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DOE OSTI2025

Multi-objective surrogate-assisted calibration of CPFEM models using macroscopic response and in situ EBSD measurements of grain reorientation trajectories

Crystal plasticity finite element method (CPFEM) models are widely used to simulate the deformation behaviour of polycrystalline materials, but their calibration is often limited by their high computational cost and the non-convexity of the optimisation landscape. Here, this study develops a multi-objective surrogate-assisted calibration workflow that couples a multi-objective genetic algorithm (MOGA) with an adaptively trained deep neural network (DNN) surrogate model to efficiently identify CPFEM parameters from experimental data. The workflow is demonstrated on three crystal plasticity (CP) formulations of increasing complexity — Voce hardening (VH), two-coefficient latent hardening (LH2), and six-coefficient latent hardening (LH6) — using in situ electron backscatter diffraction (EBSD) measurements of Alloy 617 under uniaxial tensile loading. The CPFEM models are calibrated against the experimentally observed stress–strain response and reorientation trajectories of eight grains, then validated against eight additional trajectories and overall texture evolution. Across the CP formulations, the macroscopic response was reproduced reliably, while differences emerged in the robustness and accuracy of the grain-scale predictions. Including grain reorientation trajectories in the multi-objective calibration improved texture evolution predictions and filtered out physically inconsistent parameter sets that can arise from calibrating against only the stress–strain data. The workflow also demonstrates good transferability of calibrated parameters from a low- to a high-fidelity microstructural model. These results provide practical guidance for integrating in situ microstructural data into CPFEM through efficient, repeatable, and physically meaningful multi-objective calibration.

Crystal plasticity finite element method↗

DOE OSTI2026

High-quality Acinetobacter genomes recovered from combat wounds via metagenomic sequencing resemble cultured isolate genomes

The ability to accurately characterize wound pathogens is critical to informing clinical decisions for wound infections with complex treatment requirements. Acinetobacter baumannii is an impactful nosocomial pathogen in combat wounds and civilian hospital-acquired infections. An informed understanding of the phylogenetics and epidemiology of A. baumannii infections in military and civilian environments could guide approaches that improve antibiotic treatment regimens for both military and civilian patients. Whole-genome data for bacterial strains can be difficult to obtain due to challenges in culturing isolates from preserved military specimens. Metagenomic sequencing and assembly create opportunities for genomic analysis of pathogens directly from clinical specimens. The ability to perform comparative analyses between metagenome-derived genomes and culture-derived genomes would support a range of comparative bacterial genomic studies. Wound tissue biopsy and effluent samples from combat injuries were subjected to metagenomic sequencing and assembly. In total, 42 microbial metagenome-assembled genomes (MAGs) were obtained directly from metagenomic sequence data, 36 of which were designated “high” quality. Thirty of these genomes corresponded to Acinetobacter, with 29 mapping specifically to A. baumannii. Other observed genera included Bordetella, Citrobacter, Escherichia, and Pseudomonas. Single-copy and multi-copy orthologs were identified across Acinetobacter MAGs and publicly available isolate genomes derived from military and civilian sources. Both MAG and military isolate genomes were annotated with antimicrobial resistance data, and MAG genomes were statistically comparable to genomes obtained from isolates. Our results highlight the potential of de novo metagenome assembly for enabling high-resolution characterization directly from clinical specimens, thereby improving diagnostic precision, guiding antimicrobial stewardship, and enhancing understanding of pathogen evolution across diverse healthcare and battlefield environments.

Acinetobacter baumannii↗

DOE OSTI2021

Chemomechanical effect of reduced graphene oxide encapsulation on hydrogen storage performance of Pd nanoparticles

Primary chemomechanical impacts of confinement on hydrogen storage performance are studied using a nanolaminate structure where reduced graphene oxide (rGO) encapsulates palladium (Pd) nanoparticles. Three contributing factors are identified that can alter the reaction enthalpy: nanosizing, chemical interaction with the encapsulant, and mechanical stress induced strain from a combination of clamping force and lateral pulling force exerted on the Pd nanoparticles. The mechanical contributions are quantified by combining transmission electron microscopy, ab initio computation, and continuum elasticity theory, from which the encapsulation is found to exert an additional strain of 4.96% and 2.99% before and after hydrogen absorption, respectively, increasing the Pd and Pd hydride (PdH x ) reaction enthalpy by 1.3–2.8 kJ (mol H 2 ) -1 . The effect of the chemical interaction with rGO also raises the reaction enthalpy by up to 1.6 kJ (mol H 2 ) -1 , while the nanosizing effect decreases the reaction enthalpy. The three contributing factors to the reaction enthalpy are found to be similar in magnitude, where the net effect is in agreement with the measured enthalpy increase of 3.7 kJ (mol H 2 ) -1 from the bulk value. Hydrogen absorption kinetics and capacity also improved, which is attributed to facile nucleation of the hydrogen-rich phase enabled by the inhomogeneous strain distribution over the encapsulated PdHx nanoparticles. These results demonstrate that the chemomechanical effect can be controlled in the nanolaminate structure, providing an ideal template for tuning hydrogen storage performance.

