A novel approach that uses tipping point analysis for identifying instrumental changes in sensor data that may not have full description of legacy hardware. The technique helps interpret changes of pattern in the data (autocorrelations) and distinguish them from climatic and environmental effects. This is particularly important for historic datasets, where instrumental changes may be undocumented or lack metadata.

We developed a correction model based on laboratory data, taking into account elements such as fluid density and viscosity. Our model showed high accuracy and outperformed other models. When applied in real situations, it significantly reduced the differences in calculated stresses for different fracturing fluids.

We tested solar regulators to find their suitability for use in powering instruments in the polar regions. We found that some models waste a lot of power and may result in instruments failing during the wintertime. We developed a model to illustrate this effect, and use it to show that a good choice of solar regulator means a greater chance of successful winter data collection and allows the use of a smaller, lighter, cheaper battery.

The effect of lightning strikes on the data with increasing distance, intensity and ground conductivity shows that higher intensity strikes has had an impact of the data and instruments. It is crucial to determine the location and configuration where the installation can avoid the effects groundwater fluctuations as well as lightning strikes. The susceptibility model along with conductivity information are used to make a selection of a new site for trial measurements.

A Python-based software was developed for real-time visualization and quality control at India’s CPL and HYB geomagnetic observatories. The tool generates plots, conducts quality checks, and computes first differences at 1s and 1min intervals with under 300s latency, aiding anomaly detection and quasi-definitive data preparation. Designed for future integration with AI/ML, this system enhances geomagnetic data accuracy and accessibility, revolutionizing research, forecasting, and visualization.

We develop a machine learning methods to detect and classify how much sea ice was present around our research vessel. We used a navigation radar common on many merchant vessels attached to a screen capture device. The captured images were classified using a convolutional neural network and the resulting classification were found to be in good agreement with direct observations and satellite-based products.

The QGIS Sentinel Indices plugin (QSVI) is an efficient, user-friendly tool designed to calculate key environmental remote sensing indices. Developed using Python library within the QGIS software, QSVI significantly reduces computation time, exceeding other popular GIS applications. This study demonstrates its effectiveness in processing large datasets, offering a cost-effective solution for environmental monitoring and analysis, particularly valuable in educational settings.

In the field of Marine Controlled-source Electromagnetic (MCSEM), the evident issue of heating caused by the demand for high power was observed during previous marine experiments, where devices and cables were damaged due to overheating. Therefore, the idea was conceived to design a non-contact temperature measurement system tailored specifically for MCSEM, with the capacity to seamlessly adapt to the existing hardware conditions at sea.

The increasing impact of climate change and human activities on greenhouse gas emissions highlights the need for effective monitoring, especially from the soil. Our design introduces a low-cost solution for measuring soil gas flux that is adaptable to various environments. Additionally, we propose a novel method for ensuring data quality before deploying these systems in the field.