scholarly journals Estimation of the Relative Arrival Time of Microseismic Events Based on Phase-Only Correlation

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2527 ◽  
Author(s):  
Peng Wang ◽  
Xu Chang ◽  
Xiyan Zhou
Keyword(s):  
Arrival Time ◽  
Linear Equations ◽  
Time Lag ◽  
Synthetic Data ◽  
Information Criterion ◽  
Peak Position ◽  
Arrival Times ◽  
Time Frequency ◽  
Microseismic Events ◽  
Microseismic Event

The arrival time of a microseismic event is an important piece of information for microseismic monitoring. The accuracy and efficiency of arrival time identification is affected by many factors, such as the low signal-to-noise ratio (SNR) of the records, the vast amount of real-time monitoring records, and the abnormal situations of monitoring equipment. In order to eliminate the interference of these factors, we propose a method based on phase-only correlation (POC) to estimate the relative arrival times of microseismic events. The proposed method includes three main steps: (1) The SNR of the records is improved via time-frequency transform, which is used to obtain the time-frequency representation of each trace of a microseismic event. (2) The POC functions of all pairs of time-frequency representations are calculated. The peak value of the POC function indicates the similarity of the traces, and the peak position in the time lag axis indicates the relative arrival times between the traces. (3) Using the peak values as weighting coefficients of the linear equations, consistency processing is used to exclude any abnormal situations and obtain the optimal relative arrival times. We used synthetic data and field data to validate the proposed method. Comparing with Akaike information criterion (AIC) and cross-correlation, the proposed method is more robust at estimating the relative arrival time and excluding the influence of abnormal situations.

Arrival-time picking of microseismic events based on MSNet

Geophysics ◽  
2021 ◽  
pp. 1-66
Author(s):  
Guanqun Sheng ◽  
Shuangyu Yang ◽  
Xiaolong Guo ◽  
Xingong Tang
Keyword(s):  
Arrival Time ◽  
Spatial Information ◽  
Synthetic Data ◽  
Microseismic Monitoring ◽  
Monitoring Data ◽  
Convolutional Network ◽  
Low Snr ◽  
Microseismic Data ◽  
Microseismic Events ◽  
Arrival Time Picking

Arrival-time picking of microseismic events is a critical procedure in microseismic data processing. However, as field monitoring data contain many microseismic events with low signal-to-noise ratios (SNRs), traditional arrival-time picking methods based on the instantaneous characteristics of seismic signals cannot meet the picking accuracy and efficiency requirements of microseismic monitoring owing to the large volume of monitoring data. Conversely, methods based on deep neural networks can significantly improve arrival-time picking accuracy and efficiency in low-SNR environments. Therefore, we propose a deep convolutional network that combines the U-net and DenseNet approaches to pick arrival times automatically. This novel network, called MSNet not only retains the spatial information of any input signal or profile based on the U-net, but also extracts and integrates more essential features of events and non-events through dense blocks, thereby further improving the picking accuracy and efficiency. An effective workflow is developed to verify the superiority of the proposed method. First, we describe the structure of MSNet and the workflow of the proposed picking method. Then, datasets are constructed using variable microseismic traces from field microseismic monitoring records and from the finite-difference forward modeling of microseismic data to train the network. Subsequently, hyperparameter tuning is conducted to optimize the MSNet. Finally, we test the MSNet using modeled signals with different SNRs and field microseismic data from different monitoring areas. By comparing the picking results of the proposed method with the results of U-net and short-term average and long-term average (STA/LTA) methods, the effectiveness of the proposed method is verified. The arrival picking results of synthetic data and microseismic field data show that the proposed network has increased adaptability and can achieve high accuracy for picking the arrival-time of microseismic events.

Joint microseismic event location and anisotropic velocity inversion with the cross double-difference method using downhole microseismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. KS63-KS73
Author(s):  
Yangyang Ma ◽  
Congcong Yuan ◽  
Jie Zhang
Keyword(s):  
Arrival Time ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
S Wave ◽  
Arrival Times ◽  
Monitoring Well ◽  
The Cross ◽  
Microseismic Event ◽  
Wave Arrival

We have applied the cross double-difference (CDD) method to simultaneously determine the microseismic event locations and five Thomsen parameters in vertically layered transversely isotropic media using data from a single vertical monitoring well. Different from the double-difference (DD) method, the CDD method uses the cross-traveltime difference between the S-wave arrival time of one event and the P-wave arrival time of another event. The CDD method can improve the accuracy of the absolute locations and maintain the accuracy of the relative locations because it contains more absolute information than the DD method. We calculate the arrival times of the qP, qSV, and SH waves with a horizontal slowness shooting algorithm. The sensitivities of the arrival times with respect to the five Thomsen parameters are derived using the slowness components. The derivations are analytical, without any weak anisotropic approximation. The input data include the cross-differential traveltimes and absolute arrival times, providing better constraints on the anisotropic parameters and event locations. The synthetic example indicates that the method can produce better event locations and anisotropic velocity model. We apply this method to the field data set acquired from a single vertical monitoring well during a hydraulic fracturing process. We further validate the anisotropic velocity model and microseismic event locations by comparing the modeled and observed waveforms. The observed S-wave splitting also supports the inverted anisotropic results.

scholarly journals LOCALIZATION OF MICROSEISMIC EVENTS USING PHYSICS-INFORMED NEURAL NETWORK SOLUTION TO THE EIKONAL EQUATION

2021 ◽  
Vol 2 (2) ◽  
pp. 32-38
Author(s):  
Serafim I. Grubas ◽  
Sergey V. Yaskevich ◽  
Anton A. Duchkov
Keyword(s):  
Neural Network ◽  
Eikonal Equation ◽  
Arrival Time ◽  
Absolute Error ◽  
Inverse Kinematic ◽  
Average Absolute Error ◽  
Inverse Kinematic Problem ◽  
Arrival Times ◽  
Network Solution ◽  
Microseismic Events

The paper demonstrates an algorithm for using physics-informed neural networks in workflow of processing microseismic data regarding the problem of localization of microseismic events. The proposed algorithm involves the use of a physics-informed neural network solution to the eikonal equation to calculate the traveltimes of the first arrivals. As a result, the network solution is compared with the observed arrival times to solve the inverse kinematic problem to determine the coordinates of the event locations. Using a synthetic 3D example, it was shown that the average absolute error of the arrival time misfit was less than 0.25 ms, and the average localization error did not exceed 4.5 meters.

