Estimation Of Source Parameters Research Articles

Source term estimation in the unsteady flow with dynamic mode decomposition

When estimating source parameters in the unsteady flow, the flow information of pollution dispersion is indispensable. It is common practice to save the flow information in the computer in advance but it requires large storage space. Besides, when contaminants are released after a time period of the flow field saved before, calculating the flow field by Computational Fluid Dynamics (CFD) model demands massive computational cost. Dynamic Mode Decomposition (DMD) is thereby proposed to solve the problems mentioned above. Firstly, the fields are decomposed by DMD. Then, the simulated concentrations are acquired by the adjoint equation based on the field synthesized by DMD. Finally, the measured concentrations and the simulated concentrations are taken into Bayesian inference to accomplish source term estimation (STE). The results show that the estimated results with high accuracy are obtained both in the reconstruction stage and in the prediction stage when using the fields obtained by DMD. Also, the efficiency of predicting the future flow by DMD is much higher than that by CFD simulation, suggesting that DMD can improve the efficiency of STE in some cases. As DMD uses a small number of dominant modes to synthesize the approximate fields with minor errors, it reduces the storage demand of flow information in STE. The sampling range and sampling resolution should be properly selected to ensure the accuracy of STE.

Scaling and Depth Variability of Source Parameters in Central and Southern Italy Using Regional Attenuation Models

ABSTRACT We study the scaling between seismic moment and corner frequency, and the spatial variability of the stress drop, in central and southern Italy. We analyze a data set generated by 28,943 earthquakes that occurred between 2005 and 2023 recorded by 1045 temporary or permanent stations. Considering the trade-off between source and propagation effects, we develop a spectral decomposition approach in which several attenuation models are derived for different subregions, and we perform an iterative decomposition to propagate the site amplification constraint to the whole data set. The use of multiple attenuation models has a significant impact on the source parameter estimation, resulting in stress-drop ratios on the order of a factor of 10 compared to values obtained without considering the attenuation differences. Once the attenuation variability is taken into account, the scaling between seismic moment and corner frequency shows different slopes for different subregions, with different degrees of self-similarity breakdown. Furthermore, we observe a clear trend in the slopes with depth for several subregions. Finally, the spatial variability of the stress-drop anomalies with respect to the average values expected for a given seismic moment shows a large lateral variability, which makes it difficult to detect increasing stress-drop trends with depth.

Source term estimation of a time-varying source around a building based on Bayesian inference and unsteady adjoint equations

In actual pollutant dispersion accidents, the location of the source is typically concealed and the intensity of the source varies with time. It is important to accurately estimate source parameters based on limited sensor data. However, previous studies were based on the assumption of stabilized sources and concentration fields, and ignored the process of sensor concentration changes over time, which affects the accuracy of the estimation. Therefore, this study applied a source term estimation (STE) method which combines the Bayesian inference method with unsteady adjoint equations to a time-varying source around building. The influences of the release forms, locations, and heights of the source were analyzed from the flow field and transient stage perspectives. We found that the estimation of the time-varying source performed worse than that of the constant source assumed in existing studies. The uncertainty of the estimated results increased with the complexity of the release forms of the source. In particular, the estimation of the location and strength of the period source had a wider probability distribution, higher uncertainty, and was more susceptible to changes in source location and height. The results showed that for time-varying sources, the estimated results fluctuated strongly over time with the pre-developmental and stabilization phases, and it was critical to estimate the source term based on sensor data at various time points.

Detector configuration optimization method considering response time for spatial source parameter estimation in complex urban wind environment

In source parameter estimation, improper arrangement of detectors may lead to ineffective concentration measurement or even failure in leakage detection, which would hold back emergency response efforts. However, current methods for optimizing detector configuration have inherent flaws and thus cannot be ideally applied in real-world scenarios. To address their limitations, this paper develops the method for optimizing detector configuration by considering response time. A novel cost function is proposed to balance response time and estimation accuracy based on specific demands. The optimum configuration is then obtained by genetic algorithm iteration. The wind tunnel test of an urban environment with buildings of varying heights is conducted for the research purpose. The superiority of the proposed method is demonstrated by comparing spatial source parameter estimations using optimum, uniform, and random configurations respectively. The results show that our method avoids placing detectors at locations with low concentrations, i.e., places where readings are invalid. It enables more detectors to obtain readings quickly, generating more accurate estimations in a shorter time. Only with the optimum configuration do detectors show readings for all 15 sources, with each unknown source having the highest number of detectors with readings. In contrast, the uniform configuration results in no readings for 4 sources and the random configuration results in no readings for 11 sources when leaking happens. The proposed method optimizes the detector configuration in three-dimensional space with complex flows, making it more feasible in practical applications.

Crucial time of emergency monitoring for reliable numerical pollution source identification

The Pollution source identification (PSI) is an important issue on river water quality management especially for urban receiving water. Numerical inversion method is theoretically an effective PSI technique, which employs monitored downstream pollutant breakthrough curves to identify the pollution source. In practice, it is important to know how much monitoring data should be accumulated to provide PSI results with acceptable accuracy and uncertainty. However, no literature reports on this key point and it seriously handers the numerical PSI technology to mature practical applications. To seek a monitoring guideline for PSI, we conducted extensively numerical experiments for single-point source instantaneous release taking Bayesian-MCMC method as the baseline inversion technique. The crucial time (Tc) phenomenon was found during the data accumulation process for Bayesian source inversion. After Tc, estimated source parameters subsequent sustained low error levels and uncertainty convergence. Results shown the presence of Tc impacted by the number and location of monitoring sections, while monitoring frequency and data error do not. Under different river hydrodynamic conditions, relative crucial time (Λ) is determined by the river's Peclet number, and minimum effective Λ was controlled by dispersion coefficient (Dx). Analytic spatial structure of Λ(U, Dx) was uncovered and this relationship successfully explained by the information entropy theory. Based on these findings, a novel design method of PSI emergency monitoring network for preparedness plan and a practical framework of PSI for emergency response were established. These findings fill the important knowledge gap in PSI applications and the guidelines provide valuable references for river water quality management.

Characterization and Evolution of Seismic Sequences in the Normal Fault Environment of the Southern Apennines

AbstractThe use of seismic catalogs enhanced through advanced detection techniques improves the understanding of earthquake processes by illuminating the geometry and mechanics of fault systems. In this study, we performed accurate hypocentral locations, source parameters estimation and stress release modeling from catalogs of microseismic sequences nucleating in the complex normal fault system of the Southern Apennines (Italy). The application of advanced location techniques resulted in the relocation of ∼30% of the earthquakes in the enhanced catalogs, with hypocenters clearly identifying local patches on kilometer‐scale structures that feature consistent orientation with the main faults of the area. When mapping the stress change on the fault plane, the inter‐event distance compared to the size of the events suggests that the dominant triggering mechanism within the sequences is static stress transfer. The distribution of events is not isotropic but dominantly aligned along the dip direction. These slip‐dominated lineations could be associated with striations related to fault roughness and could map the boundary between locked and creeping domains in Apulian platform and basement.

Directivity analysis and source parameter estimation: insights from the 2014 Arzew earthquake, Mw3.9, northwestern Algeria

Directivity analysis and source parameter estimation: insights from the 2014 Arzew earthquake, Mw3.9, northwestern Algeria

IMUPF-BIN: A new autonomous search method for radioactive sources

To solve the problem of mobile robots autonomously searching for unknown radioactive sources, this paper proposes an automatic source search strategy, abbreviated as IMUPF-BIN: the location of the radioactive source is estimated by improved unscented particle filtering (IMUPF) algorithm, and the search path is predicted and planned by bioinspired neural network (BIN) model. First, the search environment is expressed as an occupied grid map, and the BIN model is combined with the grid occupied map to obtain the environment information in the search process. Then, the unscented particle filter (UPF) which is used for source parameter estimation is improved by introducing the idea of linear optimization to improve the efficiency of particle usage and the diversity of particles. Further, the BIN model is introduced to reduce the influence of obstacles on the movement of the robot, thereby improving the search accuracy and efficiency of radioactive source. After the robot detects the radiation count value, the parameter information of the source is estimated through the IMUPF algorithm. By using the activity value of the BIN model and distributed model predictive control (DMPC) to plan the path, while continuously iterating the search process, the location of the radioactive source is finally determined. The simulation results show that in without obstacles environment, the localization accuracy of the IMUPF algorithm is improved by 85.6% and 52.8% compared to the standard particle filtering (PF) and UPF algorithm respectively, and the search time of the IMUPF-BIN algorithm is reduced by 75.9% compared to the gradient search (GS) algorithm. In obstacles environment, the localization accuracy of the IMUPF algorithm is improved by 80.8% and 35.9% compared to the PF and UPF algorithms respectively. In addition, the GS algorithm fails to search for the radioactive source due to the shield of obstacles, while the IMUPF-BIN algorithm can successfully search for radioactive source in a shorter search time.

A UK MW catalogue derived from coda envelopes

Summary The United Kingdom (UK) experiences low-to-moderate levels of seismicity; only 12 onshore earthquakes with local magnitude (ML) ≥ 4.0 have been recorded in the past 20 years. It is therefore difficult to estimate moment magnitude (MW) using conventional moment tensor inversion for the majority of UK seismicity, resulting in limited reliable estimates of MW. To address this, we calibrated coda envelopes at 16 broadband seismic stations distributed across the UK, to produce a MW catalogue for 100 events with MW≥2.13 that occurred since 2006. This was achieved using the open-source Coda Calibration Tool, which requires independent source parameter estimates for calibration. For 13 UK events between 2006 and 2022, we used spectral modelling to estimate apparent stress (0.32 to 1.74 MPa), and moment tensor inversion to estimate MW (3.35 to 4.52). These independent source parameters formed a subset of the inputs into the final calibration, which used seismic data from 33 events with coda-derived values of 2.57$\le $Mw$\le $4.49. The resultant coda calibration parameters were applied to 67 further events (MW≥2.13). The coda envelopes exhibit slow seismic coda decay across the UK, with significant energy up to 20 Hz, consistent with other regions of low tectonic activity. This MW catalogue, and the application of the calibration to future UK seismic events, will be useful for both assessing seismic hazard and event characterisation.

A Reciprocity‐Based Efficient Method for Improved Source Parameter Estimation of Submarine Earthquakes With Hybrid 3‐D Teleseismic Green's Functions

AbstractAccurate source parameters of global submarine earthquakes are essential for understanding earthquake mechanics and tectonic dynamics. Previous studies have demonstrated that teleseismic P coda waveform complexities due to near‐source 3‐D structures are highly sensitive to source parameters of marine earthquakes. Leveraging these sensitivities, we can improve the accuracy of source parameter inversion compared to traditional 1‐D methods. However, modeling these intricate 3‐D effects poses significant computational challenges. To address this issue, we propose a novel reciprocity‐based hybrid method for computing 3‐D teleseismic Green's functions. Based on this method, we develop a grid‐search inversion workflow for determining reliable source parameters of moderate‐sized submarine earthquakes. The method is tested and proven on five Mw5+ earthquakes at the Blanco oceanic transform fault (OTF) with ground truth locations resolved by a local ocean bottom seismometer array, using ambient noise correlation and surface‐wave relocation techniques. Our results show that fitting P coda waveforms through 3‐D Green's functions can effectively improve the source location accuracy, especially for the centroid depth. Our improved centroid depths indicate that all the five Mw5+ earthquakes on the Blanco transform fault ruptured mainly above the depth of 600°C isotherm predicted by the half‐space cooling model. This finding aligns with the hypothesis that the rupture zone of large earthquakes at OTFs is confined by the 600°C isotherm. However, it is noted that the Blanco transform fault serves as a case study. Our 3‐D source inversion method offers a promising tool for systematically investigating global oceanic earthquakes using teleseismic waves.

Estimation of source parameters of local earthquakes originated near Idukki Reservoir, Kerala

Estimation of source parameters of local earthquakes originated near Idukki Reservoir, Kerala

Estimation of Source and Spectral Decay Parameters for Local Earthquakes in Siang Region of Arunachal Himalaya and Its Implication to the Tectonics and Crustal Heterogeneity

Estimation of Source and Spectral Decay Parameters for Local Earthquakes in Siang Region of Arunachal Himalaya and Its Implication to the Tectonics and Crustal Heterogeneity

Detector configuration optimization based on wind tunnel tests using normalized adjoint concentration gradient for urban spatial source parameters estimation

When hazardous gas leaks occur, accurate and timely identification of the leakage location and rate is crucial to ensure environmental quality and public safety. However, obtaining accurate source parameter estimations becomes challenging if detectors are not appropriately arranged, regardless of estimation algorithms used. Therefore, this paper proposes a method for detector configuration optimization in three-dimensional space. Based on the flow field and with clear physical meaning, this method defines source parameters sensitive areas through the normalized gradient magnitude of the adjoint concentration field. Genetic algorithm is utilized to search for the detector combination that maximizes source parameters sensitive areas, i.e., the optimum configuration. Two types of urban environmental wind tunnel tests are designed as research objects. The rationality of obtained optimum configurations is analyzed combined with the flow field characteristics. Using optimum configurations increase estimation accuracy by 186% (S4 in urban type Ⅰ) and 398% (S5 in urban type Ⅱ) compared to the uniform and random configurations, respectively. Additionally, variation patterns of estimation accuracies with different numbers of optimum detectors are analyzed to determine the proper numbers of detectors. For urban type Ⅰ, 20 detectors are ideal, but 10 is sufficient due to space and cost limitations. For urban type Ⅱ, 22 detectors are recommended, while 12 is also decent for estimation. Although some factors may affect the estimations, the proposed method exhibits high robustness; moreover, the research scenario resembles what happens in reality, indicating its strong potential for practical application.

Real‐Time Fault Tracking and Ground Motion Prediction for Large Earthquakes With HR‐GNSS and Deep Learning

AbstractEarthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real‐time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR‐GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine‐Learning Assessed Rapid Geodetic Earthquake model (M‐LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M‐LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real‐time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.

Estimation of Site Effects and Equivalent Source Parameters of Wenchuan Earthquake Based on Generalized Chaotic Particle Inversion Technique

Abstract Based on the advantages of the chaos particle swarm optimization algorithm and the generalized inversion technology, this article estimates the source parameters and site effects of the Wenchuan earthquake. We used 440 sets of strong-motion records obtained from 43 aftershocks, and the area covered by the records was divided into subregions A and B. Initial separation of source, path, and site from the seismic spectra of subregions A and B using generalized inversion technique and then the source-site optimization model is established using chaotic particle swarm technology. From path-corrected records, we obtained absolute site effects for 33 stations and equivalent source parameters for 43 earthquakes. We made the following conclusions: (1) The moment magnitude Mw was lower than the local magnitude MLdetermined by China Earthquake Network Center. The self-similarity of the Wenchuan earthquake was confirmed. The stress drop averaged 2.31 MPa, and it was independent of the magnitude size and focal depth. (2) In the frequency 1–10 Hz, the quality factor values in subregions A and B are 110.9f0.6 and 116.1f1.2. The decay rate of the crustal medium in the western region of the west Sichuan plateau is significant compared to the eastern part. (3) Bedrock stations 51MXT and L2007 have site effects within a certain frequency. The effect of slope topography on site predominant frequency is not apparent, and the site effects increase with the increase in elevation. The shape of the site amplification curve is more similar in the middle- and low-frequency bands, and different attenuation phenomena will appear in the high-frequency band.

Particle Filtering for Source Depth and Water Depth Joint Tracking in Shallow Water

Environmental mismatch degrades the performance of source localization and tracking methods in shallow water. One solution is to estimate source parameters and the key environmental parameters simultaneously from the acoustic data. In this paper, an unconventional approach of joint tracking source depth and water depth parameters by a particle filter is proposed. This approach is free of prior environmental knowledge and numerical calculation of any forward model. First, a state-space model based on modal nature behavior is established driving the shallow-water propagation, instead of modeling in time or space, as was done previous works. Subsequently, particle filtering is employed for joint tracking, in which the evolution with mode-order of vertical wavenumbers and the relationship between state parameters and beam-wavenumber outputs transformed from the data are exploited. Final, the particle smoother reduces the uncertainty of state parameters at initial steps, and improves the overall tracking accuracy. Our approach is demonstrated using simulated data in an ideal waveguide and applied to shallow-water SWellEx-96 experimental data to substantiate its superior performance.

