Identifying the Lineament Structure Cooperatively Using the Airborne Gravimetric, Magnetic, and Remote Sensing Data: A Case Study From the Pobei Area, NW China

Identification of lineament structure plays a vital role in determining the metallogenic area and distribution of the geologic structure. Edge detection methods are mostly used to recognize the lineaments and define the geologic boundaries. Cooperatively using edge detection results of the gravity, magnetic, and remote sensing (RS) data to recognize lineaments would obtain more geologic information. In this article, new edge detectors of potential field derivatives are proposed to determine the sources' boundary, named second tilt derivative, the tilt of vertical derivative, and the normalized second vertical derivative, respectively. Presented approaches are characterized by producing zero amplitude over sources' edges and equalizing anomalies from different depths. Compared with original edge detection techniques, including other second derivative methods, synthetic examples reveal significant superiorities of suggested approaches in providing more accurate and sharper edges and are especially effective in distinguishing superimposed anomalies. The experiments also demonstrate that the normalization of the edge detectors will make images cleaner and geologic edges more easily captured. Applied to airborne gravimetric and magnetic data in the Pobei area (NW China), the proposed methods display more geologic details and lineaments. Canny, Sobel, and Prewitt operators are applied to extract the boundaries of the RS image. Lineaments picked by the three different types of data are combined collectively to get a comprehensive lineaments structure interpretation.

Automated summary

Identification of lineament structure is crucial for determining metallogenic areas and geologic structure distribution. The use of edge detection methods with gravity, magnetic, and remote sensing data can provide more geological information. New edge detectors for potential field derivatives have advantages in producing clear edges, equalizing anomalies, and distinguishing superimposed anomalies. Combining different data types to extract lineaments results in a more comprehensive interpretation of lineament structures.