Microseismic Event Detection Research Articles

Automatic Detection and Location of Microseismic Events from Sparse Network and Its Application to Post-mining Monitoring

In post-mining regions with seismic hazard, timely decision making for risk management faces the challenge of quick and reliable detection and location of seismic events. As a response to the increasing density of monitoring stations, generating large volumes of seismic data, automatic, full waveform-based methods have been developed in recent years in global seismology. Such methods often cannot be directly applied to post-mining monitoring with a limited station coverage, as it is the case when temporarily networks are installed as an emergency response. In this paper we propose a new methodology that bridges this gap and enables the application of a full waveform, backprojection based method (BackTrackBB) to data of sparse network. The methodology was successfully tested on an abandoned and flooded underground coalmine in South-eastern France. Steps preceding BackTrackBB application were implemented in order to remove the coherent noise that otherwise results in numerous false detections. First results indicate that seismic activity in the study area is controlled by water level variation within former room-and-pillar mine works and fault segments (re)activation below them.

Microseismic event detection and classification based on convolutional neural network

Microseismic event detection is a key step in the processing and interpretation of hydraulic fracturing monitoring. Low signal-to-noise ratio (S/N) properties of microseismic data, which makes the event detection of conventional methods not effective enough. In conventional methods, microseismic event detection is based on the difference of amplitude, frequency and correlation between effective signal and ambient noise. We have developed an event detection based on convolutional neural networks (CNN). To verify the feasibility of the method, we automatically create 4000 synthetic microseismic event and random noise records, and the corresponding labels for each record, which training result shown that is a good event detection method. Because the field data set is usually continuously collected by multiple geophones, we cut the data to 2000 sampling points as input data. In the training stage, we divide the synthetic microseismic data into three categories, namely, the (1) microseismic event, (2) single-phase event, and (3) ambient noise event, which are 2000, 1000, 1000, respectively. After training, the network architecture automatically learns to calculate the important features for event detection and saves the parameters of the highest training accuracy model. Field data examples of 11 stages indicate that CNN event detection is more accurate and effective than short-term average and long-term average ratio (STA/LTA) detection, and the CNN method can automatically classify events.

The 2002–2005 Changbaishan Volcanic Unrest Triggered by the 2002 M 7.2 Wangqing Deep Focus Earthquake

One of the most active intraplate volcanoes in East Asia, Changbaishan volcano experienced unrest from July 2002 to July 2005. On 2002/06/28, the M 7.2 Wangqing deep-focus earthquake occurred ∼290 km northeast of Changbaishan volcano. While some studies have suggested a possible triggering relationship, the physical mechanism of such distant interaction is still not well understood. Using a template matching technique, which cross-correlates waveform of known events with continuous data, we perform systematic detection of microseismic events recorded by station CBS near Changbaishan volcano from July 1999 to July 2007. The detected earthquakes can be further categorized into three different types: volcano-tectonic (VT) events, long-period (LP) events and harmonic-spectra (HS) events. We detect 3763 VT events between July 2002 and July 2007. The intense VT earthquake swarm during the period from July 2002 to July 2005, along with recurring LPs and HSs and other geodetic/geochemical evidence, suggest magma movement during unrest. Compared with the hand-picked catalogue, the catalogue obtained by template matching technique reveals a delayed-triggering relationship between Wangqing deep-focus earthquake and unrest. The small magnitudes of the VT events and the limited numbers of LP and HS events suggest that the Wangqing mainshock likely triggered bubble excitation in the mid-crust magma system, resulting in overpressure and a small magma injection into the shallow magma chamber at a depth of ∼5 km, leading to the 3-years unrest.

Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling

Abstract In passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy background is a challenging task. Most of the established methods for geophysical inversion are likely to yield many false event detections. The most advanced of these schemes require thousands of computationally demanding forward elastic-wave propagation simulations. Here we train and use an ensemble of Gaussian process surrogate meta-models, or proxy emulators, to accelerate the generation of accurate template seismograms from random microseismic event locations. In the presence of multiple microseismic events occurring at different spatial locations with arbitrary amplitude and origin time, and in the presence of noise, an inference algorithm needs to navigate an objective function or likelihood landscape of highly complex shape, perhaps with multiple modes and narrow curving degeneracies. This is a challenging computational task even for state-of-the-art Bayesian sampling algorithms. In this paper, we propose a novel method for detecting multiple microseismic events in a strong noise background using Bayesian inference, in particular, the Multimodal Nested Sampling (MultiNest) algorithm. The method not only provides the posterior samples for the 5D spatio-temporal-amplitude inference for the real microseismic events, by inverting the seismic traces in multiple surface receivers, but also computes the Bayesian evidence or the marginal likelihood that permits hypothesis testing for discriminating true vs. false event detection.

Energy-stack: a fast and robust method for real-time microseismic event-detection

Energy-stack: a fast and robust method for real-time microseismic event-detection

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes, such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behavior of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding an imminent failure. Microseismic monitoring recordings are often continuous, noisy, and consist of signals emitted by various sources. Manually detecting and distinguishing the signals emitted by an unstable slope is challenging. Research on automated end-to-end denoising, detection, and classification of microseismic events, as an early warning system, is still in its infancy. To this effect, our work is focused on jointly evaluating and developing suitable approaches for signal denoising, accurate event detection, nonsite-specific feature construction, feature selection, and event classification. We propose an automated end-to-end system that can process big data sets of continuous seismic recordings fast and demonstrate applicability and robustness to a wide range of events (distant and local earthquakes, slidequakes, anthropogenic noise, etc.). Algorithmic contributions lie in novel signal processing and analysis methods with fewer tunable parameters than the state of the art, evaluated on two field data sets and benchmarked against the state of the art.

Introduction to this special section: Distributed acoustic sensing

Distributed acoustic sensing (DAS) has seen many advances in recent years with many different applications. This special section contains six papers featuring applications that vary from microseismic event detection to subsea applications to surface deployments, fracture characterization, and well irregularity identification. Each paper introduces a unique problem and then poses the use of DAS in an appropriate way to solve the problem. Special sections on DAS are relatively frequent in the literature currently, so these papers are a snapshot of the work done with the technology. We hope you enjoy these publications.

Convolutional neural networks for automated microseismic detection in downhole distributed acoustic sensing data and comparison to a surface geophone array

ABSTRACTDistributed acoustic sensing is a growing technology that enables affordable downhole recording of strain wavefields from microseismic events with spatial sampling down to ∼1 m. Exploiting this high spatial information density motivates different detection approaches than typically used for downhole geophones. A new machine learning method using convolutional neural networks is described that operates on the full strain wavefield. The method is tested using data recorded in a horizontal observation well during hydraulic fracturing in the Eagle Ford Shale, Texas, and the results are compared to a surface geophone array that simultaneously recorded microseismic activity. The neural network was trained using synthetic microseismic events injected into real ambient noise, and it was applied to detect events in the remaining data. There were 535 detections found and no false positives. In general, the signal‐to‐noise ratio of events recorded by distributed acoustic sensing was lower than the surface array and 368 of 933 surface array events were found. Despite this, 167 new events were found in distributed acoustic sensing data that had no detected counterpart in the surface array. These differences can be attributed to the different detection threshold that depends on both magnitude and distance to the optical fibre. As distributed acoustic sensing data quality continues to improve, neural networks offer many advantages for automated, real‐time microseismic event detection, including low computational cost, minimal data pre‐processing, low false trigger rates and continuous performance improvement as more training data are acquired.

