• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.78-88
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• 2021

Owing to the abnormal weather conditions due to global warming, carbon capture and storage (CCS) technology has attracted global attention as a countermeasure to reduce CO₂ emissions. In the Pohang CCS demonstration project in South Korea, 100 tons of CO₂ were successfully injected into the subsurface CO₂ storage in early 2017. However, after the 2017 Pohang earthquake, the Pohang CCS demonstration project was suspended due to an increase in social concerns about the safety of the CCS project. In this study, to reconfirm the structural suitability of the CO₂ storage site in the Pohang Basin, we employed seismic imaging based on reverse-time migration (RTM) to analyze small-scale ocean-bottom seismic data, which have not been utilized in previous studies. Compared with seismic images using marine streamer data, the continuity of subsurface layers in the RTM image using the ocean-bottom seismic data is improved. Based on the obtained subsurface image, we discuss the structural suitability of the Pohang CO₂ storage site.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.113-130
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• 2021

Recently, ground-penetrating radar (GPR) surveys have been actively employed to obtain a large amount of data on occurrences such as ground subsidence and road safety. However, considering the cost and time efficiency, more intuitive and accurate interpretation methods are required, as interpreting a whole survey data set is a cost-intensive process. For this purpose, GPR data can be subjected to attribute analysis, which allows quantitative interpretation. Among the seismic attributes that have been widely used in the field of exploration, complex trace analysis and similarity are the most suitable methods for analyzing GPR data. Further, recently proposed attributes such as edge detecting and texture attributes are also effective for GPR data analysis because of the advances in image processing. In this paper, as a reference for research on the attribute analysis of GPR data, we introduce the useful attributes for GPR data and describe their concepts. Further, we present an analysis of the interpretation methods based on the attribute analysis and past cases.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.73-77
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• 2021

Induced polarization (IP) is useful for mineral exploration and hydrogeological studies by visualizing the electrochemical reactions at the interface between polarized minerals and groundwater. Frequency-domain IP (FDIP) is not actively applied to field surveys because it takes longer to acquire data, despite its higher data quality than conventional time-domain IP. However, data quality is more important in current mineral exploration as the targets gradually shift to deep or low-grade ore bodies. In addition, the measurement time reduced by automated instrumentation increases the potential for FDIP field applications. Therefore, we demonstrate that FDIP can detect mineral exploration targets by performing geophysical logging in the boreholes of a skarn deposit, in South Korea. Alternating current (AC) resistivity, percent frequency effect (PFE) and metal factor (MF) were calculated from impedance values obtained at two different frequencies. Skarn zones containing magnetite or pyrite showed relatively low AC resistivity, high PFE, and high MF compared to other zones. Therefore, FDIP surveys are considered to be useful for mineral exploration.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.202-208
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• 2021

Induced polarization (IP) was first published in a Korean academic journal in 1973, and it was soon applied to coal and metal ore exploration. Then, in universities and research institutes, IP modeling studies using the finite element approach and experimental studies on IP responses for artificial samples were conducted. In the mid-1980s, the spectral IP (SIP) measurement module was introduced to Korea, and physical scale modeling and inversion approaches were developed. Due to the decline of the mineral resource industry, this method was not actively applied. However, the SIP method was not applied In the 1990s, IP exploration was applied in the investigation of hydrothermal deposits of sulfide minerals and bentonite mineralization zones, as well as to areas where the groundwater was contaminated by intruding seawater. In the 2000s, three-dimensional inversion of the IP approach was developed, and high-precision geophysical exploration was required to secure domestic and overseas mineral resources, so SIP experiments on rock samples and approaches for field exploration were developed. The SIP approach was proven useful for the exploration of metal deposits containing sulfide minerals by applying it to explore the mineralization zone of gold-silver deposits in the Haenam region. The IP method is considered to be effective in exploring critical minerals (lithium, cobalt, and nickel) in high-tech industries. It also is expected to be useful for environmental and geotechnical investigations.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.14-25
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• 2022

The passive surface wave method measures seismic signals from ambient noises or vibrations of natural phenomena without using an artificial source. Since passive sources are usually in lower frequencies than artificial ones being able to ensure the information on deeper geological structures, the passive surface wave method can investigate deeper geological structures. In the passive method, frequency dispersion curves are obtained after data acquisition, and the dispersion curves are analyzed by assuming 1D-layered earth, which is like the method of active surface wave survey. However, when computing dispersion curves, the passive method first obtains and analyzes coherence curves of received signals from a set of receivers based on spatial autocorrelation. In this review, we explain how passive surface wave methods measure signals, and make data processing and interpretation, before analyzing field application cases.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.180-193
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• 2021

The complex resistivity method is an exploration technique that can obtain various characteristic information of underground media by measuring resistivity and phase in the frequency domain, and its utilization has recently increased. In this paper, a three-dimensional inversion algorithm for the CR data was developed to increase the utilization of this method. The Poisson equation, which can be applied when the electromagnetic coupling effect is ignored, was applied to the modeling, and the inversion algorithm was developed by modifying the existing algorithm by adopting comlex variables. In order to increase the stability of the inversion, a technique was introduced to automatically adjust the Lagrangian multiplier according to the ratio of the error vector and the model update vector. Furthermore, to compensate for the loss of data due to noisy phase data, a two-step inversion method that conducts inversion iterations using only resistivity data in the beginning and both of resistivity and phase data in the second half was developed. As a result of the experiment for the synthetic data, stable inversion results were obtained, and the validity to real data was also confirmed by applying the developed 3D inversion algorithm to the analysis of field data acquired near a hydrothermal mine.

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.59-70
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• 2022

Recent studies demonstrate that machine learning has expanded in the field of seismic interpretation. Many convolutional neural networks have been developed for seismic sequence identification, which is important for seismic interpretation. However, expense and time limitations indicate that there is insufficient data available to provide a sufficient dataset to train supervised machine learning programs to identify seismic sequences. In this study, patch division and data augmentation are applied to mitigate this lack of data. Furthermore, to obtain spatial information that could be lost during patch division, an artificial channel is added to the original data to indicate depth. Seismic sequence identification is performed using a U-Net network and the Netherlands F3 block dataset from the dGB Open Seismic Repository, which offers datasets for machine learning, and the predicted results are evaluated. The results show that patch-based U-Net seismic sequence identification is improved by data augmentation and the addition of an artificial channel.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.120-128
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• 2022

There are 17,000 reservoir dams in Korea, of which more than 85% were built over 50 years ago. Old embankment dams are weakened by internal erosion and suffusion phenomena due to preferential leakage paths and this ongoing weakening can cause their failure. Therefore, early warning associated with leakage in an embankment dam is crucial to prevent its failure. An electrical resistivity survey is a non-destructive, real-time and in-situ technique for detecting the development of leakage zones and general conditions of embankment dams. Because of its advantages, the electrical resistivity survey is widely used for reservoir safety inspections. However, the electrical resistivity survey is still not officially included in the precise safety inspection of reservoir dams because it cannot present a quantitative index of dam safety. In this study, we propose a method for calculating the leak index according to the water content evaluated from the electrical resistivity survey and/or induced polarization survey. Particularly, by proposing a quantitative leak index calculation method from monitoring surveys and independent surveys, we provide a theoretical basis for including electrical resistivity and induced polarization surveys as components of the precise safety inspection of reservoirs dams.