Microseismic Recordings Research Articles

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings.

Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings' polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = -5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data.

Reliable Denoising Strategy to Enhance the Accuracy of Arrival Time Picking of Noisy Microseismic Recordings

We propose a method to enhance the accuracy of arrival time picking of noisy microseismic recordings. A series of intrinsic mode functions (IMFs) of the microseismic signal are initially decomposed by employing the ensemble empirical mode decomposition. Subsequently, the sample entropy values of the obtained IMFs are calculated and applied to set an appropriate threshold for selecting IMFs. These are then reconstructed to distinguish between noise and useful signals. Ultimately, the Akaike information criterion picker is used to determine the arrival time of the denoised signal. Test results using synthetic noisy microseismic recordings demonstrate that the proposed approach can significantly reduce picking errors, with errors within the range of 1–3 sample intervals. The proposed method can also give a more stable picking result when applied to different microseismic recordings with different signal-to-noise ratios. Further application in real microseismic recordings confirms that the developed method can estimate an accurate arrival time of noisy microseismic recordings.

Characterizing hydraulic fracture growth using distributed acoustic sensing-recorded microseismic reflections

Hydraulic fracturing is crucial for enhancing hydrocarbon production from unconventional reservoirs. The characterization of fracture geometry and propagation has significant value in understanding reservoir response and designing more efficient completions. Distributed acoustic sensing (DAS) is a rapidly developing technology that can be used for this purpose because it provides wide-aperture observations of microseismic wavefields that contain direct P and S arrivals as well as converted and reflected waves. In addition to traditional approaches for microseismic event location and source mechanism analysis, the high spatial resolution of DAS microseismic recordings allows the imaging of induced fractures with reflected waves. Reflections are generated by waves radiated from microseismic events that impinge on hydraulic fractures created during prior treatment stages. We use a straightforward method based on f- k filtering and ray tracing to map reflected S waves from the time domain to reflectivity in the space domain. A case study of fracture imaging indicates that inferred fracture development, based on reflection imaging, is consistent with fracture-driven interactions observed using low-frequency DAS (LF-DAS) data. In addition, this study reveals reflection images of apparent distal fractures that do not reach the fiber and thus are not directly observed by LF-DAS. Fracture images obtained from several microseismic events during the same stage provide the opportunity to observe snapshots of dynamic fracture evolution processes.

A Reliable Online Dictionary Learning Denoising Strategy for Noisy Microseismic Data

Improving the quality of microseismic recordings is a critical step in microseismic data processing. We introduce a wavelet-weighted online dictionary learning (WWODL) denoising strategy for noisy microseismic recordings. We develop an adaptive parameters estimation approach for tunable Q-factor wavelet transform, which provides accurate periodic and non-stationary subband information from microseismic data for online dictionary learning (ODL). A sliding time window is employed to divide the obtained subbands into a series of patches of equal length, which are then assembled into a matrix and fed into the ODL. The subband kurtosis information is weighted to the constraint function of the ODL, further enhancing the sparse coding ability for each subband. Shortening the oscillation duration, an improved ODL is developed with a faster convergence speed in calculating sparse coefficients. Our results confirm that the WWODL can suppress high-frequency, low-frequency, and shared-bandwidth noises and has a minimal impact on the first arrival. The time consumption and signal-to-noise ratio of the WWODL are average 1/8.675 and 56.82% higher than ODL, respectively.

Microseismic Event Classification With Time-, Frequency-, and Wavelet-Domain Convolutional Neural Networks

Passive seismics help us understand subsurface processes, e.g. landslides, mining, geothermal systems etc. and help predict and mitigate their effects. Continuous monitoring results in long seismic records that may contain various sources, which need to be classified. Manual detection and labeling of recorded seismic events is not only time consuming but can also be inconsistent when done manually, even in the case where it is done by the same expert. Therefore, an automated approach for classification of continuous microseismic recordings based on a Convolutional Neural Network (CNN) is proposed, with a multiclassifier architecture that classifies earthquakes, rockfalls and low signal to noise ratio quakes. Furthermore, we propose three CNN architectures that take as input time series data, Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) maps. The suitability of these three networks is rigorously assessed over five months of continuous seismometer recordings from the active Super-Sauze landslide in France. We observe that all three architectures have excellent and very similar performance. Furthermore, we evaluate transferability to a geographically distinct seismically active site in Larissa, Greece. We demonstrate that the proposed network is able to detect all 86 catalogued earthquake events, having only been trained on the Super-Sauze dataset and shows good agreement with manually detected events. This is promising as it could replace painstaking manual labelling of events in large recordings.

