• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.211-216
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• 2013

In general, smoothing filters regularize functions by reducing differences between adjacent values. The smoothing filters, therefore, can regularize inverse solutions and produce more accurate subsurface structure when we apply it to full waveform inversion. If we apply a smoothing filter with a constant coefficient to subsurface image or velocity model, it will make layer interfaces and fault structures vague because it does not consider any information of geologic structures and variations of velocity. In this study, we develop a selective smoothing regularization technique, which adapts smoothing coefficients according to inversion iteration, to solve the weakness of smoothing regularization with a constant coefficient. First, we determine appropriate frequencies and analyze the corresponding wavenumber coverage. Then, we define effective maximum wavenumber as 99 percentile of wavenumber spectrum in order to choose smoothing coefficients which can effectively limit the wavenumber coverage. By adapting the chosen smoothing coefficients according to the iteration, we can implement multi-scale full waveform inversion while inverting multi-frequency components simultaneously. Through the successful inversion example on a salt model with high-contrast velocity structures, we can note that our method effectively regularizes the inverse solution. We also verify that our scheme is applicable to field data through the numerical example to the synthetic data containing random noise.

• Geophysics and Geophysical Exploration
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• v.20 no.1
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• pp.1-11
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• 2017

In order to improve the accuracy of microseismic epicenter location through the inversion techniques using P and S wave first arrivals, field experiments of microseismic monitoring were performed using borehole 3-component geophones. The direction of epicenter was estimated from the hodograms of P-wave first arrivals through the weight drop experiments in which the $\times$ component of 3-component geophone was aligned to the magnetic north. The picking of S wave first arrival was possible in the polarization filtered data even if S waves are difficult to be identified in raw data. The inversion technique using only P wave first arrival times can often converge to the local minimum when the initial values for epicenter are largely apart from the true epicenter, so that the correct solution can not be found. To solve this problem, the epicenter determination method using differences between P and S wave arrival times was used to estimate proper initial values of epicenter. The inversion result using only P-wave first arrival times which started from the estimated initial values showed the improved accuracy of the epicenter location.

• Geophysics and Geophysical Exploration
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• v.20 no.1
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• pp.43-48
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• 2017

The applicability of refraction survey has been tested using a wave glider widely used in long-term ocean observations around the world. To record seismic refractions, a single channel streamer with metal weight and a seismic recording system were mounted on the wave glider. We used GPS precise time synchronization signal and radio frequency (RF) communication to synchronize shot and recorder triggers and to control acquired data quality in real time. When the wave glider is positioned close to the set point, a 2,000 J sparker is exploded along the designed track at 2 second intervals. Through the test survey, we were able to successfully acquire refractions from the subsurface.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.131-140
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• 2020

In training a classification model using machine learning, the acquisition of training data is a very important stage, because the amount and quality of the training data greatly influence the model performance. However, when the cost of obtaining data is so high that it is difficult to build ideal training data, the number of samples for each class may be acquired very differently, and a serious data-imbalance problem can occur. If such a problem occurs in the training data, all classes are not trained equally, and classes containing relatively few data will have significantly lower recall values. Additionally, the reliability of evaluation indices such as accuracy and precision will be reduced. Therefore, this study sought to overcome the problem of data imbalance in two stages. First, we introduced weighted accuracy and weighted precision as new evaluation indices that can take into account a data-imbalance ratio by modifying conventional measures of accuracy and precision. Next, oversampling was performed to balance weighted precision and recall among classes. We verified the algorithm by applying it to the problem of facies classification. As a result, the imbalance between majority and minority classes was greatly mitigated, and the boundaries between classes could be more clearly identified.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.192-207
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• 2020

We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.208-214
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• 2020

In this study, we have developed a drone magnetic exploration system (proto-type) using a fluxgate magnetic sensor. Hardware of the system consists of a fluxgate magnetometer, an inertial measurement unit (IMU), a GPS, and a communication module. And we have developed monitoring software, which enables it to transmit the measured data to the ground control system (GCS) in real time. The measured magnetic data are finally saved as 1 Hz data after passing through a notch filter and a band-pass filter. For verification of this system, a preliminary test was conducted to check the magnetic responses of a magnetic object first, then the field test was carried out in two iron mines. We tested the developed system on the field test in Pocheon, Gyeonggi and Jeongseon, Gangwon. The magnetic data from the developed drone system was very similar to those from unmanned airship system developed by Korea Institute of Geoscience and Mineral Resources (KIGAM). As a result, preliminary experiment and field test have demonstrated that this system is applicable for outdoor aeromagnetic exploration. It requires more studies to improve filter function and instrument performance to minimize noise in the future.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.141-148
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• 2020

A drone platform built by Korea Institute of Geoscience and Mineral Resources (KIGAM) is introduced. The platform consists of various drone systems developed at KIGAM for photogrammetry, remote exploration, physical exploration, field operation methods, a vehicle-based drone control center, as well as a drone data platform for data storage, sharing, analysis, and visualization of the acquired data. The performance of the drone platform is verified using results obtained with the various systems, which are tested individually and in various combined applications. Finally, the possibility of using the KIGAM drone platform for geological surveys and mineral resource exploration is discussed.

• 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.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.117-130
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• 2020

Noises can distort acquired geophysical data, leading to their misinterpretation. Potential noises sources include anthropogenic activity, natural phenomena, and instrument noises. Conventional denoising methods such as wavelet transform and filtering techniques, are based on subjective human investigation, which is computationally inefficient and time-consuming. Recently, many researchers attempted to implement neural networks to efficiently remove noise from geophysical data. This study aims to review and analyze different types of neural networks, such as artificial neural networks, convolutional neural networks, autoencoders, residual networks, and wavelet neural networks, which are implemented to remove different types of noises including seismic, transient electromagnetic, ground-penetrating radar, and magnetotelluric surveys. The review analyzes and summarizes the key challenges in the removal of noise from geophysical data using neural network, while proposes and explains solutions to the challenges. The analysis support that the advancement in neural networks can be powerful denoising tools for geophysical data.

• Geophysics and Geophysical Exploration
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• v.4 no.1
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• pp.8-18
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• 2001

To delineate the inhomogeneities including fractures and to estimate the freshness of rock borehole radar consisting of the reflection and tomography methods, and GPR surveys were conducted at a granite quarry mine. The borehole reflection survey using the direction finding antenna was also conducted to get the spatial orientations of reflectors. 20 MHz was adopted as the central frequency for the borehole radar reflection and tomography surveys and 100 MHz was for GPR. Through the interpretation of borehole reflection data using dipole and direction finding antenna as well as GPR images, which are good agreement with each other, we could determine the orientation of the major fractures in three dimensional way. Parts of travel time curves of tomography data showed the anisotropy, which is uncommon in granite quarry. By comparing the tomography data and TeleViewer images, the anisotropy effect in this area are closely related to fine fissures aligned in the same direction. The area confined by the two fractures, MF2 and MF5, might consist of the most fresh granite in the surveyed area, which was concluded from the borehole radar tomography, and GPR images as well as the distribution of anisotropy.