• Fire Science and Engineering
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• v.33 no.2
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• pp.30-38
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• 2019

Tracking, which is one of the main causes of electrical fires, is perceived as a physical phenomenon of electrical discharge. Hence tracking should be explained based on electric field analysis, conduction path by electron generation, and gas discharge physics. However, few papers have considered these details. This paper proposes a tracking mechanism including their effects on tracking progress. In order to prove this mechanism, a tracking experiment, an electric field analysis for the carbonization evolution model, and an explanation of the tracking process by gas discharge physics were conducted. From the tracking experiment, the current waveforms were measured at each stage of the tracking progress from corona discharge to tracking breakdown. The electric field analysis was carried out in order to determine the electric field on the surface of a dry-band and the high electric field region for electron generation during the generation and progress of carbonization. In this paper, the proposed tracking mechanism consisted of six stages including electron avalanche by corona discharge, accumulation of positive ions, expansion of electron avalanche, secondary electron emission avalanche, streamer, and tracking by conductive path. The pulse current waveforms measured in the tracking experiment can be explained by the proposed tracking mechanism. The results of this study will be used as the technical data to detect tracking phenomenon, which is the cause of electric fire, and to improve the proof tracking index.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.84-93
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• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• Geophysics and Geophysical Exploration
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• v.6 no.3
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• pp.126-133
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• 2003

High-resolution shallow marine seismic surveys have been carried out for the resources exploration, engineering applications and Quaternary mapping. To improve the resolution of subsurface structure image, multichannel digital technique has been applied. The quality of the image depends on the vertical and horizontal resolution and signal to noise (S/N) ratio which are associated with the data acquisition parameters such as sample interval, common midpoint (CMP) interval and CMP fold. To understand the effect of the acquisition parameters, a test survey was carried out off Yeosu and the acquired data were analyzed. A 30 $in^3$ small air gun was used as a seismic source and 8 channel streamer cable with a 5 m group interval was used as a receiver. The data were digitally recorded with a shot interval of 2 s and sample interval of 0.1 ms. The acquired data were resampled with various sample intervals, CMP intervals and CMP folds. The resampled data were processed, plotted as seismic sections and compared each other. The analysis results show that thin bed structure with ${\~}1m$ thickness and ${\~}6^{\circ}$ slope can be imaged with good resolution and continuity and low noise using the acquisition parameters with a sample interval shorter than 0.2 ms, CMP interval shorter than 2.5 m and CMP fold more than 4. Because seismic resolution is associated with the acquisition parameters, the quality of the subsurface structure can be imaged successfully using suitable and optimum acquisition parameters.

• Geophysics and Geophysical Exploration
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• v.21 no.3
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• pp.171-182
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• 2018

We performed the seismic field data processing using an open-source software (Madagascar) to verify if it is applicable to processing of field data, which has low signal-to-noise ratio and high uncertainties in velocities. The Madagascar, based on Python, is usually supposed to be better in the development of processing technologies due to its capabilities of multidimensional data analysis and reproducibility. However, this open-source software has not been widely used so far for field data processing because of complicated interfaces and data structure system. To verify the effectiveness of the Madagascar software on field data, we applied it to a typical seismic data processing flow including data loading, geometry build-up, F-K filter, predictive deconvolution, velocity analysis, normal moveout correction, stack, and migration. The field data for the test were acquired in Gunsan Basin, Yellow Sea using a streamer consisting of 480 channels and 4 arrays of air-guns. The results at all processing step are compared with those processed with Landmark's ProMAX (SeisSpace R5000) which is a commercial processing software. Madagascar shows relatively high efficiencies in data IO and management as well as reproducibility. Additionally, it shows quick and exact calculations in some automated procedures such as stacking velocity analysis. There were no remarkable differences in the results after applying the signal enhancement flows of both software. For the deeper part of the substructure image, however, the commercial software shows better results than the open-source software. This is simply because the commercial software has various flows for de-multiple and provides interactive processing environments for delicate processing works compared to Madagascar. Considering that many researchers around the world are developing various data processing algorithms for Madagascar, we can expect that the open-source software such as Madagascar can be widely used for commercial-level processing with the strength of expandability, cost effectiveness and reproducibility.