Abstract:

Due to poor positioning precision and weak recognition capability of structur edge in conventional potential-field data processing, we present normalized differential method. According to the relationship between three-directional differential and the character of potential-field anomaly in position of structured edges, we give expressions of n-order normalized differential which can protrude the character of anomaly gradient zones. It is shown in a test of single body model that x-and y-directional firstorder differential after 90° phase shift and z-directional first-order differential can compress the width of anomaly gradient zones, and the contours of anomaly gradient zones center coincide the model edges. In order to improve positioning precision, we adopt second-order differential to pilot calculation. The results show that three-directional second-order differential further enhance edge recognition capability, and reveal the true model bodies. Thus it clarifies that the second-order normalized differential has higher positioning precision in edge detecting. Numerical test with noisy data shows that second-order normalized differential has strong edge recognition capability and high positioning precision when the difference radius is small, while normalized differential can effectively reduce the noise effect on data with larger geologic bodies’ boundary when the differential radius is large. In an application, normalized differential method detects 28 faults in Hulin basin of Heilongjiang, and 13 faults confirmed by geological survey on profile line DB1.

Key words: edge detection, differential method, normalization, gradient zone, differential radius