Entao Liu

Iterative interferometry-based method for picking microseismic events

Abstract Continuous microseismic monitoring of hydraulic fracturing is commonly used in many engineering, environmental, mining, and petroleum applications. Microseismic signals recorded at the surface, suffer from excessive noise that complicates first-break picking and subsequent data processing and analysis. This study presents a new first-break picking algorithm that employs concepts from seismic interferometry and time-frequency (TF) analysis. The algorithm first uses a TF plot to manually pick a reference first-break and then iterates the steps of cross-correlation, alignment, and stacking to enhance the signal-to-noise ratio of the relative first breaks. The reference first-break is subsequently used to calculate final first breaks from the relative ones. Testing on synthetic and real data sets at high levels of additive noise shows that the algorithm enhances the first-break picking considerably. Furthermore, results show that only two iterations are needed to converge to the true first breaks. Indeed, iterating more can have detrimental effects on the algorithm due to increasing correlation of random noise.

Joint seismic data denoising and interpolation with double-sparsity dictionary learning

Seismic data quality is vital to geophysical applications, so methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the dataset without introducing pseudo-Gibbs artifacts when compared to other directional multiscale transform methods such as curvelets.

Microseismic events enhancement and detection in sensor arrays using autocorrelation-based filtering

Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose an autocorrelation-based stacking method that designs a denoising filter from all the traces, as well as a multi-channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace’s autocorrelation is centred at zero in the lag domain. The effect of white noise is concentrated near zero lag; thus, the filter design requires a predictable adjustment of the zero-lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with coloured noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.

Fast online orthonormal dictionary learning for efficient full waveform inversion

Full waveform inversion (FWI) delivers high-resolution images of a subsurface medium property by minimizing itera-tively the misfit between observed and simulated seismic data, and is commonly used by the oil and gas industry for geophysical exploration. FWI is a challenging problem because seismic surveys cover ever larger areas of interest and collect massive volumes of data. The dimensionality of the problem and the heterogeneity of the medium both stress the need for faster algorithms, so sparse regularization techniques can be used to accelerate and improve imaging results. In this paper, we propose a compressive sensing method for the FWI problem by exploiting the sparsity of geological model perturbations over learned dictionaries. Based on stochastic approximations, the dictionaries are updated itera-tively to adapt changing models during FWI iterations. Meanwhile, the dictionaries are kept orthonormal in order to maintain the corresponding transform in a fast and compact manner so that these transforms do not introduce extra computational overhead to FWI. Establishing such a sparsity regu-larization on the model enables us to significantly reduce the workload by only collecting 0.625% of the field data without introducing subsampling artifacts. Hence, the computational burden of large-scale FWI problems can be greatly reduced.

An Automatic Arrival Time Picking Method Based on RANSAC Curve Fitting

Arrival time picking is useful in both active and passive seismic processing problems. Many current time picking methods suffer the problem of high false picking rate under low SNR cases. The random noise add wrong picking points that are far from the true moveout curves. In this study, we propose a new automatic arrival time picking method based on RANSAC curve fitting algorithm. Synthetic example indicates that the propose method is robust against high noise and can be used in multiple events scenario.

Full waveform microseismic inversion using differential evolution algorithm

An accurate and fast estimation of microseismic activities from passive microseismic data is a crucial issue to many oil and gas applications. Traditional methods are mainly based on manual picking of the arrival times which require a relative high signal-to-noise ratio (SNR) to produce reliable results. When the sensor array is deployed on the surface, the microseismic events have low magnitude and might be buried in strong ambient noise. A compressive sensing scheme has been introduced to implement seismic source parameters estimation, including location (i.e., hypocenter) and moment tensor (MT). Although this scheme is efficient and accurate, it entails computing the whole dictionary composed of Greens functions in advance, which brings a high computational overhead and prohibitive storage burden. In this work, we propose the differential evolution (DE) algorithm for solving the inverse problem on the fly which avoids generating and storing the whole dictionary.

Seismic Data Compression Using Online Double-Sparse Dictionary Learning Schemes

Seismic data (traces) usually demonstrate high correlation. We propose a scheme based on online dictionary learning, which explores the resemblance among local seismic traces to facilitate compression for communication. In order to alleviate the transmission overhead caused by the slow convergence of online dictionary scheme, sparse constraints and a sliding window mechanism are applied to the incremental components of the dictionaries, which significantly improve the performance of online dictionary learning scheme in the sense of communication cost.

Enhancement of Microseismic Events Using Tensor Decomposition and Time-frequency Representation

Analysis of passive microseismic data is usually a challenging task due to low signal-to-noise ratio environment. This study introduces an approach for enhancing the microseismic events using tensor decomposition and time-frequency representation. The proposed method shows promising results when applied on microseismic data set.