Geophysical Fields Research Articles

Research on elastic parameter inversion method based on seismic facies-controlled deep learning network

Deep learning has been widely applied in the field of geophysics, and existing research literature has demonstrated that intelligent geophysical inversion methods have high vertical resolution but low horizontal resolution. The reason lies in the fact that existing horizontal constraint methods mainly adopt convolutional models, without fully considering other prior information of seismic data. Within the same sedimentary unit, seismic response characteristics vary gradually due to similar lithology and geological characteristics. Therefore, the seismic facies information extracted from seismic data is integrated into deep learning network to enhance the horizontal prediction stability of the network. Firstly, according to the spatial and temporal characteristics of seismic data, a fusion network of three-dimensional convolutional neural network (3D-CNN), gated recurrent unit (GRU) and attention mechanism is established to improve the vertical resolution of inversion results. Then, seismic facies classification of the target layer is achieved by applying the K-means clustering method. Subsequently, to improve the horizontal resolution of the inversion results, seismic facies classification is transformed into temporal encoding data using the position coding theory in natural language processing, to form a seismic facies-controlled deep learning network. Finally, the deep learning network is trained and tested in the thin interlayer model and practical application adopting a semi-supervised learning method. The results indicate that incorporating seismic facies-controlled technology in the deep learning network can improve the horizontal resolution of the inversion results.

Analysis of spatial structure and singularity of microbial biogeochemical anomalies in the Taiwan Strait Basin

The abundance of specific hydrocarbon-oxidizing bacteria (MV value) in surface soil is closely linked to the concentration of thermogenic light hydrocarbons found in the underlying layers. The identification of these bacteria using specific detection techniques offers unique insights into microbiological anomalies associated with oil and gas, underscoring the significance of delineating microbiological geochemical anomalies in the exploration process. Accurate determination of the anomaly range is crucial for forecasting potential oil and gas reservoirs. In this study, a variety of fractal and geological statistical methods were employed to analyze microbiological geochemical data from the Taiwan Strait Basin. The multifractal approach allows for the simultaneous assessment of spatial autocorrelation and singularity in geochemical fields, with the fractal distribution reflecting the localized enrichment and depletion patterns of microbiological geochemical elements in rocks and other substrates. Geological statistical methods, such as the variation function, enable the assessment of spatial autocorrelation in geochemical and geophysical fields. The results indicate that microbiological anomalies in the Jiulongjiang Depression are mainly concentrated in the southwestern part of the study area, with some anomalies also observed in the central region. Conversely, microbiological anomalies in the Jinjiang Depression are predominantly located in the southern portion of the study area. Microbiological anomalies in the study area (Jinjiang Depression and Jiulongjiang Depression) exhibit a northeast-southwest strip distribution, aligning with the trend of the depressions and faults, suggesting that faults could influence the distribution of oil and gas resources in the region. Based on microbiological statistical analyses, the Jiulongjiang Depression shows better potential for oil and gas prospects compared to the Jinjiang Depression, guiding future research efforts in the Jiulongjiang Depression.

2D Magnetotelluric Resistivity Structure Modeling Using Finite Element Method Based on Vector Triangular Grid and Its Application to Lembang Fault MT Data

Abstract Magnetotellurics is one of the geophysical exploration techniques that relies on the natural fluctuations of electromagnetic waves to delineate their influence on the Earth. The primary focus of this method is to reveal the structure of Earth’s subsurface with the value of resistivity. The application of numerical approaches in magnetotelluric modeling has proven to be an efficient method in various theoretical studies in the field of geophysics, particularly in the context of modeling two-dimensional structures. This research explains a 2D resistivity structure modeling using a vector finite element method. This approach utilizes the edges of elements as vector bases. The presented results include response values such as apparent resistivity and impedance phase at the surface. The study employs the standard model from COMMEMI as a reference to validate the modeling program. Furthermore, the results from this modeling program are compared with the outcomes of the modeling program developed by Weaver et al. The good results were obtained with error values for each model for layered and homogeneous Earth < 3%. Additionally, for the reference model COMMEMI, errors of 3.4393% and 1.4050% were obtained for TE and TM modes, respectively. Furthermore, apparent resistivity and impedance phase results closely approximated the reference values for the topography model. Subsequently, in the application to field data, specifically the Lembang Fault, errors were obtained for the TE and TM modes within the range of 1.16 – 9.16% for each MT data acquisition site.

