Microseismic Signals Research Articles

Underground microseismic monitoring of a hydraulic fracturing operation for CBM reservoirs in a coal mine

AbstractHydraulic fracturing can evidently improve the coalbed methane production in underground coal mines, but it is difficult to delimit the stimulated area accurately. In order to evaluate the stimulated area, microseismic (MS) monitoring technique is proposed to investigate the seismic responses of the induced fractures of hydraulic fracturing. Three coal seams were targeted to be treated in a coal mine. An array of geophones was set along the underground roadway to detect the MS signals caused by HF. In order to verify the result of MS monitoring, water content of each coal seam has been measured before and after HF treatment. The results showed that a series of MS events were detected during the entire HF process, and a sharp MS event usually occurred during the first hour of HF process. The energy of the sharp MS event had higher magnitude than others. The MS distribution exhibited complex morphological features. The directionless MS response was distributed over a radius of less than 40 m but tended to be significantly conjugated with a radius of more than 40 m. HF could stimulate both the coal seam and the rock layers nearby. The achievable stimulated area in the coal seam was determined to be 50 m × 50 m according to the MS density and water content. The stimulated area in terms of MS density was easily found to be broader than the area of water direct intrusion. The present study indicated that MS monitoring technique could potentially be used for evaluation of HF in underground coal mine.

Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks

Abstract. Passive monitoring of ground motion can be used for geophysical process analysis and natural hazard assessment. Detecting events in microseismic signals can provide responsive insights into active geophysical processes. However, in the raw signals, microseismic events are superimposed by external influences, for example, anthropogenic or natural noise sources that distort analysis results. In order to be able to perform event-based geophysical analysis with such microseismic data records, it is imperative that negative influence factors can be systematically and efficiently identified, quantified and taken into account. Current identification methods (manual and automatic) are subject to variable quality, inconsistencies or human errors. Moreover, manual methods suffer from their inability to scale to increasing data volumes, an important property when dealing with very large data volumes as in the case of long-term monitoring. In this work, we present a systematic strategy to identify a multitude of external influence sources, characterize and quantify their impact and develop methods for automated identification in microseismic signals. We apply the strategy developed to a real-world, multi-sensor, multi-year microseismic monitoring experiment performed at the Matterhorn Hörnligrat (Switzerland). We develop and present an approach based on convolutional neural networks for microseismic data to detect external influences originating in mountaineers, a major unwanted influence, with an error rate of less than 1 %, 3 times lower than comparable algorithms. Moreover, we present an ensemble classifier for the same task, obtaining an error rate of 0.79 % and an F1 score of 0.9383 by jointly using time-lapse image and microseismic data on an annotated subset of the monitoring data. Applying these classifiers to the whole experimental dataset reveals that approximately one-fourth of events detected by an event detector without such a preprocessing step are not due to seismic activity but due to anthropogenic influences and that time periods with mountaineer activity have a 9 times higher event rate. Due to these findings, we argue that a systematic identification of external influences using a semi-automated approach and machine learning techniques as presented in this paper is a prerequisite for the qualitative and quantitative analysis of long-term monitoring experiments.

Air-temperature control on diurnal variations in microseismicity at Laohugou Glacier No. 12, Qilian Mountains

ABSTRACTWe conducted a 9-d seismic experiment in October 2015 at Laohugou Glacier No. 12. We identified microseismic signals using the short-term/long-term average trigger algorithm at four stations and classified them as long and short-duration events based on waveform, frequency, duration and magnitude characteristics. Both categories show systematical diurnal trends. The long-duration events are low-frequency tremor-like events that mainly occurred during the daytime with only several events per day. These events lasted tens of seconds to tens of minutes and are likely related to resonance of daytime meltwater. The dominant short-duration events mostly occurred during the night time with a peak occurrence frequency of ~360 h−1. Their short-duration (<0.2 s), high frequency (20–100 Hz) and dominance of Rayleigh waves are typical of events for near-surface crack opening. A strong negative correlation between the hourly event number and temperature change rate suggests that the occurrence of night-time events is controlled by the rate of night-time cooling. We estimated the near-surface tensile stress due to thermal contraction at night to be tens of kilopascals, which is enough to induce opening of surface cracks with pre-existing local stress concentrations, although we cannot exclude the effect of refreezing of meltwater produced during the day.

