2017
DOI: 10.1088/1742-6596/877/1/012039
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Second Vertical Derivative Using 3-D Gravity Data for Fault Structure Interpretation

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Cited by 12 publications
(9 citation statements)
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“…The second vertical derivative is an analytical method used principally for the separation of residuals from regional patterns (Telford et al, 1990). Commonly known as the SVD method, this kind of data processing is also immensely useful for the identification of faults and shallower geologic features as double differentiation concerning depth tends to emphasize the smaller, shallower geologic anomalies at the expense of larger regional features (Aku, 2014; Hinze et al, 2013; Reynolds, 2011; Wahyudi et al, 2017).…”
Section: Methods and Approachmentioning
confidence: 99%
“…The second vertical derivative is an analytical method used principally for the separation of residuals from regional patterns (Telford et al, 1990). Commonly known as the SVD method, this kind of data processing is also immensely useful for the identification of faults and shallower geologic features as double differentiation concerning depth tends to emphasize the smaller, shallower geologic anomalies at the expense of larger regional features (Aku, 2014; Hinze et al, 2013; Reynolds, 2011; Wahyudi et al, 2017).…”
Section: Methods and Approachmentioning
confidence: 99%
“…These methods draw several concentric circles with different radii to the center of the point on which the calculation is performed. In this case, the value of the second derivative of the following relation is specified [36]:…”
Section: Methodsmentioning
confidence: 99%
“…In Figure 9b this filter is applied to our data, showing some imaged potential fault-lines following zero contours on the map, where horizontal gradients are highest. Being a second order filter, 2VDr enhances near surface effects at the expenses of deeper anomalies, it amplifies noise and may produce artificial second derivative anomalies (Wahyudi et al, 2017).…”
Section: Data Filtering: Operations With Directional Gradientsmentioning
confidence: 99%