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.

<div> The High Quality Geophysical Analysis (HiQGA) package is a framework for geophysical forward modelling, Bayesian inference, and deterministic imaging. A primary focus of the code is production inversion of airborne electromagnetic (AEM) data from a variety of acquisition systems. Adding custom AEM systems is simple using a modern computational idea known as multiple dispatch. For probabilistic spatial inference from geophysical data, only a misfit function needs to be supplied to the inference engine. For deterministic inversion, a linearisation of the forward operator (i.e., Jacobian) is also required. For fixed wing geometry nuisances, probabilistic inversion is carried out using Hierarchical Bayesian inference, and deterministic inversion for these nuisances is done using BFGS optimisation. The code is natively parallel, and inversions from a full day of production AEM acquisition can be inverted on thousands of CPUs within a few hours. This allows for quick assessment of the quality of the acquisition, and provides geological interpreters preliminary subsurface images together with associated uncertainties. These images are then used to create subsurface models for a range of applications from natural resource exploration to its management and conservation.</div><div> </div> This abstract was submitted to/presented at the 8th International Airborne Electromagnetics Workshop (AEM 2023) (https://www.aseg.org.au/news/aem-2023).