Application of machine learning to microseismic event detection in distributed acoustic sensing data

This study presents the first demonstration of the transferability of a convolutional neural network (CNN) trained to detect microseismic events in one fiber-optic distributed acoustic sensing (DAS) data set to other data sets. DAS increasingly is being used for microseismic monitoring in industrial settings, and the dense spatial and temporal sampling provided by these systems produces large data volumes (approximately 650 GB/day for a 2 km long cable sampling at 2000 Hz with a spatial sampling of 1 m), requiring new processing techniques for near-real-time microseismic analysis. We have trained the CNN known as YOLOv3, an object detection algorithm, to detect microseismic events using synthetically generated waveforms with real noise superimposed. The performance of the CNN network is compared to the number of events detected using filtering and amplitude threshold (short-term average/long-term average) detection techniques. In the data set from which the real noise is taken, the network is able to detect >80% of the events identified by manual inspection and 14% more than detected by standard frequency-wavenumber filtering techniques. The false detection rate is approximately 2% or one event every 20 s. In other data sets, with monitoring geometries and conditions previously unseen by the network, >50% of events identified by manual inspection are detected by the CNN.

Automatic high-resolution microseismic event detection via supervised machine learning

SUMMARYMicroseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-to-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2*wavelength is used to divide the data into segments. Later on, 191 features have been extracted and used as the input data to train the support vector machine (SVM) model. These features include 63 1-D time/spectral-domain features, and 128 2-D texture features, which indicate the continuity, smoothness, and irregularity of the events/noise. The proposed feature extraction maximally exploits the limited information of each segment. Afterward, we use a combination of univariate feature selection and random-forest-based recursive feature elimination for feature selection to avoid overfitting. This feature selection strategy not only finds the best features, but also decides the optimal number of features that are needed for the best accuracy. Regarding the training process, SVM with a Gaussian kernel is used. In addition, a cross-validation (CV) process is implemented for automatic parameter setting. In the end, a group of synthetic and field microseismic data with different levels of complexity show that the proposed workflow is much more robust than the state-of-the-art short-term-average over long-term-average ratio (STA/LTA) method and also performs better than the convolutional-neural-networks (CNN), for this case where the amount of training data sets is limited. A demo for the synthetic example is available: https://github.com/shanqu91/ML_event_detection_microseismic.

An effective polarity correction method for microseismic migration-based location

Microseismic methods are important tools for monitoring the status and consequences of hydraulic fracturing. Because microseismic data recorded at the surface have a low signal-to-noise ratio, migration-based algorithms are widely used to determine the locations of microseismic events. However, there may be polarity changes in waveforms at different receivers due to the source mechanisms, which will cause the stacking images to not reach a maximum at the event location. One way for polarity correction is to perform the source mechanism and the source location inversions simultaneously, which, however, is computationally expensive and not good for real-time monitoring. We have developed an effective polarity correction method in the data domain for migration-based location methods called the polarity correction migration-based (PCM) method. This method uses an amplitude trend least-squares fitting procedure to determine the polarities along the receiver line with low additional computational cost. Then, the fitted waveform polarities are used to convert the signs of the amplitude values to stack them consistently. Due to curve fitting, this method is more suitable for microseismic data acquired with regular arrays than with scattered arrays. Numerical experiments of synthetic and real data sets demonstrate that the proposed PCM method can improve accuracy in the detection and location of microseismic events.

Expression of Concern: Automatic high-resolution microseismic event detection via supervised machine learning

Expression of Concern: Automatic high-resolution microseismic event detection via supervised machine learning

Seismic identification of geothermal prospecting in Harrat Rahat, Northern Arabian Shield

Harrat Rahat volcanic field lies along the Makkah-Madinah-Nafud active volcanic line. It has extensive linear weak zones of wrench faults that could provide ideal conduits for upwelling magma and hydrothermal fluids as well. Passive seismic data recorded by seismic stations installed within and around Harrat Rahat has been processed for the detection of microseismic events. Moreover, ambient noise was also gathered and analyzed to construct a subsurface velocity model. This model indicates low velocity zone in Harrat Rahat, and two areas with coincident basement faults and possibly with enhanced subsurface temperatures were identified. It is concluded that Harrat Rahat has high geothermal potential and promising for geothermal renewable power.