Experimental Study on Stress Evolution and Microseismic Signals under Vibration Conditions of Coal during Excavation and Subsequent Waiting Time

An experiment designed to simulate coal during excavation was conducted. Microseismic signals of coal under vibration conditions during excavation and subsequent waiting time of the coal roadway at different excavation speeds were collected and analyzed. During the excavation and subsequent waiting time, the stress in coal is redistributed, and the concentrated stress is gradually transferred to the deeper section of the coal seam. The Hilbert–Huang transform (HHT) is used to effectively denoise the collected signals. According to the noise‐reduced signal, the amplitude and pulse number of the microseismic signals emitted during the excavation process are much larger than those of the waiting time process. During excavation, the energy and event numbers of microseismic signals increase first and then decrease as the excavation speed increases. The faster the excavation speed, the more the energy, and the higher the event numbers of the microseismic signals released during the subsequent waiting time. When the excavation speed is faster, more elastic potential accumulates in the coal seam and the concentration stress is greater. As the concentrated stress moves forward in time without excavation, more coal seams fail, and more microseismic signals are released. The microseismic signal and the stress evolution law can provide a reasonable explanation for the forward movement of the concentrated stress and coal failure during roadway excavation.

Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

Aiming at the situation that complementary ensemble empirical mode decomposition (CEEMD) noise suppression method may produce redundant noise and wavelet transform easily loses high-frequency detail information, considering wavelet packet transform can be used to perform better time-frequency localization analysis on signals containing a large amount of medium and high frequency information, according to the noise and useful signal components of both the characteristic of self-correlation function is different, the CEEMD and wavelet packet threshold jointed method is proposed. The method uses the energy concentration ratio to find noise and useful signal component demarcation point to denoise the microseismic signals. Firstly, we utilize adaptively decompose the signal from high frequency to low frequency by the CEEMD; Secondly, using the self-correlation method to select the intrinsic mode function (IMF) that needs noise suppression, the wavelet suppression method is used to suppress the noise of several high-frequency components whose self-correlation coefficient is below the critical value K; Finally, the IMF component after the wavelet packet threshold noise suppression is reconstructed with the noise-free IMF component. In order to verify the effectiveness of the proposed method on the noise suppression of microseismic signal, we added a Gaussian white noise to the Ricker wavelet signal similar to the microseismic signal. The experimental results show that the signal-to-noise ratio (SNR) of the signal is raised more than 10dB. The energy percentage is higher than 92%. In practical engineering, our proposal achieves an effective noise suppression effect on the microseismic signal.

Accurate Method for Picking Up the First Arrival Time of Microseismic Signals Based on Entropy Theory

The entropy method describes the change in the signal state from a macroscopic angle and weakens the interference generated by random noise. From the angle of chaos, which describes the first arrival of microseismic signals, it predicts the trend of noise entropy based on the grey theory. The results indicate that the entropy method is sensitive to amplitude, frequency, and seismic phase changes. Compared with traditional methods, the entropy method has better noise immunity. In terms of the influence of the pickup parameter on the resulting error, the entropy method demonstrated the best result and produces the smallest error.

An improved empirical green function method to extract microseismic signals

In mining, reservoir impoundment and hydraulic fracturing exploitation, the induced microseismic clusters may present complex waveforms at receivers since the individual events generally arrive very close. And the routine methods for arrival picking are insufficiently efficient due to these strong influences. Here, we modified the empirical green function method and applied it to extract the green functions and the radiation coefficients of microseismic events with the largest energy in clusters. Multiple-channel records were used to estimate an “average” source spectrum and then the “average” source wavelet was removed from the records by deconvolution. We applied this method to the real data, and the result indicated clear improvement in extracting the dominant event of the clusters.