08 HYDROGEN↗

DOE OSTI2021

Renewable fatty acid ester production in Clostridium

Abstract Bioproduction of renewable chemicals is considered as an urgent solution for fossil energy crisis. However, despite tremendous efforts, it is still challenging to generate microbial strains that can produce target biochemical to high levels. Here, we report an example of biosynthesis of high-value and easy-recoverable derivatives built upon natural microbial pathways, leading to improvement in bioproduction efficiency. By leveraging pathways in solventogenic clostridia for co-producing acyl-CoAs, acids and alcohols as precursors, through rational screening for host strains and enzymes, systematic metabolic engineering-including elimination of putative prophages, we develop strains that can produce 20.3 g/L butyl acetate and 1.6 g/L butyl butyrate. Techno-economic analysis results suggest the economic competitiveness of our developed bioprocess. Our principles of selecting the most appropriate host for specific bioproduction and engineering microbial chassis to produce high-value and easy-separable end products may be applicable to other bioprocesses.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Unsupervised Segmentation and Clustering Workflow for Efficient Processing of 4D-STEM and 5D-STEM Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure–strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.

4D-STEM↗

DOE OSTI2021

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k -space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

Tsymbalov, Evgenii↗

DOE OSTI2024

Zero-Strain Metal-Insulator Transition by the Local Fluctuation of Cation Dimerization

The coupled electronic and structural transitions in metal-insulator transition (MIT) hinder ultrafast switching and ultimate endurance. Decoupling these transitions and achieving a zero-strain electronic MIT can overcome the fundamental limitations of MIT in solid materials. Here, this study demonstrates that iso-valent Ti dopants in supercooled VO 2 epitaxial films cause MIT with minimal hysteresis without changing unit-cell volume and crystal symmetry. The Ti dopants in the VO 2 lattice locally alter the configuration of V-V pairs, where the long-range ordering in V-V pairs is disrupted, and the nano-domains of V-V dimers are formed. Strikingly, these local V-V dimers persist even above the electronic transition temperature (T MI ), facilitating the zero-strain electronic MIT with nanoscale structural heterogeneity. The geometrically compatible interface between insulating and metallic phases drastically enhances switching speed and endurance during electrically and optically driven zero-strain MIT. In conclusion, this discovery offers a fresh perspective on the scientific understanding of MIT and the improved functionality in terms of device speed and reliability by decoupling electronic and structural transitions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Tuning Conjugated Polymer Chain Packing for Stretchable Semiconductors

We report in order to apply polymer semiconductors to stretchable electronics, they need to be easily deformed under strain without being damaged. A small number of conjugated polymers, typically with semicrystalline packing structures, have been reported to exhibit mechanical stretchability. Herein, a method is reported to modify polymer semiconductor packing-structure using a molecular additive, dioctyl phthalate (DOP), which is found to act as a molecular spacer, to be inserted between the amorphous chain networks and disrupt the crystalline packing. As a result, large-crystal growth is suppressed while short-range aggregations of conjugated polymers are promoted, which leads to an improved mechanical stretchability without affecting charge-carrier transport. Due to the reduced conjugated polymer intermolecular interactions, strain-induced chain alignment and crystallization are observed. By adding DOP to a well-known conjugated polymer, poly[2,5-bis(4-decyltetradecyl)pyrrolo[3,4-c]pyrrole-1,4-(2H,5H)-dione-(E)-1,2-di(2,2 '-bithiophen-5-yl)ethene] (DPPTVT), stretchable transistors are obtained with anisotropic charge-carrier mobilities under strain, and stable current output under strain up to 100%.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Advancing specialized biofoundries via automated adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Advanced Algal Biofoundries for the Production of Polyurethane Precursors