Migration-based near real-time detection and location of microearthquakes with parallel computing

SUMMARYPrompt detection and accurate location of microseismic events are of great importance in seismic monitoring at local scale and become essential steps in monitoring underground activities, such as oil and gas production, geothermal exploitation and underground gas storage, for implementing effective control procedures to limit the induced seismicity hazard. In this study, we describe an automatic and robust earthquake detection and location procedure that exploits high-performance computing and allows the analysis of microseismic events in near real-time using the full waveforms recorded by a local seismic network. The implemented technique, called MigraLoc, is based on the space–time migration of continuous waveform data and consists of the following steps: (1) enhancement of P and S arrivals in noisy signals through a characteristic function, by means of the time–frequency analysis of the seismic records; (2) blind event location based on delay-and-sum approach systematically scanning the volume of potential hypocentres; (3) detection notification according to the information content of the hypocentre probability distribution obtained in the previous step. The technique implies that theoretical arrival times are pre-calculated for each station and all potential hypocentres as a solution of the seismic-ray equation in a given 3-D medium. As a test case, we apply MigraLoc to two, low-magnitude, earthquake swarms recorded by the Collalto Seismic Network in the area of the Veneto Alpine foothills (Italy) in 2014 and 2017, respectively. Thanks to MigraLoc, we can increase the number of events reported in the network catalogue by more than 25 per cent. The automatically determined locations prove to be consistent with, and overall more accurate than, those obtained by classical methods using manual time-arrival picks. The proposed method works preferably with dense networks that provide signals with some degree of coherency. It shows the following advantages compared to other classical location methods: it works on the continuous stream of data as well as on selected intervals of waveforms; it detects more microevents owing to the increased signal-to-noise ratio of the stacked signal that feeds the characteristic function; it works with any complex 3-D model with no additional effort; it is completely automatic, once calibrated, and it does not need any manual picking.

On the importance of benchmarking algorithms under realistic noise conditions

SUMMARYTesting with synthetic data sets is a vital stage in an algorithm’s development for benchmarking the algorithm’s performance. A common addition to synthetic data sets is White, Gaussian Noise (WGN) which is used to mimic noise that would be present in recorded data sets. The first section of this paper focuses on comparing the effects of WGN and realistic modelled noise on standard microseismic event detection and imaging algorithms using synthetic data sets with recorded noise as a benchmark. The data sets with WGN underperform on the trace-by-trace algorithm while overperforming on algorithms utilizing the full array. Throughout, the data sets with realistic modelled noise perform near identically to the recorded noise data sets. The study concludes by testing an algorithm that simultaneously solves for the source location and moment tensor of a microseismic event. Not only does the algorithm fail to perform at the signal-to-noise ratios indicated by the WGN results but the results with realistic modelled noise highlight pitfalls of the algorithm not previously identified. The misleading results from the WGN data sets highlight the need to test algorithms under realistic noise conditions to gain an understanding of the conditions under which an algorithm can perform and to minimize the risk of misinterpretation of the results.

Automatic Microseismic Events Detection Using Multiscale Morphological Characteristic Function

Surface-acquired microseismic events are commonly unpredictable and appear as weak signals with low signal-to-noise ratios (SNRs), which present a significant challenge in microseismic data analysis and events detection. We have developed the multiscale morphology method for automatically detecting microseismic events. The technique is based on the fact that the effective signal and noise have different features in the multiscale morphological section. According to these characteristics, the effective signal can be identified by waveform consistency analysis. The microseismic data are first decomposed into different scales by multiscale morphology decomposition. Then the multiscale morphology characteristic function is calculated by waveform cross correlation approach and the potential microseismic events are evaluated using the peak value of the multiscale morphology characteristic function. Applications of the proposed method on the synthesized data and field data have been explored in this article. Various examples demonstrate that the proposed method has the advantage of suppressing the effect of Gaussian noises adaptively. Furthermore, it is excellent in automatically detecting the low SNR signal so that more microseismic events can be identified. In addition, the method could be applied to other fields, such as in the identification of earthquake signals.