Two-sided fault parameter assessment through the utilization of a particle swarm optimizer to different gravity horizontal gradients-orders with application to various cases studies

Metaheuristic methods are increasingly being implemented to source parameter estimation of gravity anomalies. These approaches have become popular in the geophysical community because of their superior advantages. We emphasize the application of the particle swarm optimizer (PSO), which is motivated by the birds’ behaviors, to elucidate gravity anomalies. Besides, using different horizontal derivative orders for the observed data is valuable in reducing the regional field effect. The current inversion algorithm applied to other synthetic models (a two-sided dipping fault with a third-order regional, a two-sided dipping fault model interfered by a spherical structure model with and without 10% noise, and two neighboring two-sided dipping faults models with and without 10% noise) as well as two real-world cases from the United States and Tunisia. The usefulness of applying these techniques together was demonstrated by providing stable results in executing the buried source parameters and eradicating the regional field effect. Therefore, we recommend the application of these techniques in the model parameter estimation studies performed with potential field anomalies due to mineralized zones.

Quantifying Site Effects and Their Influence on Earthquake Source Parameter Estimations Using a Dense Array in Oklahoma

AbstractWe investigate the effects of site response on source parameter estimates using earthquakes recorded by the LArge‐n Seismic Survey in Oklahoma (LASSO). While it is well known that near‐surface unconsolidated sediments can cause an apparent breakdown of earthquake self‐similarity, the influence of laterally varying site conditions remains unclear. We analyze site conditions across the 1825‐station array on a river plain within an area of 40 km by 23 km using vertical ground motions from 14 regional earthquakes. While the source radiation pattern controls P‐wave ground motions below 8 Hz, the surface geology correlates with P‐wave ground motions above 8 Hz and S‐wave ground motions at 2–21 Hz. Stations installed in alluvial sediments have vertical ground motions that can exceed three times the array median. We use the variation of ground motion of regional earthquakes across the array as a proxy for site effects. The corner frequencies and stress drops of local earthquakes (ML = 0.01–3) estimated using a standard single‐spectra approach show negative correlations with the site‐effect proxy, while the seismic moments show positive correlations. In contrast, the spectral‐ratio approach effectively shows no correlation. The overall bias is small as expected for this relatively homogeneous structure; accurate estimation of site‐related biases requires at least 30 stations. Correcting for site‐related biases reduces the standard deviations of the source parameters by less than 13% of the total variations. Remaining variations are partially associated with source directivity and model misfits— as small earthquakes can have complex ruptures.

Predictive Models for Seismic Source Parameters Based on Machine Learning and General Orthogonal Regression Approaches

ABSTRACT Two sets of predictive models are developed based on the machine learning (ML) and general orthogonal regression (GOR) approaches for predicting the seismic source parameters including rupture width, rupture length, rupture area, and two slip parameters (i.e., the average and maximum slips of rupture surface). The predictive models are developed based on a compiled catalog consisting of 1190 sets of estimated source parameters. First, the Light Gradient Boosting Machine (LightGBM), which is a gradient boosting framework that uses tree-based learning algorithms, is utilized to develop the ML-based predictive models by employing five predictor variables consisting of moment magnitude (Mw), hypocenter depth, dip angle, fault-type, and subduction indicators. It is found that the developed ML-based models exhibit good performance in terms of predictive efficiency and generalization. Second, multiple source-scaling models are developed for predicting the source parameters based on the GOR approach, in which each functional form has one predictor variable only, that is, Mw. The performance of the GOR-based models is compared with existing source-scaling relationships. Both sets of the models developed are applicable in estimating the five source parameters in earthquake engineering-related applications.

Effects of calibration uncertainties on the detection and parameter estimation of isotropic gravitational-wave backgrounds

Gravitational-wave backgrounds are expected to arise from the superposition of gravitational wave signals from a large number of unresolved sources and also from the stochastic processes that occurred in the Early universe. So far, we have not detected any gravitational wave background, but with the improvements in the detectors' sensitivities, such detection is expected in the near future. The detection and inferences we draw from the search for a gravitational-wave background will depend on the source model, the type of search pipeline used, and the data generation in the gravitational-wave detectors. In this work, we focus on the effect of the data generation process, specifically the calibration of the detectors' digital output into strain data used by the search pipelines. Using the calibration model of the current LIGO detectors as an example, we show that for power-law source models and calibration uncertainties $\lesssim 10 \%$, the detection of isotropic gravitational wave background is not significantly affected. We also show that the source parameter estimation and upper limits calculations get biased. For calibration uncertainties of $\lesssim 5 \%$, the biases are not significant ($\lesssim 2 \%$), but for larger calibration uncertainties, they might become significant, especially when trying to differentiate between different models of isotropic gravitational-wave backgrounds.