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

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works

A simple and fast algorithm for K-medoids clustering

Park, H.-S., & Jun, C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.039 · Cited by 2,106 · see citing works

ObsPy: A Python Toolbox for Seismology

Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., & Wassermann, J. (2010) · Seismological Research Letters · doi:10.1785/gssrl.81.3.530 · Cited by 1,800 · see citing works

Machine learning for data-driven discovery in solid Earth geoscience

Bergen, K. J., Johnson, P. A., de Hoop, M. V., & Beroza, G. C. (2019) · Science · doi:10.1126/science.aau0323 · Cited by 1,271 · see citing works

Auto-Keras: An Efficient Neural Architecture Search System

Jin, H., Song, Q., & Hu, X. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330648 · Cited by 963 · see citing works

A comparison of select trigger algorithms for automated global seismic phase and event detection

Withers, M. M., Aster, R. C., Young, C. J., Beiriger, J. I., Harris, M. D. S., Moore, S. G., & Trujillo, J. R. (1998) · Bulletin of the Seismological Society of America · doi:10.1785/bssa0880010095 · Cited by 574 · see citing works

Generalized Seismic Phase Detection with Deep Learning

Ross, Z. E., Meier, M., Hauksson, E., & Heaton, T. H. (2018) · Bulletin of the Seismological Society of America · doi:10.1785/0120180080 · Cited by 544 · see citing works

Analysis of K-Means and K-Medoids Algorithm For Big Data

Arora, P., Deepali, & Varshney, S. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.02.095 · Cited by 530 · see citing works

P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning

Ross, Z. E., Meier, M., & Hauksson, E. (2018) · Journal of Geophysical Research Solid Earth · doi:10.1029/2017jb015251 · Cited by 513 · see citing works

Machine Learning in Seismology: Turning Data into Insights

Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J., & Gerstoft, P. (2018) · Seismological Research Letters · doi:10.1785/0220180259 · Cited by 504 · see citing works

Introduction to Seismology

Shearer, P. M. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511841552 · Cited by 497 · see citing works

An automatic phase picker for local and teleseismic events

Baer, M., & Kradolfer, U. (1987) · Bulletin of the Seismological Society of America · doi:10.1785/bssa0770041437 · Cited by 464 · see citing works

Microseismic Imaging of Hydraulic Fracturing

Maxwell, S. C. (2014) · Society of Exploration Geophysicists eBooks · doi:10.1190/1.9781560803164 · Cited by 311 · see citing works

Routine Data Processing in Earthquake Seismology

Havskov, J., & Ottemöller, L. (2010) · Springer eBooks · doi:10.1007/978-90-481-8697-6 · Cited by 277 · see citing works

Controlling fluid-induced seismicity during a 6.1-km-deep geothermal stimulation in Finland

Kwiatek, G., Saarno, T., Ader, T. J., Blümle, F., Bohnhoff, M., Chendorain, M., Dresen, G., Heikkinen, P., Kukkonen, I., Leary, P. C., et al. (2019) · Science Advances · doi:10.1126/sciadv.aav7224 · Cited by 259 · see citing works

Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier

Provost, F., Hibert, C., & Malet, J. (2016) · Geophysical Research Letters · doi:10.1002/2016gl070709 · Cited by 197 · see citing works

Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning

Huang, L., Li, J., Hao, H., & Li, X. (2018) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2018.07.006 · Cited by 186 · see citing works

Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data

da Cunha, A. L. A., Pochet, A., Lopes, H., & Gattass, M. (2019) · Computers & Geosciences · doi:10.1016/j.cageo.2019.104344 · Cited by 176 · see citing works

EQcorrscan: Repeating and Near‐Repeating Earthquake Detection and Analysis in Python

Chamberlain, C. J., Hopp, C. J., Boese, C., Warren‐Smith, E., Chambers, D., Chu, S., Michailos, K., & Townend, J. (2017) · Seismological Research Letters · doi:10.1785/0220170151 · Cited by 156 · see citing works

