Joint interpretation of magnetic and gravity data at the Golgohar mine in Iran

Geophysical modelling can take advantage of combining more geophysical data with the aim of decreasing the non-uniqueness of the resulting interpretation. The combination of gravity and magnetic data can yield useful information about the source properties by using the Poisson's analysis, which may help to infer the source properties, density and magnetic susceptibility, of the causative sources. Such estimates can be set as constraints for further interpretation. We consider first the synthetic data of a homogeneous block, for which correlation analysis allows the estimation of the Poisson ratio. The estimated source parameters are then used to invert gravity and magnetic data successfully. We applied this approach to the gravity and magnetic datasets in the Golgohar iron ore complex area, located in the Sanandaj-Sirjan zone (Iran). We performed a correlation analysis between the reduced to the pole magnetic data and the vertical gravity gradients of the 1st and 2nd order, respectively. We estimated strong magnetization contrasts between the mineral rocks and the surrounding host rocks. Further quantitative information about the source depth and geometry are obtained by 2D inverse modelling of both gravity and magnetic data, based on the damped weighted minimum-length solution. Even in this case, a-priori information from the correlation analysis leads to constrain the inverse process of both gravity and magnetic data. Inverse modelling confirms a high correlation between both susceptibility and density models, both horizontally and vertically, and show the presence of isolated sources with high density and magnetization contrasts, in good accordance with the average physical parameters of iron‑gold deposit formations

Spatial Modeling of Visceral Leishmaniasis in Iran From 2010 to 2018

Kala-Azar is the most lethal type of leishmaniasis, sporadic in most parts of Iran and prevalent in some provinces. Using the Geographical Information System (GIS) and satellite data analysis, we intended to assess the disease's incidence in Iran. Methods: Using GIS, data received from the Ministry of Health and Medical Education in Tehran, Iran, and other associated institutions between 2010 and 2018 were evaluated. The disease's geographical distribution maps were then constructed, and the disease's hotspots in Iran were identified using spatial analysis using ArcGIS10.5 software. Geographically weighted regression (GWR) analysis in ArcGIS10.5 was used to link disease-influencing variables such as temperature, relative humidity, normalized difference vegetation index (NDVI), and incidence of visceral leishmaniasis. Linear regression analysis, SPSS 21 software descriptive statistics, and chi-square test were used to analyze the data. Results: This study revealed that the provinces of Ardabil, East Azarbaijan, North Khorasan, and Fars were the hot spots of VL. The provinces of Ardabil, East Azarbaijan, North Khorasan, Fars, Bushehr, Semnan, Sistan, Baluchistan, Esfahan, Chaharmahal Bakhtiari, Qom, Golestan, and Kerman had the highest correlation between temperature, vegetation density, and the incidence of Kala Azar, as determined by geographical weighted regression analysis.&nbsp