An effective polarity correction method for microseismic migration-based location

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. KS115-KS125
Author(s):  
Jincheng Xu ◽  
Wei Zhang ◽  
Xiaofei Chen ◽  
Quanshi Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Real Data ◽  
Correction Method ◽  
Fitting Procedure ◽  
Microseismic Data ◽  
The Status ◽  
Event Location ◽  
Microseismic Events ◽  
Polarity Correction

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.

Minimum semblance weighted stacking with polarity correction for surface microseismic data processing

2019 ◽  
Vol 38 (8) ◽  
pp. 630-636 ◽  
Author(s):  
Jincheng Xu ◽  
Wei Zhang ◽  
Xaofei Chen ◽  
Quanshi Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Real Data ◽  
Data Sets ◽  
Source Imaging ◽  
Microseismic Data ◽  
True Source ◽  
Source Mechanisms ◽  
Polarity Correction ◽  
Microseismic Location ◽  
Location Methods

Diffraction-stack-based algorithms are the most popular microseismic location methods for surface microseismic data. They can accommodate microseismic data with low signal-to-noise ratio by stacking a large number of traces. However, changes in waveform polarity across the receiver line due to source mechanisms may prevent stacking methods from locating the true source. Imaging functions based on simple stacks have low resolution, producing large uncertainty in the final location result. To solve these issues, we introduce a minimum semblance weighted stacking method with polarity correction, which uses an amplitude trend least-squares fitting algorithm to correct the polarity across the receiver line. We adapt the semblance weighted stacking for better coherency measure to improve the imaging resolution. Moreover, the minimum semblance is used to further improve the resolution of location results. Application to both synthetic and real data sets demonstrates good performance of our proposed location method.

Relative location of microseismic events with multiple masters

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. KS149-KS158 ◽  
Author(s):  
Vladimir Grechka ◽  
Zhao Li ◽  
Bo Howell
Keyword(s):  
Anisotropic Media ◽  
Gradual Decrease ◽  
Relative Location ◽  
Location Method ◽  
Location Errors ◽  
Satisfactory Performance ◽  
Microseismic Data ◽  
Event Location ◽  
Microseismic Events ◽  
Paraxial Ray

A recently proposed paraxial ray-based technique for relative location of microseismicity is extended to accommodate several master events with respect to which other events, termed the slaves, are located. The multi-master extension addresses two issues inherent for the existing single-master algorithm: a gradual decrease of its accuracy with the distance from the master and less than satisfactory performance in the presence of strong velocity heterogeneity. Those deficiencies are handled by applying an improved paraxial traveltime formula, exact in homogeneous elliptically anisotropic media, and by distributing masters in the subsurface to sample its heterogeneity. The contributions of different master events to the hypocenter of a given slave are automatically weighted to enhance the influence of adjacent masters, ensuring the precise slave location, and to suppress distant ones, tending to increase the slave-location errors. Tests of the multi-master relative event-location method on synthetic and field microseismic data demonstrate its precision and flexibility as well as applicability to both surface and downhole microseismic geometries.

scholarly journals Simultaneous inversion of multiple microseismic data for event locations and velocity model with Bayesian inference

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. KS27-KS39 ◽  
Author(s):  
Zhishuai Zhang ◽  
James W. Rector ◽  
Michael J. Nava
Keyword(s):  
Bayesian Inference ◽  
Real Data ◽  
Velocity Model ◽  
Location Estimation ◽  
Geothermal System ◽  
Simultaneous Inversion ◽  
Effective Velocity ◽  
Microseismic Data ◽  
Event Location ◽  
Microseismic Event

