Agnes Apriana, Andre Rahmat Al Ansory, Tari Agustina
et al.
Bengkulu City is an area prone to flooding due to its proximity to the river estuary. Flooding in Bengkulu City usually occurs during the rainy season, when high rainfall and overflowing rivers can cause inundation in several areas around the city. This research aims to identify flood-prone areas in Bengkulu City through a Geographic Artificial Intelligence (Geo-AI) approach. Geo-AI is an artificial intelligent machine with geospatial data, including satellite images and weather data, whose data analysis is more accurate and efficient in identifying flood-prone areas. Geographic Information System (GIS) can present objects of flood-prone areas from the real world in digital form. Through Google Earth Engine (GEE), satellite imagery data and other geospatial data are processed and analyzed using artificial intelligence algorithms to identify flood patterns and frequently flooded areas. The final result of this research is a map of flood-prone areas in Bengkulu City. On the map, it can be concluded that the red-colored areas including Rawa Makmur, Tanjung Agung, Bentiring, Kebun Tebeng, Penurunan, Sukarami, Pekan Sabtu, and Air Sebakul are areas that are often flooded so that these areas can be confirmed as flood-prone.
Abstract The aim of this paper is to investigate radiative properties of thermal air plasmas in wide ranges of pressure and temperature, and to analyse the accuracy of some spectral and geometrical approximations in high-pressure radiative transfer applications. Comprehensive calculations of absorption spectra, including molecular, atomic and ionic line and continuum radiation, are presented and the dependence of these spectra on the pressure level is analysed. The high resolution spectra, in association with a rigorous ray-tracing method, are then used to study the accuracy of the P1 and the simplified SP3 geometrical approximations in 1D axisymmetric geometries. Cylindrical plasma columns at uniform pressure and with a non-uniform pressure distribution are considered. The P1 approximation provides acceptable results but the SP3 approximation is found to be more accurate. Concerning the spectral approximations, the use of band averaged Rosseland mean absorption coefficients yields volumetric radiative powers in fairly good agreement with line-by-line calculations.
In the oil and gas industry, rock physics-based forward modeling has become an essential tool that allows geoscientists to better interpret the subsurface and understand the behavior of rocks under different conditions (Avseth et al., 2005). Forward modeling aims to simulate the seismic response of a given geological model, taking into consideration the elastic properties of the rocks and the seismic acquisition parameters. This approach has been widely used in the industry to gain insights into subsurface geology and unlock potential challenging and complex reservoirs, as noted by Landro (2001) in his work on seismic attribute analysis. In recent years, there has been an increasing interest in stratigraphic traps and reservoirs, mainly channelized facies, which are commonly indicative of potential hydrocarbon reservoirs (Weber, 1986). However, these features can be very tricky and challenging to detect in seismic data due to the complexity of the subsurface geology and the limitations of seismic data. To overcome these challenges, rock physics-based forward modeling can be an ideal tool to provide a more detailed understanding of the seismic responses caused by different rock types, porosities and fluid contents (Bachrach et al., 2000). The purpose of this study is to investigate the use of rock physics-based forward modeling in detecting "Channel-like" features in seismic data. During the seismic horizon interpretation phase, a clear pattern of a "pull-up/pull-down" seismic feature was observed in channelized facies as seen in Figure 1, which may indicate the presence of a hydrocarbon reservoir (Dvorkin et al., 2014). However, it is essential to understand the underlying causes of this feature, whether it is related to geological aspects or seismic artifacts, as noted by Xu and White (1995) in their work on velocity models for clay-sand mixtures.
A technique is described for referecing images of wide-angle optical systems intended for registration own radiation of the Earth's atmosphere, to geographic coordinates. The technique is based on an automatic procedure for the stars emphasing and identification in the frames and subsequent georeferencing. An example of the technique use for calculating the characteristics of a long-lived meteor trail based on observation data of two spatially separated wide-angle optical systems is shown. Keywords: Geo-referencing, all-sky camera, star identification, atmospheric emission, long-lasting trail.