Frictional Contacts Between Rough Grains With Fractal Morphology

AbstractSurface morphology plays a crucial role in friction between two contacting geomaterial surfaces, yet many questions remain unanswered regarding how detailed frictional responses deviate from analytical solutions for smooth surfaces due to the presence of roughness. In this study, we revisit the Cattaneo‐Mindlin problem for contacts between two fractally rough elastic or elasto‐plastic spheres generated based on ultra‐high degree (e.g., up to 2,000) spherical harmonics with the corresponding wavelength less than a thousandth of the mean grain diameter. Transverse contacts are simulated by finite element method, validated by the extended Cattaneo‐Mindlin solution to full slide regime for smooth sphere contacts. Extensive simulations are conducted to study contacts between two rough spheres with various surface geometries, micro friction coefficients, normal contact distances, relative roughness, fractal dimensions, and wavelength ranges. Out results indicate that: (a) the new analytical solution can approximately predict the macro contact response except for extremely high relative roughness and narrow wavelength range; (b) deviations induced by roughness from smooth sphere contacts can be neutralized by plasticity, high normal contact interference, and high micro friction coefficient; and (c) fractal dimension impacts frictional contacts less than relative roughness. The main cause of these phenomena can be credited to the underlying microscale contact information. Contact area and stress distributions and their evolutions provide concrete evidence of these observed behavior. This work provides a pathway for applying computational contact mechanics to many geophysical fields, such as the asperity model in earthquake science and the mechanics of granular materials.

KNUST SEG Geophysics Field Camp: A project to study slavery-related archaeology in West Africa

In this article, we describe a field camp in West Africa that explored the use of geophysics in archaeological investigations related to the transatlantic slave trade. Africa's geodiversity and richness of natural resources makes geoscience education essential for social, academic, scientific, and professional growth and development. Field camps serve as a pivotal element of geoscience pedagogy, providing hands-on experiential learning opportunities for budding geoscientists. Due to a lack of funding, there is limited access to and availability of geoscience field camps for students and early-career professionals in Africa. The main goal of our field camp was to delineate archaeological relics related to the transatlantic slave trade in the southeastern part of Ghana. The field camp provided a way to conduct research and train geoscience students in planning and performing geophysical investigations. It also taught students how to process and interpret geophysical data and prepare and present scientific investigation reports. We investigated sites in Aflao, Denu, Hedzranawo, and Keta in the southeastern part of Ghana. Sites of interest included former slave markets, barracoons, and a fort. The project highlighted locations of potential slavery-related archaeological relics and shared the importance of the findings and their implications for understanding the history of transatlantic slavery in Ghana.

Analytic solutions for effective elastic moduli of isotropic solids containing oblate spheroid pores with critical porosity

AbstractAccurate characterization for effective elastic moduli of porous solids is crucial for better understanding their mechanical behaviour and wave propagation, which has found many applications in the fields of engineering, rock physics and exploration geophysics. We choose the spheroids with different aspect ratios to describe the various pore geometries in porous solids. The approximate equations for compressibility and shear compliance of spheroid pores and differential effective medium theory constrained by critical porosity are used to derive the asymptotic solutions for effective elastic moduli of the solids containing randomly oriented spheroids. The critical porosity in the new asymptotic solutions can be flexibly adjusted according to the elastic moduli – porosity relation of a real solid, thus extending the application of classic David‐Zimmerman model because it simply assumes the critical porosity is one. The asymptotic solutions are valid for the solids containing crack‐like oblate spheroids with aspect ratio < 0.3, nearly spherical pores (0.7 < < 1.3) and needle‐like prolate pores with > 3, instead of just valid in the limiting cases, for example perfectly spherical pores (= 1) and infinite thin cracks (0). The modelling results also show that the accuracies of asymptotic solutions are weakly affected by the critical porosity and grain Poisson's ratio , although the elastic moduli have appreciable dependency of and . We then use the approximate equations for pore compressibility and shear compliance as inputs into the Mori–Tanaka and Kuster–Toksoz theories and compare their calculations to our results from differential effective medium theory. By comparing the published laboratory measurements with modelled results, we validate our asymptotic solutions for effective elastic moduli.

A 85Rb transverse modulation magnetometer in the geophysical range based on atomic alignment states

Abstract We have constructed a transverse modulated magnetometer based on spin alignment in a paraffin-coated 85 Rb cell operated in a geophysical magnetic field of 47.1 µT. When an orthogonal driving magnetic field ( x ^ axis) is resonant on the Larmor frequency ( z ^ axis), we have proposed a new method to zero the static residual magnetic fields in the transverse plane and achieved a sensitivity of 1.8 pT Hz − 1 with bandwidth of 200 Hz. The repump light ( F g = 2 → F e = 2 ) redistributes the populations in the ground state, rendering the state | F g = 2 ⟩ dark. This effect significantly amplifies the optical rotation signals nearly fourfold. The numerical solution of the Liouville equation is in good agreement with the experimental results. By using perturbation treatment and employing appropriate approximations, the derived analytical expressions for optical rotation are deduced to succinctly elucidate the dynamics of atomic alignment under parametric modulation. These outcomes could be extended for the advancement of an alignment magnetometer that has been designed to detect a weak magnetic signal in the geophysical range.

Neotectonic evolution of the Caucasus: recent vertical movements and mechanism of crustal deformation

It is generally recognized that the formation of the fold-and-thrust tectonic structures of mobile belts on continents is associated with crushing and narrowing of the Earth’s crust as a result of collision of lithospheric plates. The deformation of the Caucasian lithosphere in the neotectonic time is generally consistent with these ideas. However, the block differentiation of the Caucasian lithosphere introduces specific features in the directivity of modern vertical and horizontal movements. In this paper, we analyze vertical movements of the Caucasus estimated by means of high-precision leveling over more than a century and consider their spatial correlation with tectonics, seismicity, stress-strain state, and geophysical fields. A clear relationship indicating the deep tectonic nature of the long-term uplifting of the Caucasus crust is revealed. Due to the differentiation of the Arabian plate movement, the territory of the Caucasus is divided into provinces that differ from each other in the pattern of modern movements, in the orientation of faults, and in the stress-strain state. The seismic regime in these provinces also has differences in the number of seismic events and focal mechanisms of the earthquakes. We propose a model of the deformation mechanism of the Greater Caucasus, which takes into account the long-term trend of the Caucasus uplifting in the conditions of general shortening of the Earth’s crust. The results of the analysis are used as a basis for discussion of a probable mechanism of tectonic evolution of the Greater Caucasus in the neotectonic time, which can be used in the assessment of seismic hazard in the North Caucasus.

Significance testing for cross correlation: A critical examination of correlations between ENSO and GRACE-derived terrestrial water storage variabilities

The cross correlation has a wide range of applications in geophysical fields for measuring linear connections or relationships among physical quantities. Nonetheless, there remains a dearth of comprehensive discourse regarding its statistical significance testing, which is crucial for differentiating meaningful outcomes from those merely stemming from fortuity or pure randomness. Conventional theoretical methods for significance testing, commonly used in statistical analysis tools such as SPSS and MATLAB, are only applicable when dealing with idealized circumstances such as white noise. In discretionary application of these methods to analyze geophysical signals with, say, red noise may result in potentially unjustified conclusions. This study aims to develop a comprehensive approach based on the t-distribution within a rigorous statistical context, aiming to facilitate significance tests of cross correlation for general signals with specified time shifts or ranges. Extensive Monte Carlo experiments substantiate its robustness, thereby paving the way for accurate and expeditious identification of statistically (hence potentially physically) meaningful correlations in general. As an example, we examine critically the previously purported significant correlations between ENSO (El Niño Southern Oscillation) and global terrestrial water storage variations derived from the GRACE (Gravity Recovery and Climate Experiment) satellite mission, demonstrating that they are subject to questioning in the absence of complete significance testing.

Compressing the memory variables in constant-Q viscoelastic wave propagation via an improved sum-of-exponentials approximation

Earth introduces strong attenuation and dispersion to propagating waves. The time-fractional wave equation with very small fractional exponent, based on Kjartansson's constant-Q theory, is widely recognized in the field of geophysics as a reliable model for frequency-independent Q anelastic behavior. Nonetheless, the numerical resolution of this equation poses considerable challenges due to the requirement of storing a complete time history of wavefields. To address this computational challenge, we present a novel approach: a nearly optimal sum-of-exponentials (SOE) approximation to the Caputo fractional derivative with very small fractional exponent, utilizing the machinery of generalized Gaussian quadrature. This method minimizes the number of memory variables needed to approximate the power attenuation law within a specified error tolerance. We establish a mathematical equivalence between this SOE approximation and the continuous fractional stress-strain relationship, relating it to the generalized Maxwell body model. Furthermore, we prove an improved SOE approximation error bound to thoroughly assess the ability of rheological models to replicate the power attenuation law. Numerical simulations on constant-Q viscoacoustic equation in 3D homogeneous media and variable-order P- and S- viscoelastic wave equations in 3D inhomogeneous media are performed. These simulations demonstrate that our proposed technique accurately captures changes in amplitude and phase resulting from material anelasticity. This advancement provides a significant step towards the practical usage of the time-fractional wave equation in seismic inversion.

FPGA-based cesium optical pump magnetometer data recording system design

Abstract A cesium optical pump magnetometer is a kind of magnetic detection instrument commonly used in geophysical and military fields, which has significant advantages such as high sensitivity, insensitivity to attitude change, and no zero drift. Compared with manned aircraft, rotary-wing UAVs have the advantages of flexible takeoff and landing, low cost, and suitability for small measurement areas, so the aeromagnetic system based on the integration of rotary-wing UAV platforms and cesium optical pump magnetometers has a strong practical value. This paper investigates an FPGA-based CS-3 cesium optical pump magnetometer with a high-precision magnetic field data recording system for building a miniaturized aeromagnetic measurement system based on a rotary-wing unmanned aerial vehicle (UAV) platform, and the system achieves a magnetic field measurement accuracy of 3.4 pT.

Refining tomography with generative neural networks trained from geodynamics

SUMMARY Inverse problems occur in many fields of geophysics, wherein surface observations are used to infer the internal structure of the Earth. Given the non-linearity and non-uniqueness inherent in these problems, a standard strategy is to incorporate a priori information regarding the unknown model. Sometimes a solution is obtained by imposing that the inverted model remains close to a reference model and with smooth lateral variations (e.g. a correlation length or a minimal wavelength are imposed). This approach forbids the presence of strong gradients or discontinuities in the recovered model. Admittedly, discontinuities, such as interfaces between layers, or shapes of geological provinces or of geological objects such as slabs can be a priori imposed or even suggested by the data themselves. This is however limited to a small set of possible constraints. For example, it would be very challenging and computationally expensive to perform a tomographic inversion where the subducting slabs would have possible top discontinuities with unknown shapes. The problem seems formidable because one cannot even imagine how to sample the prior space: is each specific slab continuous or broken into different portions having their own interfaces? No continuous set of parameters seems to describe all the possible interfaces that we could consider. To circumvent these questions, we propose to train a Generative Adversarial neural Network (GAN) to generate models from a geologically plausible prior distribution obtained from geodynamic simulations. In a Bayesian framework, a Markov chain Monte Carlo algorithm is used to sample the low-dimensional model space depicting the ensemble of potential geological models. This enables the integration of intricate a priori information, parametrized within a low-dimensional model space conducive to efficient sampling. The application of this approach is demonstrated in the context of a downscaling problem, where the objective is to infer small-scale geological structures from a smooth seismic tomographic image.

Integrated Amphibious Distributed Acoustic Sensing for Seismic Monitoring in the Xinfengjiang Reservoir

Abstract The rapidly advancing technology of distributed acoustic sensing (DAS) has profoundly impacted the field of underwater geophysics. Our study investigates the effectiveness of DAS in underwater geological stability monitoring, with a particular focus on microseismic monitoring in the Xinfengjiang reservoir. The 6.2 km long acquisition setup, covering both land and reservoir bottom, was verified using temporary shore-based short-period seismometers to ensure reliable data acquisition in various environments. Higher background noise was observed on the land section compared with the lakebed section during the day, whereas both sections exhibited similar noise levels at night. We confirmed that the DAS system was capable of detecting distant microseismic events, some of which were previously unreported. These detections exhibited temporal and phase consistency with neighboring seismometers. Comparison of signal-to-noise ratios indicates that the lakebed section demonstrates higher sensitivity. This system delivers cost-effective performance through natural settling, negating the requirement for costly embedding methods. Moreover, the DAS system identified “comet-like” small-scale signals on the lakebed that had eluded shore-based seismometers. This exemplifies the exceptional high-density and high-resolution capabilities of DAS technology in both aquatic and terrestrial environments. This study underscores the pivotal role of the DAS technology in conducting underwater microseismic monitoring, real-time seismic monitoring, seismic mechanism research, and earthquake hazard assessment.

