We present our aeromagnetic survey results from an investigation of the iron ore mineral distribution in Pocheon, Korea, in the west-central area of the Korean Peninsula. A manned aeromagnetic system using a helicopter for regional exploration and an unmanned aeromagnetic system using a multicopter for high-resolution exploration were used for the survey. The inversion results of the magnetic data confirmed the possibility of the existence of a new iron ore body. Drilling was carried out based on inversion results and drilling revealed amphibolite including iron ore, as indicated by a strong magnetic response. The position and depth of the iron ore were consistent with the interpretation results of the magnetic data.

Unmanned Aerial vehicles (UAV) have been utilized in many application domains. UAVs (or Airships, e.g., Drones) have been adopted to explore resources (e.g., minerals). One of the main limitations of using such airships is that flying at a fixed altitude is based on GPS altitude information. In many applications, it is important to support the ‘terrain-following flying’ function for airships when deployed and utilized in areas where the terrain is not flat but bumpy and steep (for example, in mountains areas in South Korea). This paper proposed a novel architecture of an airship autopilot system with three main contributions: First, the proposed architecture is designed to support a terrain-following function using a laser range finder (which continuously identifies the distance between an airship and terrain). Second, the proposed system provides two new algorithms for path and terrain-following functions for the proposed architecture. Third, this work designed and implemented a prototype airship and autopilot system to validate the proposed method. As a result, the proposed airship design and algorithm can guarantee a high-quality data collection (e.g., magnetic data) and demonstrate the benefits of the proposed approach with the experimental results based on real-flight operation in a test area.

In the fields of oil and gas exploration, various quantitative seismic interpretation techniques have been developed to identify lithology and pore fluids based on the rock physics. However, discrimination between oil and brine in pores is often difficult because the seismic response of oil and brine are similar. Poisson impedance (PI) is an effective indicator for distinguishing oil from brine in pores. Target correlation coefficient analysis (TCCA) is widely used to calculate the “c” factor of PI. However, PI analysis using the TCCA method can lead to misinterpretation when the resistivity log is affected by lithology and porosity, as well as the pore fluid type. To overcome this problem, we developed a more effective PI workflow composed of three steps that focus on lithology impedance, porosity impedance, and fluid impedance. In each step, we exclude the unnecessary area for hydrocarbon exploration such as shale, tight, and brine-saturated areas. We applied our method to marine field data that contained shale and tight intervals and demonstrated that the proposed approach can be used to identify potential oil reservoirs more effectively from seismic data.