Improvements in mining induced microseismic source locations at the Lucky Friday mine using an automated whole-waveform analysis system

For years, severe rockburst problems at the Lucky Friday mine in northern Idaho have been a persistent safety hazard and an impediment to production. An MP250 based microseismic monitoring system, which uses simple voltage threshold picking of first arrivals, has been used in this mine since 1973 to provide source locations and energy estimates of seismic events. Recently, interest has been expressed in developing a whole waveform microseismic monitoring system for the mine to provide more accurate source locations and information about source characteristics. For this study, we have developed a prototype whole-waveform microseismic monitoring system based on a 80386 computer equipped with a 50 kHz analog-digital convertor board. The software developed includes a data collection program, a data analysis program, and an event detection program. Whole-waveform data collected and analyzed using this system during a three-day test have been employed to investigate sources of error in the hypocenter location process and to develop an automatic phase picker appropriate for microseismic events.Comparison of hypocenter estimates produced by the MP250 system to those produced by the whole-waveform system shows that significant timing errors are common in the MP250 system and that these errors caused a large part of the scatter evident in the daily activity plots produced at the mine. Simulations and analysis of blast data show that analytical control over the solutions is strongly influenced by the array geometry. Within the geophone array, large errors in the velocity model or moderate timing errors may result in small changes in the solution, but outside the array, the solution is very sensitive to small changes in the data.Our whole-waveform detection program picks event onset times and determines event durations by analysis of a segmented envelope function (SEF) derived from the microseismic signal. The detection program has been tested by comparing its arrival time picks to those generated by human analysis of the data set. The program picked 87% of the channels that were picked by hand with a standard error of 0.75 milliseconds. Source locations calculated using times provided by our entire waveform detection program were similar to those calculated using hand-picked arrival times. In particular, they show far less scatter than source locations calculated using arrival times based on simple voltage threshold picking of first arrivals.     

基于波形相似特征的微地震事件初至拾取及全局校正

微地震事件初至拾取是井下微地震监测数据处理的关键步骤之一.初至误差的存在会使微地震震源定位结果产生较大偏差,进而影响后续的压裂裂缝解释.通常初至拾取过程对所有的微地震事件选择相同的特征函数并采用一致的拾取参数进行统一处理,然而当事件的能量、震源机制、传播路径以及背景噪声等存在明显差异时,所得初至拾取结果差别显著.为了提高微地震事件初至拾取标准一致性,本文提出基于波形相似特征的初至拾取及全局校正方法.该方法首先利用互相关函数对每个事件内的各道记录进行时差校正,得到初始初至信息并形成叠加道,再对所有事件的叠加道进行全局互相关得到事件间初至相对校正量,最终初至结果可以通过各个事件的初始初至信息与其相对校正量相加得到.方法将所有微地震事件初至结果作为一个整体处理,从而能够克服常规方法初至拾取标准一致性差的缺陷.实际资料处理结果表明,相比于常规方法,该方法可以有效提高事件初至拾取和定位结果的一致性.     

基于优化参数的地震P、S波震相到时自动拾取及质量评估

准确拾取P、S波震相到时是深入开展地震波研究工作的基础,本文改进了自动拾取参数优化函数算法和质量评估方案,引入了拾取到时优化方案,使用基于参数优化的频带-带宽拾取算法、AICD拾取算法和峰度拾取算法对腾冲地区7个宽频带地震台站记录的地震资料开展了地震P、S波到时自动拾取,对拾取结果进行了优化和质量判定.结果表明：经参数优化、拾取优化后,采用3种方法自动拾取的P、S波到时与人工拾取到时的时差在0.1 s内的记录占比分别达到74.66%、70.98%.这些参数值均优于算法改进前的同类参数,证明了优化方法的可靠性.     

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.     