The primary goal of the BEEPs project was to develop a process that could accelerate the development of algae as bioproduction platforms, from initial chemical product concept to an economically viable market supply. Under this program we elected to develop strains of algae that could generate polyurethane precursors, while simultaneously developing basic genetic tools to enable improved algal production systems. This program was specifically designed to incorporate National Laboratories as a means to utilize the expertise and facilities for new bio-production platforms. To that end, we designed a program to collaborate with the Agile BioFoundry at Lawrence Berkeley National Laboratory (LBNL) and computational platforms at Pacific Northwest National Laboratory (PNNL). In addition to these National Laboratory partners, we also had academic partners from UC Davis and Georgia Tech, as well as commercial partners Algenesis Materials and BASF. To achieve these goals, we initially focused on developing the genetic tools and high throughput screening technologies necessary to generate and assess production of polymer precursors (succinic acid) in algae and cyanobacteria, including advanced promoters and biosensors. In parallel, we computationally identified potential production bottlenecks and then used the developed genetic tools to increase production rates and yields. Constant feedback of data was used in conjunction with machine learning, high-throughput cell sorting, and synthetic biology, for additional targeted metabolic engineering. Multiple rounds of tool design, building, testing, and learning were supplied to partners at PNNL and LBNL to develop new models and tools that could expedite bioproduction platform development and increase yield performance. We targeted, and achieved, the FOA requirement yield metric of 20 g/L, as a milestone and deliverable from at least one of our engineered strains for the production of succinic acid.

09 BIOMASS FUELS↗

DOE OSTI2021

Shear Assisted Processing and Extrusion (ShAPE) of Aluminum Alloy 7075, 2024, and Al-12.4TM

The most common aluminum alloys utilized in the aerospace industry are 7075 and 2024 due to their high strength-to-weight ratio compared to advanced high strength steels and other aluminum alloys. Despite excellent performance, these aluminum alloys have seen limited use outside of the aerospace industry due in part to high cost. If high-performance aluminum extrusions could be made more cost effectively by eliminating energy intensive process steps typical of conventional extrusion, then numerous opportunities exist for more widespread adoption. A key reason for the high cost of 7075 and 2024 extrusions (25-75% higher than 6061) is their slow extrusion speed. 7075 and 2024 are limited to 2 m/min and 3.5 m/min respectively, in contrast to 6061 which can be extruded at 20–80 m/min. In addition to slow speed, aluminum alloys require numerous thermal treatments throughout the extrusion process including homogenization and pre-heating prior to extrusion, and solution heat treating and artificial aging after extrusion. Each of these steps contribute to the total energy consumed during manufacturing of extruded components. This project investigates the use of ShAPE to improve extrusion speed and reduce, or even eliminate, the typical thermal treatments for high strength aluminum alloys, all while improving material performance. The overarching goal of this project was to demonstrate that Shear Assisted Processing and Extrusion (ShAPE) can manufacture high-performance aluminum alloy tubing with lower manufacturing energy and improved mechanical properties compared to conventional extrusion. Unlike conventional extrusion where the billet is rammed against a stationary die using a strictly linear motion, the ShAPE process superimposes a rotational shear force by spinning the die while the billet is plunged. Compared to conventional linear extrusion, the ShAPE process imparts significantly more strain into the feedstock material, which enables the formation of novel microstructures. These microstructures manifest an array of property and process improvements for extrusion of high-performance aluminum alloys. The following accomplishments were achieved for this project: Extrusion of 7075 at 12.2 meters/min compared to 2 meters/min for conventional extrusion; Elimination of 7075 billet homogenization (430 °C for 20 hours) which is required prior to conventional extrusion; Elimination of 7075 billet pre-heating (400 °C for 1 hour) in a separate furnace which is required prior to conventional extrusion; Achieved 7075-T6with yield strength = 595 MPa, ultimate tensile strength = 531MPa, and elongation = 17.4% for extrusions made from unhomogenized billets. Exceeds the ASTM and ASM standard, and typical industry values; Achieved 7075-T5 (i.e., no solution heat treatment) with yield strength = 588 MPa, ultimate tensile strength = 535 MPa, and elongation = 14.8% for extrusions made from homogenized billets; Extrusion of 2024 at 7.4 meters/min compared to 3.5 meters/min for conventional extrusion; Achieved 2024-T8510 yield strength = 522 MPa, ultimate strength = 510MPa, and elongation = 7.1% for extrusions made from wrought billets. Exceeds the ASTM and ASM standard, and typical industry values; Extrusion of Al-12.4TM high-performance aluminum powder directly into tubing, in a single step, which eliminates process steps typical of powder metallurgy extrusion.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Tuning Phase Purity in Chiral 2D Perovskites

