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Characterizing fluid circulation in orogens is key to understanding orogenic processes because fluid–rock interaction modifies the physical properties of rocks, hence their response to deformation and, for example, their suitability for radioactive waste storage. Fluid circulation can be dated by applying geochronological methods to fluid-precipitated minerals. Fluid sources and associated pathways can be traced using isotope data measured in the same or in other cogenetic minerals. We applied this concept to the Aar Massif (central Swiss Alps), which was part of the former European passive continental margin that was deformed and exhumed during the (Cenozoic) Alpine orogeny. Newly collected epidote from veins and from one cleft at several localities in meta-granitoids in the Aar Massif yielded U–Pb ages ranging from 27.7 ± 3.4 to 12.4 ± 1.9 Ma, which complement previously published geochronological data revealing Permian (278 ± 29, 251 ± 50, and 275 ± 18 Ma) and Miocene (19.2 ± 4.3 and 16.9 ± 3.7 Ma) epidote veins. We used Pb–Sr–O–H isotope geochemistry of epidote to evaluate fluid sources and pathways during Permian rifting and the Miocene compressional phases of Alpine orogeny. Strontium isotope data of Permian epidote are consistent with previous work suggesting meteoric water infiltration along syn-rift faults and through syn-rift sediments. A more-complex structural framework existed in the Miocene, when a sedimentary lid covered the Aar Massif. Strontium, O, and H isotope data of Miocene epidote-forming fluids indicate (1) meteoric water, mixing with (2) fluids derived from sedimentary units being compacted during orogenesis and/or (3) metamorphic water. All three fluid endmembers may have been circulating and mixing in the Aar Massif during Miocene deformation. Strontium isotope data further indicate that Miocene fluids contributed to imprinting a highly radiogenic Sr isotope composition onto Alpine shear zones or that the fluids inherited a highly radiogenic Sr isotope component by dissolving the Rb-rich, high 87Sr / 86Sr biotite therein. Both possibilities can coexist, and they imply that external fluids could modify the chemical composition of the post-Variscan granitoids hosting the studied epidote veins by fluid–rock interaction processes during deformation. Lead, Sr, and H isotopic differences among Miocene samples further suggest complexity of large-scale fluid circulation. Our work supports the fact that the reconstruction of multifaceted and multi-stage fluid circulation in highly deformed rocks benefits from extracting geochronological and isotope data from the same mineral.
Seismic signals produced by wind turbines can have an adverse effect on seismological measurements up to distances of several kilometres. Based on numerical simulations of the emitted seismic wave field, we study the effectivity of seismic borehole installations as a way to reduce the incoming noise. We analyse the signal amplitude as a function of sensor depth and investigate effects of seismic velocities, damping parameters and geological layering in the subsurface. Our numerical approach is validated by real data from borehole installations affected by wind turbines. We demonstrate that a seismic borehole installation with an adequate depth can effectively reduce the impact of seismic noise from wind turbines in comparison to surface installations. Therefore, placing the seismometer at greater depth represents a potentially effective measure to improve or retain the quality of the recordings at a seismic station. However, the advantages of the borehole decrease significantly with increasing signal wavelength.
Plate tectonics is a key driver of many natural phenomena occurring on Earth, such as mountain building, climate evolution and natural disasters. How plate tectonics has evolved through time is still one of the fundamental questions in Earth sciences. Natural microstructures observed in exhumed ultrahigh-pressure rocks formed during continental collision provide crucial insights into tectonic processes in the Earth’s interior. Here, we show that radial cracks around SiO2 inclusions in ultrahigh-pressure garnets are caused by ultrafast decompression. Decompression rates of at least 8 GPa/Myr are inferred independently of current petrochronological estimates by using thermo-mechanical numerical modeling. Our results question the traditional interpretation of fast and significant vertical displacement of ultrahigh-pressure tectonic units during exhumation. Instead, we propose that such substantial decompression rates are related to abrupt changes in the stress state of the lithosphere independently of the spatial displacement.
Abstract
The emergence, geometry and activation of faults are intrinsically linked to frictional rheology. The latter is thus a central element in geodynamic simulations which aim at modeling the generation and evolution of fault zones and plate boundaries. However, resolving frictional strain localization in geodynamic models is problematic. In simulations, equilibrium cannot always be attained and results can depend on mesh resolution. Spatial and temporal regularization techniques have been developed to alleviate these issues. Herein, we investigate three popular regularization techniques, namely viscoplasticity, gradient plasticity and the use of a Cosserat continuum. These techniques have been implemented in a single framework based on an accelerated pseudo-transient solution strategy. The latter allows to explore the effects of regularization on shear banding using the same code and model configuration. We have used model configurations that involve three levels of complexity: from the emergence of a single isolated shear band to the visco-elasto-plastic stress buildup of a crust. All considered approaches allow to resolve shear banding, provide convergence upon mesh refinement and satisfaction of equilibrium. Viscoplastic regularization is straightforward to implement in geodynamic codes. Nevertheless, more stable shear banding patterns and strength estimates are achieved with computationally more expensive gradient and Cosserat-type regularizations. We discuss the relative benefits of these techniques and their combinations for geodynamic modeling. Emphasis is put on the potential of Cosserat-type media for geodynamic applications.
Key Points
* Regularization approaches for plastic strain localization are tested using a single code based on pseudo-transient method
* All considered schemes provide convergent result upon mesh refinement and satisfaction of equilibrium
* The use of Cosserat continuum is most robust regularization approach and is also is the most demanding
Reliable earthquake detection and seismic phase classification is often challenging especially in the circumstances of low magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data is witnessed. This makes the handling of the seismic data rather daunting based on traditional approaches and therefore fuels the need for a more robust and reliable method. In this study, we investigate two deep learningbased models, termed 1D ResidualNeuralNetwork (ResNet) and multi-branch ResNet, for tackling the problem of seismic signal detection and phase identification, especially the later can be used in the case where multiple classes is organized in the hierarchical format. These methods are trained and tested on the dataset of the Southern California Seismic Network. Results demonstrate that the proposed methods can achieve robust performance for the detection of seismic signals, and the identification of seismic phases, even when the seismic events are of small magnitude and are masked by noise. Compared with previously proposed deep learning methods, the introduced frameworks achieve 4% improvement in earthquake monitoring, and a slight enhancement in seismic phase classification.
The rapid characterisation of earthquake parameters such as its magnitude is at the heart of earthquake early warning (EEW). In traditional EEW methods, the robustness in the estimation of earthquake parameters has been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep-learning-based magnitude classifier based on data from a single seismic station and further investigate the effect of using five different durations of seismic waveform data after first P-wave arrival: 1, 3, 10, 20 and 30 s. This is accomplished by testing the performance of the proposed model that combines convolution and bidirectional long short-term memory units to classify waveforms based on their magnitude into three classes: “noise”, “low-magnitude events” and “high-magnitude events”. Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as “high-magnitude”. We show that the variation in the results produced by changing the length of the data is no more than the inherent randomness in the trained models due to their initialisation. We further demonstrate that the model is able to successfully classify waveforms over wide ranges of both hypocentral distance and signal-to-noise ratio.
The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the robustness in the estimation of earthquake parameters have been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep learning based magnitude classifier and, further we investigate the effect of using five different durations of seismic waveform data after first P wave arrival of length 1s, 3s, 10s, 20s and 30s. This is accomplished by testing the performance of the proposed model that combines Convolution and Bidirectional Long-Short Term Memory units to classify waveforms based on their magnitude into three classes "noise", "low magnitude events" and "high magnitude events". Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as high magnitude. We show that the variation in the results produced by changing the length of the data, is no more than the inherent randomness in the trained models, due to their initialisation.
Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized by relatively frequent mild events and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of the volcano and the underlying physical and chemical processes. Catalogs of volcanic eruptions may be established using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative automated approach: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This process of creating event catalogs consists of three main steps: (i) identification of potential volcanic events based on squared ground-velocity amplitudes, an adaptive MaxFilter, and a prominence threshold. (ii) catalog consolidation by comparing and verification of the initial detections based on recordings from two different seismic stations. (iii) identification and exclusion of signals from regional tectonic earthquakes. The software package is applied to publicly accessible continuous seismic recordings from two almost equidistant stations at Stromboli volcano in Italy. We tested AWESAM by comparison with a hand-picked catalog and found that around 95 percent of the eruptions with a signal-to-noise ratio above three are detected. In a first application, we derive a new amplitude-frequency relationship from over 290.000 volcanic events at Stromboli during 2019-2020. The module allows for a straightforward generalization and application to other volcanoes worldwide.
A study on small magnitude seismic phase identification using 1D deep residual neural network
(2022)
Highlights
• We use 1D-ResNet-based architecture for seismic phase detection.
• We report a 4% improvement in earthquake detection as compared to previous methods.
• We show the model generalization ability on the STanford EArthquake Dataset (STEAD).
• We show its robustness by masking phases of low magnitude with various noise levels.
Abstract
Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data has been achieved. This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods. In this study, we develop 1D deep Residual Neural Network (ResNet), for tackling the problem of seismic signal detection and phase identification. This method is trained and tested on the dataset recorded by the Southern California Seismic Network. Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases. Compared to previously proposed deep learning methods, the introduced framework achieves around 4% improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center. The model generalizability is also tested further on the STanford EArthquake Dataset. In addition, the experimental result on the same subset of the STanford EArthquake Dataset, when masked by different noise levels, demonstrates the model’s robustness in identifying the seismic phases of small magnitude.
Earthquake detection and seismic phase picking play a crucial role in the travel-time estimation of P and S waves, which is an important step in locating the hypocenter of an event. The phase-arrival time is usually picked manually. However, its capacity is restricted by available resources and time. Moreover, noisy seismic data present an additional challenge for fast and accurate phase picking. We propose a deep learning-based model, EPick, as a rapid and robust alternative for seismic event detection and phase picking. By incorporating the attention mechanism into UNet, EPick can address different levels of deep features, and the decoder can take full advantage of the multi-scale features learned from the encoder part to achieve precise phase picking. Experimental results demonstrate that EPick achieves 98.80% accuracy in earthquake detection over the STA/LTA with 80% accuracy, and for phase arrival time picking, EPick reduces the absolute mean errors of P- and S- phase picking from 0.072 s (AR picker) to 0.030 s and from 0.189 s (AR picker) to 0.083 s, respectively. The result of the model generalization test shows EPick’s robustness when tested on a different seismic dataset.