Full-waveform inversion for microseismic events using sparsity constraints

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. KS1-KS12 ◽  
Author(s):  
Bharath Shekar ◽  
Harpreet Singh Sethi
Keyword(s):  
Waveform Inversion ◽  
Synthetic Data ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Limited Memory ◽  
Full Waveform ◽  
Sparsity Constraints ◽  
Microseismic Events ◽  
Spatio Temporal ◽  
Microseismic Event

Full-waveform inversion (FWI) is a powerful tool that can be used to invert for microseismic event locations and the source signature because it can exploit the complete waveform information. We have developed an algorithm to invert for a spatio-temporal source function that encapsulates microseismic events with spatially localized or distributed locations and source signatures. The algorithm does not require assumptions to be made about the number or type of sources; however, it does require that the velocity model is close to the true subsurface velocity. We reformulate the conventional FWI algorithm based on the [Formula: see text]-norm data-misfit function by adding sparsity constraints using a sparsity promoting [Formula: see text]-norm as an additional regularization term to get more focused and less noise-sensitive event locations. The Orthant-Wise Limited-memory quasi-Newton algorithm is used to solve the optimization problem. It inherits the advantageous (fast convergence) properties of the limited memory Broyden-Fletcher-Goldfarb-Shanno method and can easily overcome the nondifferentiability of [Formula: see text]-norm at null positions. We determine the performance of the algorithm on noise-free and noisy synthetic data from the SEG/EAGE overthrust model.

Precise detection of a P‐wave in low S/N signal by using time‐frequency representations of a triaxial hodogram

Geophysics ◽  
1996 ◽  
Vol 61 (5) ◽  
pp. 1453-1466 ◽  
Author(s):  
Hirokazu Moriya ◽  
Hiroaki Niitsuma
Keyword(s):  
Signal Processing ◽  
Arrival Time ◽  
P Wave ◽  
Signal Processing Technique ◽  
Spectral Matrix ◽  
Arrival Times ◽  
Time Frequency ◽  
Microseismic Data ◽  
Test Of Hypotheses ◽  
Wave Arrival

We have developed a signal processing technique for three‐component microseismic data that allows the precise determination of P‐wave arrival times. The method is based on a time‐frequency representation of the signal that allows the evaluation of the 3-D particle motion from seismic waves in both time and frequency domains. A spectral matrix is constructed using the time‐frequency distributions. A crosscorrelation coefficient for the three‐component signal is derived through eigenvalue analysis of the spectral matrix. The P‐wave arrival time is determined through a statistical test of hypotheses using the crosscorrelation coefficient. This signal processing method is evaluated using a synthetic signal and it is compared to the local stationary autoregressive method for determining P‐wave arrival times. We also show that the proposed method is capable of determining the arrival time of a synthetic P‐wave to within 1 ms (five points in the discrete time series) in the presence of a signal‐to‐noise ratio of −5dB. The method can detect the arrival time of different frequency components of the P‐wave, which is a possibility for the evaluation of velocity dispersion of the seismic wave. We demonstrate the feasibility of the method further by applying it to microseismic data from a geothermal field.

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.

scholarly journals Detection of specific areas and densities for ultrasound tomography

2019 ◽  
pp. 121-127
Author(s):  
Victoria Erofeeva ◽  
Vasilisa Galyamina ◽  
Kseniya Gonta ◽  
Anna Leonova ◽  
Oleg Granichin ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Ultrasound Imaging ◽  
Arrival Time ◽  
Detection Accuracy ◽  
Problem Solution ◽  
Ultrasound Tomography ◽  
Arrival Times ◽  
Whole Process ◽  
Imaging Reconstruction ◽  
Tomography Data

In this paper we consider the problem of ultrasound tomography. Recently, an increased interest in ultrasound tomography has been caused by non-invasiveness of the method and increased detection accuracy (as compared to radiation tomography), and also ultrasound tomography does not put at risk human health. We study possibilities of detection of specific areas and determining their density using ultrasound tomography data. The process of image reconstruction based on ultrasound data is computationally complex and time consuming. It contains the following parts: calculation of the time-of-flight (TOF) of a signal, detection of specific areas, calculation of density of specific areas. The calculation of the arrival time of a signal is a very important part, because the errors in the calculation of quantities strongly influence the total problem solution. We offer ultrasound imaging reconstruction technology that can be easily parallelized. The whole process is described: from extracting the arrival times of signals raw data feeding from physical receivers to obtaining the desired results.

scholarly journals Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 269 ◽  
Author(s):  
Wei Zhang ◽  
Zhipeng Li ◽  
Xuyang Gao ◽  
Yanjun Li ◽  
Yibing Shi
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Arrival Time ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Information Criteria ◽  
Echo Signal ◽  
Time Frequency ◽  
Ultrasonic Echo ◽  
Ultrasonic Anemometer

The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

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