Most complicated lock pattern-based seismological signal framework for automated earthquake detection

BACKGROUND : Seismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. MATERIAL AND METHOD : We developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selectorbased feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm. RESULTS : The presented self-organized MCLP-based feature engineering model yielded 96.82% classification accuracy with 10-fold cross-validation using the seismic signal dataset. CONCLUSIONS : Our model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.The South African National Library and Information Consortium (SANLiC).https://www.elsevier.com/locate/jagam2024Electrical, Electronic and Computer EngineeringSDG-09: Industry, innovation and infrastructureSDG-13:Climate actio

Space-Based Imaging Radar Studies of U.S. Volcanoes

The arrival of space-based imaging radar as a revolutionary land-surface mapping and monitoring tool little more than a quarter century ago enabled a spate of innovative volcano research worldwide. Soon after launch of European Space Agency’s ERS-1 spacecraft in 1991, the U.S. Geological Survey began SAR and InSAR studies of volcanoes in the Aleutian and Cascades arcs, in Hawai’i, and elsewhere in the western U.S. including the Yellowstone and Long Valley calderas. This paper summarizes results of that effort and presents new findings concerning: (1) prevalence of volcano deformation in the Aleutian and Cascade arcs; (2) surface-change detection and hazard assessment during eruptions at Aleutian and Hawaiian volcanoes; (3) geodetic imaging of magma storage and transport systems in Hawai’i; and (4) deformation sources and processes at the Yellowstone and Long Valley calderas. Surface deformation caused by a variety of processes is common in arc settings and could easily escape detection without systematic InSAR surveillance. Space-based SAR imaging of active lava flows and domes in remote or heavily vegetated settings, including during periods of bad weather and darkness, extends land-based monitoring capabilities and improves hazards assessments. At Kīlauea Volcano, comprehensive SAR and InSAR observations identify multiple magma storage zones beneath the summit area and along the East Rift Zone, and illuminate magma transport pathways. The same approach at Yellowstone tracks the ascent of magmatic volatiles from a mid-crustal intrusion to shallow depth and relates that process to increased hydrothermal activity at the surface. Together with recent and planned launches of highly capable imaging-radar satellites, these findings support an optimistic outlook for near-real time surveillance of volcanoes at global scale in the coming decade

MONITORING THE 2018 ERUPTION OF KĪLAUEA VOLCANO USING VARIOUS REMOTE SENSING TECHNIQUES

Monitoring the regions that are prone to natural hazards is essential in disaster management to provide early warnings. Airborne and space-borne remote sensing techniques are cost-effective in accomplishing this task. Interferometric Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique used to detect and measure the changes in the Earth’s topography over time. Spaceborne InSAR is a precise (~mm accuracy) way to measure the land surface altitudinal changes. Persistent Scatterer Interferometry (PSI) is a powerful method of differential SAR interferometry that processes the InSAR data by automatically selecting the persistent scatterers in the region. In this thesis, I developed a new algorithm to estimate the areal coverage and volume of newly erupted lava by integrating the space-borne InSAR, thermal infrared, Light Detection and Ranging (LiDAR), and Normalized Difference Vegetation Index (NDVI) techniques. I applied this algorithm to the eruption of the East Rift Zone (ERZ) of the Kīlauea volcano that took place between May and August 2018 as a case study, and estimated the areal coverage and volume of lava erupted. I compared the results of InSAR to those derived from airborne LiDAR. I found that although air-borne LiDAR provides data with higher resolution and accuracy, InSAR is almost as good as LiDAR in monitoring deformed areas and has larger spatial and temporal coverage. I also performed the PSI analysis using the Stanford Method for Persistent Scatterers (StaMPS) algorithm, and determined the Line-of-Sight (LOS) deformations prior, during, and after the 2018 eruption of the Kīlauea volcano. Results from the PSI processing show regional subsidence on the Big Island, indicating the deflation of the southern and western part of the Big Island during the eruption at the East Rift Zone. Keywords: Kilauea

Deformation Mapping and Modeling of the Aleutian Volcanoes with InSAR and Numerical Models

Surface deformation mapping is an essential component for comprehensive monitoring of volcanic activities, serving as a vital tool for discerning crucial insights into magma dynamics, storage, and migration for accurate hazard forecasting, assessment, and mitigation. However, monitoring of the volcanic deformation across the Aleutian volcanic arc is usually limited by the lack of terrestrial sensors deployed due to their remote locations and hostile environmental conditions, necessitating alternative methodologies for data acquisition and analysis. My PhD study aims at precisely mapping the crustal deformation for the Aleutian volcanoes and tracking the evolution of the magmatic system with Interferometric Synthetic Aperture Radar (InSAR) and numerical deformation modeling. Advanced timeseries InSAR algorithms are applied to three cases: Okmok, Makushin, and western and central Aleutian. Deformation history since the 2008 eruption at Okmok mapped with PSInSAR unveils several successive inflation episodes with time-dependent rates. Finite Element Models (FEM) updated with Ensemble Kalman Filter (EnKF) find the timeseries deformation can be well explained by a spherical source with temporally steady location about 3.5 km beneath the central caldera, with cumulative volume change about from 2008 to 2021. Deformation mapped from SAR data collected across platforms have detected multiple inflation/deflation cycles characterized by temporally varying rates at Makushin volcano from 2004 to 2021. Inverse models of the crustal deformation suggest a Mogi source located to the northeast of the caldera at a depth ~6 km Beneath Sea Level (BSL). A shallow secondary deformation located to the southeast of the volcano, with rates about half that of the main deformation is also identified. A volatile intrusion/degassing dominated plumbing system is preferred by the inflation/deflation cycles with distinct magnitudes and lifetimes. A new timeseries InSAR framework is developed based on the geocoded unwrapped interferograms produced from Jet Propulsion Laboratory (JPL) Advanced Rapid Imaging and Analysis (ARIA) system. Deformation histories for volcanoes in the western and central Aleutian are retrieved with this framework with Sentinel-1 imageries from 2015 to 2021. Various deformation patterns associated with different volcanic processes have been detected and used to track the evolution of volcanic systems. New deformation patterns are observed from Tanaga, Great Sitkin and Yunaska volcano. Overall higher magmatism, which may be attributed to spatial variation in tectonic environments, is identified in the central Aleutian. To investigate the discrepancy between magmatic sources derived from geodetic deformation and the ones inferred from seismic tomography at Okmok, several numerical magma reservoir models are constructed and analyzed. The single reservoir model with magmatic chamber characterized by low P and S wave velocity (Vp and Vs) and moderate P to S wave velocity (Vp/Vs) ratio produce crustal deformation that fits the geodetic observations better than the distributed reservoir model with magma chambers represented by high Vp and Vp/Vs ratio and low Vs, which likely reconcile the geodetic deformation and seismic tomography observations and highlights the necessity of joint interpretation of geophysical observations over regions with complicated volcanic environments