PROSPECTION FOR COPPER MINERALIZATION WITH CONTRIBUTION OF REMOTE SENSING, GEOCHEMICAL AND MINERALOGRAPHICAL DATA IN ABHAR 1:100,000 SHEET, NW IRAN / POSZUKIWANIA ZASOBÓW RUD MIEDZI Z ZASTOSOWANIEM ZDALNYCH TECHNIK WYKORZYSTUJĄCYCH DANE GEOCHEMICZNE I MINERALOGICZNE W POKŁADZIE GEOLOGICZNYM ABHAR 1:100,000 W PÓŁNOCNO-ZACHODNIM IRANIE

2013 ◽  
Vol 58 (4) ◽  
pp. 1071-1084 ◽  
Author(s):  
Reza Nouri ◽  
Mohammadreza Jafari ◽  
Mehran Arian ◽  
Faranak Feizi ◽  
Peyman Afzal
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Least Square ◽  
Geochemical Data ◽  
Band Ratio ◽  
Nw Iran ◽  
Copper Mineralization ◽  
Least Square Fit ◽  
Volcano Plutonic Belt ◽  
Color Composite

Abstract Abhar 1:100,000 sheet is located within the Cenozoic Tarom volcano-plutonic belt, NW Iran. The present study is based on the integration of remote sensing techniques on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data analysis consisting of stream sediment and lithogeochemical samples, within geological field observations and mineralographical studies to identify Cu prospect. On ASTER data; using a number of selected methods including band ratio, Least Square Fit (LS-Fit) and Minimum Noise Fraction (MNF) distinguished alternation zones. These methods revealed that three types of alterations: argillic, phyllic, and iron oxide zones occurring at the NE and SE of Abhar sheet, while the propylitic and silica zones are developed in NW and SW of the studied area. Lineaments were identified by aid of false color composite, high pass filters and hill-shade DEM techniques that two NW-SE and NE-SW major trends were determined. Geochemical anomalies were separated by number-size (N-S) method. Interpretation of N-S log-log plots of Cu in the area may be a result of the three steps of enrichment, i.e., mineralization and later dispersions. Field checks and Mineralgraphical studies also confirm the existence of suitable copper mineralization.

scholarly journals Recognition of a porphyry system using ASTER data in Bideghan – Qom province (central of Iran)

2014 ◽  
Vol 6 (2) ◽  
pp. 1765-1798 ◽  
Author(s):  
F. Feizi ◽  
E. Mansouri
Keyword(s):  
Thermal Emission ◽  
Volcanic Rocks ◽  
Principal Component ◽  
Field Investigation ◽  
Least Square ◽  
Band Ratio ◽  
Aster Data ◽  
False Color ◽  
False Color Composite ◽  
Color Composite

Abstract. The Bideghan area is located south of the Qom province (central of Iran). The most impressive geological features in the studied area are the Eocene sequences which are intruded by volcanic rocks with basic compositions. Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) image processing have been used for hydrothermal alteration mapping and lineaments identification in the investigated area. In this research false color composite, band ratio, Principal Component Analysis (PCA), Least Square Fit (LS-Fit) and Spectral Angel Mapping (SAM) techniques were applied on ASTER data and argillic, phyllic, Iron oxide and propylitic alteration zones were separated. Lineaments were identified by aid of false color composite, high pass filters and hill-shade DEM techniques. The results of this study demonstrate the usefulness of remote sensing method and ASTER multi-spectral data for alteration and lineament mapping. Finally, the results were confirmed by field investigation.

scholarly journals Geological mapping of carbonatites and related ores from the Oulad Dlim massif (Dakhla Province, Morocco) using remote sensing, portable X-ray fluorescence, and mineralogical data

2021 ◽  
Author(s):  
cheikh elwali Malainine ◽  
Otmane Raji ◽  
Muhammad Ouabid ◽  
Abdou Khouakhi ◽  
Jean-Louis Bodinier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Field Investigation ◽  
Geological Mapping ◽  
Band Ratio ◽  
X Ray ◽  
Critical Elements ◽  
Exploration Tool ◽  
Color Composite ◽  
Processing Techniques

