Atmospheric Noise Research Articles

Removing Atmospheric Noise from InSAR Interferograms in Mountainous Regions with a Convolutional Neural Network

Removing Atmospheric Noise from InSAR Interferograms in Mountainous Regions with a Convolutional Neural Network

Persistently active El Niño–Southern Oscillation since the Mesozoic

The El Niño-Southern Oscillation (ENSO), originating in the central and eastern equatorial Pacific, is a defining mode of interannual climate variability with profound impact on global climate and ecosystems. However, an understanding of how the ENSO might have evolved over geological timescales is still lacking, despite a well-accepted recognition that such an understanding has direct implications for constraining human-induced future ENSO changes. Here, using climate simulations, we show that ENSO has been a leading mode of tropical sea surface temperature (SST) variability in the past 250 My but with substantial variations in amplitude across geological periods. We show this result by performing and analyzing a series of coupled time-slice climate simulations forced by paleogeography, atmospheric CO2 concentrations, and solar radiation for the past 250 My, in 10-My intervals. The variations in ENSO amplitude across geological periods are little related to mean equatorial zonal SST gradient or global mean surface temperature of the respective periods but are primarily determined by interperiod difference in the background thermocline depth, according to a linear stability analysis. In addition, variations in atmospheric noise serve as an independent contributing factor to ENSO variations across intergeological periods. The two factors together explain about 76% of the interperiod variations in ENSO amplitude over the past 250 My. Our findings support the importance of changing ocean vertical thermal structure and atmospheric noise in influencing projected future ENSO change and its uncertainty.

Performance of antenna-based and Rydberg quantum RF sensors in the electrically small regime

Rydberg atom electric field sensors are tunable quantum sensors that can perform sensitive radio frequency measurements. Their qualities have piqued interest at longer wavelengths where their small size compares favorably to impedance-matched antennas. Here, we compare the signal detection sensitivity of cm-scale Rydberg sensors to similarly sized room-temperature electrically small antennas with active and passive receiver backends. We present and analyze effective circuit models for each sensor type, facilitating a fair sensitivity comparison for cm-scale sensors. We calculate that contemporary Rydberg sensor implementations are less sensitive than unmatched antennas with active amplification. However, we find that idealized Rydberg sensors operating with a maximized atom number and at the standard quantum limit may perform well beyond the capabilities of antenna-based sensors at room temperature, the sensitivities of both lying below typical atmospheric background noise.

Blind deblurring of astronomical images using a SCGTV-based single-frame method

Atmospheric turbulence often distorts space target imaging, leading to degraded image quality. To address the issue of image quality degradation, various hardware and software approaches have been proposed, including adaptive optics, lucky imaging, and blind deconvolution. Traditional astronomical image deblurring algorithms rely on information from multiple frames, requiring extensive processing time and computational resources. This study introduces the saturation-corrected graph total variation (SCGTV) method to address low signal-to-noise ratio and pixel saturation in single-frame astronomical image deblurring. This method effectively minimizes the influence of atmospheric turbulence and noise by leveraging a reweighted graph total variation prior. Saturation correction and dark channel information are integrated in SCGTV to enhance resolution and reduce artifacts. The SCGTV method is well-designed for astronomical scenarios, successfully deblurring real-world astronomical images and demonstrating superior performance on thirty simulated dataset compared to other algorithms. Quantitative evaluations on artificially blurred datasets demonstrate that SCGTV outperforms existing methods. These enhancements provide significant benefits for astronomical observation and analysis.

