Energy-stack and Kurtosis: the dynamic duo for microseismic event identification (original) (raw)
Related papers
Adaptive microseismic event detection and automatic time picking
2012
Summary Event detection and time picking is a crucial processing step in passive seismic monitoring which is required to be accurate and time efficient considering the large size of data. The short time average and long time average ratio (STA/LTA) is considered the most popular event detection algorithm. The selection of window size and threshold criterion requires careful considerations and is a timeconsuming process. The window size selection is studied in detail. A workflow is proposed in which the selection of threshold is made dynamic for each trace in the microseismic record using the Weibull distribution fit of the STA/LTA product of the three microseismic data components. Several time picking algorithms (Akaike information criterion, modified energy ratio and short and long time average ratio) are investigated. Akaike information criterion is used for the time picking because it is stable in focused time picking and is time efficient (it requires less user input). All the e...
GEOPHYSICS, 2019
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 syntheti...
A new automatic S-onset detection technique; application in microseismic data analysis
seismo.geology.upatras.gr
Algorithms that deal with the automatic S-onset time identification problem are a topic of ongoing research. Modern dense seismic networks used for earthquake location, seismic tomography investigations, source studies, early warning, etc., demand accurate automatic S-wave picking. Most of the techniques that have been proposed up to now are mainly based on the polarization features of the seismic waves. We propose a new time domain method for the automatic determination of the S-phase arrival onsets, and present its implementation on local earthquake data. Eigenvalue analysis takes place over small time intervals, and the maximum eigenvalue which is obtained on each step is retained for further processing. In this way, a time series of maximum eigenvalues is formed, which serves as a characteristic function. We obtain a first S-phase arrival time estimation by applying the kurtosis criterion on the derived characteristic function. Furthermore, a multiwindow approach combined with an energy-based weighting scheme is also applied, to reduce the algorithm's dependence on the moving window's length and provide a weighted S-phase onset. Automatic picks were compared against manual reference picks, resulting in mean residual time of 0.051 s. Moreover, the proposed technique was subjected to a noise robustness test and sustained a good performance. The mean residual time remained lower than 0.1 s, for noise levels between −1 up to 8 dB. The proposed method is easy to implement, because it is almost parameter free and demands low computational resources.
Automated microseismic event location using Master-Event Waveform Stacking
Accurate and automated locations of microseismic events are desirable for many seismological and industrial applications. The analysis of microseismicity is particularly challenging because of weak seismic signals with low signal-to-noise ratio. Traditional location approaches rely on automated picking, based on individual seismograms, and make no use of the coherency information between signals at different stations. This strong limitation has been overcome by full-waveform location methods, which exploit the coherency of waveforms at different stations and improve the location robustness even in presence of noise. However, the performance of these methods strongly depend on the accuracy of the adopted velocity model, which is often quite rough; inaccurate models result in large location errors. We present an improved waveform stacking location method based on source-specific station corrections. Our method inherits the advantages of full-waveform location methods while strongly mitigating the dependency on the accuracy of the velocity model. With this approach the influence of an inaccurate velocity model on the results is restricted to the estimation of travel times solely within the seismogenic volume, but not for the entire source-receiver path. We finally successfully applied our new method to a realistic synthetic dataset as well as real data. The increasing number of microseismic monitoring networks for both seismological and industrial applications has led to an exponential growth of available microseismicity data in the last decade. These data typically contain a large number of weak seismic events, whose waveforms are often highly noise contaminated. Standard location routines based on automated phase picking, identification, and association tend to fail when dealing with such noisy data 1 or when simultaneous events occur 2. Therefore, an improved, fully automated, and noise robust procedure must be established in order to obtain high precision location for weak events. The main systematic limitation of traditional event location techniques is that the automated event identification is most commonly performed individually on single seismograms, thereby making little or no use of the coherency information between waveforms recorded at different stations 3. The increasing interest on microseismic monitoring operations , particularly for oil and gas reservoirs applications, motivated the development of new location methods based on waveform stacking techniques commonly used in reflection seismics 2. These methods exploit the coherence of the waveforms recorded at different stations and do not require any automated phase picking, identification and association procedure. The Source Scanning Algorithm (SSA) developed by Kao and Shan 4,5 represented the first attempt to use the signal coherency information for automated detection and location of earthquakes. In later years, several modified versions of this pioneer algorithm have been proposed and used for different applications , including monitoring of natural 6–8 and induced seismicity 1,9,10 , volcano seismology 11,12 , and landslides monitoring 13. The main advantage of this class of methods is that location results are robust even in presence of noisy waveforms 1,9,10. However, like any other absolute location method, their location performance still strongly depends on the accuracy of the a priori seismic velocity model 14,15. The adoption of inaccurate models may lead to large errors and uncertainties of the location results, which affect the output of further geological and geophys-ical analysis (e.g. estimation of source mechanism, event magnitude, etc.). The negative effect of velocity model inaccuracies on location results can be mitigated using relative location methods, which are generally based on source-specific station correction terms 14 or on the differential travel times for seismic sources that are next to
Semblance for microseismic event detection
SEG Technical Program Expanded Abstracts 2014, 2014
Microseismic monitoring from large arrays using migration-based detection and location techniques is limited by detections of false positive events, which are the interpretation of spurious/noisy signals as real events. Therefore, semblance has been considered to differentiate between false positive and true events. However, semblance by itself is not suitable for variable signals such as those caused by shear source radiation. We present a new methodology for event detection and location using semblance of amplitudes corrected by a source mechanism. Our method is suitable for multichannel processing of microseismic data sets acquired with large arrays. The amplitudes are corrected by the radiation pattern of the inverted source mechanism before the semblance computation. We show that the source mechanism correction is the key factor in maximizing the value of semblance and makes the detection based on semblance superior to simple stacking. We apply this method to a data set recorded by a large surface star-like array on synthetic as well as on field data.
Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA)
Geophysical Journal International, 2016
A new Matched Filtering Algorithm (MFA) is proposed for detecting and analysing microseismic events recorded by downhole monitoring of hydraulic fracturing. This method requires a set of well-located template ('parent') events, which are obtained using conventional microseismic processing and selected on the basis of high signal-to-noise (S/N) ratio and representative spatial distribution of the recorded microseismicity. Detection and extraction of 'child' events are based on stacked, multichannel cross-correlation of the continuous waveform data, using the parent events as reference signals. The location of a child event relative to its parent is determined using an automated process, by rotation of the multicomponent waveforms into the ray-centred coordinates of the parent and maximizing the energy of the stacked amplitude envelope within a search volume around the parent's hypocentre. After correction for geometrical spreading and attenuation, the relative magnitude of the child event is obtained automatically using the ratio of stacked envelope peak with respect to its parent. Since only a small number of parent events require interactive analysis such as picking P-and S-wave arrivals, the MFA approach offers the potential for significant reduction in effort for downhole microseismic processing. Our algorithm also facilitates the analysis of single-phase child events, that is, microseismic events for which only one of the S-or P-wave arrivals is evident due to unfavourable S/N conditions. A real-data example using microseismic monitoring data from four stages of an open-hole slickwater hydraulic fracture treatment in western Canada demonstrates that a sparse set of parents (in this case, 4.6 per cent of the originally located events) yields a significant (more than fourfold increase) in the number of located events compared with the original catalogue. Moreover, analysis of the new MFA catalogue suggests that this approach leads to more robust interpretation of the induced microseismicity and novel insights into dynamic rupture processes based on the average temporal (foreshock-aftershock) relationship of child events to parents.
Microseismic Event Location Using Multiple Arrivals: Demonstration of Uncertainty Reduction
Proceedings of the 3rd Unconventional Resources Technology Conference, 2015
Event location is the basis of hydraulic fracture characterization using microseismic data. However, the traditional method of using direct arrival times and Pwave polarizations leads to increased error due to the large uncertainty in polarization. Due to shale's low velocity nature and the configuration of horizontal stimulation and monitoring wells, the head wave can often be the first arrival rather than the direct arrival. Finite difference modeling was used to validate the character of head waves in field data gathered from the Marcellus shale and the situations under which a head wave can be the first arrival were carefully analyzed. With careful processing, we reveal the presence of high number of head waves in the Marcellus Shale. Head wave and direct arrivals were used instead of the conventional Pwave polarization to estimate microseismic event location. A Bayesian inference program was also developed for joint event location and velocity model calibration. Validation of the developed method was performed on perforation shots and shows that using head waves instead of polarization can achieve much better resolution in microseismic event location. The application of the developed method on field data shows a more reasonable result than that provided by contractor. Our results show that the head wave can be a contributor instead of a detractor in the process of accurate event location. This will eliminate the necessity for polarization which has large uncertainty due to poor geophoneborehole coupling, multiple arrivals, and low signal to noise ratio. The developed method can effectively improve the accuracy of microseismic event location and proposes a better acquisition geometry and strategy to reduce microseismic monitoring cost and improve event location accuracy.
Journal of Physics: Conference Series
Nowadays, microseismic monitoring has been utilized widely to detect fractures and permeability zones in many geophysics applications for exploration: geothermal resources, unconventional hydrocarbon resources, and many else. It is required to process microseismic data effectively and efficiently, but, integrated software for microseismic data processing is not available yet. We developed GMSTech (Ganesha Microseismic Technology), a Windows C# language based software which integrates complete functions and modules for microseismic characterization. In this paper, we discuss an implementation of one of the modules which can be used for calculating microseismic event source parameters: focal mechanism, using data recorded in the certain geothermal field. For estimating the focal mechanism, we developed a new simple algorithm based on grid-searching, clustering, and statistical analysis. In the results, our modules have successfully calculated the source parameters, and it is reliable for geothermal exploration. However, several factors such as coverage and number of stations may influence the results significantly, and moreover, this preliminary results still require further validation. Nevertheless, the GMSTech shows a remarkable performance, and it is more practical to be utilized for industry purposed compared to other software.
Microseismic event locations using the double-difference algorithm
In this work we describe a reprocessing technique to relocate microseismic events based on the double-difference method. First, we use a crosscorrelation technique to assess waveform similarity between events and identify multiplet groups. Then, we correct relative arrival time inconsistencies between doublets. Next we apply the double-difference algorithm, which is a relative location method that tries to minimize the residuals between observed and calculated travel-time differences for pairs of microseismic events at each station, by iteratively adjusting the differences between all pairs of events in each multiplet group. For this study, we use a data set from a microseismic system installed near a mine, and relocate the microseismic events detected. Results are shown for a cluster of microseismic events, where it collapses the diffuse locations into a sharper image of seismicity, which is most likely related to shaft activities. We also show plots that reveal uncertainties in time picks and location for quality control purposes. A higher accuracy in the locations can provide a better detection of zones of ground instabilities; prevent activation processes and potential rock bursts that can severely impact the safety and productivity of the mine.