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

In order to mitigate economic and safety risks during mine life, a microseismic monitoring system is installed in a number of underground mines. The basic step for successfully analyzing those microseismic data is the correct detection of various event types, especially the rock mass rupture events. The visual scanning process is a time‐consuming task and requires experience. Therefore, here we present a new method for automatic classification of microseismic signals based on the Gaussian Mixture Model‐Hidden Markov Model (GMM‐HMM) by using only Mel‐frequency cepstral coefficient (MFCC) features extracted from the waveform. The detailed implementation of our proposed method is described. The performance of this method is tested by its application to microseismic events selected from the Dongguashan Copper Mine (China). A dataset that contains a representative set of different microseismic events including rock mass rupture, blasting vibration, mechanical drilling, and electromagnetic noise is collected for training and testing. The results show that our proposed method obtains an accuracy of 92.46%, which demonstrates the effectiveness of the method for automatic classification of microseismic data in underground mines.

Experimental Study on Microseismic Monitoring Highway Bridge Pile Foundation Construction

With the development of urban construction, it is inevitable that new or expanded highways cross or parallel with existing buried pipelines. The impact vibration caused by the construction of perforated pile foundation of highway bridge is bound to have adverse effects on the safe operation of adjacent buried pipelines. Based on the micro seismic monitoring technique, the characteristics of pile foundation construction vibration is researched. A series of sensors are placed on the surface and adjacent pipelines to collect micro seismic events of impact hammer on different hoisting height and different excavation depth of pile foundation construction. Thus, the wave characteristics, spectrum characteristics and energy attenuation law of microseismic signal are analyzed, and the influencing factors of construction vibration are studied. It is found that the peak value of acceleration vibration in the vertical direction is the largest, followed by the peak value of radial acceleration vibration, the vibration energy decays in power relation with the propagation distance, and with the increase of the pile hole depth, the vibration velocity of the same horizontal distance decreases gradually. The attenuation is related to the geological condition of a certain pile hole depth. The research results have some guiding significance for the vibration control of pile foundation construction.

Damping measurements from microseismic signals to infer rock mass damaging

Damping measurements from microseismic signals to infer rock mass damaging

Artificial Seismic Source Field Research on the Impact of the Number and Layout of Stations on the Microseismic Location Error of Mines

A site experiment is performed herein within a 100 m range using a high‐frequency structure activity monitor to explore the impact of different factors on the microseismic source location and analyze the range of influence of the velocity model, number of stations, and array surface on the seismic source location. Moreover, the impact of wave velocity, velocity‐free location algorithm, and position of the seismic source on the microseismic location error of mines is discussed by establishing the ideal theoretical model of the wave velocity location and with particle swarm optimization. The impact of the number of stations and tables on the location precision is also explored by using the microseismic signals produced by the artificial seismic source. The results show that, for the location model containing the velocity, the velocity error would greatly affect the location precision, and the velocity‐free algorithm receives good location results. The location result is more satisfactory when the seismic source point falls in between array envelope lines. The seismic source location precision is in direct proportion to the number of stations. According to the experiment, within a 100 m range, when the number of stations is over 12, the effect does not significantly grow with the increase of stations; the number of tables affects the location precision; and the multitable location effect is significantly superior to the single‐table effect. The research shows that the optimal station density is 0.0192%, and the appropriate sensor layout to form a multitable monitoring network may effectively enhance the microseismic source precision of mines through the selection of a velocity‐free location model. On the contrary, the number of stations can be reduced on the premise of the allowable error of the seismic source location, which may effectively reduce the monitoring cost.