Multi-band ultrathin reflective metasurface for linear and circular polarization conversion in Ku, K, and Ka bands

Linear polarization (LP) and circular polarization (CP) holds paramount importance in Ku, K, and Ka bands for satellite based communication, and remote sensing applications. Satellite based remote sensing applications face challenges like atmospheric attenuation, noise & interference, and signal degradation. Moreover, satellite based communication application demands CP in two distinct, non-adjacent frequency bands with orthogonal polarizations at greater oblique angles, considering the unpredictable incidence angles of electromagnetic (EM) waves. Addressing these challenges, an innovative metasurface polarization converter is proposed to operate efficiently across the Ku-band (13.5–18.0 GHz), K-band (18.0–26.5 GHz), and Ka-band (26.5–38.5 GHz) frequency ranges. The converter achieves left-handed circular polarization (LHCP) in the Ku- and Ka-bands within the frequency ranges of 14.57–15.65 GHz and 27.47–33.85 GHz for y-polarized incident EM waves. Additionally, it provides right-handed circular polarization (RHCP) in the K- and Ka-bands at 17.27–23.92 GHz and 35.87–38.32 GHz for y-polarized incident EM waves. The LP conversion ratio exceeds 95% in the frequency bands of 15.97–16.85 GHz, 24.70–26.65 GHz, and 34.37–35.45 GHz for y-polarized, LHCP, and RHCP incident EM waves, respectively. The metasurface exhibits robust performance up to incidence angles of 45 degrees under oblique conditions. Experimental validation using traditional board-circuit manufacturing demonstrates close agreement between measured co- and cross-polarized reflection coefficients and simulations in the 13.5–18 GHz, and 24–38.5 GHz frequency range. Thin metasurface with a thickness of only 0.64 = 0.013λo mm, the proposed design outperforms existing studies in the literature, establishing its competitive edge in terms of structure and performance.

Environmental Aspects and Impacts of Porto Seco: the Case of the Largest Dry Port in Latin America, Located in Foz do Iguaçu (Brazil)

Objective: This work aims to present and describe the process of identifying and classifying environmental aspects in a Brazilian dry port. Theoretical Framework: The Climate Change Adaptation Strategies aim to reduce greenhouse gas (GHG) emissions, increase the share of renewable energy in total energy production and increase the energy efficiency. Unfortunately, road-based dry ports increase heavy truck congestion, energy consumption and, inherently, GHG emissions. Method: The general approach includes a literature review on the importance of managing environmental aspects and impacts in dry ports, followed by the description of the holistic procedure that involves the inventory of port activities linked to environmental aspects, the Leopold matrix and specialized knowledge about the local context (e.g., compliance with law, regional climate change adaptation strategy, stakeholder concerns) to perform the classification. Results and Discussion: The results showed that atmospheric emissions, noise and resource consumption are among the most significant environmental aspects of dry port. The absence of a railway that enables a multimodal transport system and the fact that the port is located in the city center constitute a disadvantage that worsens road congestion and air emissions, since the train would reduce GHG emissions. Research Implications: The work contributes to the planning and management of the activities of Foz do Iguaçu Dry Port considering environmental aspects and impacts, direct and indirect, from a holistic perspective. Originality/Value: The work presents a ranking of the environmental aspects and impacts of dry port to help defining priority actions in light of climate change strategies.

A comparative evaluation of measures to assess randomness in human-generated sequences

Whether and how well people can behave randomly is of interest in many areas of psychological research. The ability to generate randomness is often investigated using random number generation (RNG) tasks, in which participants are asked to generate a sequence of numbers that is as random as possible. However, there is no consensus on how best to quantify the randomness of responses in human-generated sequences. Traditionally, psychologists have used measures of randomness that directly assess specific features of human behavior in RNG tasks, such as the tendency to avoid repetition or to systematically generate numbers that have not been generated in the recent choice history, a behavior known as cycling. Other disciplines have proposed measures of randomness that are based on a more rigorous mathematical foundation and are less restricted to specific features of randomness, such as algorithmic complexity. More recently, variants of these measures have been proposed to assess systematic patterns in short sequences. We report the first large-scale integrative study to compare measures of specific aspects of randomness with entropy-derived measures based on information theory and measures based on algorithmic complexity. We compare the ability of the different measures to discriminate between human-generated sequences and truly random sequences based on atmospheric noise, and provide a systematic analysis of how the usefulness of randomness measures is affected by sequence length. We conclude with recommendations that can guide the selection of appropriate measures of randomness in psychological research.

