Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning

Abstract

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine learning validated that automation based on the proposed supervised and unsupervised learning applications can classify the acquired microseismic data in real time.

1. Introduction

Microseismic data are useful for mining safety [1], microearthquake observations [2], landslide detection [3], subsidence monitoring [4], and locating underground oil storage caverns [5] because they can provide clues to detect the generation of cracks, fracture networks, or even unknown faults. During mining, different kinds of seismic data are generated, including background noise (electromagnetic noise), by typical works for mining such as mechanical drilling, blasting vibration (setting off of dynamite), and collapse (rock mass rupture) [6,7], which have to be properly recognized and categorized. In addition, any signs of collapses or earthquakes should be dealt with appropriately as mine collapses are bound to cause casualties [7,8].

A micro-earthquake (microseismicity), which is typically defined as an earthquake with a magnitude less than 2 [9], is a critical issue that needs to be analyzed and predicted using microseismic data because natural or induced/triggered earthquakes can seriously affect the structural stability of anything on the surface. Micro-earthquakes and resultant microseismic data can be utilized to monitor and forecast behaviors of injected fluids when carbon capture and storage (CCS) or geothermal projects are implemented [10,11]. As it is challenging to identify the location or flowing behavior of injected CO₂ or water directly, we can infer them by tracing back to the origin of the micro-earthquake from the obtained seismic data using monitoring devices [12]. Sensing the first arrival of a seismic signal is important to identify the distance and location of the origin of the microseismic event, and thus, considerable research on this has been performed [13].

Although recognition and classification of seismic data are critical, enormous amounts of time and energy of experts are required to perform these tasks manually [14]. Previous works have proposed automatic classification algorithms for microseismic data using machine learning and previously acquired seismic data [7,13,15,16,17].

Automatic classification through improved signal and noise classification performance by feature extraction such as duration, rising time, maximum amplitude, and relative amplitude of seismic data have been proposed [18]. In addition, machine learning and deep learning methods have been applied to the seismic data obtained from fields to utilize them in a data-driven environment with techniques such as principal component analysis, support vector machine, deep neural network, and convolutional neural network (CNN) [6,15,16,19,20,21,22,23,24,25,26].

In spite of these complicated algorithms and techniques, the efficiency of machine learning is based on qualified and trustworthy data from the target fields [27]. This study details how microseismic data from the Pohang enhanced geothermal system (EGS) site were obtained, processed, and utilized for data-driven machine learning, including both supervised and unsupervised learning methods. As the Pohang is on an active fault of the southeastern part of Korea, its critical position is of significance from a geological perspective [28,29,30].

An earthquake with a magnitude of 5.4 occurred in 2017 in Pohang. An unknown active fault exists in the region, and it must be thoroughly investigated. The government of South Korea carried out investigations to understand the correlation between the Pohang earthquake and the enhanced geothermal system project as the occurrence of the earthquake caused the suspension of many ongoing CCS and EGS projects. Many monitoring projects including the southeastern part monitoring project by the Korea Institute of Geoscience and Mineral Resources (KIGAM) have targeted this area after the occurrence of the Gyeoungju (2016) and Pohang (2017) earthquakes. These projects intend to study active faults to protect the nation from disastrous earthquakes. Therefore, the monitoring of microseismic data related to natural and/or human sources must be identified, and the geological environment in which these occur must be modeled to provide earthquake early-warning and prevent serious damages.

This study validates the potential of the automatic classification method using a simple and fast machine learning application when compared to previous studies using unique microseismic data obtained for the first time during the hydraulic-stimulation test conducted in the first EGS project in Korea. The research article is structured as follows. The Introduction section is followed by Section 2, Methodology, which explains how microseismic data were obtained and preprocessed and the machine learning methods applied to the acquired data. In Section 3, Results, machine-learning performance classifying the given data into noise and signal is presented in terms of supervised and unsupervised learning. Section 4, Conclusions, proposes the novel consequences and contributions of this study and presents the scope for further applications and research.

2. Methodology

2.1. Acquisition and Preprocessing of Microseismic Data

Pohang basin has relatively rare geological characteristics in the Korean Peninsula in terms of its high geothermal potential and relatively thicker sedimentary layers. The Pohang basin has a higher geothermal gradient of 35–40 °C/km compared to other areas (25 °C/km) [31], which is the motivation for the birth of the first and largest geothermal projects in Korea. In addition, to harness geothermal energy and for petroleum prospecting, 6 deep wells over 1 km deep have been drilled including PX-1 and PX-2, which are >4 km deep.

