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New Zealand's Alpine Fault is a large, platebounding strike-slip fault, which ruptures in large (M-w > 8) earthquakes. We conducted field and laboratory analyses of fault rocks to assess its fault zone architecture. Results reveal that the Alpine Fault Zone has a complex geometry, comprising an anastomosing network of multiple slip planes that have accommodated different amounts of displacement. This contrasts with the previous perception of the Alpine Fault Zone, which assumes a single principal slip zone accommodated all displacement. This interpretation is supported by results of drilling projects and geophysical investigations. Furthermore, observations presented here show that the young, largely unconsolidated sediments that constitute the footwall at shallow depths have a significant influence on fault gouge rheological properties and structure.
The eruption frequency of geysers can be studied easily on the surface. However, details of the internal structure including possible water and gas filled chambers feeding eruptions and the driving mechanisms often remain elusive. We used a multidisciplinary network of seismometers, video cameras, water pressure sensors and one tiltmeter to study the eruptive cycle, internal structure, and mechanisms driving the eruptive cycle of Strokkur geyser in June 2018. An eruptive cycle at Strokkur always consists of four phases: (1) Eruption, (2) post-eruptive conduit refilling, (3) gas filling of the bubble trap, and (4) regular bubble collapse at shallow depth in the conduit. For a typical single eruption 19 +/- 4 bubble collapses occur in Phase 3 and 8 +/- 2 collapses in Phase 4 at a mean spacing of 1.52 +/- 0.29 and 24.5 +/- 5.9 s, respectively. These collapses release latent heat to the fluid in the bubble trap (Phase 3) and later to the fluid in the conduit (Phase 4). The latter eventually reaches thermodynamic conditions for an eruption. Single to sextuple eruptions have similar spacings between bubble collapses and are likely fed from the same bubble trap at 23.7 +/- 4.4 m depth, 13-23 m west of the conduit. However, the duration of the eruption and recharging phase linearly increases likely due to a larger water, gas and heat loss from the system. Our tremor data provides documented evidence for a bubble trap beneath a pool geyser.
We construct and examine the prototype of a deep learning-based ground-motion model (GMM) that is both fully data driven and nonergodic. We formulate ground-motion modeling as an image processing task, in which a specific type of neural network, the U-Net, relates continuous, horizontal maps of earthquake predictive parameters to sparse observations of a ground-motion intensity measure (IM). The processing of map-shaped data allows the natural incorporation of absolute earthquake source and observation site coordinates, and is, therefore, well suited to include site-, source-, and path-specific amplification effects in a nonergodic GMM. Data-driven interpolation of the IM between observation points is an inherent feature of the U-Net and requires no a priori assumptions. We evaluate our model using both a synthetic dataset and a subset of observations from the KiK-net strong motion network in the Kanto basin in Japan. We find that the U-Net model is capable of learning the magnitude???distance scaling, as well as site-, source-, and path-specific amplification effects from a strong motion dataset. The interpolation scheme is evaluated using a fivefold cross validation and is found to provide on average unbiased predictions. The magnitude???distance scaling as well as the site amplification of response spectral acceleration at a period of 1 s obtained for the Kanto basin are comparable to previous regional studies.
Landslides
(2022)
Erosion by landslides is a common phenomenon in mountain regions around the globe, affecting all climatic zones. Landslides facilitate bedrock weathering, pedogenesis and ecological succession, being key drivers of biodiversity. Landslide chronosequences have long been used for studies of vegetation succession in initial ecosystems, but they further offer ideal model systems for studies of soil development and microbial community succession. In this review we synthesize the state of knowledge on the role of landslides in ecosystems, their influence on element cycles and interactions with biota. Further, we discuss feedback mechanisms between global warming, landslide activity and greenhouse gas emissions. In the view of increasing anthropogenic influence and climate change, soils are becoming a critical resource. Due to their ubiquity, landslide chronosequences have the potential to provide critical insights into soil development under different climates and thereby contribute to future soil restoration efforts.
We have developed a 1D laterally constrained inversion of surface-wave dispersion curves based on the minimum gradient support regularization, which allows solutions with tunable sharpness in the vertical and horizontal directions. The forward modeling consists of a finite-elements approach incorporated in a flexible nonparametric gradient-based inversion scheme, which has already demonstrated good stability and convergence capabilities when tested on other kinds of data. Our deterministic inversion procedure is performed in the shear-wave velocity log space as we noticed that the associated Jacobian indicates a reduced model dependency, and this, in turn, decreases the risks of local nonconvexity. We show several synthetics and one field example to demonstrate the effectiveness and the applicability of the proposed approach.
We present a new autoclave that enables in situ characterization of hydrothermal fluids at high pressures and high temperatures at synchrotron x-ray radiation sources. The autoclave has been specifically designed to enable x-ray absorption spectroscopy in fluids with applications to mineral solubility and element speciation analysis in hydrothermal fluids in complex compositions. However, other applications, such as Raman spectroscopy, in high-pressure fluids are also possible with the autoclave. First experiments were run at pressures between 100 and 600 bars and at temperatures between 25 degrees C and 550 degrees C, and preliminary results on scheelite dissolution in fluids of different compositions show that the autoclave is well suited to study the behavior of ore-forming metals at P-T conditions relevant to the Earth's crust.
The response of rapidly compressed highly oriented pyrolytic graphite (HOPG) normal to its basal plane was investigated at a pressure of & SIM;80 GPa. Ultrafast x-ray diffraction using & SIM;100 fs pulses at the Materials Under Extreme Conditions sector of the Linac Coherent Light Source was used to probe the changes in crystal structure resulting from picosecond timescale compression at laser drive energies ranging from 2.5 to 250 mJ. A phase transformation from HOPG to a highly textured hexagonal diamond structure is observed at the highest energy, followed by relaxation to a still highly oriented, but distorted graphite structure following release. We observe the formation of a highly oriented lonsdaleite within 20 ps, subsequent to compression. This suggests that a diffusionless martensitic mechanism may play a fundamental role in phase transition, as speculated in an early work on this system, and more recent static studies of diamonds formed in impact events. Published by AIP Publishing.
