Gulf of Aden spreading does not conform to triple-junction formation

Geology ◽  
2021 ◽  
Author(s):  
Khalid A. Almalki ◽  
Peter G. Betts

The Gulf of Aden represents an evolving example of a juvenile ocean system and is considered the most evolved rift arm of the Afar triple junction. We have undertaken analysis of recent coupled satellite and marine potential-field data to understand the first-order crustal architecture along the entire length of the gulf. Our interpretation suggests the Gulf of Aden has three domains with distinct free-air gravity and magnetic characteristics. These domains record a progression from active seafloor spreading in the eastern domain, through isolated and discontinuous spreading segments in the central domain, to active continental rifting in the western domain immediately adjacent to the Afar triple junction. Forward models suggest the presence of transitional crust, which displays linear magnetic stripe–like anomalies that bound oceanic stripes in the central domain and covering the majority of the western domain. Magnetic anomalies differ from magnetic stripes sensu stricto because they are discontinuous and cannot be correlated along the length of the gulf. Detection of northwest-southeast extension in the central domain based on magnetic stripe orientation is inconsistent with the regional northeast-southwest extension. Our observations reflect heterogeneous opening of the Gulf of Aden basins, in which spreading is migrating toward Afar as a series of isolated spreading segments, rather than initiating at the junction as proposed by classical platetectonic theory. This mechanism of ocean initiation is inconsistent with transtensional models that involve wholesale tearing of continental crust and contradicts conceptual models that rely on the Afar plume in initiating or driving the extension.

Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Nicole Debeglia ◽  
Jacques Corpel

A new method has been developed for the automatic and general interpretation of gravity and magnetic data. This technique, based on the analysis of 3-D analytic signal derivatives, involves as few assumptions as possible on the magnetization or density properties and on the geometry of the structures. It is therefore particularly well suited to preliminary interpretation and model initialization. Processing the derivatives of the analytic signal amplitude, instead of the original analytic signal amplitude, gives a more efficient separation of anomalies caused by close structures. Moreover, gravity and magnetic data can be taken into account by the same procedure merely through using the gravity vertical gradient. The main advantage of derivatives, however, is that any source geometry can be considered as the sum of only two types of model: contact and thin‐dike models. In a first step, depths are estimated using a double interpretation of the analytic signal amplitude function for these two basic models. Second, the most suitable solution is defined at each estimation location through analysis of the vertical and horizontal gradients. Practical implementation of the method involves accurate frequency‐domain algorithms for computing derivatives with an automatic control of noise effects by appropriate filtering and upward continuation operations. Tests on theoretical magnetic fields give good depth evaluations for derivative orders ranging from 0 to 3. For actual magnetic data with borehole controls, the first and second derivatives seem to provide the most satisfactory depth estimations.


2021 ◽  
Author(s):  
Alexey Shklyaruk ◽  
Kirill Kuznetsov ◽  
David Arutyunyan ◽  
Ivan Lygin

<p>At the stage of small and medium-scale geological and geophysical studies, in addition to seismic exploration, methods of potential fields (gravimetry and magnetometry) are usually actively used. These methods, in contrast to the profile seismic observations, taking into account modern satellite and aviation technologies, provide a high-quality areal density and magnetic characteristics of the study area. The main tasks of modern gravimetry and magnetometry include the task of constructing areal models, contrasting in density and magnetization of surfaces. Among a large number of algorithmic solutions, the most effective are methods using an integrated approach, in which seismic data on the morphology of reflecting horizon is used as a reference.</p><p>Reconstruction of the structural surface morphology by geophysical data can be considered as the problem of finding the relationship between the input information (potential fields, geophysical data, and available a priori information) and the desired surface. To assess the dependence, it is proposed to use the reference plots on which both input and output data are presented. Currently, one of the trends in solving such problems is methods based on neural networks. Neural networks can be of various configurations (feedforward networks, radial-basis function networks, backpropagation networks, convolutional networks, etc.), have a different number of layers and neurons.</p><p>In this research, we consider the test and real-world example. A site with a known position of the sedimentary cover bottom is considered as a test model. To verify and compare the algorithms, the gravity and magnetic effects of the layer are calculated. The gravity and magnetic fields were supplied to the input to the algorithms for constructing regression dependence and training the neural network. An incomplete model of the sedimentary cover was supplied to the input for training neural networks. The task was to restore the missing part. The parameter of the standard deviation of the original and reconstructed model was less than 2% for all types of neural networks.</p><p>As a real model, a site was considered where basement cover is only partially available. It was obtained as a result of seismic interpretation. All available geological and geophysical data were used to reconstruct the horizon. Models obtained using reconstruction algorithms can be additional information for further detailed description of the geological structure.</p><p>It should be noted that since neural networks help to find complex functional relationships between field parameters and attributes of the studied environment, they could be used in the tasks of complex interpretation of geological and geophysical data.</p>


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