<p>During the last decades, carbonatites and associated rocks have received increased interest from mining companies and the scientific community. They represent a classic source of a variety of critical elements required by certain emerging technologies and industries such as niobium, rare earth elements (REE), and phosphorus. Morocco like many other countries have several Alkaline igneous complexes, however, their potential in terms of REE-P-rich carbonatites is poorly explored and needs to be investigated. This study is an attempt to develop an advanced exploration tool for the detection and mapping of these rocks using remote sensing.  Preliminary investigations were focused on the Oulad Dlim massif at the western Reguibat Shield (Southeast of Dakhla province) where several carbonatite structures were reported, including Gleibat Lafhouda, Twihinate, Lamlaga, Lahjayra. Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) data were used to: (i) identify and map carbonatites and associated rocks liable to contain REE-P mineralization, (ii) investigate their spectral features, and produce predictive maps. Several image processing techniques, have been performed including band ratio, color composite image, principal component analysis and minimum noise fraction. The combination of these techniques appears to more effectively detect carbonatites and associated rocks. The effectiveness of this approach was verified using field investigation, in-situ geochemical analysis with portable X-ray fluorescence, and petrography. The field data were used to train classifiers to better delineate the spatial distribution of the different lithological facies. The results are generally consistent with available geological maps indicating that this approach can be satisfactorily applied in the early stages of geological exploration.</p>

scholarly journals Mapping of Intrusive Complex on a Small Scale Using Multi-Source Remote Sensing Images

2020 ◽  
Vol 9 (9) ◽  
pp. 543
Author(s):  
Yuzhou Zhang ◽  
Dengrong Zhang ◽  
Jinwei Duan ◽  
Tangao Hu
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Northwest China ◽  
Fusion Process ◽  
Small Scale ◽  
Intrusive Complex ◽  
Statistical Regression ◽  
Band Ratio

Multi-stage intrusive complex mapping plays an important role in regional mineralization research. The similarity of lithology characteristics between different stages of intrusions necessitates the use of richer spectral bands, while higher spatial resolution is also essential in small-scale research. In this paper, a multi-source remote sensing data application method was proposed. This method includes a spectral synergy process based on statistical regression and a fusion process using Gram–Schmidt (GS) spectral sharpening. We applied the method with Gaofen-2 (GF2), Sentinel-2, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to the mapping of the Mountain Sanfeng intrusive complex in northwest China in which Carboniferous intrusions have been proven to be directly related to the formation of Au deposits in the area. The band ratio (BR) and relative absorption band depth (RBD) were employed to enhance the spectral differences between two stage intrusions, and the Red-Green-Blue (RGB) false colour of the BR and RBD enhancement images performed well in the west and centre. Excellent enhancement results were obtained by making full use of all bands of the synergistic image and using the Band Ratio Matrix (BRM)-Principal Component Analysis (PCA) method in the northeast part of the study area. A crucial improvement in enhancement performance by the GS fusion process and spectral synergy process was thus shown. An accurate mapping result was obtained at the Mountain Sanfeng intrusive complex. This method could support small-scale regional geological survey and mineralization research in this region.

scholarly journals SUPERPIXEL BASED FACTOR ANALYSIS AND TARGET TRANSFORMATION METHOD FOR MARTIAN MINERALS DETECTION

2018 ◽  
Vol XLII-3 ◽  
pp. 1901-1906
Author(s):  
X. Wu ◽  
X. Zhang ◽  
H. Lin
Keyword(s):  
Factor Analysis ◽  
Near Infrared ◽  
Thermal Emission ◽  
Real Data ◽  
Transformation Method ◽  
Hyperspectral Data ◽  
Least Square ◽  
Mixed System ◽  
Martian Surface ◽  
Least Square Fit

The Factor analysis and target transformation (FATT) is an effective method to test for the presence of particular mineral on Martian surface. It has been used both in thermal infrared (Thermal Emission Spectrometer, TES) and near-infrared (Compact Reconnaissance Imaging Spectrometer for Mars, CRISM) hyperspectral data. FATT derived a set of orthogonal eigenvectors from a mixed system and typically selected first 10 eigenvectors to least square fit the library mineral spectra. However, minerals present only in a limited pixels will be ignored because its weak spectral features compared with full image signatures. Here, we proposed a superpixel based FATT method to detect the mineral distributions on Mars. The simple linear iterative clustering (SLIC) algorithm was used to partition the CRISM image into multiple connected image regions with spectral homogeneous to enhance the weak signatures by increasing their proportion in a mixed system. A least square fitting was used in target transformation and performed to each region iteratively. Finally, the distribution of the specific minerals in image was obtained, where fitting residual less than a threshold represent presence and otherwise absence. We validate our method by identifying carbonates in a well analysed CRISM image in Nili Fossae on Mars. Our experimental results indicate that the proposed method work well both in simulated and real data sets.