Deep Learning Models Augment Analyst Decisions for Event Discrimination

Linville, L. M., Pankow, K. L., & Draelos, T. J. (2019) · Geophysical Research Letters · doi:10.1029/2018gl081119 · Cited by 155 · see citing works

An In‐Depth Seismological Analysis Revealing a Causal Link Between the 2017 MW 5.5 Pohang Earthquake and EGS Project

Woo, J., Kim, M., Sheen, D., Kang, T., Rhie, J., Grigoli, F., Ellsworth, W. L., & Giardini, D. (2019) · Journal of Geophysical Research Solid Earth · doi:10.1029/2019jb018368 · Cited by 124 · see citing works

Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de la Fournaise volcano using a Random Forest algorithm

Hibert, C., Provost, F., Malet, J., Maggi, A., Stumpf, A., & Ferrazzini, V. (2017) · Journal of Volcanology and Geothermal Research · doi:10.1016/j.jvolgeores.2017.04.015 · Cited by 118 · see citing works

Characterizing rockbursts and analysis on frequency-spectrum evolutionary law of rockburst precursor based on microseismic monitoring

Liang, Z., Xue, R., Xu, N., & Li, W. (2020) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2020.103564 · Cited by 102 · see citing works

The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data

Foody, G. M., Pal, M., Rocchini, D., Garzón‐López, C. X., & Bastin, L. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5110199 · Cited by 100 · see citing works

Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM

Bi, L., Xie, W., & Junjie, Z. (2018) · Computers & Geosciences · doi:10.1016/j.cageo.2018.10.008 · Cited by 86 · see citing works

Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification

Dong, L., Li, X., & Xie, G. (2014) · Abstract and Applied Analysis · doi:10.1155/2014/459137 · Cited by 85 · see citing works

Revised stratigraphy and reinterpretation of the Miocene Pohang basinfill, SE Korea: sequence development in response to tectonism and eustasy in a back-arc basin margin

Sohn, Y. K., Rhee, C. W., & Shon, H. (2001) · Sedimentary Geology · doi:10.1016/s0037-0738(01)00100-2 · Cited by 75 · see citing works

Discrimination of Seismic Signals from Earthquakes and Tectonic Tremor by Applying a Convolutional Neural Network to Running Spectral Images

Nakano, M., Sugiyama, D., Hori, T., Kuwatani, T., & Tsuboi, S. (2019) · Seismological Research Letters · doi:10.1785/0220180279 · Cited by 69 · see citing works

Protocol for induced microseismicity in the first enhanced geothermal systems project in Pohang, Korea

Kim, K., Min, K., Kim, K.-Y., Choi, J. W., Yoon, K.-S., Yoon, W. S., Yoon, B., Lee, T. J., & Song, Y. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.04.062 · Cited by 66 · see citing works

Observations and analyses of the first two hydraulic stimulations in the Pohang geothermal development site, South Korea

Park, S., Kim, K., Xie, L., Yoo, H., Min, K., Kim, M., Yoon, B., Kim, K. Y., Zimmermann, G., Guinot, F., et al. (2020) · Geothermics · doi:10.1016/j.geothermics.2020.101905 · Cited by 62 · see citing works

Current developments on micro-seismic data processing

Wang, H., Li, M., & Shang, X. (2016) · Journal of Natural Gas Science and Engineering · doi:10.1016/j.jngse.2016.02.058 · Cited by 60 · see citing works

Simple K-Medoids Partitioning Algorithm for Mixed Variable Data

Budiaji, W., & Leisch, F. (2019) · Algorithms · doi:10.3390/a12090177 · Cited by 60 · see citing works

Estimating rock properties using sound signal dominant frequencies during diamond core drilling operations

Kumar, C. V., Vardhan, H., Murthy, C. S. N., & Karmakar, N. C. (2019) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2019.01.001 · Cited by 59 · see citing works

Determination of an infill well placement using a data-driven multi-modal convolutional neural network

