Geophysical Monitoring Research Articles

Monitoring soil cracking using OFDR-based distributed temperature sensing framework

Soil cracking induced by extreme drought represents a widespread natural phenomenon occurring across the earth surface, capable of triggering multiple weakening mechanisms within surface soils, potentially leading to the instability and failure of slopes and agricultural infrastructures. This study proposes an innovative geophysical monitoring framework for detecting field soil cracking by combining the actively heated fiber-optic (AHFO) method and distributed fibre optical sensing (DFOS) based on optical frequency domain reflectometry (OFDR) technique, referred to as AH-OFDR framework. Laboratory calibration tests, field monitoring tests, numerical simulations, and sensitivity analyses were employed to comprehensively evaluate the feasibility, effectiveness, and limitations of the AH-OFDR framework for soil crack monitoring. Laboratory calibration confirmed that the DFOS-OFDR technique achieves a minimum spatial resolution and readout accuracy of 1 mm, along with a temperature measurement accuracy of ±0.1 °C. Field monitoring verified that the AH-OFDR framework can accurately detect soil cracks ranging in width from 0.01 m to 0.12 m. Additionally, numerical simulations not only validated the effectiveness of the AH-OFDR framework across a broader range of crack widths, from 0.01 m to 0.50 m, but also established a quantitative relationship between temperature changes and the spatial distribution of crack positions and widths. Notably, a critical crack width threshold of 0.30 m was identified within the AH-OFDR framework, significantly impacting the prediction of soil crack widths. Sensitivity analysis demonstrated the remarkable crack detection capabilities of the AH-OFDR framework, irrespective of the soil crack width and spacing. The AH-OFDR framework holds substantial potential as an innovative and high-resolution observational method for advancing our understanding of diverse geological and hydrogeological processes.

High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning

High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning

Electrical geophysical monitoring of subsurface solute transport in low-relief agricultural landscapes in response to a simulated major rainfall event

Electrical geophysical monitoring of subsurface solute transport in low-relief agricultural landscapes in response to a simulated major rainfall event

Geophysical Monitoring Technologies for the Entire Life Cycle of CO2 Geological Sequestration

Geophysical monitoring of CO2 geological sequestration represents a critical technology for ensuring the long-term safe storage of CO2 while verifying its characteristics and dynamic changes. Currently, the primary geophysical monitoring methods employed in CO2 geological sequestration include seismic, fiber optic, and logging technologies. Among these methods, seismic monitoring techniques encompass high-resolution P-Cable three-dimensional seismic systems, delayed vertical seismic profiling technology, and four-dimensional distributed acoustic sensing (DAS). These methods are utilized to monitor interlayer strain induced by CO2 injection, thereby indirectly determining the injection volume, distribution range, and potential diffusion pathways of the CO2 plume. In contrast, fiber optic monitoring primarily involves distributed fiber optic sensing (DFOS), which can be further classified into distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This technology serves to complement seismic monitoring in observing interlayer strain resulting from CO2 injection. The logging techniques utilized for monitoring CO2 geological sequestration include neutron logging methods, such as thermal neutron imaging and pulsed neutron gamma-ray spectroscopy, which are primarily employed to assess the sequestration volume and state of CO2 plumes within a reservoir. Seismic monitoring technology provides a broader monitoring scale (ranging from dozens of meters to kilometers), while logging techniques operate at centimeter to meter scales; however, their results can be significantly affected by the heterogeneity of a reservoir.

Progress in carbon dioxide capture, storage and monitoring in geological landform