Methodology for Full Waveform Near Real-Time Automatic Detection and Localization of Microseismic Events Using High (8 kHz) Sampling Rate Records in Mines: Application to the Garpenberg Mine (Sweden)

AbstractRecent studies have demonstrated the success of automatic full-waveform detection and location methods in analyzing and monitoring natural and induced seismicity. These approaches have been shown to provide a significant improvement in events detectability, increasing the significance of statistical analysis that permits to identify small changes of seismicity rates in space and time. Although currently nontrivial and by far nonstandard, application of such methods to seismic monitoring of active mines could significantly improve forecasting of potential destructive rockburst events. The main challenges of such applications are related to the presence of a wide range of seismic noise sources that have to do with mining activity and a high sampling rate of recorded data (several kHz), posing problems for real-time data transfer and processing.In this study, we propose an adapted full-waveform-based automatic method for the detection and location of microseismic events that makes use of continuous seismic records from an in-mine seismic network and can be adjusted to a near-real-time monitoring scheme. The method consists of two steps: (1) event extraction and amplitude ratio-based preliminary location and (2) event relocation using a coherency-based backprojection approach. The event extraction, based on multiband signal characterization implemented in the first step, allows us to overcome the challenge of high sampling rate data (8 kHz), reducing the overall volume of transferred data and providing an energy-based signal classification scheme. This allows us to remove a significant number of machinery noise sources. The technique is developed and tested on the case study of the Garpenberg mine (Sweden) monitored by a local seismic network that is maintained by Ineris. We demonstrate the improvement in event detection capacity by a factor of 50, compared with the standard triggered-based monitoring schemes. This increased number of detected microseismic events permits us to investigate the migration pattern of induced microseismicity that is generated in response to production blast.

Ak-mean characteristic function for optimizing short- and long-term-average-ratio-based detection of microseismic events

Event detection is an essential component of microseismic data analysis. This process is typically carried out using a short- and long-term-average-ratio (STA/LTA) method, which is simple and computationally efficient but often yields inconsistent results for noisy data sets. We have aimed to optimize the performance of the STA/LTA method by testing different input forms of 3C waveform data and different characteristic functions (CFs), including a proposed [Formula: see text]-mean CF. These tests are evaluated using receiver operating characteristic (ROC) analysis and are compared based on synthetic and field data examples. Our analysis indicates that the STA/LTA method using a [Formula: see text]-mean CF improves the detection sensitivity and yields more robust event detection on noisy data sets than some previous approaches. In addition, microseismic events are detected efficiently on field data examples using the same detection threshold obtained from the ROC analysis on synthetic data examples. We recommend the use of the Youden index based on ROC analysis using a training subset, extracted from the continuous data, to further improve the detection threshold for field microseismic data.

Automatic high-resolution microseismic event detection via supervised machine learning

Automatic high-resolution microseismic event detection via supervised machine learning

Theory of the detection function for migration-based methods for the detection of microseismic events

A detection function is used during the migration-based detection of microseismic events. A potential event is detected when a detection function exceeds some specified threshold value. However, the value of this threshold is usually selected manually; therefore, the detection process is subjective and lacks constraints on the probability of exceeding the threshold. In practice, any change in the detection algorithm results in a change in the distribution of the detection-function values. Consequently, the threshold value must be adjusted to have the same probability of exceedance as before. I have developed a general probabilistic theory of the detection function that is valid for any migration-based detection method. For the sake of transparency and the interchangeability of the results, I recommend the definition of a threshold in terms of the exceedance probability. To do so, it is necessary to define a probability distribution of the detection function. I have also developed a general method for assessing the detectability of a given stacking method for the purpose of comparing different migration-based methods. A simple synthetic example indicates how to use the developed theory in practice.