High temporal resolution monitoring of small variations in crustal strain by dense seismic arrays

Abstract We demonstrate the feasibility of detecting very weak deformation in the shallow crust with high temporal resolution by monitoring the relative changes in seismic wave velocity (dv/v) using dense arrays of seismometers. We show that the dv/v variations are consistent between independent measurements from two seismic arrays. Dominant peaks in the observed dv/v spectrum suggest that tides and temperature changes are the major causes of daily and sub-daily velocity changes, in accordance with theoretical strain modeling. Our analysis illustrates that dv/v perturbations of the order of 10-4, corresponding to crustal strain changes of the order of 10-8, can be measured from ambient seismic noise with a temporal resolution of one hour. This represents a low-cost technique for high precision and high time-resolution monitoring of crustal deformation that is complementary to existing geodetic measurements and is instrumental in both the detection and understanding of low-amplitude precursory processes of natural catastrophic events.Peer reviewe

Monitoring and Imaging Seismic Velocity Changes Across Temporal and Spatial Scales

Relative changes in seismic velocity (dv/v) are associated with changes in mechanical properties of crustal materials, and can reflect the perturbations in stress fields, rock damage, and fluid content. Interferometry of seismic ambient noise enables the continuous monitoring of dv/v. In this thesis, I first explore the sensitivity and resolution limits of noise-based monitoring of dv/v. By employing dense seismic arrays at La Réunion Island, I demonstrate the feasibility of using noise-based interferometry to detect tidally-induced deformation (volumetric strain ∼ 10-8, dv/v ∼ 10-4) with ∼ hourly time resolution. I further extend the applications of noise-based monitoring by not only detecting the temporal changes but also imaging the spatial variations of dv/v. Based on the spacetime dv/v observations, I investigate groundwater fluctuations in the Coastal Los Angeles Basins during 2000–2020. The spatial imaging of dv/v reveals pronounced seasonal variability in confined aquifers. The spatial patterns of dv/v are consistent with surface deformation inferred from InSAR but also constrain aquifers and their hydrology at different depths. Moreover, I propose a method for measuring seismic travel-time shifts based on wavelet cross-spectrum analysis. This new method provides stable time-shift measurements with optimal time-frequency joint resolution that can enhance the high-resolution spatial imaging of dv/v. This dissertation advances the techniques and applications of temporal monitoring as well as spatial imaging of dv/v via seismic interferometry. Monitoring dv/v in time and space is shown to be a promising tool, which can be used in concert with other geophysical observations, to identify and decipher dynamic processes in the crust.Ph.D

Space-time monitoring of groundwater fluctuations with passive seismic interferometry

International audienceHistoric levels of drought, globally, call for sustainable freshwater management. Under pressing demand is a refined understanding of the structures and dynamics of groundwater systems. Here we present an unconventional, cost-effective approach to aquifer monitoring using seismograph arrays. Employing advanced seismic interferometry techniques, we calculate the space-time evolution of relative changes in seismic velocity, as a measure of hydrological properties. During 2000–2020 in basins near Los Angeles, seismic velocity variations match groundwater tables measured in wells and surface deformations inferred from satellite sensing, but the seismological approach adds temporal and depth resolutions for deep structures and processes. Maps of long-term seismic velocity changes reveal distinct patterns (decline or recovery) of groundwater storage in basins that are adjacent but adjudicated to water districts conducting different pumping practices. This pilot application bridges the gap between seismology and hydrology, and shows the promise of leveraging seismometers worldwide to provide 4D characterizations of groundwater and other near-surface systems

Space-time monitoring of groundwater fluctuations with passive seismic interferometry

International audienceHistoric levels of drought, globally, call for sustainable freshwater management. Under pressing demand is a refined understanding of the structures and dynamics of groundwater systems. Here we present an unconventional, cost-effective approach to aquifer monitoring using seismograph arrays. Employing advanced seismic interferometry techniques, we calculate the space-time evolution of relative changes in seismic velocity, as a measure of hydrological properties. During 2000–2020 in basins near Los Angeles, seismic velocity variations match groundwater tables measured in wells and surface deformations inferred from satellite sensing, but the seismological approach adds temporal and depth resolutions for deep structures and processes. Maps of long-term seismic velocity changes reveal distinct patterns (decline or recovery) of groundwater storage in basins that are adjacent but adjudicated to water districts conducting different pumping practices. This pilot application bridges the gap between seismology and hydrology, and shows the promise of leveraging seismometers worldwide to provide 4D characterizations of groundwater and other near-surface systems

On the measurement of seismic travel-time changes in the time-frequency domain with wavelet cross-spectrum analysis

