• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.18-25
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• 2005

We derive refraction statics for seismic data recorded in a hard rock terrain, in which there are large and rapid variations in the depth of weathering. The statics corrections range from less than 10 ms to more than 70 ms, often over distances as short as 12 receiver intervals. This study is another demonstration of the importance in obtaining accurate initial refraction models of the weathering in hard rock terrains in which automatic residual statics may fail. We show that the statics values computed with a simple model of the weathering using the Generalized Reciprocal Method (GRM) and the Refraction Convolution Section (RCS) are comparable in accuracy to those computed with a more complex model of the weathering, using least-mean-squares inversion with the conjugate gradient algorithm (Taner et al., 1998). The differences in statics values between the GRM model and that of Taner et al. (1998) systematically vary from an average of 2ms to 4ms over a distance of 8.8 km. The differences between these two refraction models and the final statics model, which includes the automatic residual values, are generally less than 5 ms. The residuals for the GRM model are frequently less than those for the model of Taner et al. (1998). The RCS statics are picked approximately 10 ms later, but their relative accuracy is comparable to that of the GRM statics. The residual statics values show a general correlation with the refraction statics values, and they can be reduced in magnitude by using a lower average seismic velocity in the weathering. These results suggest that inaccurate average seismic velocities in the weathered layer may often be a source of short-wavelength statics, rather than any shortcomings with the inversion algorithms in determining averaged delay times from the traveltimes.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.49-55
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• 2022

In this paper, we propose an obstacle detection method that can operate robustly even in external environmental factors such as weather. In particular, we propose an obstacle detection system that can accurately inform dangerous situations in AR through DB-based feature matching and RANSAC-based multiplane method. Since the approach to detecting obstacles based on images obtained by RGB cameras relies on images, the feature detection according to lighting is inaccurate, and it becomes difficult to detect obstacles because they are affected by lighting, natural light, or weather. In addition, it causes a large error in detecting obstacles on a number of planes generated due to complex terrain. To alleviate this problem, this paper efficiently and accurately detects obstacles regardless of lighting through DB-based feature matching. In addition, a criterion for classifying feature points is newly calculated by normalizing multiple planes to a single plane through RANSAC. As a result, the proposed method can efficiently detect obstacles regardless of lighting, natural light, and weather, and it is expected that it can be used to secure user safety because it can reliably detect surfaces in high and low or other terrains. In the proposed method, most of the experimental results on mobile devices reliably recognized indoor/outdoor obstacles.

• Economic and Environmental Geology
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• v.6 no.1
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• pp.29-64
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• 1973

A land resources survey in the semi-arid area, Mula in S. E. Spain, of $400km^2$ is compiled. The basic aim of the project is to investigate the intrinsic qualities of the land resources of the area by means of applying an integrated method of natural resources survey mainly concerning with analysis and synthesis of land complexes, each of them represents an area or a group of areas with similar patterns of landforms, soils and vegetation, based on a geomorphological approach. The area is characterized by a linear arrangement of relief pattern with an asymmetric homoclinal repetition of slope attitudes elongating WSW-ENE, dipping steeply on the NW sides and gently on the SE sides, which have been resulted from the post-Alpine folding of the Triassic to Cretaceous limestone, the Eocene limestone, the Oligocene sandstone and the lower Miocene limestone and marl, and the post-lower Miocene faulting, tilting and subsequent differential erosion of the Miocene sedimentary formations. An integrated body of information in geology, landforms, soils and vegetation, which are significantry interrelated as an environmental complex, has been obtained. Using this data, 26 land complexes developing on the various situations of landforms, such as folded mountain ranges, tilted tablelands, bevelled cuestas, degraded hill-lands associating with enormous foots lopes, undulating terrains and terraced or flat plains, have been differentiated, mapped and described. The soils of the area are mostly light colored calcic lithosols which have been derived dominantly from the marly parent materials and developed into remarkable slope catenas in some places depending on the relief conditions. The land uses of the area are mainly characterized by the perennially irrigated cultivation of citrus orchards along the terraced alluvial deposits fringing the Segura and Mula River, and the dry-land cereal cultivation on gentler slopes. Pioneer dry-land cultivations within the shrubs on steeper slopes are restricted to the unchannelled tributary drainage floors. The availability of water is a fundamental controlling factor for existence of native and cultivated vegetation as a whole, and a number of active processes including sheet wash and gully erosion, especially on the scarp slopes, are the other important factors to be considered in conservation and management of the land in the area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.146-157
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• 2019

One of the most distinctive features of the South Korean rural environment is that the variation of weather or climate is large even within a small area due to complex terrains. The Geospatial Schemes based on Topo-Climatology (GSTP) was developed to simulate such variations effectively. In the present study, we reviewed the progress of the geospatial schemes for production of farm-scale agricultural weather data. Efforts have been made to improve the GSTP since 2000s. The schemes were used to provide climate information based on the current normal year and future climate scenarios at a landscape scale. The digital climate maps for the normal year include the maps of the monthly minimum temperature, maximum temperature, precipitation, and solar radiation in the past 30 years at 30 m or 270 m spatial resolution. Based on these digital climate maps, future climate change scenario maps were also produced at the high spatial resolution. These maps have been used for climate change impact assessment at the field scale by reprocessing them and transforming them into various forms. In the 2010s, the GSTP model was used to produce information for farm-specific weather conditions and weather forecast data on a landscape scale. The microclimate models of which the GSTP model consists have been improved to provide detailed weather condition data based on daily weather observation data in recent development. Using such daily data, the Early warning service for agrometeorological hazard has been developed to provide weather forecasts in real-time by processing a digital forecast and mid-term weather forecast data (KMA) at 30 m spatial resolution. Currently, daily minimum temperature, maximum temperature, precipitation, solar radiation quantity, and the duration of sunshine are forecasted as detailed weather conditions and forecast information. Moreover, based on farm-specific past-current-future weather information, growth information for various crops and agrometeorological disaster forecasts have been produced.