Separation of Water Level Change From Atmospheric Artifacts Through Application of Independent Component Analysis to InSAR Time Series

AbstractIn recent years, synthetic aperture radar (SAR) interferometry (InSAR) has emerged as a valuable tool for measuring water level change (WLC) to study hydrodynamic processes in coastal wetlands. However, the highly dynamic wet atmosphere conditions common in these areas have a significant impact on InSAR observations, producing errors in the derived values. Standard methods for estimating atmospheric noise in InSAR time series lack the spatial or temporal resolution needed to adequately correct for wet tropospheric delays. In this study, we utilize the Independent Component Analysis (ICA) signal decomposition technique to identify the likely WLC signal and eliminate atmospheric noise in a time series derived from rapid repeat measurements made with the L‐band uninhabited aerial vehicle synthetic aperture radar airborne instrument. The method compares in‐situ water level measurements with the independent components (IC) to identify the ICA components corresponding to WLC. The signal‐to‐noise ratio between the WLC after the ICA‐based filtering and in situ water level gauges used for validation reaches 16 dB compared to an average of 2.6 dB before filtering. The excluded IC are used to generate maps showing a time series of likely atmospheric features. The identified features in the maps generally correspond to atmospheric features identifiable in Next Generation Weather Radar (NEXRAD) S‐band ground weather radar reflectivity maps collected during the SAR acquisitions. The method is sufficiently general to be applied to any InSAR‐derived surface displacement time series.

Analysis of a visible light communication system with QAM-OFDM modulated white LEDs using FSO

The popularity of visible light communication (VLC) has increased recently because of the advancement of LEDs and the quantity of lighting bandwidth, which enables faster and more reliable transmission. High-speed transmission via LED networks is made possible by QAM-OFDM technology, which maximizes spectral efficiency and interference robustness to improve the VLC. For the proposed work, two OFDM modulation techniques (a PSK-OFDM and a QAM-OFDM with white LED, RZ/NRZ encoding schemes, and EDFA/SOA amplifiers) are used in the design of the VLC transmitter and receiver of the FSO channel. The design and investigation make use of the OptiSystem v21 simulation tool to assess the performance of the VLC system with an emphasis on the bit error rate, extinction ratio, and signal quality criteria. The results show that the QAM-OFDM with an RZ scheme and EDFA amplifier is the more competent than PSK-OFDM, with a Q factor of 3.94, a BER of 3.71∗10−5, and an extinction ratio of 10.18. The proposed network has a 10–60 Gbps data throughput limit and a 10 m link range in the presence of atmospheric conditions and noise effects.

Shortwave Radiative Flux Variability Through the Lens of the Pacific Decadal Oscillation

AbstractThe variability of the shortwave radiative fluxes at the surface and top of atmosphere (TOA) is examined in a pre‐industrial modeling setup using the Pacific Decadal Oscillation (PDO) as a possible pacemaker of atmospheric decadal‐scale variability. Within models from the Coupled Model Intercomparison Project—Phase 6, downwelling shortwave radiation at the surface, the net shortwave fluxes at the surface and TOA, as well as cloud radiative effects show remarkably similar patterns associated with the PDO. Through ensemble simulations designed with a pure PDO pattern in the North Pacific only, we show that the PDO relates to about 20%–40% of the unforced year‐to‐year variability of these shortwave fluxes over the Northern Hemispheric continents. The sea surface temperature imprint on shortwave‐flux variability over land is larger for spatially aggregated time series as compared to smaller areas, due to the blurring effect of small‐scale atmospheric noise. The surface and TOA radiative flux anomalies associated with the PDO index range of [−1.64; 1.64] are estimated to reach up to ±6 Wm−2 for North America, ∓3 Wm−2 for India and ±2 Wm−2 for Europe. We hypothesize that the redistribution of clouds in response to a North Pacific PDO anomaly can impact the South Pacific and North Atlantic SSTs.

Atmospheric Noise Measurements in the Garden: Detecting Universalities in Inter-Spheric Waiting Time Statistics

Lightning flashes result in an instantaneous emission of electromagnetic (EM) waves that encompass a broad spectrum of frequencies in the domain of radio waves. The signature of these impulses in the region of the very low frequency (VLF) of radio waves and bellow are called spherics. These impulsive signals traverse great distances in the waveguide between the Earth’s ionosphere and its surface. Due to the abundance of lightnings the lower end of the EM spectrum is dominated by the waveforms of these emission. It is called atmospheric radio noise because of its origin. This article outlines a straightforward approach for capturing spherics activity within the VLF radio wave range. Employing data processing techniques, we pinpoint the timestamps of spherics within time series data. The distribution of inter-spheric times is analyzed across various detection threshold levels. Utilizing recordings spanning two distinct years and seasons, we replicate the established form of the inter-spheric time distribution described in the literature. Notably, we demonstrate that by rescaling the time intervals between spherics with the mean time for a specific recording and given detection threshold, the distributions collapse to a master curve. This universal pattern is accurately characterized by a single-parameter mean-scaled Gamma distribution. Additionally, we note the similarities in the distribution of inter-spheric times with patterns found in earthquake recurrence times.

Impacts of Atmospheric Internal Variations on the Variability of Sea Surface Temperature Based on the Hydra‐SINTEX Model

AbstractOcean–atmosphere interactions largely control the variabilities of the climate system on Earth. However, how much atmospheric internal signals contribute to climate variabilities remains uncertain over many parts of the globe. Here, we develop an interactive ensemble coupled model (called Hydra‐SINTEX) to investigate the influences of atmospheric internal variations (AIVs) on the mean‐states and variability of the climate system. The results show that, while climatological mean‐states are little affected, the AIVs can largely influence climate variabilities over the globe. We pay particular attention to two regions, that is, the tropical eastern Indian Ocean, which is the key area of the Indian Ocean Dipole (IOD), and the subtropical North Pacific. We found that sea surface temperature (SST) variabilities in these two regions are much reduced without the AIVs but with distinct mechanisms. Without the AIVs, the intensity of the IOD is largely reduced in association with weakened air–sea coupling in the tropics. This indicates the importance of atmospheric noise forcing on the development of the IOD. In contrast, the reduction of SST variability in the subtropical North Pacific is caused by the absence of the AIVs that are generated by both mid‐latitude atmospheric processes and weakened remote influence of the tropical SST in accordance with the reduced SST signals there.

Slow response of surface water temperature to fast atmospheric variability reveals mixing heterogeneity in a deep lake

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes.

Dual-Channel Mapping–Gas Column Concentration Inversion Method Based on Multispectral Imaging

Infrared multispectral imaging technology can achieve the long-distance, wide-ranging and fast detection of target gas, and has been widely used in the fields of dangerous-gas detection and environmental monitoring. However, due to the difficulty in acquiring background radiation as well as atmospheric disturbance and noise interference in the detection process, the quantitative detection of gas concentration has become a difficult problem to solve. Therefore, this paper proposes an inversion method for gas column concentration based on infrared multispectral imaging technology. Firstly, infrared background radiation images of the non-target gas absorption spectrum band were collected and converted into background radiation images of the target gas absorption spectrum band according to the dual-channel mapping relationship. Then, combined with the gas radiation images of the target gas absorption spectrum band, the column concentration distribution of the gas was obtained by using the measured calibration relationship between absorbance and column concentration. Experiments of gas detection in different environments were carried out, and the column concentration distribution of the target gas was inverted using this method; the results showed that the average relative error of the inversion of the gas column concentration was 4.84%, which enables the quantitative detection of gas column concentration in a complex environment.

Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

AbstractThe North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño‐Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric‐ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions.

MACHINE LEARNING FOR AUTOMATIC EXTRACTION OF WATER BODIES USING SENTINEL-2 IMAGERY

Context. Given the aggravation of environmental and water problems, there is a need to improve automated methods for extracting and monitoring water bodies in urban ecosystems. The problem of efficient and automated extraction of water bodies is becoming relevant given the large amount of data obtained from satellite systems. The object of study is water bodies that are automatically extracted from Sentinel-2 optical satellite images using machine learning methods.
 Objective. The goal of the work is to improve the efficiency of the process of extracting the boundaries of water bodies on digital optical satellite images by using machine learning methods.
 Method. The paper proposes an automated information technology for delineating the boundaries of water bodies on Sentinel-2 digital optical satellite images. The process includes eight stages, starting with data download and using topographic maps to obtain basic information about the study area. Then, the process involved data pre-processing, which included calibrating the images, removing atmospheric noise, and enhancing contrast. Next, the EfficientNet-B0 architecture is applied to identify water features, facilitating optimal network width scaling, depth, and image resolution. ResNet blocks compress and expand channels. It allows for optimal connectivity of large-scale and multi-channel links across layers. After that, the Regional Proposal Network defines regions of interest (ROI), and ROI alignment ensures data homogeneity. The Fully connected layer helps in segmenting the regions, and the Fully connected network creates binary masks for accurate identification of water bodies. The final step of the method is to analyze spatial and temporal changes in the images to identify differences, changes, and trends that may indicate specific phenomena or events. This approach allows automating and accurately identifying water features on satellite images using machine learning.
 Results. The implementation of the proposed technology is development through Python software development. An assessment of the technology’s accuracy, conducted through a comparative analysis with existing methods, such as water indices and K-means, confirms a high level of accuracy in the period from 2017 to 2023 (up to 98%). The Kappa coefficient, which considers the degree of consistency between the actual and predicted classification, confirms the stability and reliability of our approach, reaching a value of 0.96.
 Conclusions. The experiments confirm the effectiveness of the proposed automated information technology and allow us to recommend it for use in studies of changes in coastal areas, decision-making in the field of coastal resource management, and land use. Prospects for further research may include new methods that seasonal changes and provide robustness in the selection and mapping of water surfaces.

Deblurring using Reverse Filtering Techniques: An Experimental Analysis

Images are an integral part of life. Blurring an image is a process that makes the image less sharp and distorts the detail of an image. The images got blurred due to atmospheric noise, improper camera setting, and relative motion between the camera and the scene. Deblurring is the process of recovering the sharp image from the blurred image. The types of blurs include Average blur, Motion blur, Defocus blur, Gaussian blur, etc. Mainly deblurring techniques are categorised into blind image deconvolution and nonblind image deconvolution. There exist many deblurring techniques in the literature. Deblurring using reverse filtering is to suppress the effect of the filter using the filter itself as many times as needed but without knowing its internal structure. The filter is regarded as a black box. This paper presents the experimental analysis of existing reverse filtering techniques such as zero order reverse filtering (T-method), Rendition method (R-method), P-method, and TDA (Total Derivative Approximation). The methods considered in the paper are spatial domain methods.

An Assessment of Spatio-temporal Land Use/Land Cover Dynamics Using Landsat Time Series Data (2008-2022) in Kuliyapitiya West Divisional Secretariat Division in Kurunagala District, Sri Lanka

The main aim of the study is to detect the changes of land use land cover (LULC) in Kuliyapitiya West Divisional Secretariat division during 2008-2022. Following the download of Landsat images in the study area, several procedures were taken to pre-process the images. This included performing radiometric and geometric corrections to eliminate undesired sensor data and atmospheric noise. The Maximum Likelihood Classification (MLC) was used as the main technique of image post-processing to derive land use maps for the target years. To evaluate the accuracy of outputs 525 ground control points were verified using the Google Earth Pro engine. The classification accuracy of the study was 86% for 2008, 79 % in 2015, and 80 % in 2022. The results showed that over the past 15 years, settlements and built-up areas increased from 20.84% to 28.14% and 0.33% to 2.71%, respectively, whereas coconut lands decreased from 58.3% to 48.6%. The settlement, which showed an increase of land area of 11.9 km2 throughout the period, was identified as the main land use gainer while coconut was the main land use that lost 15.9 km2 of its land area over the past fifteen years. The built-up area showed a 3.96 km2 overall gain during the period due to urbanization and the expansion of the industrial, educational, and service sectors in the study area. The other four land use classes have not undergone any significant changes throughout the relevant time. The study highlights the importance of combining accuracy evaluation and image classification algorithms to gain a more comprehensive understanding of the LULC changes. Hence, our findings could assist decision-makers in land use planning to efficiently guide the sustainable land management.

ANALYSIS OF TELEVISION SIGNAL BOOSTERS USING DIGITAL TELEVISION BOOSTERS AND ANTENNA

A digital antenna is a device used to capture digital signals. Digital television broadcasts are capable of transmitting better reception quality image signals on the television screen than analog broadcasts. Using an antenna without a booster causes less than optimal reception for several stations that are quite far from the receiving antenna. The television antenna booster functions as an amplifier for signal losses received from the antenna via coaxial cable. The signal that enters the antenna through the cable is a very weak RF signal. The booster itself works as an RF signal amplifier and also acts as a frequency selector used by the television receiver (VHF or UHF). This research aims to analyze the comparison of the performance of two antenna boosters, namely the TOYOSAKI Booster and MATRIX Booster, using digital antennas, namely the TOYOSAKI, MATTRIX and TAFFWARE antennas, to find a comparison of which antenna and booster are the best. This research uses the SINPO method, namely a number code to assess signal strength, interference, atmospheric noise, propagation conditions, and general impression of reception. Ratings are made with numbers ranging from 5 (very good) to 1 (very bad). The research results show that the quality of the images produced by the TOYOSAKI and MATRIX antennas is better using the MATRIX Booster than using the TOYOSAKI Booster. Meanwhile, for the TAFFWARE antenna, it is better to use a TOYOSAKI Booster than to use a MATRIX.

Towards a geodetic earthquake catalogue for Central America: detecting coseismic deformation in Costa Rica using Sentinel-1 InSAR

SUMMARY Earthquake source parameters can be estimated using seismological observations, but the identification of the fault responsible is often complicated by location uncertainties and the inherent ambiguity between nodal planes. Satellite Interferometric Synthetic Aperture Radar (InSAR) can be used to observe ground deformation and model fault geometry but is limited by climate conditions (water vapour) and ground coverage (dense vegetation). In the tropics, the atmosphere is dynamic and most regions are densely vegetated, making detecting coseismic deformation challenging. Here, we perform a systematic inspection of coseismic interferograms from Sentinel-1 SAR images, to assess their suitability for detecting coseismic deformation in Costa Rica. Using data from the seismological network, we target seven earthquakes between 2016 and 2020 with depths $\le \, 20$ km and magnitudes Mw 5.3–6.2. For each event, we use the seismic parameters to compute line-of-sight displacements for ascending and descending geometries and for both nodal planes and generate 12- and 24-d coseismic interferograms where available. We obtain interferograms with coseismic displacement signals for three of the seven earthquakes. We invert the geodetic data to retrieve the earthquake source parameters but the lack of offshore geodetic coverage causes trade-offs between parameters and large uncertainties. The Jacó and Golfito earthquakes likely occurred on the subduction interface and the geodetic locations were 6–9 km closer to the coast than previous seismic estimates. The Burica earthquake occurred on a shallow steeply dipping thrust fault in the outer forearc. For the other earthquakes, no coseismic deformation was detected due to atmospheric noise or poor coherence. These results demonstrate the suitability of 12-d Sentinel-1 interferograms for monitoring shallow earthquakes of magnitude > Mw 5.7 in Central America. This approach can be used to begin a surface deformation catalogue for the region, which will ultimately help improve the understanding of active deformation processes and improve hazard maps.