A Self‐Supervised Framework for Refined Reconstruction of Geophysical Fields via Domain Adaptation

AbstractReconstructing fine‐grained, detailed spatial structures from time‐evolving coarse‐scale geophysical fields has been a long‐standing challenge. Current deep learning approaches addressing this issue generally require massive fine‐scale fields as supervision, which is often unavailable due to limitations in existing observational systems and the scarcity of widespread high‐precision sensors. Here, we present AdaptDeep, a self‐supervised framework for refined reconstruction of geophysical fields via domain adaptation from the coarse‐scale source domain to the fine‐scale target domain. This method incorporates two pretext tasks, cropped field reconstruction and temporal augmentation‐assisted contrastive learning, to leverage spatial and temporal correlations in the target domain. A global propagation structure is proposed in the feature extraction network to leverage bidirectional information for enhanced long‐range dependencies and robustness against estimation errors. In experiments, AdaptDeep correctly identifies local, fine structures and significantly recovers 81.2% detailed information in sea surface temperature fields.

Using the methodology of transformation of geophysical fields in the study of dike-type ore systems in the zone of influence of the Chai-Yurinsky deep fault

Identification of prospecting geophysical signs of promising areas at the level of ore nodes and fields, deposits and ore structures (ore-localizing structures) has always been one of the main contents of geophysical research. However, especially at the deposit – ore structures level, very often the solution of these tasks becomes very difficult and difficult to implement. The problem is that, in general, rocks in the research area are quite contrasting in their physical properties; however, ore-bearing dike bodies represented by beresitized diorite-porphyrites do not always confidently differ in their physical characteristics from the sandy, carbonaceous, sulfidized and shale sedimentary rocks that contain them. The use of approaches based on statistical processing techniques in the interpretation of geophysical data made it possible to establish geological and geophysical signs of gold-quartz mineralization of the dike type within the study area and thus predict gold mineralization.The paper presents the results of processing geophysical materials in order to create a list of features characteristic of gold-quartz deposits of the dyke type in the Central-Kolyma region (Magadan Oblast).The geophysical research complex consisted of electrical profiling and magnetic exploration. MMPOS-2 magnetometers were used to measure the geomagnetic field. The coordinates of the measurement points were determined using a GPS receiver structurally connected to a magnetometer. The methodology of the work provided for the introduction of variations of the geomagnetic field. The variation of the Earth’s magnetic field was taken into account using the SURV software. The work by the electrical profiling method was carried out using an instrument complex: AIE-2 using the median gradient method in modification of induced polarization (SG-VP) at a frequency of 0.3 Hz. Supply line AB=800-900 m, receiving line MN=10 m.The presented set of works is capable of solving the problems of forecasting gold mineralization in the zone of influence of the Chai-Yurinsky fault of the Yana-Kolyma metallogenic system. Based on the interpretation of the geophysical data, prospecting geological and geophysical signs of gold-quartz mineralization within the ore field are outlined.One of the promising methodological methods of interpretation in determining the search signs of potentially ore zones is electrical exploration by the method of induced polarization.

Local fractal and wavelet analysis of aerogeophysical data: An integrated approach in target generation of Cu-Au-Fe skarn and gold-polymetallic epithermal porphyry-related systems in eastern Transbaikalia, Russia

Three geophysical fields (anomalous magnetic, exposure dose rate, and apparent resistance) were investigated by local fractal and two-dimensional wavelet analyses for ore prospectivity mapping (scale 1:25000). The study region is part of the Gazimuro-Zavodsky gold-polymetallic ore district (Transbaikalia, South-East Russia) and includes 9 recognized hypogene ore deposits and 40 ore occurrences of Au, Fe, Cu, Pb, W, Sb, As, boron, talc, and fluorite (skarn and hydrothermal formations related to felsic and mafic magmatism). The results show that fractal and wavelet analyses of the geophysical fields demonstrate a certain specialization in the delineation of different mineralization types. The best prospective efficiency (in terms of quality/cost) was achieved by integrating the six individual prospective maps using the ensemble approach.