Figure 1a represents a drilling log of a drill hole located near the EGS site. The Pohang basin originated from the pull-apart process that occurred during the Middle Tertiary along with the opening of the East Sea [32]. Cretaceous sedimentary rocks, including volcanic tuff, lie beneath the Tertiary sediments. Permian granodiorite forms the basement of this region. In the shallow part of the Pohang basin, near the EGS site, unconsolidated mudstone with a thickness of 100–500 m occurs among the Tertiary sediments with its thickness increasing from north to south. This thick mudstone environment was not a favorable condition for the installation location of seismometers in terms of signal-to-noise ratio, because the harder rock is more likely to attenuate the surface noise with depth [33].

Figure 1b shows the microseismic monitoring network for the EGS site near Pohang. White squares are shallow borehole accelerometers named Pohang borehole stations (PHBSs) with installation depths of 100–130 m, which indicates that sensors were located in unconsolidated mudstone layers. White circles are temporary stations with surface seismometers called mobile surface stations (MSSs). As seen in Figure 1c, a sensor and an amplifier manufactured by DJB and Guralp DM24 digitizer, respectively, constitute each station. The DJB accelerometer is a piezoelectric sensor with three orthogonal axes to receive broadband signals such as those from microseismic events.

These stations were originally designed to have 3 km and 5 km radii of coverage centered on the EGS construction site. The installation of the shallow borehole sensor network was completed in April, 2012, and the temporary surface stations were only operated during 2016–2017, when the hydraulic stimulation was performed. Deep borehole sensors were also installed temporarily during the stimulation period to catch smaller events. The depths of installation of these two different types of sensor systems were around 2 km and 1.3–1.5 km, respectively. Among these installed sensors, we chose data from PHBS8 for this research, as they were the most consistent and robust during 2012–2017.

Routine pre-processing procedures used for data preparation are described in Figure 2. We mainly used the InSite software provided by Itasca international [35] for EGS microseismic monitoring and processing. The event detections were conducted based on short-time averaging (STA) and long-time averaging (LTA) methodology with variable parameters regarding the availability and sensitivity of pilot sensors. The collected data from four hydraulic-stimulation were saved and processed in the InSite software. Among them, relatively clear data of 99 events were manually selected and converted to the miniSEED, which is the standard format used by seismologists. In this procedure, raw amplitude information was pre-processed, including the removal of a linear trend or offset from the zero amplitude (Det. in Figure 2). Amplitude normalization (Norm. in Figure 2) for each trace was additionally conducted and saved for checking the difference between using absolute and relative amplitude information for machine learning. As a normalization procedure, we divided the amplitude of all data points by the maximum absolute amplitude of each individual axis.

After converting raw data to miniSEED format and pre-processing including linear offset removal and normalization, the standard STA/LTA (short-time averaging/long-time averaging) algorithm was employed for signal and noise sample generation. Equations (1) and (2) and Figure 3 describes the basic concept of the short-time averaging and long-time averaging triggering method. In Equations (1) and (2) and Figure 3, i is an index of the test data point and $l_{1,2}$ are window-lengths of the STA and LTA. By examining the Equations and Figure, STA is literally averaging the amplitude of the short periods; thus, it reflects an abrupt amplitude change in the waveform (Equation (1) and green line in Figure 3). Conversely, LTA means the long trend of the data representing a mean energy level of noise (Equation (2) and red solid line in Figure 3). In principle, the STA/LTA ratio will increase as it approaches the P-wave onset and then decrease. Thus, the STA/LTA algorithm is commonly used for P-wave onset picking (triggered time) and event triggering as well. We used the standard STA/LTA subroutine provided in the ObsPy library [36] for already triggered events. There are precise algorithms to pick first arrival [37,38,39,40]. However, a simple STA/LTA algorithm is sufficient to work out for the purpose of dividing the data into signal and noise.

$$STA={\displaystyle \sum }_{j=i}^{i-l_1}{\{Amplitude \left(i\right)\}}^2/l_1$$

(1)

$$LTA={\displaystyle \sum }_{j=i}^{i-l_2}{\{Amplitude \left(i\right)\}}^2/l_2 where l_2=10\times l_1$$

(2)

Figure 4 describes the preparation of sample data for machine learning with the triggered data in detail. We divided the time trace of sample data into the noise and signal based on the event’s triggered time. To prevent the omission of signal due to error of triggering by STA/LTA algorithm, a 0.1 s before the triggered time calculated from the STA/LTA algorithm was used as a triggered time.

