Application of Remote Sensing In Two Southern Iranian Oil Fields

Geoscientists have long applied photographic cameras, radar, lasers, infrared (IR) scanners, radiometers, spectrometers, microwaves, and multi spectral scanners (MSS) in the search for hydrocarbons. With introduction of satellite remote sensing, basic techniques were then coupled with this new technology. This produced enhanced views of the Earth’s surface. Although oil and gas reservoirs are deep below the surface, they have some indicators, which can be detected on the ground. To reduce the exploration costs for hydrocarbons during the reconnaissance stage of exploration, satellite images and available surface data by combining with other current conventional exploration techniques could be used. In recent years, geological reconnaissance has been augmented by sophisticated terrace data-gathering techniques, which have been categorized as remote sensors. GIS allows petroleum engineers or functional group within to communicate information and make spatial and temporal decisions about assets, activities and natural resources. The present paper deals with the study of two existing petroleum-rich reservoirs. The selected area contains thermally unprocessed VNIR, SWIR and TIR ASTER images for granule of the study area covering Ab-teymur and Darquin reservoirs. Each granule covers an area of 3600 Km2 (60 km x 60 km) of land of onshore Iran. Besides the main geological units and the gas geological analysis within the boundary of these granules have been studied. For this work three layers of information are considered: geology, geochemistry and vegetation cover. The main geological units within the boundary of the granules have been discussed for both fields. The basis of gas geochemical prospecting methods is that no oil or gas reservoir cap rock is completely impermeable. Hydrocarbons and other compounds and elements escape from the reservoirs and the more volatile components migrate to the surface where they may be trapped in soils or diffuse in atmosphere or ocean. Vegetation cover within the boundaries of oil field influenced zones was taken into consideration as an individual layer of information which will complement the other layers of information by its corresponding statistical weight

Selective insensitivity to income held by the richest

This is the author accepted manuscriptData availability The HTML code of the experiments, complete surveys and materials, the anonymized data, the preprocessing, and the analysis codes for each study are publicly available on the Open Science Framework (https://osf.io/hszyp/). Data for Study 2 is available for download at https://issp.org/data-download/by-year/.:The misperception of income inequality is often touted as a critical barrier to more widespread support of redistributive policies. Here, we examine to what extent and why (mis)perceptions vary systematically across the income distribution. Drawing on data from four studies (N = 2,744)—including a representative sample and pre-registered incentive-compatible experiments—we offer converging evidence that people specifically underestimate the amount of income held by the top of the income distribution. While this selective underestimation is likely driven by multiple mechanisms, including systemic factors, we find that cognitive biases contribute to the observed pattern of results. The rise of inequality in many developed countries has been documented before, and the fact that this growing inequality is largely driven by the outsized gains of the richest individuals may pose new challenges previously underappreciated: our theory and findings highlight that cognitive biases pose a key obstacle to people’s recognition of the concentration of income among the richest individuals, and may potentially distort their preferences for redistribution. We conclude by discussing future directions for research and the importance of incorporating behavioral and cognitive limitations into the design of redistributive public policy

Mass Reproducibility and Replicability: A New Hope

This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes