Microseismic full waveform modeling and location

Seismic waveforms generated by earthquakes contain valuable information about the Earth's interior. Effectively utilizing seismic waveforms is critical for understanding earthquake source mechanism, imaging subsurface structure and monitoring earthquake hazard. In contrast to large earthquakes, microearthquakes have much lower magnitudes and are difficult to detect. Recorded waveforms of microearthquakes have small amplitudes and can be easily contaminated by noise. In this thesis, I develop an automatic method to fully utilize seismic waveforms to locate earthquakes, especially microearthquakes. A seismic modeling tool which can simulate seismic wave propagation in complex media using various seismic sources is also developed to generate synthetic seismic waveforms for testing and analysis. As seismic anisotropy is common in shale and fractured rocks, I develop an efficient finite-difference full waveform modeling tool for simulating wave propagation in heterogeneous and anisotropic media. In order to model both double-couple and non-double-couple sources, an arbitrary moment tensor source is implemented in the forward modeling tool. The modeling tool can serve as an efficient Eikonal solver for the waveform migration used in source location or subsurface imaging. The modeling tool also provides an efficient way to obtain the Green's function in anisotropic media, which is the key of anisotropic moment tensor inversion and source mechanism characterization. The modeling tool can be used to generate seismic waveforms in complex, anisotropic models using various source-receiver geometries and source mechanisms. I generate and analyse synthetic datasets for vertical downhole arrays and surface arrays using this modeling tool. Due to the influence of seismic anisotropy, seismic location can have a deviation of a few hundred meters. Through analysing the synthetic seismic waveforms, I find that it is feasible to evaluate the seismic anisotropy of the subsurface and further estimate the orientation and density of potential cracks in the subsurface by examining the traveltimes and amplitudes of recorded seismic waveforms. I propose a novel waveform coherency-based method to locate earthquakes from continuous seismic data. The method can automatically detect (micro-)earthquakes and find the locations and origin times of seismic events directly from recorded seismic waveforms. By continuously calculating the coherency between waveforms from different receiver pairs or groups, this method greatly expands the available information which can be used for event location and has high imaging resolution. This method does not require phase identification and picking, which allows for a fully automated seismic location process. I have tested and compared this method to other migration-based methods (i.e. envelope, STA/LTA and kurtosis migration) in noise-free and noisy synthetic data. The tests and analysis show that the new developed method is very noise resistant and can obtain reliable location results even when the signal-to-noise ratio is below 0.1. By utilizing waveform coherency, the new method is able to suppress strong interference from other seismic sources occurring at a similar time and location and shows excellent performance in imaging weak seismic events. It can be used with arbitrary 3D velocity models and is able to obtain reasonable location results with smooth but inaccurate velocity models. Computational efficiency test shows the new method can achieve very high speedup ratio easily. This new method exhibits excellent location performance and can be easily parallelized giving it large potential to be developed as a real-time location method for very large datasets. I apply the new method to automatically locate the induced and volcano-tectonic seismicity using sparse and irregular monitoring arrays. Compared to other migration-based methods, in spite of the often sparse and irregular distribution of monitoring arrays, the new method shows better location performance and obtains more consistent location results with the catalogue obtained by manual picking. The new method successfully locates many volcano-tectonic earthquakes with local magnitude smaller that 0 beneath Uturuncu, where seismicity is triggered by the passage of surface waves caused by the M 8.8 2010 Maule earthquake. The case at Uturuncu demonstrates that this new method can be used to automatically detect and locate microseismic events in large or streaming seismic datasets, which are time consuming and difficult to manually pick. 98.25% of 114 triggered seismic events in the published catalogue have been successfully detected and located. In addition, the new location method also automatically detect and locate 322 verified additional seismic events whose magnitude is smaller than 0. Using this new location method, a more complete seismic catalogue with much lower magnitude threshold can be obtained, which can benefit further seismic analysis. The new location results at Uturuncu show that seismicity is likely deeper than previously thought, down to 7 km below the surface. The event location example at the Aquistore carbon capture and storage site shows that continuous and coherent drilling noise in industrial settings will pose great challenges for source imaging. However, automatic quality control techniques are used to automatically select high quality data, and can thus effectively reduce the effects of continuous drilling noise and improve source imaging quality. By utilizing this new location method in combination with the automatic quality control techniques, event location down to signal-to-noise ratios of 0.025 (1/40) is possible in tests. The location performance of the new method for synthetic and real datasets demonstrates that this new method can perform as a reliable and automatic seismic waveform analysis tool to locate microseismic events

Minimising the computational time of a waveform based location algorithm

Accurate and fast localisation of microseismic events is a requirement for a number of applications, e.g. mining, enhanced geothermal systems. New methods for event localisation have been proposed over the last decades. The waveform-based methods are of the most recent developed ones and their main advantage is the ability to locate weak seismic events. Despite this, these methods are demanding in terms of computational time, making real-time seismic event localisation very difficult. In this work, we further develop a waveform-based method, the Multichannel coherency migration method (MCM), to improve the computational time. The computational time for the MCM algorithm has been reported to linearly depend on several parameters, such as the number of stations, the length of the waveform time window, the computer architecture, and the volume of the area we are searching for the hypocentre. To minimise the computational time we need to decrease one or more of the above parameters without compromising the accuracy of the result. We break the localisation procedure into several steps: (1) we locate the event with a relatively large spatial grid interval which will give less potential hypocentral locations and less calculations as a result. (2) Based on the results of step (1) and the locations of maximum coherencies we decrease the grid volume to a quarter of the original volume and the spatial interval to half the original, focusing only around the area identified in step (1). Step (2) is repeated several times for decreased grid volumes and spatial intervals until the hypocentral location does not significantly change any more. We tested this approach on both synthetic and real data. We find that while the accuracy of the hypocentre is not compromised, the computational time is up to 125,000 times shorter

Data-driven methods for situation awareness and operational adjustment of sustainable energy integration into power systems

In the context of increasing complexity in power system operations due to the integration of renewable energy sources, two main challenges arise: accurate short-term wind power forecasting and power flow convergence control. Accurate wind power forecasting plays a crucial role in power system scheduling, while controlling power flow convergence is essential for system stability. This study proposes a concise short-term wind power generation prediction model that combines a feature selection-based convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) model. By effectively screening multidimensional feature datasets, the model optimizes the selection of highly correlated feature parameters and assigns weights to input data based on feature correlation. The CNN-BiLSTM combination model is then employed to establish a predictive model for wind power generation based on multiple features. Additionally, this study introduces an automatic adjustment model for power flow convergence using the D3QN (Double Dueling Q Network) reinforcement learning algorithm. This addresses the challenge of power imbalance leading to flow non-convergence, enabling effective control of power flow convergence and adaptive adjustment of operating modes. Experiments conducted using the KDD Cup 2022 wind power prediction dataset validate the wind power prediction method. The results demonstrate that the CNN-BiLSTM model effectively utilizes time-series data, surpassing other neural networks in prediction accuracy. Simulation results based on the PYPOWER case39 standard case reveal that the reinforcement learning model’s reward value increases with training rounds and stabilizes at 40. Remarkably, more than 72% of abnormal flow samples achieve rapid convergence within 10 steps, affirming the proposed method's efficacy and computational efficiency. The findings of this study contribute to enhancing the accurate awareness of new energy integration into power systems and provide a novel adaptive control method for power flow

Janus monolayers of transition metal dichalcogenides.

Structural symmetry-breaking plays a crucial role in determining the electronic band structures of two-dimensional materials. Tremendous efforts have been devoted to breaking the in-plane symmetry of graphene with electric fields on AB-stacked bilayers or stacked van der Waals heterostructures. In contrast, transition metal dichalcogenide monolayers are semiconductors with intrinsic in-plane asymmetry, leading to direct electronic bandgaps, distinctive optical properties and great potential in optoelectronics. Apart from their in-plane inversion asymmetry, an additional degree of freedom allowing spin manipulation can be induced by breaking the out-of-plane mirror symmetry with external electric fields or, as theoretically proposed, with an asymmetric out-of-plane structural configuration. Here, we report a synthetic strategy to grow Janus monolayers of transition metal dichalcogenides breaking the out-of-plane structural symmetry. In particular, based on a MoS2 monolayer, we fully replace the top-layer S with Se atoms. We confirm the Janus structure of MoSSe directly by means of scanning transmission electron microscopy and energy-dependent X-ray photoelectron spectroscopy, and prove the existence of vertical dipoles by second harmonic generation and piezoresponse force microscopy measurements

Microseismic Full Waveform Modeling in Anisotropic Media with Moment Tensor Implementation

Seismic anisotropy which is common in shale and fractured rocks will cause travel-time and amplitude discrepancy in different propagation directions. For microseismic monitoring which is often implemented in shale or fractured rocks, seismic anisotropy needs to be carefully accounted for in source location and mechanism determination. We have developed an efficient finite-difference full waveform modeling tool with an arbitrary moment tensor source. The modeling tool is suitable for simulating wave propagation in anisotropic media for microseismic monitoring. As both dislocation and non-double-couple source are often observed in microseismic monitoring, an arbitrary moment tensor source is implemented in our forward modeling tool. The increments of shear stress are equally distributed on the staggered grid to implement an accurate and symmetric moment tensor source. Our modeling tool provides an efficient way to obtain the Green’s function in anisotropic media, which is the key of anisotropic moment tensor inversion and source mechanism characterization in microseismic monitoring. In our research, wavefields in anisotropic media have been carefully simulated and analyzed in both surface array and downhole array. The variation characteristics of travel-time and amplitude of direct P- and S-wave in vertical transverse isotropic media and horizontal transverse isotropic media are distinct, thus providing a feasible way to distinguish and identify the anisotropic type of the subsurface. Analyzing the travel-times and amplitudes of the microseismic data is a feasible way to estimate the orientation and density of the induced cracks in hydraulic fracturing. Our anisotropic modeling tool can be used to generate and analyze microseismic full wavefield with full moment tensor source in anisotropic media, which can help promote the anisotropic interpretation and inversion of field data

An active learning framework for microseismic event detection

Induced microseismic monitoring has gained increased interest recently, to support various subsurface activities, including geothermal exploration and oil and gas production. To accurately detect and locate origins of microseismisity, deep learning-based methods have become popular due to their high accuracy when trained on large well-labelled datasets. However, though a huge amount of publicly available seismic measurements is available, laballed data to train models is very scarce, since labelling is time consuming and requires very specialist knowledge. Building on our prior work on active learning for time-series data, we propose an active learning method that cleverly picks only a small number of samples to query and stops when the proposed stopping criterion is met. We demonstrate that the proposed approach can save up to 83% of labelling effort even when transferred to a well with different sensing equipment from those used to build the training set

Opportunities and challenges for renewable energy policy in China

Renewable energy is the inevitable choice for sustainable economic growth, for the harmonious coexistence of human and environment as well as for the sustainable development. Government support is the key and initial power for developing renewable energy. In this article, an overall review has been conducted on renewable energy development policy (including laws and regulations, economic encouragement, technical research and development, industrialized support and government model projects, etc.) in China. On this basis, a systematic analysis has been conducted on the disadvantages of renewable energy development policy. On the point of long-term effective system for renewable energy development, a series of policy advice has been offered, such as strengthening the policy coordination, enhancing regional policy innovation, echoing with clean development mechanism, implementing process management, constructing market investment and financing system. It is expected that the above advices could be helpful to ever-improvement of renewable energy development policy.Renewable energy Resources Energy policy China

Controllable Preparation of Highly Crystalline Sulfur-Doped Π-Conjugated Polyimide Hollow Nanoshell for Enhanced Photocatalytic Performance

In this study, a series of highly crystalline π-conjugated polyimide photocatalysts with porous nano hollow shell (HSPI) was prepared for the first time by the hard template method by adjusting the addition ratio of the template precursor. SiO2 nanospheres not only serve as template agents but also as dispersants to make precursors of SPI more uniform, and the degree of polymerization will be better, resulting in significantly enhanced crystallinity of HSPI relative to bulk SPI (BSPI). More strikingly, it is found that HSPI has a larger specific surface area, stronger visible light absorption, and higher separation efficiency of photogenerated electron and hole pairs compared with BSPI by various spectral means characterization analysis. These favorable factors significantly enhanced the photocatalytic degradation of methyl orange (MO) by HSPI. This work provides a promising approach for the preparation of cheap, efficient, environmentally friendly, and sustainable photocatalysts

mRNA vaccine-a desirable therapeutic strategy for surmounting COVID-19 pandemic

As an acute respiratory infectious disease, COVID-19 threatens the safety of global public health. Given the current lack of specific treatment against this disease, research and development of vaccines have become sharp weapons for overcoming the pandemic. mRNA vaccines have become the lead in COVID-19 vaccination strategies due to their advantages, such as rapid industrial production and efficacy. A total of 137 COVID-19 vaccines have entered the clinical trial stage, among which 23 are mRNA vaccines, accounting for 17% of the total vaccines. Herein, we summarize the research and developmental processes of mRNA vaccines as well as the approach for protecting the human body against infection. Focusing on the latest clinical trial data of two COVID-19 mRNA vaccines from Pfizer and Modena, we discuss their effectiveness and safety. Finally, we analyze the challenges and problems that mRNA vaccines face in controlling the COVID-19 pandemic