基于时频稀疏性分析法的低信噪比微震事件识别与恢复

微震监测是直观评价压裂过程和压裂效果的有效手段.微震事件识别是微震监测的首要步骤.然而对于低信噪比微震监测数据,常规识别方法很难取得满意效果.基于微震事件在时频域中的稀疏性,本文提出利用Renyi熵值表示微震监测数据的时频稀疏程度,并以时频距离为约束条件,建立以低熵值的道数为判别阈值的目标函数.本文方法能在识别出微震事件的同时,恢复出较为清晰的微震事件.通过数值计算和对实际监测数据的测试,表明该方法对低信噪比的微震监测数据有较好的处理效果.     

Using Automated, High-precision Repicking to Improve Delineation of Microseismic Structures at the Soultz Geothermal Reservoir

—?An automatic, adaptive, correlation-based algorithm for adjusting phase picks in large digital seismic data sets provides significant improvement in resolution of microseismic structures using only a small fraction of the time and manpower which would be required to re-analyze waveforms manually or semi-automatically. We apply this technique to induced seismicity at the Soultz-sous-Forêts geothermal site, France. The method is first applied to a small, previously manually repicked subset of the catalogue so that we may compare our results to those obtained from painstaking, visual, cross-correlation-based techniques. Relative centroid-adjusted hypocenters show a decrease in median mislocation from 31 to 7?m for preliminary and automatically adjusted picks, respectively, compared to the manual results. Narrow, intersecting joint features not observed in the preliminary hypocenter cloud, but revealed through manual repicking, are also recovered using the automatic method. We then address a larger catalogue of ～7000 microearthquakes. After relocating the events using automatic repicks, the percentage of events clustering within 5?m of their nearest neighbor increases form 5 to 26% of the catalogue. Hypocenter relocations delineate narrow, linear features previously obscured within the seismic cloud, interpreted as faults or fractures which may correspond to fluid propagation paths, or to changes in stress as a result of elevated pore pressures. RMS travel-time residuals for the larger data set are reduced by only 0.2%; however, phase-pick biases in the preliminary catalogue have influenced both the velocity model and station correction calculations, which will affect location residuals. These pick biases are apparent on the adjusted, stacked waveforms and correcting them will be important prior to future velocity model refinements.     

基于深度学习卷积神经网络的地震波形自动分类与识别

发展高效、高精度、普适性强的自动波形拾取算法在地震大数据时代背景下显得越来越重要.波形自动拾取算法的主要挑战来自如何适应不同区域的不同类型地震事件的分类与筛选.本文针对地震事件-噪音分类这一问题,使用13839个汶川地震余震事件建立数据集,应用深度学习卷积神经网络(CNN)方法进行训练,并用8900个新的汶川余震事件作为检测数据集,其训练和检测准确率均达到95%以上.在对连续波形的检测中,CNN方法在精度和召回率上优于STA/LTA和Fbpicker传统方法,并能找出大量人工挑选极易遗漏的微震事件.最后,我们应用训练好的最优模型对选自全国台网的441个台站8天的连续波形数据进行了识别、到时挑取及与参考地震目录关联,CNN检出7016段波形,用自动挑选算法拾取到1380对P,S到时,并与540个地震目录事件成功关联,对1级以上事件总体识别准确率为54%,二级以上为80%,证明了CNN模型具有泛化能力,初步展示了CNN在发展兼具效率、精度、普适性算法,实时地震监测等应用上具有巨大潜力.     

Earthquake detection in the Jiangsu region,China using graphics-processing-unit-based Match & Locate and rapid earthquake association and location*

Earthquake detection and location are essential in earthquake studies, which generally consists of two main classes: waveform-based and pick-based methods. To evaluate the ability of two different methods, a graphics-processing-unit-based Match & Locate (GPU-M&L) method and a rapid earthquake association and location (REAL) method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for comparison. GPU-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time residuals. Twenty-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&L. The number of newly detected and located events is about 2.8 times more than those listed in the local catalog. We both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a short-term/long-term average (STA/LTA) trigger algorithm for seismic phase detection and picking by applying the REAL. We then refine seismic locations using a least-squares location method (VELEST) and a high-precision relative location method (hypoDD). By applying STA/LTA and PhaseNet, 1006 and 1893 events are associated and located, respectively. The newly detected events are mainly clustered and show steeply dipping fault planes. By analyzing the performance of these methods based on long-term continuous seismic data, the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8, respectively, which are smaller than 2.6 given by the local catalog. Although REAL provides improvement compared with GPU-M&L, REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio (SNR). Stations at southeast of the study region with low SNR may lead to few detections in the same area.     

Automatic picking of P and S phases using a neural tree

The large amount of digital data recorded by permanent and temporary seismic networks makes automatic analysis of seismograms and automatic wave onset time picking schemes of great importance for timely and accurate event locations. We propose a fast and efficient P- and S-wave onset time, automatic detection method based on neural networks. The neural networks adopted here are particular neural trees, called IUANT2, characterized by a high generalization capability. Comparison between neural network automatic onset picking and standard, manual methods, shows that the technique presented here is generally robust and that it is capable to correctly identify phase-types while providing estimates of their accuracies. In addition, the automatic post processing method applied here can remove the ambiguity deriving from the incorrect association of events occurring closely in time. We have tested the methodology against standard STA/LTA phase picks and found that this neural approach performs better especially for low signal-to-noise ratios. We adopt the recall, precision and accuracy estimators to appraise objectively the results and compare them with those obtained with other methodologies.Tests of the proposed method are presented for 342 earthquakes recorded by 23 different stations (about 5000 traces). Our results show that the distribution of the differences between manual and automatic picking has a standard deviation of 0.064 s and 0.11 s for the P and the S waves, respectively. Our results show also that the number of false alarms deriving from incorrect detection is small and, thus, that the method is inherently robust.This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to Journal of Seismology.     

微地震事件初至拾取SLPEA算法

微地震事件初至拾取是微地震数据处理的关键步骤之一.实际微地震监测资料中存在大量低信噪比事件,而传统方法对这些事件的应用效果并不理想.为了克服传统方法抗噪性弱的缺点,本文通过综合地震信号与环境噪声在振幅、偏振以及统计特征等方面的存在的差异,设计了一种针对低信噪比微地震事件的初至拾取方法——SLPEA算法.为了检验本文方法的可行性和有效性,分别对模型数据和实际资料进行了处理,并将处理结果与传统方法及手工拾取的结果进行了对比.分析表明,利用本文方法得到的初至到时与手工拾取结果的绝对误差平均值仅为1.33×10~(-3)s,小于3个采样点;方差为3.21×10~(-6)s~2;初至到时在手工拾取结果±0.005s误差范围内的个数占总数的95.8%.这些参数值均优于传统方法的同类参数,证明了本文方法的可靠性.     

2D PP/PS-stereotomography: Application to a real 2D-OBC dataset

It has been shown on an ‘ideal’ synthetic dataset that PP/PS‐stereotomography can estimate an accurate velocity model without any pairing of PP‐ and PS‐events. The P‐wave velocity model is first estimated using PP data and then, fixing this velocity field, the S‐wave velocity is estimated using the PS data. This method needed to be evaluated further and we present here the first application of PP/PS‐stereotomography to a real dataset: the 2D East‐West Mahogany OBC line (Gulf of Mexico). We are here confronted with data which do not fit our working assumptions: coherent noise (due to an approximate separation of PP‐ and PS‐events and some remaining multiples), probably some anisotropy and 3D effects. With a careful selection of the stereotomographic picks, which allows one to decrease the effect of the picked coherent noise by the automatic picker, our application can demonstrate the relevance of our approach in the upper part of the profile, where anisotropy and 3D effects might be low. We can thus estimate, without any pairing of PP‐ and PS‐events, a velocity field which provides not only flat common image gathers, but also PP‐ and PS‐depth migrated images located at the same positions. For the deeper part of the profile, a significant shift in depth appears. In addition to possible anisotropy, 3D effects and a more complex velocity field (‘salt body’), this is due to the quality of the PZ‐ and X‐components profiles: The PZ‐component profile where the PP‐stereotomographic picking is performed, is polluted by conflicting converted or multiple events and the X‐component profile, where the PS‐stereotomographic picking is performed, is highly noisy. This study emphasizes the need to develop accurate selection criteria for the stereotomographic picks.     

Application of Kalman Filtering Techniques for Microseismic Event Detection

—?Microseismic monitoring systems are generally installed in areas of induced seismicity caused by human activity. Induced seismicity results from changes in the state of stress which may occur as a result of excavation within the rock mass in mining (i.e., rockbursts), and changes in hydrostatic pressures and rock temperatures (e.g., during fluid injection or extraction) in oil exploitation, dam construction or fluid disposal. Microseismic monitoring systems determine event locations and important source parameters such as attenuation, seismic moment, source radius, static stress drop, peak particle velocity and seismic energy. An essential part of the operation of a microseismic monitoring system is the reliable detection of microseismic events. In the absence of reliable, automated picking techniques, operators rely upon manual picking. This is time-consuming, costly and, in the presence of background noise, very prone to error. The techniques described in this paper not only permit the reliable identification of events in cluttered signal environments they have also enabled the authors to develop reliable automated event picking procedures. This opens the way to use microseismic monitoring as a cost-effective production/operations procedure. It has been the experience of the authors that in certain noisy environments, the seismic monitoring system may trigger on and subsequently acquire substantial quantities of erroneous data, due to the high energy content of the ambient noise. Digital filtering techniques need to be applied on the microseismic data so that the ambient noise is removed and event detection simplified. The monitoring of seismic acoustic emissions is a continuous, real-time process and it is desirable to implement digital filters which can also be designed in the time domain and in real-time such as the Kalman Filter. This paper presents a real-time Kalman Filter which removes the statistically describable background noise from the recorded seismic traces.     

Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes

Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network (CNN) and recurrent neural network (RNN). The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90.7% in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92.6% and an error rate lower than 2%. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.     

一种改进的基于网格搜索的微地震震源定位方法

震源定位是微地震监测技术要解决的主要问题.目前,井下微地震监测多采用走时拟合法计算震源位置.常规方法受到环境噪声、初至拾取误差、速度模型误差等因素的影响,定位结果存在一定误差.为了提高定位精度,本文提出了一种改进的基于网格搜索的微地震震源定位方法.本文方法根据P波的偏振特征参数计算概率密度函数求取震源方位角,并采用改进的目标函数和搜索算法计算震源的径向距离和深度.模型数据和实际资料的处理结果表明,本文方法具有较强的抗噪性,计算得到的震源方位角更加接近真实值;与常规目标函数相比,本文方法采用的目标函数具有更好的收敛性,其定位结果受初至拾取误差和速度模型误差的影响更小;本文提出的搜索算法能够消除由于错误拾取造成的观测到时中的异常值对定位结果的影响.     

First-break picking for microseismic data based on cascading use of Shearlet and Stockwell transforms

First-break picking of microseismic data is a significant step in microseismic monitoring. There is a great error in conventional first-break picking methods based on time domain analysis in low signal to noise ratio. S-transform may provide a novel approach, it can extract the time–frequency features of the signal and reduce the picking error because of its high time–frequency resolution and good time–frequency clustering; however, the S-transform is not well suited for microseismic data with high noise. For applications to array data where the weak signal has spatial coherency as well as some distinct temporal characteristics, we propose to combine the shearlet transform with a time–frequency transform. In the proposed method, the shearlet transform is used to capture spatial coherency features of the signal. The information of the signal and noise in shearlet domain is represented by shearlet coefficients. We use the correlation of signal coefficients at adjacent fine scales to give prominence to signal features to accurately discriminate the signal from noise. The prominent signal coefficients make the signal better gathered in time–frequency spectrum of the S-transform. Finally, we can get reliable and accurate first breaks based on the change of energy. The performance of the proposed method was tested on synthetic and field microseismic data. The experimental results indicated that our method is outstanding in terms of both picking precision and adaptability to noise.     

