The 22 September 2021 (AEST) moment magnitude MW 5.9 Woods Point earthquake was the largest in the state of Victoria’s recorded history. The ground motions were felt throughout the state of Victoria and into neighbouring states New South Wales and South Australia. Minor damage was reported in the city of Melbourne and in some regional towns close to the epicentre. This event was captured on many high-quality recorders from multiple sources, including private, university, and public stations. These recordings provide a rare opportunity to test the validity of some ground motion models thought to be applicable to the southeast region of Australia. This paper presents spectral acceleration and attenuation comparisons of the Woods Point earthquake event to some ground motion models. The results of this paper provide further evidence that the attenuation characteristics of southeastern Australia may be similar to that in central and eastern United States, particularly at shorter distances to the epicentre. This paper was presented at the Australian Earthquake Engineering Society 2021 Virtual Conference, Nov 25 – 26.

To improve exploration success undercover, the UNCOVER initiative identified high-resolution 3D seismic velocity characterisation of the Australian plate as a high priority. To achieve this goal, the Australian Government and academia have united around the Australian Passive Seismic Array Project (AusArray). The aim is to obtain a national half-degree data coverage and an updatable 3D national velocity model, which grows in resolution as more data become available. AusArray combines data collected from the Australian National Seismological Network (ANSN), multiple academic transportable arrays (supported by AuScope and individual grants) and the Seismometers in Schools program. The Exploring for the Future program has enable the unification of these datasets and a doubling of the national rate of data acquisition. Extensive quality control checks have been applied across the AusArray dataset to improve the robustness of subsequent tomographic inversion and interpretation. These data and inversion code framework allow robust national-scale imaging of the Earth to be rapidly undertaken at depths of a few metres to hundreds of kilometres. <b>Citation:</b> Gorbatov, A., Czarnota, K., Hejrani, B., Haynes, M., Hassan, R., Medlin, A., Zhao, J., Zhang, F., Salmon, M., Tkalčić, H., Yuan, H., Dentith, M., Rawlinson, N., Reading, A.M., Kennett, B.L.N., Bugden, C. and Costelloe, M., 2020. AusArray: quality passive seismic data to underpin updatable national velocity models of the lithosphere. 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. http://dx.doi.org/10.11636/135284 <b>Data for this product are available on request from clientservices@ga.gov.au (see data description). Last updated 08/08/2024 - Quote eCat# 135284</b>

<div>A national compilation of airborne electromagnetic (AEM) conductivity–depth models from AusAEM (Ley-Cooper et al. 2020) survey line data and other surveys (see reference list in the attachments) has been used to train a conductivity model prediction for the 0-4 m and 30 m depth intervals. Over 460,000 training points/measurements were used in a 5 K-Fold training and validation split. A further 28,626 points/measurements were used to assess the out of sample performance (OOS; i.e. points not used in the model validation). Modelling of the conductivity values (i.e. measurements along the AEM survey lines) was performed using the gradient boosted (GB) tree algorithm. The GB model is a machine learning (ML) ensemble technique used for both regression and classification tasks (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html). Samples along the flight-line were thinned to approximately one sample per 300 m. This avoided the situation where we could have more than one sample per pixel (i.e. features or covariates used in the model prediction have a cell or pixel size of 80 m) that could otherwise lead to over fitting. In addition, out of sample set used label clusters or groups to minimise overfitting. Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th respectively) to measure the model uncertainty. Grids show conductivity (S/m) in log 10 units. The methodology used to generate these conductivity grids are overall similar to that described by Wilford, et al. 2022.</div><div>&nbsp;</div><div>Reported out-of-sample r-squares for the 0-4 m and 3 m depths are 0.76 and 0.74, respectively. The ML approach allows estimation of conductivity into areas where we do not have airborne electromagnetic survey coverage. Hence these model have a national extent. Where we do not have AEM survey coverage the model is finding relationships with the covariates and making informed estimates of conductivity in those areas. Where those relationships are not well understood (i.e. where we see a departure in the feature space characteristics from what the model can ‘see’) the model prediction is likely to be less certain. Differences in the features and their corresponding values ‘seen’ and used in the model versus the full feature space covering the entire continent are captured in the covariate shift map. High values in the shift model can indicate higher potential uncertainty or unreliability of the model prediction. Users therefore need to be mindful when interpreting this dataset, of the uncertainties shown by the 5th-95th percentiles, and high values in the covariate shift map.</div><div>&nbsp;</div><div>Datasets in this data package include:</div><div>&nbsp;</div><div>1. 0_4m_conductivity_prediction_median.tif</div><div>2. 0_4m_conductivity_lower_percentile_5th.tif</div><div>3. 0_4m_conductivity_upper_percentile_95th.tif</div><div>4. 30m_conductivity_prediction_median.tif</div><div>5.30m_conductivity_lower_percentile_5th.tif</div><div>6. 30m_conductivity_upper_percentile_95th.tif</div><div>7. National_conductivity_model_shift.tif</div><div>8. Full list of referenced AEM survey datasets used to train the model (word document)</div><div>9. Map showing the distribution of training and out-of-sample sites</div><div><br></div><div>All the Geotiffs (1-6) are in log (10) electrical conductivity siemens per metre (S/m).</div><div>&nbsp;</div><div>This work is part of Geoscience Australia’s Exploring for the Future program which provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.</div><div><br></div><div><br></div><div><strong>Reference:</strong></div><div><br></div><div>Ley-Cooper, A. Y., Brodie, R.C., and Richardson, M. 2020. AusAEM: Australia’s airborne electromagnetic continental-scale acquisition program, Exploration Geophysics, 51:1, 193-202, DOI: 10.1080/08123985.2019.1694393</div><div><br></div><div>Wilford, J., LeyCooper, Y., Basak, S., Czarnota, K. 2022. High resolution conductivity mapping using regional AEM survey and machine learning. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/146380</div>