We have applied Bayesian inference for simultaneous inversion of multiple microseismic data to obtain event locations along with the subsurface velocity model. The traditional method of using a predetermined velocity model for event location may be subject to large uncertainties, particularly if the prior velocity model is poor. Our study indicated that microseismic data can help to construct the velocity model, which is usually a major source of uncertainty in microseismic event locations. The simultaneous inversion eliminates the requirement for an accurate predetermined velocity model in microseismic event location estimation. We estimate the posterior probability density of the velocity model and microseismic event locations with the maximum a posteriori estimation, and the posterior covariance approximation under the Gaussian assumption. This provides an efficient and effective way to quantify the uncertainty of the microseismic location estimation and capture the correlation between the velocity model and microseismic event locations. We have developed successful applications on both synthetic examples and real data from the Newberry enhanced geothermal system. Comparisons with location results based on a traditional predetermined velocity model method demonstrated that we can construct a reliable effective velocity model using only microseismic data and determine microseismic event locations without prior knowledge of the velocity model.

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

scholarly journals Automotive antenna diversity system for satellite radio with high phase accuracy in low SNR-scenarios

2018 ◽  
Vol 10 (5-6) ◽  
pp. 578-586 ◽  
Author(s):  
Simon Senega ◽  
Ali Nassar ◽  
Stefan Lindenmeier
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Multipath Fading ◽  
Correction Method ◽  
Antenna System ◽  
Antenna Diversity ◽  
Phase Detection ◽  
Low Snr ◽  
Single Antenna ◽  
Noise Correction

AbstractFor a fast scan-phase satellite radio antenna diversity system a noise correction method is presented for a significant improvement of audio availability at low signal-to-noise ratio (SNR) conditions. An error analysis of the level and phase detection within the diversity system in the presence of noise leads to a correction method based on a priori knowledge of the system's noise floor. This method is described and applied in a hardware example of a satellite digital audio radio services antenna diversity circuit for fast fading conditions. Test drives, which have been performed in real fading scenarios, are described and results are analyzed statistically. Simulations of the scan-phase antenna diversity system show higher signal amplitudes and availabilities. Measurement results of dislocated antennas as well as of a diversity antenna set on a single mounting position are presented. A comparison of a diversity system with noise correction, the same system without noise correction, and a single antenna system with each other is performed. Using this new method in fast multipath fading driving scenarios underneath dense foliage with a low SNR of the antenna signals, a reduction in audio mute time by one order of magnitude compared with single antenna systems is achieved with the diversity system.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

Correlation and Interpretation of Microseismic Responses using Pressure Measurements in Offset Observation Wells

2015 ◽  
Author(s):  
Robert Downie ◽  
Joel Le Calvez ◽  
Barry Dean ◽  
Jeff Rutledge
Keyword(s):  
Surface Pressure ◽  
Source Parameters ◽  
Reservoir Pressure ◽  
Pressure Measurements ◽  
Observation Well ◽  
Fracture Pressure ◽  
Microseismic Data ◽  
Pressure Depletion ◽  
Microseismic Events ◽  
Observation Wells

Abstract Interpretation of the microseismic data acquired during hydraulic fracture treatments is based on a variety of techniques that make use of the locations, times, and source parameters of the detected events, in conjunction with the stimulation treatment data. It is sometimes possible to observe trends or changes in the microseismic data that correspond to the surface pressure measurements; however this aspect of interpretation becomes problematic due the variability of fluid friction, slurry density, perforation restrictions, and other near-wellbore pressures when computing bottom hole fracturing pressure. An interpretation technique is proposed that uses pressure measurements in observation wells that are offset to the treatment well during microseismic interpretations. The observation well can be any well with open perforations in close proximity to the treatment well. The observation well pressures are not affected by the many complicating factors that are encountered when estimating pressure in the fracture from the surface pressure measured in the treatment well. Example data from field observations are used to demonstrate that the detection of microseismic events near an observation well and corresponding detection of fluid pressure from the fracture in the observation well validates the calculated event locations. The relationship between fracture pressure, the state of stress, and microseismic responses is discussed using Mohr-Coulomb failure criteria. Observation-well pressures and microseismic events are also used to identify instances where reservoir pressure depletion near the observation well affects surface operations at the treatment well. The results of the study show that reliable measurements of fracture pressure for use in microseismic interpretations can be obtained from offset observation wells, and where reservoir pressure depletion causes deviations from expected fracture behavior. The results also show that microseismic responses are directly related to fracture pressure, and not simply the presence of fracturing fluid itself, leading to an improved understanding of the conditions under which microseismic events occur.