During observations of 2 years of the hydraulic stimulations, source–receiver distances were generally approximately less than 5 km (we assume that the average approximate P wave velocity is around 5.6 km/s and S wave velocity is around 3.294 km/s based on the sonic log on the EGS site). Regarding the residual energy after S wave arrival, an additional 1 s was enough to contain all the P and S wave characteristics. Thus, the authors decided on the 2 s window after P wave onset for the signal.

Thus, data points in a 2 s time window before the triggered time were labeled as noise. The same time window parameter was applied to the data after the triggered time, and it was labeled as a signal. In this way, we augmented the training data, which comprise 99 noise events and 99 signal events. The total number of amplitude data points was 1001, since the typical samples per second of PHBSs was 500 (i.e., 500 data points per second), where its Nyquist frequency (250 Hz) was within the actual frequency range (<200 Hz) of signals according to the spectrograms in Figure 4.

In this study, one data sample represents each of Figure 4a–i. Thus, one data sample is composed of 1001 data points. Each data point indicates an amplitude value. There were three direction measurements from one of signal or noise events. Thus, there were 3003 data points for each event. In the machine or deep learning of this study, one training data sample is composed of three data samples from the three directions, which means that there were data points for each training data sample. Data point, data sample, and training data sample should be taken as having different meanings; they are defined separately for a better understanding of data construction and convenience of labeling and training for machine learning.

2.2. Supervised Learning: Random Forest and Convolutional Neural Network

Random forest (RF) is composed of multiple decision trees. A decision tree finds the most explanatory features, which appropriately divide a given data pool such that the divided data have a higher purity than before the division [42,43]. For example, it assumes that there are training data samples as shown in Figure 5a and that they can be categorized into noise (black) and signal (red) classes as presented. The data are scattered in two dimensions: first and second features. The features could be any data point among the 3003 data points.

If all the data were organized according to the first feature, they would be arrayed as Figure 5b. A thick red bar on Figure 5b indicates a decision boundary dividing the given data into two groups. If a decision boundary was set on the second feature, it would be arrayed as in Figure 5c. In the case of the first feature, the organized data show a distributed trend in an orderly fashion in spite of the mixed training data samples. In Figure 5c, whichever criterion is fixed to classify the entire data, it will have impurity compared to Figure 5b. After Figure 5c, the two subgroups can be divided further into another two subgroups, respectively, as displayed in Figure 5d.

Figure 5e depicts how a decision tree divides the given data pool into branches. The training data samples are labeled as 0 (signal) and 1 (noise). Each sample is composed of 3003 data points. Among the 3003 data points, we find a decision boundary to separate given data into two groups having similar features, respectively, such that it has lower impurity. Including the far lower right part, Figure 5e has four stages of dividing the given data. The first, second, and third stages correspond to Figure 5a,c,d, respectively. In the far lower right of Figure 5e, there are five samples, and they are categorized into three signals (the left dotted-line circle) and two noises (the right dotted-line circle). Although the degree of purity is likely to increase by narrowing down to multiple subgroups (Figure 5e), an excessive number of branches could cause overfitting and weakened generality.

In most cases, a randomly mixed data pool has high complexity and impurity, which must show a similar condition to the low purity of Figure 5c rather than the high purity of Figure 5b. That is, there might be no features to categorize data as clearly as Figure 5b does. Thus, we should be able to solve more complicated situations such as in Figure 5c than in Figure 5b. In such a complicated situation, we are bound to encounter many scenarios corresponding to how given data are divided. Therefore, only one single decision tree could give us a biased decision, even though a decision tree has the advantages of simplicity and understandability. To avoid this drawback, multiple decision trees are trained, and they consist of one RF model.

Figure 6 schematically describes the concept of RF composed of multiple decision trees. Typically, the stability of performance increases with the greater number of decision trees; however, computational cost also increases proportionally, and hence an optimal number of trees should be fixed. Thus, although the number of trees depends on the number of data samples and features, it is usually decided as a few hundred to a few thousand [42,44,45,46,47].

