with bindings available in multiple languages. The lower level parallel I/O library, ExSeisPIOL (Extreme-Scale Seismic Parallel I/O Library), targets SEG-Y and other proprietary formats, simplifying I/O by internally interfacing MPI-I/O and other I/O interfaces. The I/O is explicitly handled; end users only need to define the memory limits, decomposition of I/O across processes, and data access patterns when reading and writing data. ExSeisPIOL bridges the layout gap between the SEG-Y file structure and file system organization. The higher level parallel seismic workflow library, ExSeisFlow (Extreme-Scale Seismic workFlow), leverages ExSeisPIOL, further simplifying I/O by implicitly handling all I/O parameters, thus allowing geophysicists to focus on domain-specific development. Operations in ExSeisFlow focus on prestack processing and can be performed on single traces, individual gathers, and across entire surveys, including out of core sorting, binning, filtering, and transforming. To optimize memory management, the workflow only reads in data pertinent to the operations being performed instead of an entire file. A smart caching system manages the read data, discarding it when no longer needed in the workflow. As the libraries are optimized to handle spatial and temporal locality, they are a natural fit to burst buffer technologies, particularly DDN’s Infinite Memory Engine (IME) system. With appropriate access semantics or through the direct exploitation of the low-level interfaces, the ExSeisDat stack on IME delivers a significant improvement to I/O performance over standalone parallel file systems like Lustre.

Super-computing or HPC clusters are built to provide services to execute computationally complex applications. Generally, these HPC applications involve large scale IO (input/output) processing over the networked parallel file system disks. They are commonly developed on top of the C/C++ based MPI standard library. The HPC clusters MPI–IO performance significantly depends on the particular parameter value configurations, not generally considered when writing the algorithms or programs. Therefore, this leads to poor IO and overall program performance degradation. The IO is mostly left to individual practitioners to be optimised at code level. This usually leads to unexpected consequences due to IO bandwidth degradation which becomes inevitable as the file data scales in size to petabytes. To overcome the poor IO performance, this research paper presents an approach for auto-tuning of the configuration parameters by forecasting the MPI–IO bandwidth via artificial neural networks (ANNs), a machine learning (ML) technique. These parameters are related to MPI–IO library and lustre (parallel) file system. In addition to this, we have identified a number of common configurations out of numerous possibilities, selected in the auto-tuning process of READ/WRITE operations. These configurations caused an overall READ bandwidth improvement of 65.7% with almost 83% test cases improved. In addition, the overall WRITE bandwidth improved by 83% with number of test cases improved by almost 93%. This paper demonstrates that by using auto-tuning parameters via ANNs predictions, this can significantly impact overall IO bandwidth performance.

ExSeisDat is designed using standard message passing interface (MPI) library for seismic data processing on high-performance super-computing clusters. These clusters are generally designed for efficient execution of complex tasks including large size IO. The IO performance degradation issues arise when multiple processes try accessing data from parallel networked storage. These complications are caused by restrictive protocols running by a parallel file system (PFS) controlling the disks and due to less advancement in storage hardware itself as well. This requires and leads to the tuning of specific configuration parameters to optimize the IO performance, commonly not considered by users focused on writing parallel application. Despite its consideration, the changes in configuration parameters are required from case to case. It adds up to further degradation in IO performance for a large SEG-Y format seismic data file scaling to petabytes. The SEG-Y IO and file sorting operations are the two of the main features of ExSeisDat. This research paper proposes technique to optimize these SEG-Y operations based on artificial neural networks (ANNs). The optimization involves auto-tuning of the related configuration parameters, using IO bandwidth prediction by the trained ANN models through machine learning (ML) process. Furthermore, we discuss the impact on prediction accuracy and statistical analysis of auto-tuning bandwidth results, by the variation in hidden layers nodes configuration of the ANNs. The results have shown the overall improvement in bandwidth performance up to 108.8% and 237.4% in the combined SEG-Y IO and file sorting operations test cases, respectively. Therefore, this paper has demonstrated the significant gain in SEG-Y seismic data bandwidth performance by auto-tuning the parameters settings on runtime by using an ML approach.

In paleomagnetism, practical measurements are rarely made using perfect, isolated, single-phase, ferromagnetic crystals. Experimental observations are typically made using magnetic materials formed by a variety of natural processes. In this thesis, we will look at bridging the gap between current numerical modelling capability and experimental observations. First, we work towards micromagnetic modelling of multi-phase magnetic materials, including magnetostriction, embedded in a rocky matrix, along with crystal defects. We present a derivation of the Boundary Element Method formulation used by the micromagnetics package, MERRILL, and provide an extension of this from single-phase materials to multi-phase. After discussing issues with previous approaches to modelling magnetostriction, we derive and present a more robust and flexible approach. This model of magnetostriction is suitable for non-uniformmagnetizations, for multi-phase materials, and for arbitrary boundary conditions, and can be incorporated into MERRILL.We then outline a method for extending our model to materials embedded in an infinite elastic matrix of arbitrary elasticity. Finally, we present a method for modelling the magnetic response of a material due to crystal defects, along with a concrete example of a magneto-dislocation coupling energy at a magnetite-ilmenite boundary where stress due to lattice misfit is eased by regular edge dislocations. Second, we work towards being able to verify micromagnetic models against nano-scale experimental data. To do this, we present two techniques for simulating electron holograms from micromagnetic modelling results, a technique capable of imaging magnetic structures at the nano-scale. We also present example electron holograms of commonly occurring magnetic structures in nano-scale rock and mineral magnetism, and highlight some distinguishing features, which may be useful for interpreting experimental electron holography data.