Carbon Capture and Storage (CCS) is recognized as a potent strategy for managing the accumulation of human-generated CO2 in the atmosphere, helping to alleviate climate change’s effects. The CO2 gas is captured from the point source through methods such as pre-treating fossil fuels, oxy-fuel combustion, or post-combustion capture; thereafter; it is transported to a storage location and injected into geological formations. This article provides an overview of carbon dioxide capture and sequestration, focusing on its key principles, technologies, associated risks, and challenges. Direct Air Capture (DAC) and Scalable Modelling, Artificial intelligence (Al), Rapid Theoretical calculations SMART technologies are detailed as emerging and promising approaches to CO2 capture. Numerous pilot and commercial projects commissioned to manage carbon dioxide emissions are presented. Additionally, the paper explores approaches combining geological, geophysical, geochemical, and environmental monitoring techniques to ensure the secure and sustainable storage of CO2 underground. These are essential to address uncertainties, minimize risks, and build public confidence in CCS as a viable climate mitigation strategy. The successful deployment of these technologies on a global scale will require continued innovation, particularly in the areas of monitoring, risk management, and public engagement. Emerging technologies such as AI and SMART systems could play a crucial role in enhancing the efficiency and safety of CCS operations. However, the integration of these advancements with existing infrastructure and regulatory frameworks remains a challenge. Ultimately, a multi-disciplinary approach, combining technological, economic, and regulatory perspectives, will be vital to realizing the full potential of CCS in combating climate change.

Dynamics of rain-triggered lahars and destructive power inferred from seismo-acoustic arrays and time-lapse camera correlation at Volcán de Fuego, Guatemala

AbstractLahars, or volcanic mudflows, are one of the most devastating natural, volcanic hazards. Deadly lahars, such as the one that occurred after the Nevado del Ruiz, Columbia eruption in 1985, in which at least 23,000 people tragically lost their lives, threaten the safety and well-being of humans, the economy, and the infrastructure of many of the communities living in the vicinity of volcanoes. Due to their complex flow behaviors, lahars remain a major challenge to those studying them. We present an analysis of several rain-triggered lahar events at Volcán Fuego in Guatemala using both seismic and infrasound monitoring to quantify both ground vibrations and low-frequency atmospheric sound waves associated with these mudflows. Geophysical data collected over this field campaign quantifies flow parameters such as velocities, stage and the frequency of these rain-triggered lahars. Time-lapse imagery of lahar flows is compared with filtered seismo-acoustic signal characteristics to ascertain stage predictions and relationship to stage fluxes. Using random forest regression models, we establish moderate correlations (correlation coefficient modes 0.48–0.53) with statistical significance (p value = 0.01–0.02) between signal energetics and respective stage. Compiling a catalog of rain-triggered lahar events in Volcán de Fuego’s drainages over a season permits a dataset amenable to statistical analysis. Our goal is the development of new-generation geophysical monitoring tools that will be capable of remote and real-time estimation of flow parameters.

Stochastic control of geological carbon storage operations using geophysical monitoring and deep reinforcement learning

Geological carbon storage (GCS) is the process of injecting and storing carbon dioxide (CO2) in the subsurface to reduce greenhouse gas emissions. Safe and profitable GCS operations require effective decision-making in the presence of uncertain geological models, a process which can often be facilitated with geophysical monitoring. In this study, we examine how sequential decision-making algorithms can be combined with geophysical measurements for the optimal control of GCS operations. Specifically, we develop an artificial intelligence model using deep reinforcement learning (DRL) that takes geophysical time-lapse gravity and well pressure monitoring data as input and delivers an optimal CO2 injection policy. The objective of the problem at hand is to maximize the profit of a hypothetical GCS operation while mitigating the potential for induced seismicity, by training DRL agents using combined geostatistical, reservoir and geophysical simulation. Comparisons against two benchmarks – a constant injection strategy and an injection schedule optimized using a commercial reservoir simulator toolbox – show that the stochastic control of such operations from subsurface monitoring data using deep reinforcement learning is feasible. Evaluation results show that DRL agent behavior generates profits which are on average 1 to 8 percent higher than what is possible through a constant injection approach. Furthermore, we show that DRL can generate optimal injection policies applicable to the true (yet previously unseen) subsurface given carefully managed levels of uncertainty.

Statistical analysis of the ground deformation of Vulcanian explosions at Sakurajima volcano, Japan

The forecast of pulsatory explosions during volcanic unrest periods is an essential issue for the assessment and mitigation of volcanic hazards. Although various precursors are detectable through geophysical and geochemical monitoring, difficulties remain in precisely constraining possible scenarios. A probabilistic approach is effective in assessing risk while considering various uncertainties. Sakurajima volcano characterized by frequent Vulcanian activity is one of the suitable fields for the probabilistic forecast of pulsatory explosions. Their inflation-deflation patterns of ground deformation related to Vulcanian explosions are useful for evaluating the imminence and size of the next event. The large database obtained from its vigorous activity can contribute to statistical analysis. In this study, aiming the probabilistic forecast of the timing and size of explosions, we investigated the duration of inflation and volume changes at the pressure source using strain records of over 5000 events of Sakurajima volcano. Then, a stochastic model was estimated to explain the distribution of these events. The log-logistic distribution was found to be an appropriate model for data distribution, indicating the presence of competing processes, such as pressurization and depressurization, in the conduit. The model parameters of the log-logistic distribution temporally fluctuated reflecting the volcanic activity, especially increasing the magma supply from a deep region. We also suggested a methodology to constrain the probabilities of the likely timing and size of an imminent explosion using real-time strain monitoring and an estimated model distribution. Although some improvements would be needed for practical forecasting, our approach could be useful in predicting possible ash hazards.

Analysis of Naturally Occurring Radioactive Material (NORM) in shifting earth layers in Piyungan, Bantul

In recent decades, understanding the correlation between radon gas emissions and geodynamic processes has been pivotal in enhancing geophysical monitoring. This study advances current methodologies by refining radon data analysis and employing mathematical models to further elucidate this relationship. We developed a radon well geometry for Piyungan using Finite Element Method principles, and conducted simulations via GNU Octave to assess normal and anomalous conditions. The results indicate a basal radon concentration of 2.2420 Bq/L at a depth of 14.2308 m. Notably, radon levels inversely correlate with the water volume and diffusivity, yet proportionally increase with soil moisture. These findings offer a nuanced understanding of radon behavior in response to geodynamic activity, paving the way for improved environmental monitoring.

Introduction to this special section: Geophysics and sustainability

Unprecedented levels of greenhouse gasses in the atmosphere drive climate change by trapping heat and raising global temperatures. This warming effect disrupts weather patterns, increases the frequency of natural disasters, and contributes to rising sea levels. Applied geophysics can significantly alleviate anthropogenic emissions and accelerate the transition to cleaner energy sources such as wind, solar, geothermal energy, and hydrogen. One solution at the core of energy transition is the broad application of carbon capture, utilization, and geologic storage (CCUS) at the scale of gigatonnes of sequestered CO2 to reduce greenhouse gas emissions during the transition to a carbon-neutral future. Geophysical monitoring is central to the success of CCUS. In particular, seismic methods serve as a primary tool to ensure compliance and safety in CCUS projects. However, the role of geophysics in sustainability is a broader topic extending beyond carbon geosequestration.

Multiphysics framework for cost-effective, long-term monitoring of carbon storage in a carbonate reservoir

We evaluate an effective and cost-efficient multiphysics approach for long-term monitoring of carbon storage in carbonate reservoirs. A key focus of our study is to advocate for a cost-effective, long-term monitoring strategy for carbon storage projects through the integration of surface and borehole multiphysics measurements. By leveraging technologies related to electromagnetic (EM) and gravity methods, we present a framework that addresses the monitoring of the dynamic behavior of injected CO2 over an extended period. The modeling setup integrates realistic static reservoir models with simulated dynamic saturation models to provide results as close as possible to real experimental conditions. Results demonstrate the high sensitivity of EM fields to changes in reservoir CO2 saturation, especially when surface-to-reservoir acquisition configurations are adopted. Additionally, gravity simulations, employing three-axis vector surface gravity measurements and borehole vector gravity, effectively capture density variations over time. Our proposed framework highlights the sequential use of seismic imaging for preinjection site characterization and the transition to EM and gravity surveys for continuous monitoring during and after the injection phases. The multiphysics simulation results performed on realistic carbon storage conditions contribute to the evolving landscape of effective geophysical CO2 monitoring technology for reliable long-term reservoir management.

Production processes optimization through machine learning methods based on geophysical monitoring data

The purpose of the article is to create an effective method for low-delay monitoring of the operating state of a drill string and a drill bit without interfering with the proper drilling process. For the drilling process to be continuously controlled, an experimental setup that operates by utilizing the phase-metric method of control was developed. Any movement of the bit causes a change in the electrical characteristics of the probing signal. To obtain a stable signal from a bit immersion depth of up to 250 m, a frequency of probing electrical signals of 166 Hz and an amplitude of up to 500 V were used; the sampling rate of an analog-to-digital converter (ADC) was 10101 Hz. To identify the state of the drill string and the bit based on graphs of time-dependences of changes in the probing signal electrical characteristics, the present authors investigated a number of deep learning methods. Based on the results of the study, a series of capsular neural network methods ( CapsNet ) was chosen. The authors developed modifications of 2D-CapsNet: windowed Fourier transform (WFT) - 2D-CapsNet and frequency slice wavelet transform (FSWT) - 2D-CapsNet. Both of these methods showed a 99% accuracy in determining the transition between two layers of rocks with different properties, which is 2–3% higher than the currently used measurement-while-drilling (MWD) and logging-while-drilling (LWD) rock surveys. Both of these methods unambiguously reveal self-oscillations in the drill string. When determining a fully serviceable bit in the case of self-oscillations, the (FSWT) - 2D-CapsNet method showed an accuracy of 99%.

Recent Expansion of the Cascades Volcano Observatory Geophysical Network at Mount Rainier for Improved Volcano and Lahar Monitoring

Abstract The U.S. Geological Survey Cascades Volcano Observatory (CVO) recently expanded its continuous geophysical monitoring at Mount Rainier, an active stratovolcano in Washington state. CVO monitors volcanoes in Oregon, Washington, and Idaho to characterize volcanic systems and detect unrest. Mount Rainier has a history of large lahar occurrences in the Holocene, including at least one that may not have been associated with volcanic activity. Pierce County, Washington, is one of the areas most at risk from large lahars. In the 1990s, CVO collaborated with Pierce County to install the Rainier lahar detection system (RLDS), an automated system designed to detect large lahars in high-risk drainages and mitigate hazards to heavily populated areas. The system was designed to detect lahars within 5–10 min of their occurrence and alert authorities of the need to evacuate populated low-lying areas before lahar arrival. In addition, CVO and the Pacific Northwest Seismic Network (PNSN) maintained and expanded a network of seismic and geodetic monitoring stations on and near the edifice to provide adequate volcano monitoring capabilities. Since 2016, CVO has worked to upgrade the existing RLDS and to expand its capabilities into other drainages around Mount Rainier. This expansion includes installation of 25 new broadband seismic stations with many including infrasound along high-risk drainages, as well as support for equipment upgrades at existing PNSN and CVO volcano monitoring sites. All stations transmit continuous, near-real-time data with dramatically improved spatial coverage for volcano monitoring and lahar hazard mitigation compared to the previous system.

Integrated geochemical and geophysical monitoring for the assessment of natural pollutant mitigation in heavy metal-contaminated areas of South Korea, Deagu.

The evaluation and monitoring of sites contaminated with heavy metals are essential for pollution remediation and prevention. In this study, we conducted geophysical and geochemical investigations at a site exhibiting heavy metal contamination downstream from an abandoned mine, with the aim of analyzing the extent of contamination and its temporal variation. We employed geophysical survey methods including electrical resistivity and induced polarization surveys of areas contaminated with heavy metals. Repeated surveys were conducted over time using the electrical resistivity method. Numerical simulations were employed to mitigate and eliminate electrical noise stemming from topography on the site. Additionally, time-lapse inversion was conducted on the resistivity data sets to analyze the changes in resistivity caused by variations in heavy metal contaminants. In the geochemical survey, soil samples were collected from the same locations as the geophysical survey, and chemical properties including pH, water content, electrical conductivity, and cation exchange capacity were analyzed. Our results showed that with the reduction of major sources of As and Zn contamination by 50%, the time-lapse electrical resistivity inversion results indicated that the resistivity of the subsurface materials increased by a factor of two. This paper demonstrated the natural reduction of the heavy metal contaminants at the site due to rainfall, aiming to comprehensively analyze the resultant alteration of both geochemical and geophysical properties.

