scholarly journals Magnetic data interpretation to determine the depth of basement rocks and structural elements of Mandisha village, El-Bahariya Oasis, Western Desert, Egypt

2019 ◽  
Vol 1 (2) ◽  
pp. 7-19
Author(s):  
GAWEISH Wael Ragab ◽  
◽  
MARZOUK Hossam Hassan ◽  
Keyword(s):  
Data Interpretation ◽  
Western Desert ◽  
Magnetic Data ◽  
Structural Elements ◽  
Basement Rocks ◽  
Bahariya Oasis

Integrated Geophysical study on the subsurface structural characterization of West Beni Suef area, Western Desert, Egypt

2020 ◽  
Author(s):  
Ahmed Khalil ◽  
Ahmed El Emam ◽  
Tharwat Abdel Hafeez ◽  
Hassan Saleh ◽  
Waheed Mohamed
Keyword(s):  
Spectral Analysis ◽  
Edge Detection ◽  
Bouguer Anomaly ◽  
Western Desert ◽  
Magnetic Data ◽  
Magnetic Survey ◽  
Euler Deconvolution ◽  
Detection Techniques ◽  
Basement Rocks ◽  
Geophysical Study

<p>The aim of this work is to study the subsurface structures in the west Beni Suef area of the Western Desert in Egypt and to determine their effects on surface geologic structures. A detailed land magnetic survey has been carried out for the total component of the geomagnetic field using two proton magnetometers. The necessary corrections concerning daily variation, the regional gradient and time variations have been applied. Then, the total magnetic intensity anomaly map (TMI) has been constructed and transformed to the reduced to the pole magnetic map (RTP). The reduction-to-pole magnetic and Bouguer anomaly maps were used to obtain regional extensions of this subsurface structure. Regional–residual separation is carried out using the power spectrum. Also, Edge detection techniques are applied to delineate the structure and hidden anomalies. Data analysis was performed using trend analysis, Euler deconvolution, the results indicate that the area is affected by tectonic forces in the N-S, NW-SE, NE-SW and E-W trends, which are correlated with the directions of surface geologic lineaments. In addition, depths to the basement rocks have been estimated using spectral analysis technique. The computed depths have been used to construct the basement relief map which resulted from gravity and magnetic data. They show that the depth to the basement rocks ranges from 2.3 km to 4.7 km.</p><p><strong>KEYWORDS</strong><strong><br></strong>Land magnetic, Gravity, Euler deconvolution, Edge detection and Spectral analysis.</p>

Integrated Geophysical Interpretation of Kerio Valley Basin Stratigraphy, Kenya Rift

2016 ◽  
Author(s):  
Godfred Osukuku ◽  
Abiud Masinde ◽  
Bernard Adero ◽  
Edmond Wanjala ◽  
John Ego
Keyword(s):  
Gravity Data ◽  
Gravity Anomalies ◽  
Fault System ◽  
Magnetic Data ◽  
Data Sets ◽  
Seismic Profiles ◽  
The West ◽  
Seismic Reflection Data ◽  
Basement Rocks ◽  
Western Side

Abstract This research work attempts to map out the stratigraphic sequence of the Kerio Valley Basin using magnetic, gravity and seismic data sets. Regional gravity data consisting of isotactic, free-air and Bouguer anomaly grids were obtained from the International Gravity Bureau (BGI). Magnetic data sets were sourced from the Earth Magnetic Anomaly grid (EMAG2). The seismic reflection data was acquired in 1989 using a vibrating source shot into inline geophones. Gravity Isostacy data shows low gravity anomalies that depict a deeper basement. Magnetic tilt and seismic profiles show sediment thickness of 2.5-3.5 Km above the basement. The Kerio Valley Basin towards the western side is underlain by a deeper basement which are overlain by succession of sandstones/shales and volcanoes. At the very top are the mid Miocene phonolites (Uasin Gishu) underlain by mid Miocene sandstones/shales (Tambach Formation). There are high gravity anomalies in the western and southern parts of the basin with the sedimentation being constrained by two normal faults. The Kerio Valley Basin is bounded to the west by the North-South easterly dipping fault system. Gravity data was significantly of help in delineating the basement, scanning the lithosphere and the upper mantle according to the relative densities. The basement rocks as well as the upper cover of volcanoes have distinctively higher densities than the infilled sedimentary sections within the basin. From the seismic profiles, the frequency of the shaley rocks and compact sandstones increases with depths. The western side of the basin is characterized by the absence of reflections and relatively higher frequency content. The termination of reflectors and the westward dip of reflectors represent a fault (Elgeyo fault). The reflectors dip towards the west, marking the basin as an asymmetrical syncline, indicating that the extension was towards the east. The basin floor is characterized by a nearly vertical fault which runs parallel to the Elgeyo fault. The seismic reflectors show marked discontinuities which may be due to lava flows. The deepest reflector shows deep sedimentation in the basin and is in reasonable agreement with basement depths delineated from potential methods (gravity and magnetic). Basement rocks are deeper at the top of the uplift footwall of the Elgeyo Escarpment. The sediments are likely of a thickness of about 800 M which is an interbed of sandstones and shales above the basement.