Novel Wavelet Threshold Denoising Method to Highlight the First Break of Noisy Microseismic Recordings

We proposed a novel wavelet threshold denoising method based on the discrete wavelet transform for noisy microseismic recordings. This algorithm can simultaneously suppress both the high- and low-frequency noises of the microseismic recordings and further highlight the first break of the noisy microseismic recordings. First, we design an adaptive threshold calculation method based on all the wavelet coefficients in different decomposition levels. Then, a continuous and derivable thresholding function is constructed. By adjusting the preset shape adjustment parameters, a more reasonable thresholding function is determined to achieve the denoising processing for highlighting the first break of the microseismic recordings. Finally, different comparison tests are performed to evaluate the performance of the developed method, and the results indicate that the new method can achieve a more effective denoising result. Application on the real microseismic recordings further confirms that the proposed method can make the corresponding first break more clear.

Strong Noise-Tolerance Deep Learning Network for Automatic Microseismic Events Classification

Identifying useful microseismic events is one of the key steps in monitoring tunnel rockbursts. Here, we propose a strong noise-tolerance deep learning (SNTDL) network for the automatic classification of noisy microseismic events. The training set, validation set, and test set of the SNTDL network consist of 27,989 unfiltered microseismic recordings. First, to comprehensively characterize the microseismic events, we extract 10 weakly correlated features of the microseismic recordings as the input of the SNTDL network. Then, the skip connection and concatenation structure are added to this network, which can further enhances its generalization ability. Additionally, the SNTDL, AlexNet, Inception, Visual Geometry Group, and ResNet are compared using the synthetic microseismic recordings with different signal-noise ratios. The results demonstrate that the SNTDL network has a higher accuracy and stronger noise-tolerance capability than the other approaches. Application to a dataset collected from a different construction environment confirms that the SNTDL network can still achieve an accurate classification result, which further verifies that the proposed network has a reliable generalization performance.

Arrival picking method for microseismic phases based on curve fitting

Microseismic phase detection and arrival picking are critical steps in the processing of hydraulic fracturing microseismic monitoring data. As the signal-to-noise ratios of P- and S-waves differ because of the influence of focal mechanisms, geometry, and background noise, difficulties are introduced in the effective discrimination of seismic phases and the accurate acquisition of arrivals in conventional processing methods. In this paper, we propose a method for identifying microseismic phase arrival time by comprehensively analyzing the variation of moveout curves and combining the intra-event waveform similarities with the energy ratio of multitrace signals. First, a curve-fitting formula is constructed with perforation arrivals, and event detection is achieved by adopting an energy-weighted similarity coefficient that seeks plausible fitting curves with a sliding time window in continuous microseismic recordings. Then, the P- and S-waves are separated by the fitting parameters. The known arrival time trend of the microseismic phase is employed to calculate residual time corrections. Finally, the accurate arrival results of the microseismic phases can be obtained by picking the arrivals of stacked traces. The reliability and effectiveness of the proposed method for microseismic phase detection and arrival picking were determined through tests using field data. Arrival results indicate that the proposed method can improve accuracy compared with the traditional energy ratio method.

Characteristics of microseismic data recorded by distributed acoustic sensing systems in anisotropic media

Fiber-optic distributed acoustic sensing (DAS) cables are now used to monitor microseismicity during hydraulic-fracture stimulations of unconventional gas reservoirs. Unlike geophone arrays, DAS systems are sensitive to uniaxial strain or strain rate along the fiber direction and thus provide a 1C recording, which makes identifying the directionality and polarization of incoming waves difficult. Using synthetic examples, we have shown some fundamental characteristics of microseismic recordings on DAS systems for purposes of hydraulic fracture monitoring in a horizontal well in anisotropic (vertical transverse isotropy [VTI]) shales. We determine that SH arrivals dominate the recorded signals because their polarization is aligned along the horizontal cable at the near offset, although SV will typically dominate for events directly above or below the array. The amplitude of coherent shear-wave (S-wave) arrivals along the cable exhibits a characteristic pattern with bimodal peaks, the width of which relates to the distance of the event from the cable. Furthermore, we find that S-wave splitting recorded on DAS systems can be used to infer the inclination of the incoming waves, overcoming a current limitation of event locations that have constrained events to lie in a horizontal plane. Low-amplitude SV arrivals suggest an event depth similar to that of the DAS cable. Conversely, steep arrivals produce higher amplitude SV-waves, with S-wave splitting increasing with offset along the cable. Finally, we determine how polarity reversals observed in the P and SH phases can be used to provide strong constraints on the source mechanisms.

Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network

High-accuracy microseismic phase picking is fundamental to microseismic signal processing. Phase picking methods based on deep learning show great potential dealing with low signal to noise ratio (SNR) data but need enormous training data. However, it's not easy to obtain a big size of field datasets and label them manually to train the neural network. In this paper, a novel method is proposed by applying feature extraction (Gammatone Feature) and neural networks to pick phases automatically. The feature extraction scheme aims to train the neural network using a relatively small size of training datasets. To test the performances of the proposed method, synthetic datasets were obtained by numerical simulation and used to train the neural network. Both synthetic datasets and field datasets were used to test the neural network for picking P- and S-phases of microseismic events. The results of phase picking illustrate that: (1) feature extraction scheme in the training stage can help reduce the size of training datasets; (2) The neural network can be trained well just by synthetic data and phase picking results are accurate and satisfying when the method was tested by both synthetic and field datasets.

Deep neural networks design and analysis for automatic phase pickers from three-component microseismic recordings

SUMMARYIt is essential to pick P-wave and S-wave arrival times rapidly and accurately for the microseismic monitoring systems. Meanwhile, it is not easy to identify the arrivals at a true phase automatically using traditional picking method. This is one of the reasons that many researchers are trying to introduce deep neural networks to solve these problems. Convolutional neural networks (CNNs) are very attractive for designing automatic phase pickers especially after introducing the fundamental network structure from semantic segmentation field, which can give the probability outputs for every labelled phase at every sample in the recordings. The typical segmentation architecture consists of two main parts: (1) an encoder part trained to extracting coarse semantic features; (2) a decoder part responsible not only for recovering the input resolution at the output but also for obtaining sparse representation of the objects. The fundamental segmentation structure performs well; however, the influence of the parameters in the structure on the pickers has not been investigated. It means that the structure design just depends on experience and tests. In this paper, we solve two main questions to give some guidance on network design. First, we show what sparse features will learn from the three-component microseismic recordings using CNNs. Second, the influence of two key parameters in the network on pickers, namely, the depth of decoder and activation functions, is analysed. Increasing the number of levels for a certain layer in the decoder will increase the burden of demand on trainable parameters, but it is beneficial to the accuracy of the model. Reasonable depth of the decoder can balance prediction accuracy and the demand of labelled data, which is important for microseismic systems because manual labelling process will decrease the real-time performance in monitoring tasks. Standard rectified linear unit (ReLU) and leaky rectified linear unit (Leaky ReLU) with different negative slopes are compared for the analysis. Leaky ReLU with a small negative slope can improve the performance of a given model than ReLU activation function by keeping some information about the negative parts.

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.

Microseismic event location using multi-scale time reversed imaging

Locating passive seismic sources in a reservoir monitoring system has attracted considerable interest recently due to its ability to image the induced fracture geometry that results from fluid injection and fracturing processes. Time reversed imaging (TRI) techniques are well recognized localization tools, which back-propagates the received microseismic recordings to focus at its real source location without picking various arrival phases. However, the time reversed images are often contaminated due to the strong noise, coherent noise and poor spatial constraints especially in surface microseismic measurements, leading to unreliable location estimations. To minimize these interferences, we propose a multi-scale TRI technique using the shift-invariant dual-tree complex wavelet transform (DTCWT) to decompose the original waveforms into multiple time-frequency domains (different levels). The Birge-Massart threshold is applied to the wavelet coefficients at each level to further attenuate the noise amplitude. The TRI are then applied to the reconstructed waveform component at each level. Both the synthetic and field studies show that the multi-scale cross-correlation of TR images at effective levels substantially improved the quality of the final image of subsurface microseismic events with a much sharper and brighter focus. On the other hand, the images at noise levels may reveal the velocity structure, which is helpful in event location. In addition, the noise components recognized by the multi-scale TRI can be applied to estimate the background noise level, which can be further used in microseismic sensitivity and location uncertain analysis.