Evaluating the contribution of geophysics to the assessment of natural hazards

This paper discussed the role of geophysics in the evaluation of natural hazards with the view to enhancing familiarity with earth shakes, ground characteristics, landslide likelihood, and volcanic deliveries. Semantic and geophysical data were collected from seismic surveys, measurement of groundwater, electrical resistivity of the subsoil and gas emission. Seismic data analysis revealed diverse activity across sites, with magnitudes ranging from 3.234 to 6.456 Mw, emphasizing the importance of geophysical monitoring in identifying high-risk areas. Groundwater measurements indicated spatial variations in water table depth from 1.234 to 6.789 m, essential for effective resource management. Soil resistivity values ranged from 75.123 to 145.901 Ohm-m, providing insights into soil properties relevant for geotechnical and environmental studies. Landslide susceptibility assessment used the factor of slope angle, vegetation cover, and rainfall intensity; these were 10. 901° to 28. 345°, 55. 234% to 88. 456%, and 6. 789 to 20. 901 mm/hr respectively. The gas emission of SO2 ranged between 0. 123–0. 901 kg/s, that of CO2, 0. 456–1. 234 kg/s, while H2S ranged between 0. 789–1. 567 kg/s based on the emission of the several sites suitable for eruption prediction models. A statistical approach was used in which Histogram, scatter plot and radar chart were used to explain the data collected. The study conclude that the application of various geophysical methods is strikingly useful in natural hazard and risk assessment and planning and therefore beneficial in reducing disaster risks. These results provide compelling evidence about the importance of geophysics in propagating awareness in the Earth processes and improvement of hazards.

Integrated geophysical observations and information resources of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences (Kamchatka Peninsula)

The article presents data on the types of observations and information resources of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences (as of 2022), which can be used by scientific and educational organizations of the Russian Federation for studying precursors and developing methods for predicting earthquakes and volcanic eruptions, solving a wide range of research tasks in the field of geophysical monitoring of seismically active areas and others. Information resources and observation data are illustrated on the example of the Unified Information System of Seismological Data of the KB GS RAS and time series data of meteorological, hydrogeological observations and observations of volumetric radon activity in the soil gas at the Moroznaya station.

Intrusive mechanism of the 2018 Sierra Negra Galápagos eruption, constrained by 4D tomographic images

Understanding the mechanisms of magma intrusion underpins our ability to interpret geophysical monitoring signals at volcanoes and thus issue reliable forecasts of future activity. The basaltic caldera volcanoes of the western Galápagos Islands, Ecuador, exhibit exceptionally high rates of deformation driven by shallow magma accumulation and migration. However, the nature and evolution of magma storage at these volcanoes is poorly constrained by earthquake hypocentre locations or geodetic inversions. Here we show that transient variations in seismic velocity before, during, and after the 2018 eruption of the Sierra Negra volcano track the accumulation of magma in a shallow sill complex and the emplacement of a lateral flank intrusion. A four-dimensional tomographic technique applied to the P-wave arrivals of local earthquakes provides high spatiotemporal resolution of changes in the physical properties of the shallow volcanic system. In the month before the eruption, the expansion of a low-velocity zone above the sub-caldera sill complex coincides with caldera uplift and near-surface fracturing, driven by persistent shallow magma accumulation. A new low-velocity anomaly appeared progressively in the western flank of the volcano in the days after the onset of the eruption, coinciding with the opening of a curved sill that supplied magma to secondary eruptive fissures. The anomaly disappeared as the curved sill deflated after the initial opening, despite it remaining the conduit for magma from the caldera complex to the flank fissures. Low velocities across the shallow caldera after the end of the eruption likely result from rapid inflation due to recharge with fresh magma from depth. These results indicate a previously unknown complexity to the magmatic plumbing system at Sierra Negra and suggest velocity changes resulting from an interplay of thermal and stress perturbations.

4D Electrical Resistivity Imaging of Stress Perturbations Induced During High‐Pressure Shear Stimulation Tests

AbstractFluid flow through fractured media is typically governed by the distribution of fracture apertures, which are in turn governed by stress. Consequently, understanding subsurface stress is critical for understanding and predicting subsurface fluid flow. Although laboratory‐scale studies have established a sensitive relationship between effective stress and bulk electrical conductivity in crystalline rock, that relationship has not been extensively leveraged to monitor stress evolution at the field scale using electrical or electromagnetic geophysical monitoring approaches. In this paper we demonstrate the use time‐lapse 3‐dimensional (4D) electrical resistivity tomography to image perturbations in the stress field generated by pressurized borehole packers deployed during shear‐stimulation attempts in a 1.25 km deep metamorphic crystalline rock formation.