CNN is one of the neural networks used for deep learning. CNN extracts the best features from data by pooling and convolution [46,48,49,50,51,52]. Figure 7 describes how pooling and convolution work during data processing. Figure 7a,b are examples of global average pooling and max pooling. Global average pooling calculates the mean of 16 values (each convolution layer of Figure 7a) and brings one averaged value as the representative of each convolution layer. Max pooling outputs maximum value from each assigned area by scanning window and stride. Here, stride is the moving step of the scanning window. The sizes of window and stride are 2-by-2 and 2, respectively, as in Figure 7b, such that this process brings four representative figures. Figure 7c is an example of convolution with a 3-by-3 kernel, 1 stride, and padding. A kernel is similar to a scanning window of max pooling, and the size or elements can be different to determine different trends of an image as shown in Figure 7d. Padding consists of supplementary grids surrounding an original input layer to preserve data size. Although the principles of pooling and convolution are different, the idea behind them is to extract essential information from a given image’s data. In general, a series of convolution or pooling layers are stacked to suitably deal with images and to achieve users’ needs such as denoising, detection, location, and classification of seismic data [48,53].

We designed CNN using AutoKeras [54], which automatically and efficiently searches for an optimal neural architecture. Schematically, the overall CNN application was constructed as shown in Figure 8. The number of data points for an axis is 1001, and there are three axes (Figure 8a). One training sample is composed of the three axes such that a sample is composed of 3003 data points in total (Figure 8a). Those with multiple samples consist of training data. As shown in Figure 8b, one sample is taken as 1001 by 1 by 3 and the last 3 means the three channels of CNN input form. In Figure 8c, CNN is constructed with convolutional layers for convolutions and poolings. Then, it is ended to a fully connected network and sigmoid [55] to give values of 0 to 1, which indicate the probability of whether it is a noise or a signal (Figure 8d).

2.3. Unsupervised Learning: K-Medoids Clustering

K-medoids is a data clustering algorithm to categorize given data into k groups with k centroids. K-means clustering, also a clustering algorithm, decides center location as averaged coordinates of each data group, k. However, the K-means algorithm has a couple of drawbacks compared to K-medoids [56,57,58]. First, it is highly dependent on the initial locations of k representative points. Second, it does not properly work for different sizes and densities of clusters. Third, it is vulnerable to noise or outliers such that the clustering result could be biased. On the other hand, K-medoids clustering gives less overlapping clustering results, is less sensitive to outliers, and is more representative of centers for each cluster. There are variant versions of the K-medoids algorithm, and this study utilizes the partitioning around medoids (PAM) [58] algorithm as outlined in the following sequence and depicted in Figure 9:

• Given training data samples are presented in the dimension of data features and randomly select k data points as the representatives (medoids) among the entire n data points (Figure 9a).
• The rest of the data points are assigned to each of the k center points when data have the closest distance from the medoids (Figure 9b).
• The locations of the medoids are changed, and the sum of within-cluster distances for before and after (Figure 9c) situations is computed.
• Medoids option showing the lower sum of within-cluster distances is selected (Figure 9c).
• Steps 2 to 4 are repeated until there is no change in the locations of the medoids.

As mentioned in Figure 4, there were 198 seismic data samples composed of 99 signal and 99 noise samples, and they were presented as one data point as described in Figure 9a. According to previous studies and experiences, the vertical component is the first data to be analyzed. The vertical component reflects the P-wave energy most clearly in many cases [59,60]. Besides, we can save computational costs by using one component rather than three components for event classification. For that reason, the vertical component was selected as the only representative feature of the 198 seismic data samples analyzed in this study. Distance (similarity–dissimilarity) between the samples is defined as Euclidean in this study as the following Equation (3):

$${\mathrm{C}}_{mm}={\displaystyle \sum }_{j=1}^{n_e}{\displaystyle \sum }_{i=i}^{n_e}\left(m_i-m_j\right){\left(m_i-m_j\right)}^{\mathrm{T}},$$

(3)

where ${\mathrm{C}}_{mm}$ means covariance matrix representing relationship among the microseismic samples and a component of ith row and jth column in ${\mathrm{C}}_{mm}$ is Euclidean distance between ith sample and jth sample, $n_e$ is the number of given training samples and it comprises 198 seismic data in this study. Thus, the matrix ${\mathrm{C}}_{mm}$ is 198 by 198 symmetric covariance matrix with zero components on the diagonal line.

Coordinates of each data sample were computed using classical multidimensional scaling in MATLAB. To visualize the samples in two dimensions, the X and Y axes of Figure 9 were defined corresponding to the two largest eigenvalues [61].

