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  • The term 'modelling while interpreting' refers to the use of 3D models during the interpretation of reflection seismic data in order to inform that process. Rather than using 3D models at a final stage of the project just to display results, new software tools are emerging to enable development of 3D models in parallel with the seismic interpretation work. These tools provide additional means to help interpreters make informed decisions such as where to pick basement and to check the 3D integrity of their geological models. Applications of this new workflow are illustrated through a recently completed petroleum prospectivity assessment of the Capel and Faust frontier deep-water basins located 800 km to the east of Brisbane. Geoscience Australia acquired 2D geophysical data across these basins in 2007 and subsequently mapped the complex distribution of sub-basins by integrating 2D time-domain seismic interpretation with 3D gravity modelling. Forward and inverse 3D gravity models were used to inform the seismic interpretation and test the seismic basement pick. The identification of basement was problematic due to a lack of wells and the likelihood that acoustic basement represented older sedimentary material intruded by igneous rocks. Sonobuoy refraction data were modelled to achieve conversion of travel times to depth and estimate densities. Modelling gravity while interpreting reflection seismic data improved confidence in the mapping of the extent and thickness of sediments in these basins, and has potential to be used more widely in mapping projects to reduce exploration risk.

  • Increasing the knowledge of ocean current patterns in Torres Strait region is of direct interest to indigenous communities and industries such as fisheries and shipping that operate in the region. Ocean circulation in Torres Strait influences nearly all aspects of the ecosystem, including sediment transport and turbidity patterns, primary production in the water column and bottom sediments, and recruitment patterns for organisms with pelagic phases in their life cycles. This study is the first attempt to describe the water circulation and transport patterns across Torres Strait and adjacent gulfs and seas, on time scales from hours to years. It has also provided a framework for an embedded model describing sediment transport processes (described in Margvelashvili and Saint-Cast, 2006). The circulation model incorporated realistic atmospheric and oceanographic forcing, including winds, waves, tides, and large-scale regional circulation taken from global model outputs. Simulations covered a hindcast period of eight years, allowing the tidal, seasonal, and interannual flow characteristics to be investigated. Results demonstrated that instantaneous current patterns were strongly dominated by the barotropic tide and its spring-neap cycle. However, longer-term transport through Torres Strait was mainly controlled by seasonal winds, which switch from north-westerly monsoon winds in summer to south-easterly trades in winter. Model results were shown to be relatively insensitive to internal model parameters. However, model performance was strongly dependent on the quality of the forcing fields. For example, the prediction of low-frequency inner-shelf currents was improved substantially when temperature and salinity forcing based on the average seasonal climatologies was replaced by that from global model outputs. Uncertainties in the tidal component of the forcing also limited model skill, particularly predictions to the west of Cape York which were strongly dependent on the sealevels imposed along the open boundary in Gulf of Carpentaria.

  • The MODIS (or Moderate Resolution Imaging Spectroradiometer) 250 m EVI dataset provides a valuable ongoing means of characterising and monitoring changes in land use and resource condition. However the multiple factors that influence a time series of greenness data make the data difficult to analyse and interpret. Without prior knowledge, underlying models for time series in a given remote sensing image are often heterogeneous. So while conventional time series analysis methods such as wavelet transform and Fourier analysis may work well for part of the image, these models are either invalid or must be substantially re-parameterised for other parts of the image. To overcome these challenges we propose a new approach to distil information from earth observation time series. The characteristics of a remote sensing time series are represented by a set of statistics (which we call coefficients) selected to correspond to the dynamics of a natural system. To ensure the coefficients are robust and generic, statistics are calculated independently by applying statistical models with less complexity on shorter segments within the time series. An International Standards Organization (ISO) Land Cover classification was generated for cropping regions in the Gwydir and Namoi catchments, in Australia. Areas identified in the classification as irrigated and rain fed cropping were analysed using a tailored time series analysis tool. The crop analysis tool identifies time series features such as the number and duration of fallow periods, crop timing, presence/absence of a crop during a year for a specific growing season. This information is combined with paddock boundaries derived from Landsat imagery to provide detailed year-by-year insight into cropping practices in the Gwydir and Namoi catchments.

  • Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences. <b>Citation:</b> Li, J. A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R. Appl. Sci. 2019, 9, 2048. https://doi.org/10.3390/app9102048

  • To perform a realistic 3D inversion of gravity data covering a significant proportion of the surface of the Earth, it is necessary to take into account the curvature of the Earth. We have developed an algorithm for inverting gravity data in spherical coordinates and have demonstrated this using data covering the continental mass of Australia and surrounding ocean areas. The density structures evident in the crust and uppermost mantle of the resultant 3D inversion model are in broad agreement with knowledge of the geological features for the region and with variations in the depth to the Moho that are present in the AusMoho model.

  • Geothermal energy has been harnessed in Australia for several decades for both direct use applications and power generation, but only at very small scale installations. Australia's geothermal resources are amagmatic and unconventional by the accepted definitions in other parts of the world centred on active volcanism or plate margin collision. Worldwide, there is a lack of experience in exploring for and developing unconventional resources, and few "deposit" or resource models to aide exploration. The conceptualisation of a range of geological environments amenable to geothermal resource development will underpin the large scale development of geothermal utilisation in Australia. This will include developing exploration models spanning the range of unconventional geothermal resources; from "EGS" or "Hot (Dry) Rock" where permeability stimulation is a pre-requisite, to "Hot Sedimentary Aquifer" where no permeability stimulation is required.