The introduction of chiral organic spacers in low-dimensional metal-halide perovskites triggers chiroptical activity, which is appealing for spintronic applications. However, a comprehensive understanding of structure formation and the ability to control phase purity in such materials have yet to be developed. Herein, the impact of processing conditions on the phase purity, microstructure, and chiroptical properties of chiral 2D perovskites is explored. The anisotropic emergence of a 1D perovskite inside the 2D matrix and its dependence on the organic cation chirality, solvent, and thermal annealing conditions are shown. By controlling these parameters, the in-plane conductivity of the films is nearly doubled. Furthermore, it is demonstrated, for the first time, that solvent choice and the spatial configuration of the organic cation can have an impact on the residual lattice strain and the energetic disorder of the system. The fundamentals presented here can help to improve the film deposition methods for low-dimensional chiral perovskites, offering strategies to control phase purity.

36 MATERIALS SCIENCE↗

DOE OSTI2025

The tier system: a host development framework for bioengineering

Development of microorganisms into mature bioproduction host strains has typically been a slow and circuitous process, wherein multiple groups apply disparate approaches with minimal coordination over decades. To help organize and streamline host development efforts, we introduce the Tier System for Host Development, a conceptual model and guide for developing microbial hosts that can ultimately lead to a systematic, standardized, less expensive, and more rapid workflow. The Tier System is made up of three Tiers, each consisting of a unique set of strain development Targets, including experimental tools, strain properties, experimental information, and process models. By introducing the Tier System, we hope to improve host development activities through standardization and systematization pertaining to nontraditional chassis organisms.

09 BIOMASS FUELS↗

DOE OSTI2026

Microstructural evolution, defect mitigation, and precipitation behavior in AA6061 via laser powder bed fusion with high-temperature substrate heating

Defects, particularly solidification cracking, remain persistent challenges in the laser powder bed fusion (LPBF) processing of AA6061 aluminum alloy. This study systematically investigates defect mitigation, microstructural evolution, and mechanical properties associated with high-temperature substrate preheating at 500 °C. Comprehensive microstructural analyses, including characterization of defects, grain structures, and precipitation behavior, were performed on samples in both as-built and T6 heat-treated states. Elevated preheating substantially reduced solidification cracking across a wide processing window, while demonstrating decreased crack sensitivity to laser parameters. Columnar cracks along the build direction were observed despite substrate preheating. Lack-of-fusion and keyhole porosity were effectively eliminated, though gas-induced microporosity persisted at higher powers. In-depth characterization of two distinct laser power and speed conditions confirmed the formation of micron-sized, non-coherent Mg 2 Si precipitates under heated substrate conditions, alongside α-AlFeCrMnSi intermetallic phases indicative of in-situ thermal effects during fabrication. Subsequent T6 heat treatment revealed the formation of fine, coherent needle-shaped β″ strengthening precipitates. Despite substantial differences in processing parameters, comparable mechanical properties were measured in the as-built samples (∼52 MPa yield strength, ∼130 MPa tensile strength), primarily due to reduced strain hardening effects and consistent precipitation characteristics. Meanwhile, the T6 heat treatment led to significant improvement in properties, enhancing yield strength by over 400%, aligning closely with the performance of conventional wrought AA6061-T6. These findings underscore that high-temperature substrate preheating offers an effective means to suppress cracks, control precipitation, and enhance mechanical performance in LPBF-processed AA6061.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Grain refinement in titanium prevents low temperature oxygen embrittlement

Interstitial oxygen embrittles titanium, particularly at cryogenic temperatures, which necessitates a stringent control of oxygen content in fabricating titanium and its alloys. Here, we propose a structural strategy, via grain refinement, to alleviate this problem. Compared to a coarse-grained counterpart that is extremely brittle at 77 K, the uniform elongation of an ultrafine-grained (UFG) microstructure (grain size ~ 2.0 µm) in Ti-0.3wt.%O is successfully increased by an order of magnitude, maintaining an ultrahigh yield strength inherent to the UFG microstructure. This unique strength-ductility synergy in UFG Ti-0.3wt.%O is achieved via the combined effects of diluted grain boundary segregation of oxygen that helps to improve the grain boundary cohesive energy and enhanced dislocation activities that contribute to the excellent strain hardening ability. The present strategy will not only boost the potential applications of high strength Ti-O alloys at low temperatures, but can also be applied to other alloy systems, where interstitial solution hardening results into an undesirable loss of ductility.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Experimental testing of additively manufactured embedded fiber optic smart devices for clean energy applications

Abstract An additively manufactured prototype smart device was created to investigate in-flow temperature distributions using embedded high-definition fiber optic sensors within a component for clean energy systems. The devices were created using Ultrasonic Additive Manufacturing to create a unique embedded pathway within a flow conditioner for the high-definition fiber optic sensors to be placed within. The fibers used allowed for temperature measurements to be taken every 0.65 mm along the fiber. The high-resolution fibers were thermally calibrated enable the 2D reconstruction of the temperature profile in the flow path of the structure. This is due to the temperature-related strain response of the material and strain measurements of the fibers. Hot airflow testing of these devices showed the ability to identify localized temperature differences in the flow. The observed strain response within the smart device had higher strain concentrations in the thicker web regions than in the thinner web regions. These higher strain regions resulted in higher uncertainties for the temperature responses. Further calibration is needed to improve the accuracy of the smart devices, specifically within the inner web structures of a flow straightening component. Further investigations of the devices within flow showed the temperature sensing to be independent of the effects of flow velocity. The devices were able to distinguish temperature differences within single and two-phase flow and showed local sensitivity to the temperature changes with the identification of hot and cold spots. The presented results showed the viability of the smart device for obtaining detailed temperature distributions using common industrial components. Eventually, the goal for these smart devices will be to withstand higher temperature and pressure environments such as those experienced in nuclear, fusion, and concentrated solar energy systems.

Donlan, Connor F. (ORCID:0000000223317882)↗

DOE OSTI2024

Applying a polysaccharide lyase from Stenotrophomonas maltophilia to disrupt alginate exopolysaccharide produced by Pseudomonas aeruginosa clinical isolates

Pseudomonas aeruginosa is considered one of the most challenging, drug-resistant, opportunistic pathogens partly due to its ability to synthesize robust biofilms. Biofilm is a mixture of extracellular polymeric substances (EPS) that encapsulates microbial cells, leading to immune evasion, antibiotic resistance, and thus higher risk of infection. In the cystic fibrosis lung environment, P. aeruginosa undergoes a mucoid transition, defined by overproduction of the exopolysaccharide alginate. Alginate encapsulation results in bacterial resistance to antibiotics and the host immune system. Given its role in airway inflammation and chronic infection, alginate is an obvious target to improve treatment for P. aeruginosa infection. Previously, we demonstrated polysaccharide lyase Smlt1473 from Stenotrophomonas maltophilia strain k279a can catalyze the degradation of multiple polyuronides in vitro, including D-mannuronic acid (poly-ManA). Poly-ManA is a major constituent of P. aeruginosa alginate, suggesting that Smlt1473 could have potential application against multidrug-resistant P. aeruginosa and perhaps other microbes with related biofilm composition. In this study, we demonstrate that Smlt1473 can inhibit and degrade alginate from P. aeruginosa. Additionally, we show that tested P. aeruginosa strains are dominant in acetylated alginate and that all but one have similar M-to-G ratios. These results indicate that variation in enzyme efficacy among the isolates is not primarily due to differences in total EPS or alginate chemical composition. Overall, these results demonstrate Smlt1473 can inhibit and degrade P. aeruginosa alginate and suggest that other factors including rate of EPS production, alginate sequence/chain length, or non-EPS components may explain differences in enzyme efficacy.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Daily and Seasonal Variations of Shallow Seismic Velocities in Southern California From Joint Analysis of H/V Ratios and Autocorrelations of Seismic Waveforms

Establishing a baseline of ongoing secular velocity variations at the subsurface can improve the accuracy of detecting and interpreting short-term velocity changes, and advance the understanding of observed seismic motions and the behavior of subsurface materials. Toward these goals, we develop and apply a deconvolved autocorrelation (DA) method to estimate regional daily and seasonal changes of seismic velocities in southern California. The DA method combines advantages of traditional autocorrelation and Horizontal-to-Vertical Spectral Ratio, and is used to analyze over 10 years of data recorded by 50 stations. The results indicate widespread daily and seasonal changes of up to 10% and 4%, respectively, in the top tens of meters of the crust. The thickness of the surface layer, distance from the coast, and topographic variations are important factors controlling the amplitudes of the resolved velocity variations. The results suggest that changes of soil moisture and thermoelastic strain are likely dominant factors affecting the daily and seasonal variations, respectively. The developed DA method can improve the accuracy and robustness of estimated changes of subsurface materials at other locations.

58 GEOSCIENCES↗