Chu, M.-G., Min, B., Kwon, S., Park, G., Kim, S., & Huy, N. X. (2019) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2019.106805 · Cited by 56 · see citing works

Site Selection, Preparation and Installation of Seismic Stations

Trnkoczy, A., Bormann, P., Hanka, W., Holcomb, L. G., Nigbor, R. L., Shinohara, M., Shiobara, H., & Suyehiro, K. (2012) · Publication Database GFZ (GFZ German Research Centre for Geosciences) · doi:10.2312/gfz.nmsop-2_ch7 · Cited by 52 · see citing works

Microseismic monitoring and analysis of induced seismicity source mechanisms in a retreating room and pillar coal mine in the Eastern United States

Leake, M. R., Conrad, W. J., Westman, E., Afrouz, S. G., & Molka, R. J. (2017) · Underground Space · doi:10.1016/j.undsp.2017.05.002 · Cited by 46 · see citing works

Automatic Classification of Microseismic Signals Based on MFCC and GMM‐HMM in Underground Mines

Peng, P., He, Z., & Wang, L. (2019) · Shock and Vibration · doi:10.1155/2019/5803184 · Cited by 41 · see citing works

Machine Learning for Data-Driven Discovery

Sengupta, P. P., & Shrestha, S. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.06.030 · Cited by 40 · see citing works

Automatic classification of multi-channel microseismic waveform based on DCNN-SPP

Bi, L., Wei, X., Junjie, Z. H. A. O., & Hui, Z. H. A. O. (2018) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2018.09.022 · Cited by 30 · see citing works

Fully Convolutional Networks for Handwriting Recognition

Such, F. P., Peri, D., Brockler, F., Hutkowski, P., & Ptucha, R. (2018) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr-2018.2018.00024 · Cited by 30 · see citing works

High-Accuracy Discrimination of Blasts and Earthquakes Using Neural Networks With Multiwindow Spectral Data

Miao, F., Carpenter, N. S., Wang, Z., Holcomb, A. S., & Woolery, E. W. (2020) · Seismological Research Letters · doi:10.1785/0220190084 · Cited by 30 · see citing works

Three Dimensional Geological Model of Pohang EGS Pilot Site, Korea

Lee, T. J., Song, Y., Jeon, J., & Yoon, W. S. (2015) · Cited by 29 · see citing works

Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images

Kim, S., Lee, K., Lee, M., Ahn, T., Lee, J., Suk, H., & Ning, F. (2020) · Energies · doi:10.3390/en13195032 · Cited by 28 · see citing works

Discrimination of different blasting and mine microseismic waveforms using FFT, SPWVD and multifractal method

Li, B., Wang, E., Li, Z., Niu, Y., Li, N., & Li, X. (2021) · Environmental Earth Sciences · doi:10.1007/s12665-020-09330-7 · Cited by 28 · see citing works

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

Kang, B., Kim, S., Jung, H., Choe, J., & Lee, K. (2019) · Energies · doi:10.3390/en12101859 · Cited by 26 · see citing works

Microseismic event monitoring of highly stressed rock mass around underground oil storage caverns

Hong, J., Lee, H. S., Lee, D. H., Kim, H. Y., Choi, Y.-C., & Park, Y.-J. (2006) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2005.12.151 · Cited by 23 · see citing works

Artificial Neural Networks as Emerging Tools for Earthquake Detection

Rojas, O., Otero, B., Alvarado, L., Mus, S., & Tous, R. (2019) · Computación y Sistemas · doi:10.13053/cys-23-2-3197 · Cited by 23 · see citing works

Aseismic Mining Subsidence in an Abandoned Mine: Influence Factors and Consequences for Post-Mining Risk Management

Contrucci, I., Balland, C., Kinscher, J. L., Bennani, M., Bigarré, P., & Bernard, P. (2018) · Pure and Applied Geophysics · doi:10.1007/s00024-018-2015-6 · Cited by 22 · see citing works