International audienceThe spatial distribution of temporal variations in seismic wavespeed is key to understanding the sources and physical mechanisms of various geophysical processes. The imaging of wavespeed changes requires accurate measurements of traveltime delays with both high lapse-time and frequency resolutions. However, traditional methods for time-shift estimation suffer from their limited resolutions. In this paper we propose a new approach, the wavelet method, to measure the traveltime changes in the time-frequency domain. This method is based on wavelet cross-spectrum analysis, and can provide optimal time-frequency joint resolution while being computationally efficient. It can deal not only with coda but also dispersive surface waves even in the presence of cycle skipping. Using synthetic coda, we show that the wavelet method can retrieve traveltime shifts more stably and accurately than traditional methods. An application at Salton Sea Geothermal Field indicates that the wavelet method is less affected by spectral smearing and better discriminates dv/v variations at different frequencies. Furthermore, upon investigations on synthetic coda, we illustrate that the bias on dv/v measurements due to changes in source frequency content is likely to be negligible, either with traditional methods or with the new wavelet method. The wavelet method sheds lights on applications of seismic interferometry that aim to locate changes in space

Crashworthiness analysis of novel cactus-inspired multi-cell structures under axial crushing

This study introduces an innovative cactus-inspired bionic tube (CBT) designed for enhanced energy absorption, drawing inspiration from the ribbed structure of cacti. Validation is achieved through quasi-static crushing experiments, confirming the numerical model's accuracy. Numerical simulations investigate critical factors, including structural mass, wall thickness, loading velocity, and cross-sectional configuration, revealing that proper cross-section design can boost the specific energy absorption (SEA) of the original CBT by 15.84 %. Additionally, a theoretical model is developed to forecast the mean crushing force of CBTs. Comparative analysis demonstrates the superior crashworthiness of CBTs over contemporary bionic and widely adopted multicell structures with the same mass, achieving a remarkable SEA of 11.44 J/g—exceeding maximum and minimum SEA values of these structures by 28.3 % and 127.9 %, respectively. This research significantly contributes to advancing the development of high-performance bionic energy-absorbing structures for crash applications

Uncovering the Molecular Mechanism of Actions between Pharmaceuticals and Proteins on the AD Network

<div><p>This study begins with constructing the mini metabolic networks (MMNs) of beta amyloid (Aβ) and acetylcholine (ACh) which stimulate the Alzheimer’s Disease (AD). Then we generate the AD network by incorporating MMNs of Aβ and ACh, and other MMNs of stimuli of AD. The panel of proteins contains 49 enzymes/receptors on the AD network which have the 3D-structure in PDB. The panel of drugs is formed by 5 AD drugs and 5 AD nutraceutical drugs, and 20 non-AD drugs. All of these complexes formed by these 30 drugs and 49 proteins are transformed into dyadic arrays. Utilizing the prior knowledge learned from the drug panel, we propose a statistical classification (dry-lab). According to the wet-lab for the complex of amiloride and insulin degrading enzyme, and the complex of amiloride and neutral endopeptidase, we are confident that this dry-lab is reliable. As the consequences of the dry-lab, we discover many interesting implications. Especially, we show that possible causes of Tacrine, donepezil, galantamine and huperzine A cannot improve the level of ACh which is against to their original design purpose but they still prevent AD to be worse as Aβ deposition appeared. On the other hand, we recommend Miglitol and Atenolol as the safe and potent drugs to improve the level of ACh before Aβ deposition appearing. Moreover, some nutrients such as NADH and Vitamin E should be controlled because they may harm health if being used in wrong way and wrong time. Anyway, the insights shown in this study are valuable to be developed further.</p></div

Estimation of Soil Cations Based on Visible and Near-Infrared Spectroscopy and Machine Learning

Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate and efficient acquisition of information on soil cation content is of great importance for the monitoring of soil quality and pollution prevention. At present, few scholars focus on soil exchangeable cations using remote sensing technology. This study proposes a new method for estimating soil cation content using hyperspectral data. In particular, we introduce Boruta and successive projection (SPA) algorithms to screen feature variables, and we use Guangdong Province, China, as the study area. The backpropagation neural network (BPNN), genetic algorithm–based back propagation neural network (GABP) and random forest (RF) algorithms with 10-fold cross-validation are implemented to determine the most accurate model for soil cation (Ca2+, K+, Mg2+, and Na+) content estimations. The model and hyperspectral images are combined to perform the spatial mapping of soil Mg2+ and to obtain the spatial distribution information of images. The results show that Boruta was the optimal algorithm for determining the characteristic bands of soil Ca2+ and Na+, and SPA was the optimal algorithm for determining the characteristic bands of soil K+ and Mg2+. The most accurate estimation models for soil Ca2+, K+, Mg2+, and Na+ contents were Boruta-RF, SPA-GABP, SPA-RF and Boruta-RF, respectively. The estimation effect of soil Mg2+ (R2 = 0.90, ratio of performance to interquartile range (RPIQ) = 3.84) was significantly better than the other three elements (Ca2+: R2 = 0.83, RPIQ = 2.47; K+: R2 = 0.83, RPIQ = 2.58; Na+: R2 = 0.85, RPIQ = 2.63). Moreover, the SPA-RF method combined with HJ-1A HSI images was selected for the spatial mapping of soil Mg2+ content with an R2 of 0.71 and RPIQ of 2.05. This indicates the ability of the SPA-RF method to retrieve soil Mg2+ content at the regional scale