3. Results and Discussion

Figure 10 presents two examples of signal and noise samples, respectively. One sample consists of three directions: vertical, horizontal 1, and 2 in Figure 10a–f. The red dotted line and arrows indicate the onset time of P- and S-wave energies. In Figure 10a–c, one can clearly observe that the amplitude level is above the average amplitude level before and after the red dotted lines. The start of the P-wave is the most important feature to determine whether it is an event or not. In the middle of Figure 10a–c, S-wave energy is also clearly distinguishable. In the horizontal components, the S-wave energy is orthogonal to the P-wave vibration that was successfully detected. On the other hand, Figure 10d–f display an example of ambient noise, where the average level of the amplitude is comparably consistent with the fluctuations through all data points. For this study, the amplitude information is used directly as an input data sample for supervised and unsupervised learning. We presumed that P and S wave information is not reflected in machine learning explicitly but implicitly.

Table 1. Conditions for training of RF and CNN.

Parameters		Conditions
Entire data		198 (Signal 99 + Noise 99)
Training data		160 (Signal 1–80 + Noise 1–80)
Test data		38 (Signal 81–99 + Noise 81–99)
Size of one sample		3003 (1001 points for each of the three axes)
RF	Maximum depth	10
	Number of trees	200
	Number of properties	55
CNN	Optimization	Automated deep learning by Auto-Keras
	Maximum epochs	300
	Validation split	20%

shows the training condition for RF and CNN. For all machine learning processes, we used a workstation with an Intel Xeon Gold 6136 central processing unit using 3 and 2.99 GHz processors with 128 GB of random access memory. The number of data was 198, which was composed of 99 signal and 99 noise samples. Training data used 80% of the samples, and the test data used 20%. The size of one sample is 3003 for axes in three directions. The maximum depth for RF was set at 10 for proper classification performance. The number of trees was optimized at 200 considering affordable computation cost for less than 30 min with a stable performance regardless of randomness. In the case of CNN, its neural structure was optimized according to Auto-Keras, the automated deep learning algorithm [54]. The number of maximum epochs and the validation split ratio was decided at typical levels.

Figure 11 displays how RF and CNN predict the original microseismic data in a confusion matrix form. Green and red color indicate right and wrong prediction, respectively. Figure 11a,b are the training and test results from RF, and Figure 11c,d present those from CNN. Both RF and CNN show 100% accuracy in training results. The RF shows only one false noise, which means it is a signal that was evaluated as noise, was observed. On the other hand, CNN brings one false signal, that is, it predicted noise as signal. Although it turns out that both RF and CNN have the same accuracy of performance, the two methods must be compared from the perspective of uncertainty.

Figure 12 describes the probability of a given sample being assigned to the signal group. Both RF and CNN can output continuous probability values between 0 and 1 (0: signal, 1: noise). The RF model is composed of multiple trees, and each tree gives a bimodal result, 0 or 1. The CNN model ends with the sigmoid model giving stochastic values from 0 to 1. In Figure 12, each point indicates the probability to be a signal for each data sample in order (see Table 1). In Figure 12a,c, the training samples 1 to 80 and 81 to 160 mean noise and signal data, respectively. Accordingly, the probability values are well matched to the data distribution, which indicates that the training samples are properly categorized into signal and noise by both the RF and the CNN.

Conversely, Figure 12b shows the probability values of around 0.5, which means that those samples cause a difficulty in classification. In the case of test sample number 20 with the red circle, it should be in the signal, but it is classified as noise. The CNN model still presents clear stochastic indications to classify noise and signal in the test samples (Figure 12d). However, the test sample number 19 with the red circle, which is supposed to be noise, is wrongly classified as signal.

Figure 13 displays the false noise and false signal from the results of RF and CNN. Figure 13a–c and Figure 13d–f correspond to false noise and false signal of Figure 11b and Figure 11d, respectively. In that, the left column sample of Figure 13 is actually signal and the right one of Figure 13 is noise. The model constructed in machine learning is the black-box, and it does not explicitly provide the reason for classification, and it needs to infer what the RF and CNN models did for sample numbers 20 and 19. Thirty-eight test data samples of signals and noises (Appendix A) were thoroughly investigated.

It is hypothesized that the RF-trained model confused signal as noise because of the amplitude fluctuation pattern of the vertical component. The energy of the vertical component in Figure 13a is weak compared to the ambient noise level. For the CNN model case, we found that the overall amplitude fluctuation pattern of the vertical component from the noise samples is similar except for that of sample number 19, which has an abrupt elevation in amplitude at the end of the data points (Figure 13d). This implies that the trained CNN model might determine noise according to the vertical component and sample 19 was peculiarly patterned to be noise.

