Case study: Cloud-based visual analytics and processing for geophysical data

Dr Pavel Golodoniuc1, Mr Samuel Bradley1, Mr John Hille1, Dr Aaron Davis1

1CSIRO, Kensington, Australia

Abstract:

Pavel’s professional focus is on data-intensive multi-disciplinary sciences, machine learning, and scientific data visualisation techniques. He is now a research team leader of the Informatics Platforms team at CSIRO Mineral Resources.

Geophysical survey datasets used for resource exploration and detection are large in volume, dense in time and space, and have many dimensions. The Geophysics Processing Toolkit was conceived as geophysical survey data processing toolkit for interpretation and inversion that is scalable and deployable to various compute infrastructures.

Interactive visualisation and signal processing tools make the process more efficient. We have developed the toolkit using a cross-platform technology stack designed to work in a containerised environment in a Cloud and be accessible via a web browser. This approach makes it intrinsically scalable and cost-efficient to operate. The toolkit architecture allows for a greater degree of extensibility offering a range of interactive visualisations and integration of a suite of signal processing tools for noise detection and removal. Decoupling of visualisation components from cloud compute and storage nodes allows on-the-fly substitution of analytical codes, e.g., forward modelling, inversions. This brings greater flexibility in experimental research through the ability to apply various numerical methods and compare results via elaborate visualisations and through the application of statistical methods.

Initially developed as a processing toolkit for airborne electromagnetic data, it can be extended well beyond that domain to incorporate multiple geophysical datasets such as gravity and magnetics. Delegation of computing tasks and storage requirements to a third-party cloud provider (a) minimises procurement and maintenance costs of computing/storage infrastructure and (b) eliminates clients’ privacy concerns as data are stored and processed in an isolated cloud environment.


Biography:

Dr Pavel Golodoniuc is trained in Computer Sciences with an emphasis on system architectures and numerical modelling and holds a PhD in Exploration Geophysics from Curtin University, Western Australia. Before joining CSIRO in 2008, he gained extensive experience in system design and implementation for commercial, industrial and governmental sectors. His experience in software engineering includes the development of hardware drivers, SCADA systems, software for mobile and embedded platforms as well as scientific software for geophysical numerical modelling. He has held technical leadership roles in national spatial data infrastructure projects in Australia involving multi-disciplinary collaborators from Australian geological surveys, universities, and international Open Geospatial Consortium community.

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