Misinterpreting proxy data for paleoclimate signals: A reply to Srivastava and Jovane, 2020

The Holocene ◽  
2020 ◽  
Vol 30 (12) ◽  
pp. 1874-1883
Author(s):  
Tanuj Shukla ◽  
Manish Mehta ◽  
Dwarika Prasad Dobhal ◽  
Archna Bohra ◽  
Bhanu Pratap ◽  
...  
Keyword(s):  
Late Holocene ◽  
Data Interpretation ◽  
High Energy ◽  
Magnetic Data ◽  
Climate Variations ◽  
Magnetic Mineralogy ◽  
Proxy Data ◽  
Glacier Valley ◽  
Future Work ◽  
Rule Out

Srivastava and Jovane (2020) have made several comments on our assessment of proxy data and challenged the outcome of Shukla et al. (2020) based mainly on interpretation of environmental magnetic parameters. We respond to their criticisms and re-evaluate our paper, remove ambiguities and validate our conclusions through additional proxies (grain-size and geochemistry). We welcome their comments and do not entirely rule out their interpretation for magnetic mineralogy. We highlight the importance of proxy validation for high-energy environments like Chorabari lake. However, single proxy data correlation is likely to produce biased results with no relevant meaning. The objective of our study was to understand complexities in the glacial-climate system by reconstructing late-Holocene climate variations using the glacial lake sediment records from the Mandakini River Basin, Central Himalaya, India. We presented the complexities in Shukla et al. (2020), and this was also highlighted by Srivastava and Jovane (2020). In response, we provide additional justification of proxy response and substantiate our results with present-day estimates from the Chorabari glacier valley. We disagree with the thesis put forward by Srivastava and Jovane (2020) in their conclusion as they overemphasize the interpretation of a single proxy. We maintain that the investigation of present-day glacial settings is an important precursor of paleoclimatic data interpretation and that this supports our conclusions. We will try to incorporate the important suggestions of Srivastava and Jovne (2020) relating to the interpretation of magnetic data in future work.

COMPUTER‐ASSISTED ANALYSIS TECHNIQUES FOR REMOTE SENSING DATA INTERPRETATION

Geophysics ◽  
1977 ◽  
Vol 42 (3) ◽  
pp. 468-481 ◽  
Author(s):  
Paul E. Anuta
Keyword(s):  
Gamma Ray ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Magnetic Data ◽  
Computer Assisted ◽  
Data Sets ◽  
Visual Interpretation ◽  
Computer Based ◽  
Interpretation Process ◽  
Assisted Analysis

The development of airborne and satellite multispectral scanning radiometers has created widespread interest in the application of such sensors to mapping of earth resources. The energy sensed in each band can be used as a parameter in a computer‐based, multidimensional‐pattern‐recognition process to aid in the interpretation of the nature of elements in the scene. Images from each band can also be interpreted visually. Visual interpretation of 5 or 10 multispectral images simultaneously becomes impractical, especially as the area studied increases; hence, great emphasis has been placed on machine (computer‐assisted) techniques in the interpretation process. A number of other data sets have recently been studied and integrated by digital registration with the multispectral reflectance and radiance phenomena. Topographic data, which have been registered with four‐band Landsat multispectral scanner (MSS) data, are being studied to determine relationships between spectral and topographic variables. Geophysical variables. including gamma‐ray and magnetic data, have also been registered and studied using the multivariate analysis approach.

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