  • Basin evolution of the Vlaming Sub-basin and the deep-water Mentelle Basin, both located offshore on the southwest Australian continental margin, were investigated using 2D and 3D petroleum system modelling. Compositional kinetics, determined on the main source sequences, were used to predict timing of hydrocarbon generation and migration as well as GOR evolution and phase behaviour in our 2D and 3D basin models. The main phase of petroleum generation in the Vlaming Sub-basin occurred at 150 Ma and ceased during following inversion and erosion episodes. Only areas which observed later burial have generated additional hydrocarbons during the Tertiary and up to present day. The modelling results indicate the likely generation and trapping of light oils for the Jurassic intervals for a variety of structural traps. It is these areas which are of greatest interest from an exploration point of view. The 2D numerical simulations in the Mentelle Basin indicate the presence of active hydrocarbon generating kitchen areas. Burial histories and generalized petroleum evolutionary histories are investigated.

  • Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management, but they are either unavailable or unknown. Hence we applied random forest (RF), generalised linear model (GLM) and their hybrid methods with geostatistical techniques to SSR data by addressing relevant issues with variable selection and model selection. It was found that: 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model selection methods are unsuitable for identifying GLM predictive models and joint application of RF and AIC can select accuracy-improved models; 3) highly correlated predictors may improve RF predictive accuracy; 4) hybrid methods for RF can accurately predict count data; and 5) effects of model averaging are method-dependent. This study depicted the non-linear relationships of SSR and predictors, generated spatial distribution of SSR with high accuracy and revealed the association of high SSR with hard seabed features. <b>Citation:</b> Jin Li, Belinda Alvarez, Justy Siwabessy, Maggie Tran, Zhi Huang, Rachel Przeslawski, Lynda Radke, Floyd Howard, Scott Nichol, Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness, <i>Environmental Modelling & Software</i>, Volume 97, 2017, Pages 112-129, https://doi.org/10.1016/j.envsoft.2017.07.016

  • A Bayesian inversion technique to determine the location and strength of trace gas emissions from a point source in open air is presented. It was tested using atmospheric measurements of nitrous oxide (N2O) and carbon dioxide (CO2) released at known rates from a source located within an array of eight evenly spaced sampling points on a 20 m radius circle. The analysis requires knowledge of concentration enhancement downwind of the source and the normalized, three-dimensional distribution (shape) of concentration in the dispersion plume. The influence of varying background concentrations of ~1% for N2O and ~10% for CO2 was removed by subtracting upwind concentrations from those downwind of the source to yield only concentration enhancements. Continuous measurements of turbulent wind and temperature statistics were used to model the dispersion plume. The analysis localized the source to within 0.8 m of the true position and the emission rates were determined to better than 3% accuracy. This technique will be useful in assurance monitoring for geological storage of CO2 and for applications requiring knowledge of the location and rate of fugitive emissions.

  • The inversion analyses presented in our paper and now extended in this Reply were ultimately only one part of the AEM system selection process for the BHMAR project. Both Derivative and Inversion analyses are in their nature theoretical, and it is impossible, in a theoretical analysis, to capture all of the aspects relevant for real surveys with little margin for error in political time frames. In reality, neither the Derivative nor Inversion analysis provided the degree of certainty required (by the project manager and client) to ascertain whether any of the candidate AEM systems were able to map the key managed aquifer recharge targets recognized in the study area. Consequently, a decision was made to acquire data over a test line with the 2 systems (SkyTEM and TEMPEST) that performed best in the Derivative and Inversion analysis studies. This approach was vindicated with quite distinctive and very different performance observed between these two systems, especially when compared with borehole and ground geophysical and hydrogeological data over known targets. Data were inverted both with contractors' software and with reference software common to all systems and the results were compared. Ultimately, it was the test line, particularly in the near-surface (top 20metres), thatmade the SkyTEM system stand out as the best system for the particular targets in the project area. SkyTEM mapped the key multi-layered hydrostratigraphy and water quality variability in the key aquifer that defined the key MAR targets, although the TEMPEST system had a superior performance at depths exceeding 100metres. Importantly, the SkyTEM system also mapped numerous, subtle fault-offsets in the shallow near-surface. These structures were critical to mapping recharge and inter-aquifer leakage pathways. Further analysis has demonstrated that selection of the most appropriate AEM system and inversion can result in order of magnitude differences in estimates of potential groundwater resources. The acquisition of SkyTEM data was an outstanding success, demonstrating the capability of AEM systems to provide high-resolution data for the rapid mapping and assessment of groundwater and strategic aquifer storages in Australia's complex and highly salinized floodplain environments. The SkyTEM data were used successfully to identify 14 major new groundwater targets and multiple MAR targets, and these have been validated by an extensive drilling program (Lawrie et al., 2012a-e). Increasingly, the demand from clients for higher certainty in project decision making, and quantifying errors, will see development of new system comparative analysis approaches such as the Inversion analysis approach documented in our initial paper. Ultimately, system fly-offs are likely in high-profile projects where budgets permit.