Using Airborne Electromagnetic Data To Construct A Sediment Textural Model And Salt Budget For Use In Hydrogeological Applications: A Case Study From New South Wales, Australia

Abstract

This paper reports on the research to quantify salt budgets and sediment and saprolite textures in a sub-catchment of the Lachlan Catchment, NSW. Data were obtained from airborne electromagnetic (AEM), ground and borehole electromagnetic (EM) data, and laboratory petrophysical and geochemical studies. Algorithms have been derived from statistical relationships between AEM conductivity data, down-hole induction logs and petrophysical attributes. These algorithms are then used in ArcGISTM software to model the salt budgets and textural distribution model. Introduction Salinity is one of the major environmental issues in Australia, and includes salinisation of both urban and agricultural areas, potential salinisation of major rivers and waterways from saline water seeping out of catchments, and seawater intrusion in coastal irrigation areas. Airborne geophysical surveys, specifically AEM, have been used to improve salinity management outcomes. Critical to the successful use of airborne geophysics for salinity management is identification of the key management questions, integration of AEM data with appropriate hydrogeological data, and incorporation of interpreted products into hydrogeological models. One of the important criteria in managing salinity is the ability to quantify the amount of salt in the environment and its potential interaction with groundwater flow paths. The total amount of salt, including both immobile and mobile, is termed salt budget. EM systems are useful because they measure the bulk electrical conductivity (EC) response from various geological materials, including sediments and bedrock. The EC is mainly attributed to the volume of saline pore fluid and its ionic concentrations. This paper reports on research to quantify salt budgets and sediment and saprolite textures in a sub-catchment of the Lachlan Catchment in NSW. Data were obtained from AEM, ground and borehole EM data, and laboratory petrophysical and geochemical studies. Algorithms have been derived from statistical relationships between AEM conductivity data, down-hole induction logs, and petrophysical attributes collected from drill cores and cuttings (moisture content, porosity, salinity and texture). These algorithms are then used in ArcGIS software to model the salt budgets. This approach is also used to derive a textural distribution model of the sub-catchment. From the textural model, preferential flowpaths and flow impedance at a scale resolvable on the conductivity depth-slice intervals (10 – 20 m thick) are established. The research methodology established in this project expands the utility of AEM datasets for calculating salinity budgets and groundwater flow paths. The methodology also provides better constraints on the interpretation of aquifer systems and geological frameworks.