Use of artificial neural networks for classification of noisy seismic signals

Кислов, К. В., & Гравиров, В. В. (2017) · Seismic Instruments · doi:10.3103/s0747923917010054 · Cited by 20 · see citing works

Development of ensemble smoother–neural network and its application to history matching of channelized reservoirs

Kim, S., Lee, K., Lim, J.-T., Jeong, H., & Min, B. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107159 · Cited by 20 · see citing works

Automatic first-arrival picking method based on an image connectivity algorithm and multiple time windows

Pan, S., Qin, Z., Lan, H., & Badal, J. (2018) · Computers & Geosciences · doi:10.1016/j.cageo.2018.12.001 · Cited by 19 · see citing works

Modeling and prediction of slug characteristics utilizing data-driven machine-learning methodology

Kim, T.-W., Kim, S., & Lim, J.-T. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107712 · Cited by 16 · see citing works

Data-Driven Three-Phase Saturation Identification from X-ray CT Images with Critical Gas Hydrate Saturation

Kim, S., Lee, K., Lee, M., & Ahn, T. (2020) · Energies · doi:10.3390/en13215844 · Cited by 16 · see citing works

Discrimination of the natural and artificial quakes in the Eastern Marmara Region, Turkey

Sertçelik, F., Yavuz, E., Birdem, M., & Merter, G. (2020) · Acta Geodaetica et Geophysica · doi:10.1007/s40328-020-00315-3 · Cited by 9 · see citing works

Tailoring distributed acoustic sensing techniques for the microseismic monitoring of future CCS sites: Results from the field

Wilks, M., Wüestefeld, A., Øye, V., Thomas, P. J., & Kolltveit, E. (2017) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/segam2017-17153208.1 · Cited by 4 · see citing works

The emergence of AI-based wearable sensors for digital health technology: a review

Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535 · see citing works

Recent advances in earthquake seismology using machine learning

Kubo, H., Naoi, M., & Kano, M. (2024) · Earth Planets and Space · doi:10.1186/s40623-024-01982-0 · Cited by 114 · see citing works

Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities

Abdalzaher, M. S., Elsayed, H. A. E. A., Fouda, M. M., & Salim, M. M. (2023) · Energies · doi:10.3390/en16010495 · Cited by 109 · see citing works

Microseismic event waveform classification using CNN-based transfer learning models

Dong, L., Shu, H., Tang, Z. S., & Yan, X. (2023) · International Journal of Mining Science and Technology · doi:10.1016/j.ijmst.2023.09.003 · Cited by 64 · see citing works

IoT-Driven Microseismic Sensing System and Monitoring Platform for Landslide Detection

Indukala, P. K., Gosh, U. G., & Ramesh, M. V. (2024) · IEEE Access · doi:10.1109/access.2024.3427386 · Cited by 26 · see citing works

Evaluation of saturation changes during gas hydrate dissociation core experiment using deep learning with data augmentation

Kim, S., Lee, K., Lee, M., Lee, J., Ahn, T., & Lim, J.-T. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109820 · Cited by 26 · see citing works

Nuclear‐medicine probes: Where we are and where we are going

González-Montoro, A., Vera‐Donoso, C. D., Konstantinou, G., Sopeña, P., Martinez, M., Ortiz, J. B., Carles, M., Benlloch, J., & González, A. J. (2022) · Medical Physics · doi:10.1002/mp.15690 · Cited by 23 · see citing works

Improved prediction of shale gas productivity in the Marcellus shale using geostatistically generated well-log data and ensemble machine learning

Kim, S., Hong, Y., Lim, J.-T., & Kim, K. H. (2023) · Computers & Geosciences · doi:10.1016/j.cageo.2023.105452 · Cited by 22 · see citing works

Mine Microseismic Time Series Data Integrated Classification Based on Improved Wavelet Decomposition and ELM

Ding, L., Chen, Z., Pan, Y., & Song, B. (2022) · Cognitive Computation · doi:10.1007/s12559-022-09997-z · Cited by 19 · see citing works