Understanding the dynamics of enhanced light non-aqueous phase liquids (LNAPL) remediation at a polluted site: Insights from hydrogeophysical findings and chemical evidence

This study intricately unfolds a pioneering methodology for remediating contaminants in a persistent light non-aqueous phase liquids (LNAPL)-contaminated site. The remediation strategy seamlessly integrates enhanced desorption and in-situ chemical oxidation (ISCO), orchestrating the injection of PetroCleanze® (a desorbent) and RegenOx® (an oxidizer) through meticulously designed wells. These injections, based on detailed geological and hydrogeological assessments, aim at mobilizing residual contaminants for subsequent extraction. Real-time subsurface dynamics are investigated through geophysical monitoring, employing electrical resistivity tomography (ERT) to trace reagent migration pathways via their effect on bulk electrical conductivity. The integration of groundwater sampling data aims at providing additional insights into the transformations of contaminants in the spatiotemporal context. Vivid two-dimensional time-lapse ERT sections showcase the evolution of resistivity anomalies, providing high-resolution evidence of the heterogeneity, dispersion pathways of desorbent and oxidant, and residual LNAPL mobilization. Hydrochemical analyses complement this, revealing effective mobilization processes with increasing aqueous concentrations of total petroleum hydrocarbons (TPH) over time. Speciation analysis unveils the intricate interplay of desorption and oxidation, portraying the dynamic fractionation of hydrocarbon components. The hydrogeophysical and data-driven framework not only delivers qualitative and quantitative insights into reagent and contaminant distribution but also enhances understanding of spatial and temporal physio-chemical changes during the remediation process. Time-lapse ERT visually narrates the reagent's journey through time, while chemical analyses depict the unfolding processes of desorption and oxidation across space and time. The coupling of hydrogeophysical and chemical findings pictures the transformations of pollutants following the sequence of product injection and the push and pull activities, capturing the removal of mobilized contaminants through hydraulic barrier wells. This enhanced understanding proves instrumental towards optimizing and tailoring remediation efforts, especially in heterogeneous environmental settings. This study establishes a new standard for a sophisticated and innovative contaminant remediation approach, advancing environmental practices through the harmonized analysis of geophysical and chemical data.

Experimental study of geophysical and transport properties of salt rocks in the context of underground energy storage

AbstractArtificial caverns in salt rock formations play an important role in the net‐zero energy transition challenge, both for covering short‐term fluctuations in energy demand and serving as safe locations for long‐term underground gas storage both for hydrogen and natural gas. Geophysical tools can serve for monitoring geomechanical changes in the salt cavern during selection and development, and during gas storage/extraction activities, but the use of common geophysical monitoring techniques has been very limited in this area. Here, we present experimental work on physical and transport properties of halite rocks within the energy storage context and assess the potential of seismic and electromagnetic data to monitor gas storage activities in salt formations. First, we analysed the stress‐dependency of the elastic and transport properties of five halite rocks to improve our understanding on changes in the geological system during gas storage operations. Second, we conducted two dissolution tests, using cracked and intact halite samples, monitored with seismic (ultrasonic P‐ and S‐waves velocities and their attenuation factors) and electromagnetic (electrical resistivity) sources to evaluate (i) the use of these common geophysical sensing methods to remotely interpret caverning development and (ii) the effect of structural discontinuities on rock salt dissolution. Elastic properties and permeability showed an increasing trend towards rock sealing and mechanical enhancement with increasing pressure for permeabilities above 10−21 m2, with strong linear correlations up to 20 MPa. In the dissolution tests, the ultrasonic waves and electrical resistivity showed that the presence of small structural discontinuities largely impacts the dissolution patterns. Our results indicate that seismic and electromagnetic methods might help in the selection and monitoring of the caverning process and gas storage operations, contributing to the expected increase in demand of large‐scale underground hydrogen storage.