The horizontal components of noise samples have a variety of patterns compared to the vertical component. Inconsistent noise trends are similar to signal samples. Therefore, there is a good chance that the RF model and the neural structure of the CNN model assign low weights for the horizontal components compared to the vertical one because the horizontal components lack consistent features, so they are less helpful for the identification of noise.

Figure 14 shows the k-medoids clustering results of the normalized and fast Fourier transformed (FFT) samples. FFT is widely utilized to analyze waveform data such as mircroseismic from drilling, blasting, and earthquake [62,63,64]. FFT transforms given mircroseismic data from the time domain to the frequency domain such that it makes it easier to identify the specific pattern or trend, leading to reliable characterization or classification of signal and noise. The 198 training data samples were composed of 99 signal and 99 noise (Figure 14a,d). They were clustered into 10 groups indicated in the upper right corner of Figure 14a,d. Centers of each group are also marked with black empty circles. Figure 14b,c,e,f are pictured to separately show samples corresponding to each center of 10 clusters. They are put into odd and even groups to avoid overlapping.

In Figure 14a, cluster number nine is the signal group, which is indicated with green colored balls with a black edge as shown in the label. Figure 14b shows the center sample of cluster number nine, and it has a general pattern of signal (P and S waves). In Figure 14b,c, the rest of the nine center samples present an irregular noise pattern. In Figure 14d, cluster number one, the red cross marks indicate the signal group, and they also show the certain trend of P and S waves. We can manually guess whether a cluster is assigned to a signal or noise based on the 10 center samples in this study.

The fact that the clustering properly categorizes 198 samples into signal and noise is validated. Figure 15a,b show the accuracies of k-medoids clustering for the normalized and FFT samples at 89% and 97%, respectively. The difference in accuracy of 8% means that FFT helps to extract essential features of microseismic data. In the cases of the classifications by RF and CNN, there was no need to reconsider their machine learning performances because both methods were workable and affordable. In terms of the application of k-medoids on the normalized samples, 89% accuracy is insufficient for application to field data for practical purposes, and hence, FFT is the better option.

A comparison of Figure 15a,b shows that the number of false noises is 3. On the other hand, the number of false signals is reduced from 19 to 3, which is encouraging because it saves us time spent on checking false signals. In Figure 15a,b, the false noise is critical because we could miss important events by treating them as noise. Sorting out these confusing samples needs attention in a further study.

4. Conclusions

This study validated the potential of successful machine learning applicability for signal–noise classification of microseismic data from the Pohang EGS project. The seismic data presented in this study are the first and unique microseismic data obtained during the hydraulic-stimulation test for the first EGS project in Korea. The number of training data samples was 198, composed of 99 signal and 99 noise, which has an advantageous data composition of 50:50 between two classes for reliable classification performance based on machine learning.

Supervised and unsupervised learning methods were utilized to address the classification and the results showed decent accuracy. The two supervised methods (RF and CNN) brought satisfying accuracies of 100% and 97.4% for the training and test sets, respectively, in both RF and CNN. The unsupervised method, K-medoids clustering, gave 88.9% and 97.0% accuracy for the normalized data and FFT data, respectively. Generally, the classification performance seems to be sufficiently trustworthy if it would be applied for practical utilization in the field in real-time. RF worked appropriately for classification because the quality of training data was well-qualified and came from one water injection well, which must have led to consistency of the signal data.

The quantity of the utilized data might be insufficient; however, extra data would be added in the future after resolving the public acceptance problem by thorough government investigation on the EGS project after the Pohang earthquake in 2017 and contribute to further studies on the overall geological environment to understand complex fracture and fault systems present in Pohang. Therefore, this study is a suitable starting point for the processing and understanding of microseismic activities that occur after the Pohang earthquake, which still needs to be understood. Furthermore, a new project planned by KIGAM is expected to drastically improve the quantity of microseismic data. In addition, Korea Meteorological Administration and Earthquake Research Center in KIGAM are trying to install many seismometers to detect micro-earthquakes and delineate fault systems.

Accurate and fast event triggering and classification of signal and noise will be required in the immediate future as an enormous amount of data will be generated by investigations of complex fracture and fault networks. Therefore, we expect to expand the generality of the proposed method by refining using new data and continuously updating the data pool. Such recurrent application of the method will lead to the construction of the automatic process and learning system for a signal–noise classification.