Introducing Au Potential Areas, Using Remote Sensing and Geochemical Data Processing Using Fractal Methods in Chartagh, Western Azerbijan – Iran

2016 ◽  
Vol 61 (2) ◽  
pp. 397-414 ◽  
Author(s):  
Edris Mansouri ◽  
Faranak Feizi
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Geochemical Data ◽  
Field Observations ◽  
Band Ratio ◽  
Fractal Method ◽  
Noise Fraction ◽  
Geochemical Data Analysis ◽  
Minimum Noise ◽  
Spectral Angle

Abstract The studied area - Chartagh - is located in the East of Azerbaijan gharbi Province, Iran. In this paper, geology map, ASTER satellite images were used and after processing these images with ENVI softwares, geochemical data analysis consisting of lithogeochemical samples, within geological field observations. On ASTER data; using a number of selected methods including band ratio, Minimum Noise Fraction (MNF) and Spectral Angle Maper (SAM) distinguished alternation zones. Geochemical anomalies were separated by number - size (N-S) fractal method. (N-S) fractal method was utilized for High intensive Au, As and Ag anomalies.

Least-square refinement of structure parameters from CBED integrated intensities: application to determination of temperature factors in Y BA2CU3O7

1992 ◽  
Vol 50 (2) ◽  
pp. 1456-1457
Author(s):  
Kjersti Gjønnes ◽  
Jon Gjønnes
Keyword(s):  
Least Square ◽  
Measured Intensity ◽  
Structure Information ◽  
Accurate Temperature ◽  
Least Square Fit ◽  
Case Application ◽  
Integrated Intensities ◽  
Temperature Factors ◽  
Narrow Bands

Electron diffraction intensities can be obtained at large scattering angles (sinθ/λ ≥ 2.0), and thus structure information can be collected in regions of reciprocal space that are not accessable with other diffraction methods. LACBED intensities in this range can be utilized for determination of accurate temperature factors or for refinement of coordinates. Such high index reflections can usually be treated kinematically or as a pertubed two-beam case. Application to Y Ba2Cu3O7 shows that a least square refinememt based on integrated intensities can determine temperature factors or coordinates.LACBED patterns taken in the (00l) systematic row show an easily recognisable pattern of narrow bands from reflections in the range 15 < l < 40 (figure 1). Integrated intensities obtained from measured intensity profiles after subtraction of inelastic background (figure 2) were used in the least square fit for determination of temperature factors and refinement of z-coordinates for the Ba- and Cu-atoms.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

Array design

1968 ◽  
Vol 58 (3) ◽  
pp. 977-991
Author(s):  
Richard A. Haubrich
Keyword(s):  
High Resolution ◽  
Data Processing ◽  
Least Square ◽  
Wave Number ◽  
Array Design ◽  
Least Square Fit ◽  
The Difference ◽  
Set Of Points ◽  
Array Output

abstract Arrays of detectors placed at discrete points are often used in problems requiring high resolution in wave number for a limited number of detectors. The resolution performance of an array depends on the positions of detectors as well as the data processing of the array output. The performance can be expressed in terms of the “spectrum window”. Spectrum windows may be designed by a general least-square fit procedure. An alternate approach is to design the array to obtain the largest uniformly spaced coarray, the set of points which includes all the difference spacings of the array. Some designs obtained from the two methods are given and compared.

scholarly journals From Geometric-Optical Remote Sensing Modeling to Quantitative Remote Sensing Science—In Memory of Academician Xiaowen Li

2018 ◽  
Vol 10 (11) ◽  
pp. 1764 ◽  
Author(s):  
Qinhuo Liu ◽  
Guangjian Yan ◽  
Ziti Jiao ◽  
Qing Xiao ◽  
Jianguang Wen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Scale Effects ◽  
Remote Sensing Data ◽  
Geometric Optic ◽  
Optical Remote Sensing ◽  
Research Activities ◽  
Quantitative Remote Sensing ◽  
Vegetation Canopies ◽  
High Regard

The academician Xiaowen Li devoted much of his life to pursuing fundamental research in remote sensing. A pioneer in the geometric-optical modeling of vegetation canopies, his work is held in high regard by the international remote sensing community. He codeveloped the Li–Strahler geometric-optic model, and this paper was selected by a member of the International Society for Optical Engineering (SPIE) milestone series. As a chief scientist, Xiaowen Li led a scientific team that made outstanding advances in bidirectional reflectance distribution modeling, directional thermal emission modeling, comprehensive experiments, and the understanding of spatial and temporal scale effects in remote sensing information, and of quantitative inversions utilizing remote sensing data. In addition to his broad research activities, he was noted for his humility and his dedication in making science more accessible for the general public. Here, the life and academic contributions of Xiaowen Li to the field of quantitative remote sensing science are briefly reviewed.

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