Advanced Observation for Geophysics, Climatology and Astronomy

This Special Issue covers a wide range of scientific tasks carried out in the fields of geophysics, climatology and astronomy [...]

THE ROLE OF ARTIFICIAL INTELLIGENCE IN GEOSCIENCES: CONCEPTUAL REVIEW

Geophysics is the branch of Earth science that applies the principles and methods of physics to the study of the Earth. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are transforming the field by providing powerful tools for data processing, analysis, and prediction. Traditional geophysical methods are often challenged by data complexity, non-unique solutions, and human interpretation limitations. AI algorithms trained on large datasets can identify intricate patterns and relationships within the data, leading to improved accuracy and efficiency. In this review, we provide an overview of the development of AI in the geophysical field such as seismic exploration. Specific examples illustrate the application of AI in geophysical workflows, highlighting the results achieved in areas such as seismic data processing, reservoir characterization, and automated seismic interpretation. Keywords: geophysics, seismic prospecting, exploration geophysics, artificial intelligence, machine learning, deep learning

STATISTICAL CHARACTERISTICS OF THE GEOPHYSICAL FIELDS DISTURBED BY WEATHER FRONTS

The Earth (internal spheres) — atmosphere — ionosphere — magnetosphere (EAIM) formation is a single integrated system with direct and reverse, positive and negative coupling, as well as with their combination. The high-energy sources of natural and anthropogenic origins activate coupling between the components of the EAIM. The effects that the sources of various physi- cal nature have on the EAIM system have been studied quite well, while the influence of the weather fronts and other powerful atmospheric sources on the EAIM system and its components has been studied only partly. The scientific objective of this study is to conduct a statistical analysis of variations in the basic parameters of the geophysical fields that accompany the movement of atmospheric fronts. The histograms have been constructed that show the atmospheric pressure difference, atmospheric tem- perature difference, duration of the action of the atmospheric front, and the rate of change in the pressure and temperature, as well as the histograms showing the distribution of variations in the atmospheric electric field, the atmospheric current density, and in the magnetic field. The analysis undertaken has shown that these parameters exhibit variations within a broad range of values. The mean values of these parameters are estimated to be 145 Pa, 6 °C, 70 min, 2.4 Pa/min, 0.23 °C/min, 3.2 kV/m, 63 nA/m2, and 20 nT, respectively. The analysis of the scatter diagrams shows that the correlation between the variation in physi- cal parameters is almost always absent. This means that a single governing parameter along the path of the atmospheric front does not exi st. A simplified analytical relation has been derived to estimate the perturbation in the electric field strength caused by the atmospheric front, which yields ~6–60 kV/m values that increase by an order of magnitude during thunderstorms. Under disturbed conditions, the atmospheric current density is shown to increase from 10 –12 A/m2 to 10–11—10–10 A/m 2. The fol- lowing three mechanisms of an increase in the magnetic induction under the influence of the atmospheric front are considered: the disturbances of the external current density, electromagnetic induction, and the magnetic effect of turbulence. All these mechanisms yield the value of the effect less than ~1 nT. Only the magnetic effect of the ionosphere can explain an increase of 10—70 nT in the magnetic field variations. The energetics of the pressure, temperature, electric, and magnetic fields has been estimated to be (~10 16—10 17 J, ~1013—1014 W), (~1018—1019 J, 1015—1016 W), (~109—1011 J, ~106—108 W), (~1010 — 10 11 J, 107—108 W), respectively. The following channels have been validated through which the components of the EAIM sys- tem couple under the action of atmospheric fronts: atmospheric pressure differences, ionospheric electron density differences, the generation of infrasound and gravity waves, the generation of electromagnetic waves by lightning flashes, and the perturba- tions in the global electric circuit.

Fifteen Years of Integrated Terrestrial Environmental Observatories (TERENO) in Germany: Functions, Services, and Lessons Learned

AbstractThe need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future.