First Arrival Picking on Microseismic Signals Based on K-Means with a ReliefF Algorithm

The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study’s proposed method and its potential in processing complicated microseismic datasets.

[1]  Yangkang Chen,et al.  Automatic microseismic event picking via unsupervised machine learning , 2020, Geophysical Journal International.

[2]  Microseismic monitoring illuminates phases of slope failure in soft soils , 2021 .

[3]  Yanchun Wang,et al.  A first arrival detection method for low SNR microseismic signal , 2018, Acta Geophysica.

[4]  Shaohuan Zu,et al.  Low-frequency noise attenuation in seismic and microseismic data using mathematical morphological filtering , 2017, Geophysical Journal International.

[5]  E. K. Park,et al.  Detection and identification of seismic P-Waves using Artificial Neural Networks , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[6]  Xibing Li,et al.  Collaborative localization method using analytical and iterative solutions for microseismic/acoustic emission sources in the rockmass structure for underground mining , 2018, Engineering Fracture Mechanics.

[7]  Tianbin Li,et al.  Microseismic monitoring and stability evaluation for the large scale underground caverns at the Houziyan hydropower station in Southwest China , 2015 .

[8]  Yue Li,et al.  First arrival time picking for microseismic data based on shearlet transform , 2017 .

[9]  Yue Li,et al.  Arrival-time picking method based on approximate negentropy for microseismic data , 2018 .

[10]  Alberto Michelini,et al.  Automatic picking of P and S phases using a neural tree , 2006 .

[11]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[12]  Tieyuan Zhu,et al.  AEnet: Automatic Picking of P-Wave First Arrivals Using Deep Learning , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Mirko van der Baan,et al.  Comparison of the STA/LTA and power spectral density methods for microseismic event detection , 2015 .

[14]  Azzedine Zerguine,et al.  Observation-Driven Method Based on IIR Wiener Filter for Microseismic Data Denoising , 2018, Pure and Applied Geophysics.

[15]  Mao Chen Ge,et al.  Efficient mine microseismic monitoring , 2005 .

[16]  Chao Zhang,et al.  Automatic Time Picking for Microseismic Data Based on a Fuzzy C-Means Clustering Algorithm , 2016, IEEE Geoscience and Remote Sensing Letters.

[17]  S. Mostafa Mousavi,et al.  Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform , 2016 .

[18]  Yangkang Chen,et al.  Unveiling the signals from extremely noisy microseismic data for high-resolution hydraulic fracturing monitoring , 2017, Scientific Reports.

[19]  Sebastián Ventura,et al.  Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context , 2015, Neurocomputing.

[20]  Jubran Akram,et al.  A fuzzy c-means assisted AIC workflow for arrival picking on downhole microseismic data , 2019 .

[21]  Chun An Tang,et al.  Microseismic monitoring and stability analysis of the left bank slope in Jinping first stage hydropower station in southwestern China , 2011 .

[22]  Michael Fehler,et al.  Petroleum reservoir characterization using downhole microseismic monitoring , 2010 .

[23]  Debotyam Maity,et al.  Neuro-evolutionary event detection technique for downhole microseismic surveys , 2016, Comput. Geosci..

[24]  Min Bai,et al.  Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network , 2019, Earth and Space Science.

[25]  Jing Zheng,et al.  Microseismic event denoising via adaptive directional vector median filters , 2017, Acta Geophysica.

[26]  Fred Aminzadeh,et al.  Novel hybrid artificial neural network based autopicking workflow for passive seismic data , 2014 .

[27]  Yangkang Chen,et al.  Automatic microseismic-event detection via supervised machine learning , 2018, SEG Technical Program Expanded Abstracts 2018.

[28]  JingLan Zhang,et al.  First arrival picking of microseismic signals based on nested U-Net and Wasserstein Generative Adversarial Network , 2020 .

[29]  Lihua Zhang,et al.  Method for Identifying Micro-seismic P-Arrival by Time-frequency Analysis Using Intrinsic Time-Scale Decomposition , 2015, Acta Geophysica.

[30]  Mauricio D. Sacchi,et al.  Radon transform-based microseismic event detection and signal-to-noise ratio enhancement , 2015 .

[31]  Yi Wang,et al.  Locating Mine Microseismic Events in a 3D Velocity Model through the Gaussian Beam Reverse-Time Migration Technique , 2020, Sensors.

[32]  Erick Baziw,et al.  Microseismic Event Detection Kalman Filter: Derivation of the Noise Covariance Matrix and Automated First Break Determination for Accurate Source Location Estimation , 2004 .

[33]  Antonio Morales-Esteban,et al.  Identifying P phase arrival of weak events: The Akaike Information Criterion picking application based on the Empirical Mode Decomposition , 2017, Comput. Geosci..

[34]  Teng Wang,et al.  A Time Picking Method for Microseismic Data Based on LLE and Improved PSO Clustering Algorithm , 2018, IEEE Geoscience and Remote Sensing Letters.

[35]  T. Wang,et al.  Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal , 2012 .

[36]  Yilin Fan,et al.  Microseismic Monitoring of the Left Bank Slope at the Baihetan Hydropower Station, China , 2016, Rock Mechanics and Rock Engineering.

[37]  Ru Zhang,et al.  Microseismic Monitoring of Strainburst Activities in Deep Tunnels at the Jinping II Hydropower Station, China , 2016, Rock Mechanics and Rock Engineering.

[38]  Yi Duan,et al.  Identifying microseismic events in a mining scenario using a convolutional neural network , 2020, Comput. Geosci..

[39]  Zhu Quan-jie.  Automated microseismic event arrival picking and multichannel recognition and location , 2013 .

[40]  Detection of the Long Period Long Duration (LPLD) Events in Time-and Frequency-Domain , 2015, Acta Geophysica.

[41]  Ruili Wang,et al.  A Reliable Strategy for Improving Automatic First‐Arrival Picking of High‐Noise Three‐Component Microseismic Data , 2019, Seismological Research Letters.

[42]  Mirko van der Baan,et al.  Unsupervised Dictionary Learning for Signal‐to‐Noise Ratio Enhancement of Array Data , 2018, Seismological Research Letters.

[43]  Yi Liu,et al.  A novel method for automatic identification of rock fracture signals in microseismic monitoring , 2021 .

[44]  Daniel Peter,et al.  A robust neural network-based approach for microseismic event detection , 2017 .