We produce climate projections through the 21st century using the fractional energy balance equation (FEBE): a generalization of the standard energy balance equation (EBE). The FEBE can be derived from Budyko-Sellers models or phenomenologically through the application of the scaling symmetry to energy storage processes, easily implemented by changing the integer order of the storage (derivative) term in the EBE to a fractional value.
The FEBE is defined by three parameters: a fundamental shape parameter, a timescale and an amplitude, corresponding to, respectively, the scaling exponent h, the relaxation time tau and the equilibrium climate sensitivity (ECS). Two additional parameters were needed for the forcing: an aerosol recalibration factor alpha to account for the large aerosol uncertainty and a volcanic intermittency correction exponent upsilon. A Bayesian framework based on historical temperatures and natural and anthropogenic forcing series was used for parameter estimation. Significantly, the error model was not ad hoc but rather predicted by the model itself: the internal variability response to white noise internal forcing.
The 90 % credible interval (CI) of the exponent and relaxation time were h = [0.33, 0.44] (median = 0.38) and tau = [2.4, 7.0] (median = 4.7) years compared to the usual EBE h = 1, and literature values of tau typically in the range 2-8 years. Aerosol forcings were too strong, requiring a decrease by an average factor alpha = [0.2, 1.0] (median = 0.6); the volcanic intermittency correction exponent was upsilon = [0.15, 0.41] (median = 0.28) compared to standard values alpha = upsilon = 1. The overpowered aerosols support a revision of the global modern (2005) aerosol forcing 90 % CI to a narrower range [ -1.0, -0.2] W m(-2). The key parameter ECS in comparison to IPCC AR5 (and to the CMIP6 MME), the 90 % CI range is reduced from [1.5, 4.5] K ([2.0, 5.5] K) to [1.6, 2.4] K ([1.5, 2.2] K), with median value lowered from 3.0 K (3.7 K) to 2.0 K (1.8 K) Similarly we found for the transient climate response (TCR), the 90 % CI range shrinks from [1.0, 2.5] K ([1.2, 2.8] K) to [1.2, 1.8] K ([1.1, 1.6] K) and the median estimate decreases from 1.8 K (2.0 K) to 1.5 K (1.4 K). As often seen in other observational-based studies, the FEBE values for climate sensitivities are therefore somewhat lower but still consistent with those in IPCC AR5 and the CMIP6 MME. <br /> Using these parameters, we made projections to 2100 using both the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP) scenarios, and compared them to the corresponding CMIP5 and CMIP6 multi-model ensembles (MMEs). The FEBE historical reconstructions (1880-2020) closely follow observations, notably during the 1998-2014 slowdown ("hiatus"). We also reproduce the internal variability with the FEBE and statistically validate this against centennial-scale temperature observations. Overall, the FEBE projections were 10 %-15 % lower but due to their smaller uncertainties, their 90 % CIs lie completely within the GCM 90 % CIs. This agreement means that the FEBE validates the MME, and vice versa.
Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.
Watershed management requires an understanding of key hydrochemical processes. The Pra Basin is one of the five major river basins in Ghana with a population of over 4.2 million people. Currently, water resources management faces challenges due to surface water pollution caused by the unregulated release of untreated household and industrial waste into aquatic ecosystems and illegal mining activities. This has increased the need for groundwater as the most reliable water supply. Our understanding of groundwater recharge mechanisms and chemical evolution in the basin has been inadequate, making effective management difficult. Therefore, the main objective of this work is to gain insight into the processes that determine the hydrogeochemical evolution of groundwater quality in the Pra Basin. The combined use of stable isotope, hydrochemistry, and water level data provides the basis for conceptualizing the chemical evolution of groundwater in the Pra Basin. For this purpose, the origin and evaporation rates of water infiltrating into the unsaturated zone were evaluated. In addition, Chloride Mass Balance (CMB) and Water Table Fluctuations (WTF) were considered to quantify groundwater recharge for the basin. Indices such as water quality index (WQI), sodium adsorption ratio (SAR), Wilcox diagram, and salinity (USSL) were used in this study to determine the quality of the resource for use as drinking water and for irrigation purposes. Due to the heterogeneity of the hydrochemical data, the statistical techniques of hierarchical cluster and factor analysis were applied to subdivide the data according to their spatial correlation. A conceptual hydrogeochemical model was developed and subsequently validated by applying combinatorial inverse and reaction pathway-based geochemical models to determine plausible mineral assemblages that control the chemical composition of the groundwater. The interactions between water and rock determine the groundwater quality in the Pra Basin. The results underline that the groundwater is of good quality and can be used for drinking water and irrigation purposes. It was demonstrated that there is a large groundwater potential to meet the entire Pra Basin’s current and future water demands. The main recharge area was identified as the northern zone, while the southern zone is the discharge area. The predominant influence of weathering of silicate minerals plays a key role in the chemical evolution of the groundwater. The work presented here provides fundamental insights into the hydrochemistry of the Pra Basin and provides data important to water managers for informed decision-making in planning and allocating water resources for various purposes. A novel inverse modelling approach was used in this study to identify different mineral compositions that determine the chemical evolution of groundwater in the Pra Basin. This modelling technique has the potential to simulate the composition of groundwater at the basin scale with large hydrochemical heterogeneity, using average water composition to represent established spatial groupings of water chemistry.