• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.168-177
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• 2020

Seabed multiple reflections (seabed multiples) are the main cause of misinterpretations of primary reflections in both shot gathers and stack sections. Accordingly, seabed multiples need to be suppressed throughout data processing. Conventional model-driven methods, such as prediction-error deconvolution, Radon filtering, and data-driven methods, such as the surface-related multiple elimination technique, have been used to attenuate multiple reflections. However, the vast majority of processing workflows require time-consuming steps when testing and selecting the processing parameters in addition to computational power and skilled data-processing techniques. To attenuate seabed multiples in seismic reflection data, input gathers with seabed multiples and label gathers without seabed multiples were generated via numerical modeling using the Marmousi2 velocity structure. The training data consisted of normal-moveout-corrected common midpoint gathers fed into a U-Net neural network. The well-trained model was found to effectively attenuate the seabed multiples according to the image similarity between the prediction result and the target data, and demonstrated good applicability to field data.

• Journal of the Korean Geophysical Society
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• v.5 no.2
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• pp.143-151
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• 2002

Bulku temple in the city of Kyungju, Korea, built in 791 and reconstructed in the 20th century, is the home of seven national treasures including two three-story stone pagodas, Dabotap (height 10.4m, width 7.4m, weight 123.2ton) and Seokgatap (height 10.8m, width 4.4m, weight 82.3 ton). An earlier archaeological investigation shows that stone pagodas have experienced severe weathering process which will threaten their stability. At the base part of Dabotap, an offset of the stone alignment is also observed. For the purpose of the structural safety diagnosis of two pagodas, we introduce the nondestructive geophysical methods. Site characteristics around the pagodas are determined by the measurement of multiple properties such as seismic velocity, resistivity, image of GPR(ground-penetrating radar). Near the pagodas, the occurrence of high resistivity (up to 2200 Ωm) is obvious whereas their outskirts have as low as 200 Ωm. For the velocity of the P wave, the site of Dabotap has the range of 500~800 m/s which is higher than counterpart of Seokgatap with the velocity of 300~500m/s, indicating the solider stability of Dabotap site. Consequently, in addition to GPR images, the foundation boundaries beneath each stone pagodas are revealed. The Dabotap site is in the form of an octagon having 6-m-long side with the depth of ~4m, whereas the Seokgatap site the 9m × 10m rectangle with the depth of 3m. These subsurface structures appear to reflect the original foundations constructed against the stone load of ~8 ton/㎡. At the subsurface beneath the northeast of each pagoda, low seismic velocity as well as low resistivity is prominent. It is interpreted to represent the weak underground condition.

• 한국지구물리탐사학회:학술대회논문집
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• 2004.08a
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• pp.70-83
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• 2004

The safety diagnosis of cultural assets is Primarily focused on its non-destructiveness. Research on the nondestructive diagnosis and conservation of masonry cultural heritage is the key which is considered by technologic kernel. Geophyscial Prospecting as nondestructive diagnostic technology plays an important role in the characterization of the foundation of stone pagodas. It is natural that understanding of shallow subsurface condition beneath them is essential for their structural safety diagnosis. As an example, the nondestructive geophysical methods were applied to two three-story stone pagodas, Seokgatap (height 10.8 m, width 4.4 m, weight 82.3 ton) and Dabotap (height 10.4 m, width 7.4 m, weight 123.2 ton) which were built in 791 at Bulkuksa temple. An earlier archaeological investigation shows that stone pagodas have experienced severe weathering process and are slightly leaning, which will threaten their stability At the base part of Dabotap, an offset of the stone alignment is also observed. Direct measurements of ultrasonic velocities was introduced for the mechanical properties of the stone The velocity ranges of ultrasonic waves for Dabotap and Seokgatap are 1217${\~}$4403 m/s and 584${\~}$5845 m/s, respectively, and the estimated averages of the uniaxial compressive strength are 463 kg/$cm^2$ and 409 kg/$cm^2$, respectively. Site characteristics, around the pagodas are determined by the measurement of multiple properties such as seismic velocity, resistivity, image of ground-penetrating radar, On the basis of the higher velocity structure, the site of Seokgatap appears to have solider stability than the Seokgatap site. Near the pagodas, higher(up to 2200 $\Omega$m) resistivity is present whereas their outskirts have as low as 200 $\Omega$m. By the combined results of each geophyscial methods, the subsurface boundaries of two stone pagodas are revealed. The Dabotap site is in the form of an octagon having 6-m-long side with the depth of ${\~}$4 m, whereas the Seokgatap site is the 8 ${\times}$ 10 m rectangle with the depth of 3 m. These subsurface structures appear to reflect the original foundations constructed against the stone load of ${\~}8 ton/m^2$. At the subsurface beneath the northeast of each pagoda, low seismic velocity as well as low resistivity is prominent. It is interpreted to represent the weak underground condition which Is the possible cause of the slightly leaning pagodas toward the NNW.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.185-198
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• 2023

In marine seismic surveys, the inevitable occurrence of trace gaps in the near offset resulting from geometrical differences between sources and receivers adversely affects subsequent seismic data processing and imaging. The absence of data in the near-offset region hinders accurate seismic imaging. Therefore, reconstructing the missing near-offset information is crucial for mitigating the influence of seismic multiples, particularly in the case of offshore surveys where the impact of multiple reflections is relatively more pronounced. Conventionally, various interpolation methods based on the Radon transform have been proposed to address the issue of the nearoffset data gap. However, these methods have several limitations, leading to the recent emergence of deep-learning (DL)-based approaches as alternatives. In this study, we conducted an in-depth analysis of two representative DL-based studies to scrutinize the challenges that future studies on near-offset interpolation must address. Furthermore, through field data experiments, we precisely analyze the limitations encountered when applying previous DL-based trace interpolation techniques to near-offset situations. Consequently, we suggest that near-offset data gaps must be approached by extrapolation rather than interpolation.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.