The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition

The problems of mode mixing, mode splitting, and pseudocomponents caused by intermittence or white noise signals during empirical mode decomposition (EMD) are difficult to resolve. The partly ensemble EMD (PEEMD) method is introduced first. The PEEMD method can eliminate mode mixing via the permutation entropy (PE) of the intrinsic mode functions (IMFs). Then, bilateral permutation entropy (BPE) of the IMFs is proposed as a means to detect and eliminate mode splitting by means of the reconstructed signals in the PEEMD. Moreover, known ingredient component signals are comparatively designed to verify that the PEEMD method can effectively detect and progressively address the problem of mode splitting to some degree and generate IMFs with better performance. The microseismic signal is applied to prove, by means of spectral analysis, that this method is effective.

Adaptive microseismic data compressed sensing method based on dictionary learning

The theory of compressed sensing brings a revolutionary breakthrough to signal acquisition technology, it can sample the signal far below the Nyquist frequency, and accurately reconstruct the original signal through numerical optimization method. In order to improve the precision of microseismic signal after reconstruction and improve the sampling efficiency, an adaptive microseismic data compressed sensing method based on dictionary learning is proposed in this paper. Firstly, the adaptive redundancy dictionary is constructed according to the characteristics of the microseismic signal. Then the energy and the sparsity under redundant dictionary of signal are calculated. Finally, according to the comprehensive index of energy and sparseness, an adaptive sampling strategy of compressed sensing is developed. Simulation experiment show that, the algorithm reduces the number of samples by 10% compared with traditional compressed sensing, and improves the reconstruction accuracy.

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

The recognition of multi-channel microseismic waveform is important for hazard prediction. This method is widely used to design the corresponding waveform feature of microseismic waveform recognition by hand. However, manual design process is challenging with unsatisfactory classification effect. Thus, this study presents a method with deep convolutional neural network and Spatial Pyramid Pooling (DCNN-SPP) to identify automatically microseismic waveform. We constructed DCNN-SPP to classify multi-channel microseismic waveform. Given the inconsistent channel number of each receiving microseismic signal, we used spatial pyramid pooling to fix the features of multi-channel waveforms. This signal was extracted from the last convolution layer, which can achieve end-to-end training. To extend the dataset, we pre-process raw data by filtering and de-nosing. The experiment shows the practical value of the recognition method with an accuracy rate of up to 91.13%.

СЕЗОННЫЕ ВАРИАЦИИ АМПЛИТУДНО-ЧАСТОТНЫХ ХАРАКТЕРИСТИК МИКРОСЕЙСМИЧЕСКИХ КОЛЕБАНИЙ ПО ДАННЫМ СЕЙСМИЧЕСКИХ СТАНЦИЙ БАЙКАЛЬСКОЙ СЕТИ

Purpose. The purpose of the paper is evaluation and analysis of the amplitude-frequency patterns of background microseisms (local ambient noises) and their possible seasonal variations in the territory of the Baikal seismic zone. Methods. The method of spectral relations of the vertical and horizontal components of the microseism (H/V-ratio) or Nakamura method is used. The continuous seismic recording data obtained by 23 short-period seismic stations of the Baikal Branch of the Federal Research Centre “Single Unified Geophysical Survey of the Russian Academy of Science” located in the Baikal seismic zone are used as an input material for the analysis. Only long segments of seismic records with the duration of at least 1 hour have been selected for the analysis in order to exclude possible random errors related to human-made noises, earthquakes, industrial explosions, etc. Results. The obtained curves of H/V-ratios and their seasonal variations characterize the amplitude-frequency patterns of local ambient noises in the frequency range from 0.5 to 10.0 or 20.0 Hz depending on the type of the recording equipment used. Conclusions. The obtained H/V-ratio curves can be conditionally divided into two groups: stable and featuring significant seasonal variations. The first group includes the stations with stable amplitude-frequency characteristics of the microseismic signal, which do not depend on the season. The second group includes the stations for which the H/V-ratio curves demonstrating significant seasonal variations have been obtained. Having compared the position of the seismic stations and the obtained H/V-ratio curves, the authors identified the dependence of the amplitude-frequency characteristics of the microseismic signal on their local conditions in the station setup area (at the outcrops of bedrock, in the zone of large active faults or in the rift depressions with a thick sedimentary cover).

