Volcano Monitoring Application Research Articles

Design of Real-Time Object Detection in Mobile Robot for Volcano Monitoring Application

Indonesia is one of the countries located at the ring of fire which should be monitored to predict the eruption earlier and set the risk zones around with no human involvement especially while eruption taking place. Therefore, in this research, it is used a 4 wheeled mobile robot called PRAWIRA for this purpose. The robot should have the ability to avoid the obstacles in front of it in this area. It has been designed a real-time object detection system for volcano monitoring application using deep learning from the YOLOv5s model for 4 objects (trees, persons, stones, and stairs). It was used 484 images for the dataset after the pre-train process was conducted with several steps: object identification; dataset downloading (Google Chrome Extension and Open Images v6); image labeling (LabeImg); augmentation process (flip, blur, and rotation); and data training for varies epochs and batches by Jupyter Notebook GPU. The preliminary result for this research was presented in the mean average precision (mAP) of YOLOv5s (the smallest version). The first variation (batch = 16, epochs = 100) resulted in mAP_0.5 = 17.9% and mAP_0.5:0.95 = 7.27% with 0.262 hours of training time. The second (batch = 16, epochs = 500) resulted in mAP_0.5 = 25.7% and mAP_0.5:0.95 = 12.3% with 1.296 hours of training time, while the third (batch = 80, epochs = 100) resulted in mAP_0.5 = 17.7% and mAP_0.5:0.95 = 5.63% with 0.232 hours of training time. Furthermore, the last variation (batch = 80, epochs = 500) resulted in mAP_0.5 = 19.5% and mAP_0.5:0.95 = 8.92% with 1.085 hours of training time. Therefore, the second variation is the best result for the model with 14.8 MB of size. Moreover, interfaces for the best model were displayed to show the result of the training.

A Review of Mobile Robot Navigation System for Volcano Monitoring Application

Volcano is a geological environment including magma, eruption, volcanic edifice and its basements. For continuous monitoring after eruption, a mobile robot could be proposed as an alternative to prevent hazardous effect to volcanologist who perform up close monitoring. In this paper, the robots were divided into 3 types according to their different structures: legged, track-legged and wheeled mobile robots. Meanwhile, the navigation system were implemented in 4 steps suitable for volcano condition: environment mapping, trajectory design, motion control and obstacle avoidance. These navigation system also tested in different locations: indoor, outdoor and real volcano with different testing method for these robots. The testing result was discussed in robot kinematics parameter such as trajectory, velocity, slope angle, rollover and sideslip angels.

A Review of Mobile Robot Navigation System for Volcano Monitoring Application

Volcano is a geological environment including magma, eruption, volcanic edifice and its basements. For continuous monitoring after eruption, a mobile robot could be proposed as an alternative to prevent hazardous effect to volcanologist who perform up close monitoring. In this paper, the robots were divided into 3 types according to their different structures: legged, track-legged and wheeled mobile robots. Meanwhile, the navigation system were implemented in 4 steps suitable for volcano condition: environment mapping, trajectory design, motion control and obstacle avoidance. These navigation system also tested in different locations: indoor, outdoor and real volcano with different testing method for these robots. The testing result was discussed in robot kinematics parameter such as trajectory, velocity, slope angle, rollover and sideslip angels.

Combined ground and aerial measurements resolve vent-specific gas fluxes from a multi-vent volcano

Volcanoes with multiple summit vents present a methodological challenge for determining vent-specific gas emissions. Here, using a novel approach combining multiple ultraviolet cameras with synchronous aerial measurements, we calculate vent-specific gas compositions and fluxes for Stromboli volcano. Emissions from vent areas are spatially heterogeneous in composition and emission rate, with the central vent area dominating passive emissions, despite exhibiting the least explosive behaviour. Vents exhibiting Strombolian explosions emit low to negligible passive fluxes and are CO2-dominated, even during passive degassing. We propose a model for the conduit system based on contrasting rheological properties between vent areas. Our methodology has advantages for resolving contrasting outgassing dynamics given that measured bulk plume compositions are often intermediate between those of the distinct vent areas. We therefore emphasise the need for a vent-specific approach at multi-vent volcanoes and suggest that our approach could provide a transformative advance in volcano monitoring applications.

