Geoscience frontiers 

About: Geoscience frontiers is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Geology & Zircon. It has an ISSN identifier of 1674-9871. It is also open access. Over the lifetime, 1446 publications have been published receiving 43609 citations.

IsoplotR: A free and open toolbox for geochronology

Abstract: This paper reviews the basic principles of radiometric geochronology as implemented in a new software package called IsoplotR, which was designed to be free, flexible and future-proof. IsoplotR is free because it is written in non-proprietary languages (R, Javascript and HTML) and is released under the GPL license. The program is flexible because its graphical user interface (GUI) is separated from the command line functionality, and because its code is completely open for inspection and modification. To increase future-proofness, the software is built on free and platform-independent foundations that adhere to international standards, have existed for several decades, and continue to grow in popularity. IsoplotR currently includes functions for U-Pb, Pb-Pb, 40 Ar/ 39 Ar, Rb-Sr, Sm-Nd, Lu-Hf, Re-Os, U-Th-He, fission track and U-series disequilibrium dating. It implements isochron regression in two and three dimensions, visualises multi-aliquot datasets as cumulative age distributions, kernel density estimates and radial plots, and calculates weighted mean ages using a modified Chauvenet outlier detection criterion that accounts for the analytical uncertainties in heteroscedastic datasets. Overdispersion of geochronological data with respect to these analytical uncertainties can be attributed to either a proportional underestimation of the analytical uncertainties, or to an additive geological scatter term. IsoplotR keeps track of error correlations of the isotopic ratio measurements within aliquots of the same samples. It uses a statistical framework that will allow it to handle error correlations between aliquots in the future. Other ongoing developments include the implementation of alternative user interfaces and the integration of IsoplotR with other data reduction software.

Rare earth elements:A review of applications, occurrence, exploration, analysis, recycling, and environmental impact

Abstract: Rare earth elements (REE) include the lanthanide series elements (La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu) plus Sc and Y. Currently these metals have become very critical to several modern technologies ranging from cell phones and televisions to LED light bulbs and wind turbines. This article summarizes the occurrence of these metals in the Earth's crust, their mineralogy, different types of deposits both on land and oceans from the standpoint of the new data with more examples from the Indian subcontinent. In addition to their utility to understand the formation of the major Earth reservoirs, multi-faceted updates on the applications of REE in agriculture and medicine including new emerging ones are presented. Environmental hazards including human health issues due to REE mining and large-scale dumping of e-waste containing significant concentrations of REE are summarized. New strategies for the future supply of REE including recent developments in the extraction of REE from coal fired ash and recycling from e-waste are presented. Recent developments in individual REE separation technologies in both metallurgical and recycling operations have been highlighted. An outline of the analytical methods for their precise and accurate determinations required in all these studies, such as, X-ray fluorescence spectrometry (XRF), laser induced breakdown spectroscopy (LIBS), instrumental neutron activation analysis (INAA), inductively coupled plasma optical emission spectrometry (ICP-OES), glow discharge mass spectrometry (GD-MS), inductively coupled plasma mass spectrometry (including ICP-MS, ICP-TOF-MS, HR-ICP-MS with laser ablation as well as solution nebulization) and other instrumental techniques, in different types of materials are presented.

Machine learning in geosciences and remote sensing

Abstract: Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.) that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin

Abstract: Soil erosion is a serious problem arising from agricultural intensification, land degradation and other anthropogenic activities. Assessment of soil erosion is useful in planning and conservation works in a watershed or basin. Modelling can provide a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions. In the present study, the soil loss model, Revised Universal Soil Loss Equation (RUSLE) integrated with GIS has been used to estimate soil loss in the Nethravathi Basin located in the southwestern part of India. The Nethravathi Basin is a tropical coastal humid area having a drainage area of 3128 km 2 up to the gauging station. The parameters of RUSLE model were estimated using remote sensing data and the erosion probability zones were determined using GIS. The estimated rainfall erosivity, soil erodibility, topographic and crop management factors range from 2948.16 to 4711.4 MJ/mm·ha − 1 hr − 1 /year, 0.10 to 0.44 t ha − 1 ·MJ − 1 ·mm − 1 , 0 to 92,774 and 0 to 0.63 respectively. The results indicate that the estimated total annual potential soil loss of about 473,339 t/yr is comparable with the measured sediment of 441,870 t/yr during the water year 2002–2003. The predicted soil erosion rate due to increase in agricultural area is about 14,673.5 t/yr. The probability zone map has been derived by the weighted overlay index method indicate that the major portion of the study area comes under low probability zone and only a small portion comes under high and very high probability zone. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Nethravathi Basin.

Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques

Abstract: Integration of remote sensing data and the geographical information system (GIS) for the exploration of groundwater resources has become a breakthrough in the field of groundwater research, which assists in assessing, monitoring, and conserving groundwater resources. In the present paper, various groundwater potential zones for the assessment of groundwater availability in Theni district have been delineated using remote sensing and GIS techniques. Survey of India toposheets and IRS-1C satellite imageries are used to prepare various thematic layers viz. lithology, slope, land-use, lineament, drainage, soil, and rainfall were transformed to raster data using feature to raster converter tool in ArcGIS. The raster maps of these factors are allocated a fixed score and weight computed from multi influencing factor (MIF) technique. Moreover, each weighted thematic layer is statistically computed to get the groundwater potential zones. The groundwater potential zones thus obtained were divided into four categories, viz., very poor, poor, good, and very good zones. The result depicts the groundwater potential zones in the study area and found to be helpful in better planning and management of groundwater resources.