A first arrival detection method for low SNR microseismic signal

Most of the microseismic signals have low signal-to-noise ratio (SNR) due to the strong background noise, which makes it difficult to locate the first arrival time. Both accuracy and stability of conventional methods are poor in this situation. To overcome this problem, here we proposed a new method based on the adaptive Morlet wavelet and principal component analysis process in wavelet coefficients matrix. The three components of microseismic signal make it possible to extract the features in wavelet coefficients domain. Then the reconstructed signal from weighted features presents an obvious first arrival. Tests on synthetic signals and real data provide a solid evidence for its feasibility in low SNR microseismic signal.

Downhole microseismic signal denoising via empirical wavelet transform and adaptive thresholding

Downhole microseismic data have the characteristics of low signal-to-noise ratio and high frequency, which pose a major challenge to noise attenuation. In this paper, we propose a novel downhole microseismic denoising approach based on the empirical wavelet transform combined with adaptive thresholding. According to the frequency characteristics of the signal, a spectrum segmentation strategy is designed. It can adaptively decompose the signal and noise into different modes. Through analyzing the spectrum and energy of the modes, different threshold functions are applied. The mode that contains more valid signals is processed by hard thresholding to reserve the amplitude; the other modes, which contain less useful signals, are processed by modified threshold functions to maintain the continuity of the restructured signal. We have determined this approach's potential for microseismic denoising by comparing its performance on synthetic and field data using complete ensemble empirical mode decomposition, wavelet transform, and synchrosqueezed wavelet transform.

Characterizing the complexity of microseismic signals at slow-moving clay-rich debris slides: the Super-Sauze (southeastern France) and Pechgraben (Upper Austria) case studies

Abstract. Soil and debris slides are prone to rapid and dramatic reactivation. Deformation within the instability is accommodated by sliding, whereby weak seismic energies are released through material deformation. Thus, passive microseismic monitoring provides information that relates to the slope dynamics. In this study, passive microseismic data acquired at Super-Sauze (southeastern France) and Pechgraben (Upper Austria) slow-moving clay-rich debris slides (“clayey landslides”) are investigated. Observations are benchmarked against previous similar case studies to provide a comprehensive and homogenized typology of microseismic signals at clayey landslides. A thorough knowledge of the various microseismic signals generated by slope deformation is crucial for the future development of automatic detection systems to be implemented in landslide early-warning systems. Detected signals range from short-duration (< 2 s) quake-like signals to a wide variety of longer-duration tremor-like radiations (> 2 s – several min). The complexity of seismic velocity structures, the low quantity and low quality of available signal onsets and non-optimal seismic network geometry severely impedes the source location procedure; thus, rendering source processes characterization challenging. Therefore, we constrain sources' locations using the prominent waveform amplitude attenuation pattern characteristic of near-source area (< about 50 m) landslide-induced microseismic events. A local magnitude scale for clayey landslides (ML−LS) is empirically calibrated using calibration shots and hammer blow data. The derived ML−LS returns daily landslide-induced microseismicity rates that positively correlate with higher average daily displacement rates. However, high temporal and spatial resolution analyses of the landslide dynamics and hydrology are required to better decipher the potential relations linking landslide-induced microseismic signals to landslide deformation.

Study on energy band characteristic of microseismic signals in water inrush channel

Study on energy band characteristic of microseismic signals in water inrush channel

Identification of blasting vibration and coal-rock fracturing microseismic signals

Α new method based on variational mode decomposition (VMD) is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition.