A new signal processing method for acoustic emission/microseismic data analysis

The acoustic emission/microseismic technique (AE/MS) has emerged as one of the most important techniques in recent decades and has found wide applications in different fields. Extraction of seismic event with precise timing is the first step and also the foundation for processing AE/MS signals. However, this process remains a challenging task for most AE/MS applications. The process has generally been performed by human analysts. However, manual processing is time consuming and subjective. These challenges continue to provide motivation for the search for new and innovative ways to improve the signal processing needs of the AE/MS technique. This research has developed a highly efficient method to resolve the problems of background noise and outburst activities characteristic of AE/MS data to enhance the picking of P-phase onset time. The method is a hybrid technique, comprising the characteristic function (CF), high order statistics, stationary discrete wavelet transform (SDWT), and a phase association theory. The performance of the algorithm has been evaluated with data from a coal mine and a 3-D concrete pile laboratory experiment. The accuracy of picking was found to be highly dependent on the choice of wavelet function, the decomposition scale, CF, and window size. The performance of the algorithm has been compared with that of a human expert and the following pickers: the short-term average to long-term average (STA/LTA), the Baer and Kradolfer, the modified energy ratio, and the short-term to long-term kurtosis. The results show that the proposed method has better picking accuracy (84% to 78% based on data from a coal mine) than the STA/LTA. The introduction of the phase association theory and the SDWT method in this research provided a novelty, which has not been seen in any of the previous algorithms --Abstract, page iii

Applications of pattern classification to time-domain signals

Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%

Seismic Applications of Interactive Computational Methods

Effective interactive computing methods are needed in a number of specific areas of geophysical interpretation, even though the basic algorithms have been established. One approach to raise the quality of interpretation is to promote better interaction between human and the computer. The thesis is concerned with improving this dialog in three areas: automatic event picking, data visualization and sparse data imaging. Fully automatic seismic event picking methods work well in relatively good conditions. They collapse when the signal-to-noise ratio is low and the structure of the subsurface is complex. The interactive seismic event picking system described here blends the interpreter's guidance and judgment into the computer program, as it can bring the user into the loop to make subjective decisions when the picking problem is complicated. Several interactive approaches for 2-D event picking and 3-D horizon tracking have been developed. Envelope (or amplitude) threshold detection for first break picking is based on the assumption that the power of the signal is larger than that of the noise. Correlation and instantaneous phase pickers are designed for and better suited to picking other arrivals. The former is based on the cross-correlation function, and a model trace (or model traces) selected by the interpreter is needed. The instantaneous phase picker is designed to track spatial variations in the instantaneous phase of the analytic form of the arrival. The picking options implemented into the software package SeisWin were tested on real data drawn from many sources, such as full waveform sonic borehole logs, seismic reflection surveys and borehole radar profiles, as well as seven of the most recent 3-D seismic surveys conducted over Australian coal mines. The results show that the interactive picking system in SeisWin is efficient and tolerant. The 3-D horizon tracking method developed especially attracts industrial users. The visualization of data is also a part of the study, as picking accuracy, and indeed the whole of seismic interpretation depends largely on the quality of the final display. The display is often the only window through which an interpreter can see the earth's substructures. Display is a non-linear operation. Adjustments made to meet display deficiencies such as automatic gain control (AGC) have an important and yet ill-documented effect on the performance of pattern recognition operators, both human and computational. AGC is usually implemented in one dimension. Some of the tools in wide spread use for two dimensional image processing which are of great value in the local gain control of conventional seismic sections such as edge detectors, histogram equalisers, high-pass filters, shaded relief are discussed. Examples are presented to show the relative effectiveness of various display options. Conventional migration requires dense arrays with uniform coverage and uniform illumination of targets. There are, however, many instances in which these ideals can not be approached. Event migration and common tangent plane stacking procedures were developed especially for sparse data sets as a part of the research effort underlying this thesis. Picked-event migration migrates the line between any two points on different traces on the time section to the base map. The interplay between the space and time domain gives the interpreter an immediate view of mapping. Tangent plane migration maps the reflector by accumulating the energy from any two possible reflecting points along the common tangent lines on the space plane. These methods have been applied to both seismic and borehole-radar data and satisfactory results have been achieved

Seismic Applications of Interactive Computational Methods

Effective interactive computing methods are needed in a number of specific areas of geophysical interpretation, even though the basic algorithms have been established. One approach to raise the quality of interpretation is to promote better interaction between human and the computer. The thesis is concerned with improving this dialog in three areas: automatic event picking, data visualization and sparse data imaging. Fully automatic seismic event picking methods work well in relatively good conditions. They collapse when the signal-to-noise ratio is low and the structure of the subsurface is complex. The interactive seismic event picking system described here blends the interpreter's guidance and judgment into the computer program, as it can bring the user into the loop to make subjective decisions when the picking problem is complicated. Several interactive approaches for 2-D event picking and 3-D horizon tracking have been developed. Envelope (or amplitude) threshold detection for first break picking is based on the assumption that the power of the signal is larger than that of the noise. Correlation and instantaneous phase pickers are designed for and better suited to picking other arrivals. The former is based on the cross-correlation function, and a model trace (or model traces) selected by the interpreter is needed. The instantaneous phase picker is designed to track spatial variations in the instantaneous phase of the analytic form of the arrival. The picking options implemented into the software package SeisWin were tested on real data drawn from many sources, such as full waveform sonic borehole logs, seismic reflection surveys and borehole radar profiles, as well as seven of the most recent 3-D seismic surveys conducted over Australian coal mines. The results show that the interactive picking system in SeisWin is efficient and tolerant. The 3-D horizon tracking method developed especially attracts industrial users. The visualization of data is also a part of the study, as picking accuracy, and indeed the whole of seismic interpretation depends largely on the quality of the final display. The display is often the only window through which an interpreter can see the earth's substructures. Display is a non-linear operation. Adjustments made to meet display deficiencies such as automatic gain control (AGC) have an important and yet ill-documented effect on the performance of pattern recognition operators, both human and computational. AGC is usually implemented in one dimension. Some of the tools in wide spread use for two dimensional image processing which are of great value in the local gain control of conventional seismic sections such as edge detectors, histogram equalisers, high-pass filters, shaded relief are discussed. Examples are presented to show the relative effectiveness of various display options. Conventional migration requires dense arrays with uniform coverage and uniform illumination of targets. There are, however, many instances in which these ideals can not be approached. Event migration and common tangent plane stacking procedures were developed especially for sparse data sets as a part of the research effort underlying this thesis. Picked-event migration migrates the line between any two points on different traces on the time section to the base map. The interplay between the space and time domain gives the interpreter an immediate view of mapping. Tangent plane migration maps the reflector by accumulating the energy from any two possible reflecting points along the common tangent lines on the space plane. These methods have been applied to both seismic and borehole-radar data and satisfactory results have been achieved

Elastic wave measurement in rock engineering

Imperial Users onl

Crosshole seismic processing of physical model and coal measures data

Crosshole seismic techniques can be used to gain a large amount of information about the properties of the rock mass between two or more boreholes. The bulk of this thesis is concerned with two crosshole seismic processing techniques and their application to real data. The first part of this thesis describes the application of traveltime and amplitude tomographic processing in the monitoring of a simulated EOR project. Two physical models were made, designed to simulate 'pre-flood' and 'post-flood' stages in an EOR project. The results of the tomography work indicate that it is beneficial to perform amplitude tomographic processing of cross-well data, as a complement to traveltime inversion, because of the different response of velocity and absorption to changes in liquid/gas saturations for real reservoir rocks. The velocity tomograms image the flood zone quite accurately. Amplitude tomography shows the flood zone as an area of higher absorption but does not image its boundaries as precisely, because multi-pathing and diffraction effects are not accounted for by the ray-based techniques used. Part two is concerned with the crosshole seismic reflection technique, using data acquired from a site in northern England. The processing of these data is complex and includes deconvolution, wavefield separation and migration to a depth section. The two surveys fail to pin-point accurately the position of a large fault; the disappointing results, compared to earlier work in Yorkshire, are attributed to poorer generation of compressional body waves in harder Coal Measures strata. The final part of this thesis describes the results from a pilot seismic reflection test over the Tertiary igneous centre on the Isle of Skye, Scotland. The results indicate that the base of a large granite body consists of interlayered granites and basic rocks between 2.1 and 2.4km below mean sea level

Effects of mining subsidence observed by time-lapse seismic reflection profiling

Extracting coal from underground mineworkings causes the overlying rocks to subside with associated changes in the stress regime. The aim of the study reported here was to apply the surface seismic reflection method to study the effect of subsidence on seismic velocity. Two sets of time-lapse surveys were carried out over two longwall mining panels in the Selby Coalfield. Seismic lines were profiled parallel and perpendicular to adjacent panels H45 and H46, respectively. A total of twenty-one repeated surveys were carried out along the two lines over a period of three years. The effect monitored was due to mining in the Bamsley Seam, at 550 m depth. As mining progressed, the traveltime of a strong reflection event from an anhydrite bed at 150 m depth was measured after processing the data with standard techniques. An overall increase in traveltime of about 4 % was observed. The progressive increase in traveltime over panel H45 correlated well with empirical calculations of differential subsidence between the surface and the anhydrite. However, the magnitude of the change must principally be accounted for by a decrease in seismic velocity, associated with a reduction in the vertical effective stress. Although the traveltime over panel H46 was also found to increase, and to correlate quite well with die expected differential subsidence, the agreement was less good along this transverse profile. This is attributed to asymmetric subsidence effects because the ground on the SW side of the panel had already been worked by panel H45, but the ground on the NE side was unworked. At the time of each seismic survey across panel H46, the profile was also levelled, and it was found that surface subsidence values along the profile increased towards panel H45. As most of the subsidence caused by mining panel H45 would have been completed by the time the H46 profile was surveyed, the effect must be at least partly attributed to asymmetric subsidence due to panel H46. Where the ground had been weakened by subsidence due to mining H45, near-total subsidence from mining H46 took place rapidly; but in the previously unworked ground on the NE side of panel H46, the residual subsidence was presumably delayed by competent strata in the overburden. Further work is needed to confirm whether this explanation is correct

Temporal and spatial analysis of near fault stations in terms of impulsive behavior

Increasing number of seismic stations located in close proximity to active faults allows analysis of seismic signals that are recorded in near fault regions. Unique seismic signals, called \u201cimpulsive\u201d or \u201cpulse shaped\u201d signals, are captured in velocity waveforms in numerous large magnitude earthquakes. In such waveforms, the earthquake is recorded as a one or several long period high amplitude signals. Long period signals are important in engineering seismology due to their large loads on structures. Ground motion prediction equations and design codes fail to capture the amplitudes in long periods of the impulsive signals. In this thesis nature of impulsive signals and their spatial distribution in near fault regions are investigated. To do that two different algorithm are developed in order to distinguish impulsive signals from non-impulsive signals. Moreover, the probability of the pulse shaped signal occurrence is estimated. In order to investigate the impulsive signals, near fault records from major crustal earthquakes are merged into a dataset. It contains waveforms that are coming from well known seismogenic zones. Waveforms in the dataset are also analyzed by implementing several previous studies to make comparison. The first pulse shaped signal classification algorithm is developed using wavelet analysis. Wavelet analysis decomposes the signal into time-frequency domain which provides the energy variation with time and frequency. The wavelet power spectrum of velocity waveforms are analyzed by using Ricker and Morlet wavelets. A threshold of minimum amplitude is applied. A comparison is made between the total energy of a signal and the energy of the time incidence where peak ground velocity is measured. Furthermore time incidence where maximum spectral energy is located in time is also taken into consideration. Energy ratios are used for determination of impulsive signals. It is found that a Ricker wavelet explains the features of the impulsive part of the velocity waveforms more accurately than the Morlet wavelet. It can measure the period of the pulse and the phase shift of the impulsive parts of the waveform. Spectral features of the impulsive signals are also captured successfully using a Ricker wavelet. The second classification algorithm uses convolutional neural networks. In order to train the convolutional neural networks, synthetic impulsive signals are created. A model is developed using real non-impulsive velocity waveforms from the dataset and synthetic impulsive waveforms. Impulsive signals are manually labeled as impulsive or non-impulsive. The trained model is run on the real manually picked impulsive signals of the dataset and the performance of the convolutional neural network, the wavelet method and various previously published methods are benchmarked. The convolutional neural networks approach correctly identifies almost 97% of the impulsive signals. Accuracy rate of the model is superior than other models. In order to understand the probability of the impulsive signals on earthquakes, a multi-variate Bayes classifier method is implemented on the dataset. Various information about the fault, earthquake and station are analyzed and 3 parameters that are correlated with the impulsive signals are used for the probability calculations. Probability models are developed for normal, reverse and strike slip faults. The validity of this model is tested on the data set. Developed models can provide pulse probability distributions without requiring earthquake-specific parameters. A relation between the period of the pulses and the moment magnitude is also developed