Development of a robust mobile robot for volcano monitoring application

Indonesia is one of the countries that lies in the pacific ring of fire, the highlighted area that known to be active by seismic and volcano activities. Indonesia has a total of 129 active volcanoes that make the land fertile, but also vulnerable to disaster. When a volcanic eruption occurs, the current fixed monitoring system is not fully reliable. On the other hand, monitoring of further volcano activities is critically needed in this situation. Therefore, a volcano monitoring system that can move freely and controlled safely is needed. To solve this problem, a mobile robot that capable of moving in volcano area has been developed. The robot locomotion system is designed with 2 DC motor using 4-wheel drive configuration. Each motor implements a PID Controller to adjust the speed that has been set. In addition, the robot is also equipped with a camera (Logitech C920), vibration sensor (ADXL 345), temperature sensor (DHT 11), carbon dioxide gas sensor (MG-811), and sulphur dioxide gas sensor (TGS 2602) to retrieve volcanic condition data, as its function for volcano monitoring. The microcontroller used to adjust motor control and read sensors data is Nucleo STM32-F466RE, while the mini-PC that being used for integrated data communication and processing is Raspberry PI 3B+. PID Controller has been successfully applied with average deviation of 2.5% for the left motor, and 2.75% for the right motor.

Determining the Main CSMA Parameters for Adequate Performance of WSN for Real-Time Volcano Monitoring System Applications

This paper presents a study in order to identify the value range of the main parameters within carrier sense multiple access (CSMA) defined in IEEE 802.15.4 that guarantees a satisfactory wireless sensor network (WSN) performance for a volcano monitoring application. Moreover, this study performs the comparison among several test-beds in outdoor scenarios with the purpose of distinguishing the optimal number of nodes for each gateway according the main constraints given by an existing sensor network for real-time (RT) volcano monitoring system such as sampling time, packet loss, and delay. We used a mathematical model that works with Markovian techniques and involves some parameters of CSMA mechanism within the model, such as the minimum value of the backoff exponent (BEmin), the contention window length (W), and the number of slots (L). We obtained the approximate values of these parameters by the interpolation of the normalized throughput curves from the deployment, and thus, we could obtain a mathematical model with the specifications required for the RT volcano monitoring. After validating the model with test-bed outdoor deployments we found that BEmin, W and L are key factors for determining the performance of a WSN, these parameters guarantee the range in which the WSN works according to the constraints imposed for this particular volcano monitoring application.

Volcano monitoring applications of the Ozone Monitoring Instrument

AbstractThe Ozone Monitoring Instrument (OMI) is a satellite-based ultraviolet (UV) spectrometer with unprecedented sensitivity to atmospheric sulphur dioxide (SO2) concentrations. Since late 2004, OMI has provided a high-quality SO2dataset with near-continuous daily global coverage. In this review, we discuss the principal applications of this dataset to volcano monitoring: (1) the detection and tracking of large eruption clouds, primarily for aviation hazard mitigation; and (2) the use of OMI data for long-term monitoring of volcanic degassing. This latter application is relatively novel, and despite showing some promise, requires further study into a number of key uncertainties. We discuss these uncertainties, and illustrate their potential impact on volcano monitoring with OMI through four new case studies. We also discuss potential future avenues of research using OMI data, with a particular emphasis on the need for greater integration between various monitoring strategies, instruments and datasets.

Design of smart sensing components for volcano monitoring

In a volcano monitoring application, various geophysical and geochemical sensors generate continuous high-fidelity data, and there is a compelling need for real-time raw data for volcano eruption prediction research. It requires the network to support network synchronized sampling, online configurable sensing and situation awareness, which pose significant challenges on sensing component design. Ideally, the resource usages shall be driven by the environment and node situations, and the data quality is optimized under resource constraints. In this paper, we present our smart sensing component design, including hybrid time synchronization, configurable sensing, and situation awareness. Both design details and evaluation results are presented to show their efficiency. Although the presented design is for a volcano monitoring application, its design philosophy and framework can also apply to other similar applications and platforms.

Optimizing the Number of Double-Differenced Observations for GPS Networks in Support of Deformation Monitoring Applications

Deformation monitoring using GPS is usually carried out by installing and operating a local network of GPS receivers mounted on the deforming body, e. g., the flanks of a volcano. For continuous monitoring applications a near-real-time, epoch-by-epoch solution obtained from multi-baseline processing is desired in order to take into account between-baseline correlations and to detect movements over as short a period of time as possible. In the case of the volcano monitoring application, the sides of the volcano will block out part of the sky, hence the receivers are not likely to track a lot of satellites that are visible from all receiver stations at the same time. If the usual base-station/base-satellite approach is used in the baseline processing, only the common satellites are considered, resulting in the number of possible double-differenced observables being comparatively low; hence a ot of valuable information may be lost. The proposed method, based on the work by Saalfeld (1999), considers satellites that are visible from a small number of network stations only. Thus the number of independent double-differenced observables can be maximized in order to obtain a better solution. A numerical example is given that verifies the improved solutions that can be obtained using this data processing approach. © 2001 John Wiley & Sons, Inc.