The magnetotelluric (MT) method is increasingly being applied to a wide variety of geoscience problems. However, the software available for MT data analysis and interpretation is still very limited in comparison to many of the more mature geophysical methods such as the gravity, magnetic or seismic reflection methods. MTPy is an open source Python package to assist with MT data processing, analysis, modelling, visualization and interpretation. It was initiated at the University of Adelaide in 2013 as a means to store and share Python code amongst the MT community (Krieger and Peacock 2014). Here we provide an overview of the software and describe recent developments to MTPy. These include new functionality and a clean up and standardisation of the source code, as well as the addition of an integrated testing suite, documentation, and examples in order to facilitate the use of MT in the wider geophysics community.

The magnetotelluric (MT) method is becoming more widely used in the geoscience community as it becomes increasingly recognised as a useful exploration tool. However, while the analysis and inversion tools available to the MT community have increased over recent years, the software available to work with these tools is still somewhat limited and often costly in comparison to some of the more mature techniques like gravity, magnetics and seismic. The MTpy python library is open source software that aims to assist MT practitioners in carrying out the processing and analysis steps that need to be carried out with MT data and in working with the various inversion codes that are available. However, MTpy still contains coding issues, bugs and gaps in functionality, which have limited its use to date. We are currently developing MTpy to rectify these problems and expand the functionality, and thus facilitate the use of MT as an exploration technique. Key improvements include adding new functions and modules, refactoring the code to give better quality and consistency, fixing bugs and adding new Graphic User Interfaces. Abstract prepared for the Australian Exploration Geoscience Conference (AEGC) 18 -21 February 2018, Sydney, NSW. (https://www.aig.org.au/events/first-australian-exploration-geoscience-conference/)

<p>The Isotopic Atlas of Australia is one of the fundamental datasets in Geoscience Australia (GA)’s Exploring for the Future program. It is underpinned by a nationwide coverage of high-quality U-Th-Pb radiometric dates, mostly determined by Sensitive High Resolution Ion Micro Probe (SHRIMP). For the past decade, GA and the international SHRIMP community have relied on SQUID 2.50 software to process isotopic data acquired by SHRIMP for U-Th-Pb geochronology. However, SQUID 2.50 is obsolete because of dependency on Excel 2003, which is unsupported by Microsoft and will not operate on Windows 10. As a result, GA collaborated with the Cyber Infrastructure Research and Development Laboratory for Earth Sciences (CIRDLES.org) at the College of Charleston (USA) to redeploy SQUID 2.50 algorithms in an open-source, platform-independent and freely available Java application (Squid3). Squid3 replicates (rather than seeking to enhance) SQUID 2.50 logic and arithmetic, with substantial improvements in flexibility and interactivity. In this paper, we review documentation detailing widely trusted but little-known SQUID 2.50 algorithms and provide an overview of Squid3, focusing on the implementation and improvement of SQUID 2.50 functionality. The beta version of Squid3 is capable of end-to-end U-Th-Pb data processing, from ingestion of raw SHRIMP .xml files, through finalised summary calculations, to reporting of data arrays suitable for visualisation via packages such as Isoplot, Topsoil and IsoplotR. In production, Squid3 will enable users to sever links with Excel 2003, while ensuring the sustainability, reliability and relevance of SHRIMP data. <p><b>Citation:</b> Bodorkos, S., Bowring, J.F., and Rayner, N.M., 2020. Squid3: Next-generation data processing software for Sensitive High Resolution Ion Micro Probe (SHRIMP). In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

<div>Elastic seismic waves propagate in the solid Earth and contain valuable information for inferring subsurface physical properties. Traditionally, the data acquisition requires active artificial sources for generating seismic waves, which can be air-gun arrays for offshore seismic surveys, and explosive or vibroseis sources for onshore seismic surveys. Active-source seismic data have a wide frequency spectrum often from 4 Hz to 50 Hz and contain reflected and refracted P and S waves and surface waves, which have been widely used for obtaining high-resolution images of subsurface structures across industry and academia in the past decades. In this report, we study the feasibility of combining seismic data from borehole drilling (drilling noise) and the state-of-the-art seismic imaging technique, reverse time migration, for a direct migration (imaging) of drilling-generated seismic noise data without intermediate steps (such as seismic interferometry for virtual common-source seismic data), in the aim of obtaining subsurface structural images. The developed imaging method enables informed decision-making during the drilling process by revealing the three-dimensional structure beneath the drill rig. With precise knowledge of the depth to bedrock and the surrounding geological formations, better planning can be achieved, significantly reducing both costs and time.</div>

Seismological data are used for a variety of purposes, from earthquake hazard zonation to mapping Earth structure and mineral resource exploration. The immense volumes of seismic data now available challenge the application of routine seismic analysis techniques using existing tools. These tools fail to take advantage of recent advances in computing hardware and data formats. Given the scale of data to process and the computational complexity of algorithms involved, a more efficient approach that scales on high-performance computing and data (HPC-HPD) platforms is needed. In addition, different agencies have tended to use bespoke and ad hoc data formats, data curation processes and quality standards, hindering large-scale analyses and modelling. High-performance seismological tools (HiPerSeis) facilitate the transformation of source seismological data into formats geared towards HPC-HPD platforms. HiPerSeis also implements optimised seismological workflows that can be run at large scale on HPC-HPD platforms. <b>Citation:</b> Hassan, R., Hejrani, B., Medlin, A., Gorbatov, A. and Zhang, F., 2020. High-performance seismological tools (HiPerSeis). In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

The magnetotelluric (MT) method is increasingly being applied to map tectonic architecture and mineral systems. Under the Exploring for the Future (EFTF) program, Geoscience Australia has invested significantly in the collection of new MT data. The science outputs from these data are underpinned by an open-source data analysis and visualisation software package called MTPy. MTPy started at the University of Adelaide as a means to share academic code among the MT community. Under EFTF, we have applied software engineering best practices to the code base, including adding automated documentation and unit testing, code refactoring, workshop tutorial materials and detailed installation instructions. New functionality has been developed, targeted to support EFTF-related products, and includes data analysis and visualisation. Significant development has focused on modules to work with 3D MT inversions, including capability to export to commonly used software such as Gocad and ArcGIS. This export capability has been particularly important in supporting integration of resistivity models with other EFTF datasets. The increased functionality, and improvements to code quality and usability, have directly supported the EFTF program and assisted with uptake of MTPy among the international MT community. <b>Citation:</b> Kirkby, A.L., Zhang, F., Peacock, J., Hassan, R. and Duan, J., 2020. Development of the open-source MTPy package for magnetotelluric data analysis and visualisation. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.