## 2 Background  
Information fusion is the process of combining data gathered from two or more sensors to produce a more comprehensive dataset through mathematical modeling. The integration and concurrent use of multiple sensors has long been utilized in remote sensing and mechatronics for the perception of particular aspects of different environments and in decision making about the environment being sensed. It is well understood that any task using parameter estimates from multiple sources of data can benefit from the use of information fusion. Since different instrument types have their individual capabilities and limitations, the synergistic combination of multiple analyzers and aggregation of the unique information from the individual components of a multianalyzer system is expected to improve overall system performance [1]. Amongst the advantages and benefits of such data fusion are extended parameter coverage yielding a more complete view of the system being interrogated, provision of information about the independent features of the system, increased characterization of information content, improved resolution, reduced uncertainty, increased robustness and reliability of the analysis, increased hypothesis discrimination, and increased confidence in the result [2]. Traditionally, multisensor data fusion is perceived in a three-level hierarchy, namely data/information fusion, feature fusion, and decision fusion [3]. The success of such information fusion is determined by how well the new knowledge produced by fusion processes represents reality, which consequently depends on the quality of the data, the adequacy of the uncertainty model(s) used, and the accuracy or applicability of prior knowledge. It has been observed across a variety of application domains as diverse as agriculture and food safety [4; 5; 6], archaeology and cultural heritage [7; 8; 9], the geosciences [10; 11; 12; 13; 14], metallurgy [15; 16], remote sensing [17; 18; 19; 20], robotics [21; 22], and defense, security, and forensics [23; 24; 25] that, by synthesizing raw data from multiple sources (data fusion), processing and integrating this data (information fusion), and then analyzing the integrated data using the tools of multivariate statistics, statistical estimation and inference, information theory, artificial intelligence, and machine learning, it is possible to generate more meaningful results than can be obtained with data from any single source and that such fused information can be of enhanced value due to the use of complementary information. For example, fusion of measurements from different kinds of analytical instruments can reduce limits of detection and decrease uncertainty, whereas the fusion of information from different types of sensors with different physical characteristics can enhance insight about the environment being observed [26; 27; 28; 29; 30]. XRFS and RS are well-established laboratory techniques for chemical analysis [31; 32; 33; 34; 35] and have been applied to a very wide range of natural and man-made materials for more than half a century. LIBS is a less well known analytical methodology, but there is a robust technical literature from the last two decades that documents and validates that LIBS can analyze the elemental content of gases, liquids, and solids [36; 37; 38; 39; 40; 41; 42]. Recent work has demonstrated that LIBS can be used effectively for geomaterial classification and discrimination [43; 44; 45; 46; 47] and that LIBS analysis of soils is an effective methodology for characterizing elemental chemistry [48; 49; 50]. Like XRFS and RS before it, LIBS technology has recently progressed rapidly from laboratory systems to commercial handheld units for field use [51]. Since all analytical methods have certain strengths and limitations, the intentional combination of complementary techniques can provide for a more robust and informative analysis. XRFS and LIBS are both analysis methods with unique features and different sensitivities to a wide range of elements.