Seismic attenuation is an important reservoir characteristic, as it is highly sensitive to changes in rock pore fluids. The sensitivity of attenuation makes it a valuable parameter for monitoring reservoir dynamics. This paper presents an advanced method for quality factor (Q) estimation and attenuation difference (AD) estimation, which builds upon the frequency-independent centroid frequency shift (FiCFS) and divergence-based CFS methods. In contrast to conventionalQ-value estimation techniques, our approach provides frequency independence, enhanced noise immunity and heightened sensitivity to minor attenuation variations. This method addresses the issues associated with centroid frequency discrepancies and simplifies the calculations by eliminating the necessity for traveltime differences. The superiority of the noise immunity and amplification effect of AD estimation is demonstrated by synthetic data experiments, which also facilitate easier monitoring of reservoir changes. The efficacy of this method is validated through the CO2injection monitoring at the Frio II site, which demonstrates its ability to detect fluid changes within the reservoir. The in-depth analysis of variance, divergence, and centroid frequency difference reveals the direct impact of these disparate methodologies and substantiates the sublimation of the novel approach.

The focal mechanism provides seismological constraints on the geological faults that generate the earthquakes and thus is important for regional seismotectonic research. Focal mechanism calculation based on the P-wave first-motion-polarity is a widely used method, particularly helpful for small to moderate-size earthquakes. However, determining the P-wave first-motion polarity can be challenging and subjective for smaller earthquakes. Here, we propose a deep-learning method (EQpolarity) for determining the P-wave first-motion polarity using the vertical-component seismic waveforms. The proposed deep-learning method was trained using a large-scale dataset from South California and then adapted to the Texas earthquake data via a transfer learning method. The original and secondary models obtained 95.43% and 98.82% accuracy on the Texas database, respectively, indicating the effectiveness of transfer learning. We further apply the deep learning method to thousands of events on the TexNet catalog to determine the focal mechanisms. Most of the focal mechanism solutions align well with the strikes, dips, and rakes of the known faults that were explored previously using full-waveform-based methods. The generation of the large focal mechanism database offers significant insights into the seismotectonic status of West Texas. The open-source package of EQpolarity can be accessed at https://github.com/chenyk1990/eqpolarity.

Carbon capture and storage (CCS) is critical to mitigating carbon dioxide (CO2) emissions, with geological formations serving as safe reservoirs for CO2 storage. Rigorous monitoring protocols are essential to track subsurface CO2 distribution and potential leakage to ensure the effectiveness and safety of carbon sequestration initiatives. Active seismic monitoring has emerged as a powerful tool for this purpose, offering precision in confirming CO2 sequestration and identifying potential leakage pathways. Seismic attenuation, which indicates fluid changes within reservoirs, has attracted attention due to its sensitivity to reservoir fluid changes. This study presents a novel divergence-based centroid frequency shift (CFS) method for monitoring attenuation changes induced by CO2 injection. The method, derived without assumptions beyond a constant Q model, exhibits frequency independence, enhancing sensitivity to frequency components and facilitating accurate observation of their response to fluid composition changes. Validity and feasibility are demonstrated through application to the Frio II CO2 injection project, which reveals distinct frequency-dependent attenuation changes. Lower frequencies show increased sensitivity to CO2 injection, outperforming conventional monitoring methods that rely on velocity and travel time variations. This method offers increased sensitivity to CO2 injection and provides critical frequency-based features for monitoring CO2 transport and detecting potential leaks.

Signal attenuation estimation is a critical task in signal processing and is essential for analyzing media characteristics and compensating for energy loss. Current centroid frequency shift-based methods for estimating attenuation are mostly based on the assumptions of full-band analysis and frequency-dependent or independent quality factor (Q). In this paper, we propose a novel frequency-independent centroid frequency shift (FiCFS) method for signal attenuation estimation with higher adaptability. It is based on arbitrary frequency bands instead of the full-band spectrum defined by the conventional centroid frequency shift (CFS) method, and accordingly, the derivation is performed by incomplete gamma functions instead of ordinary gamma functions. Through rigorous mathematical derivations, the first moment (centroid frequency) and the second moment (variance) are proved to be frequency insensitive for arbitrary frequency bands, and then the arbitrary frequency band-based CFS method, i.e., the FiCFS method, is derived with the frequency-weighted exponential spectrum assumption. The matching ability of the frequency-weighted exponential spectrum to other signal spectra is verified, demonstrating the method’s adaptability to most attenuated signals. Experimental results using synthetic and field data sets demonstrate that the proposed method is adaptive, noise-immune, and reliable.

In this work, a Robust Worst-Case (RWC) estimation is presented for recovering sparse reflectivity series from uniformly quantized seismic signals. First, theOrthogonal Matching Pursuit(OMP) algorithm is applied on a quantized seismic trace. A set of conservative estimates of the reflectivity impulses and a dimension-reduced system are accordingly obtained. Second, the error induced by the quantization is modeled as a systemic uncertainty in a multiplication way. This modeling imposes a greater model uncertainty on the estimated reflectivity impulses with small values and vice versa. Finally, a RWC deconvolution scheme is designed for the dimension-reduced system. Among those roughly estimated impulses from the OMP algorithm, the ones statistically less affected by the quantization process are assigned with higher confidence weights and the ones statistically with higher magnitudes of quantization error are assigned with lower confidence weights. By rescaling the quantization error, the proposed scheme significantly increases the robustness of the solution to the quantization error and hence better improves the visual saliency of seismic signals than that of the OMP algorithm. This scheme is tested on both synthetic and real seismic data and the performance is evaluated by compared to that of the OMP algorithm. The results shows that the new scheme significantly outperforms the OMP algorithm by observing the following two aspects: first, the falsely or overly estimated impulses are significantly suppressed in the RWC estimation compared with that of the OMP algorithm, and second, the RWC estimation exhibits enhanced robustness to the change of the quantization interval.

In this work, we develop a novel abrupt-jump detection algorithm for seismic signal deconvolution. This new method significantly suppresses the effect of side lobes. It begins with transforming a seismic trace to a series of innovations with a Kalman filter and then estimates the likelihood ratios of the reflectivity impulses from the innovations. Secondly, it modifies the likelihood ratios by imposing an additional punishment on its asymmetry. Therefore, the likelihood ratios induced by the side lobes are highly suppressed. Hence the reflectivity impulses recovered from the modified likelihood ratios are less effected by the side lobes, leading to significantly enhanced resolution. The efficacy of the proposed method is numerically validated on a synthetic and a field dataset. The experimental results show that the proposed scheme is efficient and practical in enhancing signal quality of seismograms than the original likelihood-ratio-based abrupt-jump detection.

This article proposes an innovative scheme for recovering sparse reflectivity series from uniformly quantized seismic signals. In this scheme, the statistically less affected impulses by the quantization error are assigned higher weights than the ones with a larger error. First, the orthogonal matching pursuit (OMP) algorithm is applied on a quantized seismic trace to obtain a set of conservative estimates of the reflectivity impulses. Second, the quantization error is formulated as a systematic uncertainty within a neighborhood of the obtained conservative estimates from the OMP. Finally, a robust worst case (RWC) deconvolution method is developed to recover an improved estimate of the reflectivity impulses. The proposed scheme significantly increases the robustness and enhances the recovered reflectivity impulses obtained by the OMP algorithm. This is substantiated by experiments on both synthetic and real seismic data. Specifically, the falsely or overly estimated impulses are significantly suppressed, and the robustness to the change of the quantization interval is enhanced.