compositional data

Identification of geochemical anomalies is a significant step during regional geochemical exploration. In this matter, new techniques have been developed based on deep learning networks. These simple-structure-networks act like our brains on processing the data by simulating deep layers of thinking. In this paper, a hybrid compositional-deep learning technique was applied to identify the anomalous zones in Dehsalm area which is located in 90 km of SW-Nehbandan, a town in South Khorasan province, Iran. The compositional robust factor analysis (CRFA) was applied as a tool to help select a meaningful subset as an input to Continuous Restricted Boltzmann Machine (CRBM). The dataset consists of 635 stream sediment geochemical samples analyzed for 21 elements. Using CRFA, the 3rd factor (i.e. Pb, Zn, Cu, Ag, Sb, Sr, Ba, Hg and W), indicating epithermal mineralization in the area, was considered as an input set to CRBM. The best-performed CRBM with 80 hidden units and stabilized parameters at 150 iterations was finalized and trained on all the geochemical samples of the study area. Average square contribution (ASC) and average square error (ASE) were determined as anomaly identifiers on the reconstructed error of the trained CRBM. A statistical threshold was applied on the values of the criteria (ASC & ASE) and the resulting outputs were mapped to delineate the anomalous samples. The maps indicated that ASC and ASE have the same performance in the multivariate geochemical anomaly recognition. The anomalies were spatially confirmed with the mineral indexes of Pb, Zn, Cu and Sb, as well as several active mines of Pb and Cu in the study area.

Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the correlation coefficient of a closed number system and comparing it with an open number system, one can see an increase in the values of the closed number system, which is false. Such features of this data prevent the application of standard statistical techniques to process the data. Therefore, several methods have been proposed for transforming the data from closed to open number systems. There are various geostatistical methods consisting of estimation and simulation methods in order to model a deposit. Geostatistical simulations can produce various models for a deposit with different probability percentages. The most applicable geostatistical simulation method is the sequential Gaussian simulation technique, which is highly flexible. In this work, 392 Litho-geochemical data of the Baghqloom region of Kerman in Iran consisting of 20 elements were at first converted using an open number system. Afterwards, the elements that were helpful for exploring the area and were normally standard were simulated for 100 times. After the simulations, the valid output was chosen using geostatistical validation. The maps derived from the simulations revealed the enriched concentrations of mineralization elements in the central regions.

The closed nature of geochemical data has been proven in many studies. Compositional data have special properties that mean that standard statistical methods cannot be used to analyse them. These data imply a particular geometry called Aitchison geometry in the simplex space. For analysis, the dataset must first be opened by the various transformations provided. One of the most popular of the applied transformations is the log-ratio transform. The main purpose of this research is to identify the anomalous area in the Khusf 1:100000 sheet which is located in the western part of Birjand, South Khorasan province. To achieve the goal, a dataset of 652 stream sediments geochemically analysed for 20 elements was collected. In practice, the geochemical data were first opened by CLR transformation and then the range correlation coefficient (RCC) ratio was calculated and mapped. In consequence, the robust factor analysis for compositional data was used to separate the elements, mostly in the high-value regions obtained by the method of RCC. Finally, the priority of anomalies was specified using weighted catchment analysis. The above procedures led to the recognition of some anomaly zones for elements of Cu, Bi, Sb, Ni and Cr in the study area. Such results can be useful for designing an appropriate exploratory plan for semi-detailed and detailed exploration steps.