Reliability of the automatic procedures for locating earthquakes in southwestern Alps and northern Apennines (Italy)

Reliable automatic procedure for locating earthquake in quasi-real time is strongly needed for seismic warning system, earthquake preparedness, and producing shaking maps. The reliability of an automatic location algorithm is influenced by several factors such as errors in picking seismic phases, network geometry, and velocity model uncertainties. The main purpose of this work is to investigate the performances of different automatic procedures to choose the most suitable one to be applied for the quasi-real-time earthquake locations in northwestern Italy. The reliability of two automatic-picking algorithms (one based on the Characteristic Function (CF) analysis, CF picker, and the other one based on the Akaike’s information criterion (AIC), AIC picker) and two location methods (“Hypoellipse” and “NonLinLoc” codes) is analysed by comparing the automatically determined hypocentral coordinates with reference ones. Reference locations are computed by the “Hypoellipse” code considering manually revised data and tested using quarry blasts. The comparison is made on a dataset composed by 575 seismic events for the period 2000–2007 as recorded by the Regional Seismic network of Northwestern Italy. For P phases, similar results, in terms of both amount of detected picks and magnitude of travel time differences with respect to manual picks, are obtained applying the AIC and the CF picker; on the contrary, for S phases, the AIC picker seems to provide a significant greater number of readings than the CF picker. Furthermore, the “NonLinLoc” software (applied to a 3D velocity model) is proved to be more reliable than the “Hypoellipse” code (applied to layered 1D velocity models), leading to more reliable automatic locations also when outliers (wrong picks) are present.     

地震检测与震相自动拾取研究

针对微震事件易受噪声干扰等特点,本文将STA/LTA方法和基于方差的AIC方法（var-AIC）相结合,在震相到时初步拾取的基础上,使用台站的德洛内（Delaunay）三角剖分及台站间最大走时差约束来减少噪声干扰的影响. 利用到时进行地震定位之后,根据台站预测到时,在设定的时间窗内对地震震相进行更精细的分析. 特别是针对微震事件信噪比低的特点,设计了基于偏振分析的拾取函数,根据窗内STA/LTA方法和var-AIC方法的拾取结果自动选择合适的值作为震相到时. 最后,对西昌流动地震台阵2013年304个单事件波形数据的分析处理和检验结果表明,本文方法较传统方法具有更高的地震事件检测能力和更高的震相拾取精度.      

Application of microseismic multiplet analysis to the Basel geothermal reservoir stimulation events

The stimulation of a geothermal well in Basel, Switzerland produced a distribution of microseismic event locations with an overall alignment in the direction of the maximum horizontal stress. Fault plane solutions of individual larger events indicated movements on fracture planes at an angle to the maximum horizontal stress that could not be reliably interpreted from the event locations. To obtain higher resolution images of the microseismic event locations, events with similar waveforms have been identified by multiplet analysis. A number of receivers were used in the multiplet processing to ensure each multiplet is represented by a unique group of waveforms. The location accuracy within each multiplet has been significantly improved using cross‐correlation to refine the shear‐wave traveltime picks. The distribution of events within each multiplet can be interpreted as being due to movements on a single fracture or a number of near parallel fractures. It is shown that whilst the overall distribution of events is around the direction of the maximum horizontal stress, the individual multiplets representing fracture planes have a variety of azimuths and dips.     

Source-independent wave-equation based microseismic source location using traveltime inversion

A new source location method using wave-equation based traveltime inversion is proposed to locate microseismic events accurately. With a sourceindependent strategy, microseismic events can be located independently regardless of the accuracy of the source signature and the origin time. The traveltime-residuals-based misfit function has robust performance when the velocity model is inaccurate. The new Fréchet derivatives of the misfit function with respect to source location are derived directly based on the acoustic wave equation, accounting for the influence of geometrical perturbation and spatial velocity variation. Unlike the mostly used traveltime inversion methods, no traveltime picking or ray tracing is needed.Additionally, the improved scattering-integral method is applied to reduce the computational cost. Numerical tests show the validity of the proposed method.     

基于深度学习的地震检测模型在区域台网的泛化性研究

将识别地震的深度学习算法PhaseNet应用于四川台网和首都圈台网,对该模型的泛化能力进行了测试和评估.首先利用2010年1月至2018年10月首都圈台网199个地震台站记录的29 328个事件(ML0～ML4)所对应的126761段事件波形,以及2019年4-9月四川及邻省部分台网227个地震台站记录的16595个事...