Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN) (Iron Ore Deposit, Yazd, IRAN)

The study area is located near Toot village in the Yazd province of Iran, which is considered in terms of its iron mineralization potential. In this area, due to radioactivity, radiometric surveys were performed in a part of the area where magnetometric studies have also been performed. According to geological studies, the presence of magnetic anomalies can have a complex relationship with the intensity of radioactivity of radioactive elements. Using the K-means clustering method, the centers of the clusters were calculated with and without considering the coordinates of radiometric points. Finally, the behavior of the two variables of magnetic field strength and radioactivity of radioactive elements relative to each other was studied, and a mathematical relationship was presented to analyze the behavior of these two variables relative to each other. On the other hand, the increasing and then decreasing behavior of the intensity of the Earth’s magnetic field relative to the intensity of radioactivity of radioactive elements shows that it is possible to generalize the results of magnetometric surveys to radiometry without radiometric re-sampling in this region and neighboring areas. For this purpose, using the general regression neural network and backpropagation neural network (BPNN) methods, radiometric data were estimated with very good accuracy. The general regression neural network (GRNN) method, with more precision in estimation, was used as a model for estimating the radiation intensity of radioactive elements in other neighboring areas.

Supplemental Material: The importance of oxbow lakes in the floodplain storage of pollutants

Radiometric data from Woods Pond, Massachusetts, loss-on-ignition data from cores collected along transect T1, and coordinates of core locations.<br>

Supplemental Material: The importance of oxbow lakes in the floodplain storage of pollutants

Radiometric data from Woods Pond, Massachusetts, loss-on-ignition data from cores collected along transect T1, and coordinates of core locations.<br>

Machine learning methods for Precipitable Water Vapor estimation by radiometric data in millimetre wavelength

This work deals with the first try to calculate the amount of Precipitable Water Vapor (PWV) in atmosphere by using machine learning and AI methods. We use the detector voltages series measured by radiometric system “MIAP-2” as the initial data for machine learning. The radiometer MIAP-2 works by “atmospheric dip method” in 2mm and 3mm atmospheric transparency windows. We also have PWV data series collected by Water Vapor Radiometer and GNSS receiver for data validation. The best convergence results were demonstrated by the independent component analysis (ICA) method with coefficient of determination R2= 0.53 and artificial neural network method (ANN) with R2= 0.8. These methods allow to reduce the systematic errors due to direct PWV calculation from raw radiometric data avoiding unnecessary steps opacity calculation.

Correction of Biogeochemical-Argo Radiometry for Sensor Temperature-Dependence and Drift: Protocols for a Delayed-Mode Quality Control

Measuring the underwater light field is a key mission of the international Biogeochemical-Argo program. Since 2012, 0–250 dbar profiles of downwelling irradiance at 380, 412 and 490 nm besides photosynthetically available radiation (PAR) have been acquired across the globe every 1 to 10 days. The resulting unprecedented amount of radiometric data has been previously quality-controlled for real-time distribution and ocean optics applications, yet some issues affecting the accuracy of measurements at depth have been identified such as changes in sensor dark responsiveness to ambient temperature, with time and according to the material used to build the instrument components. Here, we propose a quality-control procedure to solve these sensor issues to make Argo radiometry data available for delayed-mode distribution, with associated error estimation. The presented protocol requires the acquisition of ancillary radiometric measurements at the 1000 dbar parking depth and night-time profiles. A test on >10000 profiles from across the world revealed a quality-control success rate >90% for each band. The procedure shows similar performance in re-qualifying low radiometry values across diverse oceanic regions. We finally recommend, for future deployments, acquiring daily 1000 dbar measurements and one night profile per year, preferably during moonless nights and when the temperature range between the surface and 1000 dbar is the largest.

New insights on the Ife-Ilesha schist belt using integrated satellite, aeromagnetic and radiometric data

The present study combined analysis of satellite, aeromagnetic and radiometric data for evaluation of structural features within the Ife-Ilesha schist belt. Shuttle Radar Topographic Mission digital elevation data have been enhanced using hill-shading technique for the delineation of morphological features. The superposition of total gradient amplitude lineaments on the 3-D Euler deconvolution map revealed the trends and depth of structural features within the study region. The major trends are NE–SW, NNE–SSW, E–W and minor trends in the N–S direction, including the Ifewara shear zone that trends in the NNE–SSW. The estimated depths to the top of the sources within the shear zone varies from 90 to 200 m. Complementary analysis of the airborne radiometric data revealed that the Ifewara shear zone and adjacent regions are characterized by radiometric anomalies, indicating regional mineralization alteration zone. Generally, there is a good correlation between the satellite, radiometric, aeromagnetic maps which provides new insights and re-evaluation of structural features.