Productivity Prediction in the Upper Wolfcamp Formation Using Weighted Voting Ensemble Machine Learning Method

Kim, S., Yoon, H. C., Lim, J.-T., Jeong, D., & Kim, K. H. (2022) · Gas Science and Engineering · doi:10.1016/j.jgsce.2023.204916 · Cited by 19 · see citing works

Case Study Analysis of AI-Powered Sensor Fabrics for Continuous Health Monitoring in Chronic Disease Management

Sarker, M. T. H. (2025) · Strategic Data Management and Innovation · doi:10.71292/sdmi.v2i01.18 · Cited by 17 · see citing works

AI-driven wearable health devices with health-aware control and secure Prognostics: Experimental and Simulation-Based Validation

Jain, A. K., & Bhullar, S. (2025) · Array · doi:10.1016/j.array.2025.100532 · Cited by 17 · see citing works

Prediction of maximum slug length considering impact of well trajectories in British Columbia shale gas fields using machine learning

Kim, S., Yun, Y., Choi, J., Bizhani, M., Kim, T.-W., & Jeong, H. (2022) · Journal of Natural Gas Science and Engineering · doi:10.1016/j.jngse.2022.104725 · Cited by 17 · see citing works

Synergistic enhancement of productivity prediction using machine learning and integrated data from six shale basins of the USA

Kim, S., Kim, K. H., & Lim, J.-T. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.212068 · Cited by 13 · see citing works

Spatiotemporal interpretation of three-phase saturation behaviors in gas hydrate formation and dissociation through deep learning modeling

Kim, S., Lee, K., Lee, M., Lee, J., Ahn, T., & Lim, J.-T. (2022) · Geoenergy Science and Engineering · doi:10.1016/j.petrol.2022.111219 · Cited by 12 · see citing works

A Semisupervised Seismic Events Classifier Based on Generative Adversarial Network

Jia, L., Chen, S., Li, Y., & Zheng, P. (2024) · Seismological Research Letters · doi:10.1785/0220240301 · Cited by 3 · see citing works

Automatic recognition of mining microseismic and blasting vibration signals

Li, B., Li, N., Liu, W., Feng, J., Wang, S., & Sun, X. (2025) · Physics of Fluids · doi:10.1063/5.0276322 · Cited by 1 · see citing works

EFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS: EXAMPLE OF KAZAKHSTAN

Kazbekova, G., Aben, A., Amanov, A., Zhunissov, N., & Abibullayeva, A. (2025) · Scientific Journal of Astana IT University · doi:10.37943/21kuxz6354 · Cited by 1 · see citing works

Physics-Based Machine Learning for Vibration Mitigation by Open Buried Trenches

Pereira, L., Godinho, L., Branco, F. G., Oliveira, P. J. V., Costa, P. A., & Colaço, A. (2025) · Applied Sciences · doi:10.3390/app152111609

Data-Driven Signal Recognition- A Machine Learning Application For The Real-Time Microseismic Monitoring

Shamsa, A., & Paydayesh, M. (2018) · Proceedings · doi:10.3997/2214-4609.201803007

Intelligent Recognition of Valid Microseismic Events Based on Self-supervised Learning

Song, Y., Wang, E., Liu, C., Li, Y., Yang, H., Li, B., Chen, D., & Di, Y. (2024) · Measurement · doi:10.1016/j.measurement.2024.114760 · Cited by 21 · see citing works

Automatic microseismic signals classification with Deep Learning using multi-input Convolutional Neural Networks

Rajeul, S. (2021) · doi:10.3997/2214-4609.202132010 · Cited by 2 · see citing works

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters

Peng, K., Tang, Z., Dong, L., & Sun, D. (2021) · Sensors · doi:10.3390/s21216967 · Cited by 25 · see citing works

Classification of Microseismic Signals Using Machine Learning

Chen, Z., Cui, Y., Pu, Y., Rui, Y., Chen, J., Mengli, D., & Yu, B. (2024) · Processes · doi:10.3390/pr12061135 · Cited by 6 · see citing works