Fourth Moment Structure of Markov Switching Multivariate GARCH Models

2019 ◽  
Author(s):  
Maddalena Cavicchioli
Keyword(s):  
Closed Form ◽  
Moving Average ◽  
Markov Switching ◽  
Computational Cost ◽  
Sufficient Conditions ◽  
Impulse Response Function ◽  
Real Data ◽  
Multivariate Garch ◽  
Fourth Moment ◽  
Spectral Density Matrix

Abstract We derive sufficient conditions for the existence of second and fourth moments of Markov switching multivariate generalized autoregressive conditional heteroscedastic processes in the general vector specification. We provide matrix expressions in closed form for such moments, which are obtained by using a Markov switching vector autoregressive moving-average representation of the initial process. These expressions are shown to be readily programmable in addition of greatly reducing the computational cost. As theoretical applications of the results, we derive the spectral density matrix of the squares and cross products, propose a new definition of multivariate kurtosis measure to recognize heavy-tailed features in financial real data, and provide a matrix expression in closed form of the impulse-response function for the volatility. An empirical example illustrates the results.

scholarly journals Wind Turbine Noise Reduction from Seismological Data

2021 ◽  
Author(s):  
Janis Heuel ◽  
Wolfgang Friederich
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Computational Cost ◽  
Noise Signal ◽  
Noise Model ◽  
Threshold Function ◽  
High Signal ◽  
Time Data ◽  
Negative Effects ◽  
Before And After

<p>Over the last years, installations of wind turbines (WTs) increased worldwide. Owing to<br>negative effects on humans, WTs are often installed in areas with low population density.<br>Because of low anthropogenic noise, these areas are also well suited for sites of<br>seismological stations. As a consequence, WTs are often installed in the same areas as<br>seismological stations. By comparing the noise in recorded data before and after<br>installation of WTs, seismologists noticed a substantial worsening of station quality leading<br>to conflicts between the operators of WTs and earthquake services.</p><p>In this study, we compare different techniques to reduce or eliminate the disturbing signal<br>from WTs at seismological stations. For this purpose, we selected a seismological station<br>that shows a significant correlation between the power spectral density and the hourly<br>windspeed measurements. Usually, spectral filtering is used to suppress noise in seismic<br>data processing. However, this approach is not effective when noise and signal have<br>overlapping frequency bands which is the case for WT noise. As a first method, we applied<br>the continuous wavelet transform (CWT) on our data to obtain a time-scale representation.<br>From this representation, we estimated a noise threshold function (Langston & Mousavi,<br>2019) either from noise before the theoretical P-arrival (pre-noise) or using a noise signal<br>from the past with similar ground velocity conditions at the surrounding WTs. Therefore, we<br>installed low cost seismometers at the surrounding WTs to find similar signals at each WT.<br>From these similar signals, we obtain a noise model at the seismological station, which is<br>used to estimate the threshold function. As a second method, we used a denoising<br>autoencoder (DAE) that learns mapping functions to distinguish between noise and signal<br>(Zhu et al., 2019).</p><p>In our tests, the threshold function performs well when the event is visible in the raw or<br>spectral filtered data, but it fails when WT noise dominates and the event is hidden. In<br>these cases, the DAE removes the WT noise from the data. However, the DAE must be<br>trained with typical noise samples and high signal-to-noise ratio events to distinguish<br>between signal and interfering noise. Using the threshold function and pre-noise can be<br>applied immediately on real-time data and has a low computational cost. Using a noise<br>model from our prerecorded database at the seismological station does not improve the<br>result and it is more time consuming to find similar ground velocity conditions at the<br>surrounding WTs.</p>

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