The 2015 Mw 7.8 Gorkha Earthquake in Nepal and its aftershocks: analysis of strong ground motion

Various aspects of the 2015 Gorkha Earthquake and its aftershocks are presented, and different hypotheses about the nature of long period pulses observed in ground motion record in Kathmandu Valley are critically discussed and reviewed. The frequency-size distribution of aftershocks, as well as the temporal distribution within one year of the mainshock is discussed. Some interesting features of the strong ground motion of the mainshock recorded at different places in Kathmandu Valley are discussed. Although the root-mean-square horizontal ground accelerations recorded at rock and soft sites were similar, the peak accelerations varied significantly. Elastic response spectra of the mainshock near the centre of the Valley (KATNP station operated by USGS) contained two very distinct peaks: a narrower and larger peak at ~0.4 s and a smaller, but very wide, peak at ~5 s. The peak of the spectra was significantly larger than what is commonly adopted in design codes. The latest published uniform hazard spectra for a 475-year return period were found to significantly over-estimate seismic demands on stiff structures (fundamental period less than ~0.5 s,) and underestimate the demands on more flexible structures. The resonant site frequencies computed from microseismic recordings, as reported in published literature, are found to be much larger than what was observed in the spectral ratio of ground motion recorded in soft sediments and exposed bedrock in Kathmandu. Response spectra of aftershock motions recorded at two stations lacked the peculiar peak at 5 s observed in the spectra of mainshock motion. The relatively high Fourier spectral amplitudes (at frequencies near 0.2–0.3 Hz) of ground acceleration recorded at the rock site indicate that the long-period pulse prominent at the soft site was not entirely due to site effects. The polarization pattern of the 5 s pulse at the rock site and at soft site KATNP were found to be very different. Comparison of polarization patterns of mainshock ground motion at rock and soft sites indicate that the 5 s pulse observed at some locations in Kathmandu Valley is a combination of shear waves (the amplitudes of which were amplified by the basin layers) and Love waves.

Separation and location of microseism sources

[1] Microseisms are ground vibrations caused largely by ocean gravity waves. Multiple spatially separate noise sources may be coincidentally active. A method for source separation and individual wavefield retrieval of microseisms using a single pair of seismic stations is introduced, and a method of back azimuth estimation assuming Rayleigh-wave arrivals of microseisms is described. These methods are combined to separate and locate sources of microseisms in a synthetic model and then applied to field microseismic recordings from Ireland in the Northeast Atlantic. It is shown that source separation is an important step prior to location for both accurate microseism locations and microseisms wavefield studies.

Path effects in subsurface microseismic monitoring

Path effects represent relative changes in amplitude and phase that occur when elastic waves propagate in different directions within a medium. Path effects for downhole microseismic monitoring differ fundamentally from path effects for surface monitoring. The reason for this difference is simple: in most cases, borehole microseismic recordings consist of waves transmitted in a more-or-less horizontal direction, whereas surface microseismic data typically contain signals traveling in the vertical direction. In many geologic settings of so-called tight reservoirs this is equivalent to parallel and perpendicular to the layering.

Fracture quality from integrating time-lapse VSP and microseismic data

Tight gas reservoirs are problematic to produce, often requiring multiple stages of hydraulic fracturing in order to create connected pathways through which hydrocarbons may flow. In this paper, we propose a new methodology to characterize the quality of hydraulic fractures. Using synthetic VSP and microseismic data, we test the concept that the rock volume containing open, gas-filled fractures will scatter seismic energy more profusely than a volume containing closed, nonproductive fractures. By measuring the amount of scattered energy in a time-lapse 3D VSP study taken before and after the hydraulic fracturing episode, we hope to compare the productive flow quality of different regions of the hydraulically fractured rock. The microseismic recordings allow us to locate areas which have been hydraulically fractured and create imaging operators to extract the scattered signals from the time-lapse VSP data.

Seismic detectors for continuous bed load monitoring in a gravel stream

Abstract A field experiment is described for continuous recording of bed load transport rates in a coarse-grained alluvial channel, using instruments typically available to seismologists. The experiment was carried out in 1990 in the Italian Alps, in a small basin equipped since 1982 with instruments conventionally used in hydrological surveys and bed load transport measurements. In July 1990, six seismometers were placed along the alluvial channel for up to about 150 m upstream of the flow gauging station. Continuous microseismic recordings of seven flood flows were analysed, five being associated with coarse sediment delivery. The mechanism of bed load transport was inferred from two or three microseismic impulse peaks occurring before and after the discharge peak shown in the hydrograph. Those microseismic peaks are thought to reflect the pulsed nature of bed load transport while the periods elapsing between them are interpreted as indicating the duration of the transport process and appeared independent of peak discharges.

Variations des micros�ismes autour du Golfe de Gascogne

Microseismic recordings have been made in Brest (Hydrographic Office), Hendaye (Abbadia Observatory) and La Coruňa (Observatorio meteorologico), and compared with Paris-St Maur. The amplitude ratio Brest/St Maur is dependent on the place of low atmospheric centers and the duplication of the central isobar of these seems to give rise to stronger microseisms. Swell propagation can also be timed between La Coruna and Morocco and between station K and Hendaye. Pyramidal waves are again emphasized as the cause of microseisms.