Lunar pits, some of which are interpreted as collapse features into underlying lava tubes, expose otherwise inaccessible stratigraphy and may provide entry points to subsurface voids that preserve records of lunar volcanism and offer potential sites for future human exploration. We synthesize the current state of knowledge on lunar pits and lava tubes, covering their morphological characteristics, classification, proposed formation mechanisms, mechanical stability, and detection from orbit. We then review the open science questions that pit and pit-wall investigation is uniquely placed to address, spanning the volcanic stratigraphy of the lunar maria, the structure and lateral variability of the regolith, and the dimensions and accessibility of subsurface conduits. To evaluate how these questions can be tackled in situ, we assess the feasibility and expected performance of geophysical and remote-sensing investigations for subsurface voids and surface exposures, mainly focusing on gravity measurements, ground-penetrating radar, high-resolution imaging, and spectroscopy. Building on this, we present LunarLeaper, a small legged robot mission concept combining a gravimeter, ground-penetrating radar, high-resolution imager, spectrometer, and leg-based geomechanical experiments to deliver the first in situ investigation of a mare pit. The concept targets the Marius Hills Pit and its associated rille, with a mobility architecture optimized for the rugged terrain encountered at pit edges and funnel slopes.
Rapid gravity-driven mass movements (RGMMs), such as rock-falls, snow avalanches, glacier break-off events, debris flows, lahars and pyroclastic density currents are among the most hazardous phenomena occurring in mountain environments. In the last decades monitoring solutions based on the remote detections of the seismic and acoustic signals radiated by the flow have been developed and adopted, and have shown to provide an important, low-cost, and relatively straightforward complement to in situ measurements. Here we present a review of recent infrasound observations of the various types of RGMMs highlighting the capabilities and limits of infrasound array detections with applications for both research and monitoring.
In the spring and summer of 2020, the world broke down. A worldly breakdown often gives rise to forms of moral breakdown, or those “moments” when some worldly event or occurrence forces a person or persons to critically reflect on their until then unquestioned way of being-in-the-world (Zigon 2007). From the persistence of the global pandemic, to the collapse of the economy, to the murder of George Floyd by police officers on camera, to the worldwide response to that injustice, the world and its human inhabitants experienced a breakdown in those months and it became impossible to ever see, hear, understand, or be in the world in the same way again. This special issue of Puncta brings together anthropologists and philosophers who take up a critical phenomenological or hermeneutic approach for thinking the contemporary condition. From the possibility of inhabiting a world conditioned by a global pandemic, to the impossibility of dwelling in conditions of systemic racism, from the question of how to face a future that presents itself as looming, to a present that denies the very possibility of truth: this collection responds to these and more in the hope of showing not only the contemporary conditions of existence, but that other conditions always remain as an ever-present potentiality
The boundary between rocky mantle and iron core constitutes the most significant discontinuity within the terrestrial planets, the core itself is one of the largest, if not the largest, structural features of these planets with consequences for the entire geodynamical evolution of the planet: It contains a significant amount of the planets iron inventory, and planetary magnetic fields are generated within the core. We take the occasion of the first seismic determination of the core size of Mars to look back into the development of theories about planetary interiors and cores, starting with early mythological narrations. The renaissance produced the first geologically and physically motivated inferences about the Earth's core, which were extended to the Moon, Mars and other planets in the 19th century. Theories based on telescopic observations soon found their limits, and spacecraft missions to the Moon and to Mars provided the necessary precision of radius, mass, and moment of inertia determinations, and finally seismic data, to determine the core radius precisely. Meanwhile, interest extended to beyond the solar system, and we discuss the observational foundations on which models for the core size of exoplanets are based.
Over the past decades, global geodynamical models have been used to investigate the thermal evolution of terrestrial planets. With the increase of computational power and improvement of numerical techniques, these models have become more complex, and simulations are now able to use a high resolution 3D spherical shell geometry and to account for strongly varying viscosity, as appropriate for mantle materials. In this study we review global 3D geodynamic models that have been used to study the thermal evolution and interior dynamics of Mars. We discuss how these models can be combined with local and global observations to constrain the planet's thermal history. In particular, we use the recent InSight estimates of the crustal thickness, upper mantle structure, and core size to show how these constraints can be combined with 3D geodynamic models to improve our understanding of the interior dynamics, present-day thermal state and temperature variations in the interior of Mars.
Solid body tides provide key information on the interior structure, evolution, and origin of the planetary bodies. Our Solar system harbours a very diverse population of planetary bodies, including those composed of rock, ice, gas, or a mixture of all. While a rich arsenal of geophysical methods has been developed over several years to infer knowledge about the interior of the Earth, the inventory of tools to investigate the interiors of other Solar-system bodies remains limited. With seismic data only available for the Earth, the Moon, and Mars, geodetic measurements, including the observation of the tidal response, have become especially valuable and therefore, has played an important role in understanding the interior and history of several Solar system bodies. To use tidal response measurements as a means to obtain constraints on the interior structure of planetary bodies, appropriate understanding of the viscoelastic reaction of the materials from which the planets are formed is needed. Here, we review the fundamental aspects of the tidal modeling and the information on the present-day interior properties and evolution of several planets and moons based on studying their tidal response. We begin with an outline of the theory of viscoelasticity and tidal response. Next, we proceed by discussing the information on the tidal response and the inferred structure of Mercury, Venus, Mars and its moons, the Moon, and the largest satellites of giant planets, obtained from the analysis of the data that has been provided by space missions. We also summarise the upcoming possibilities offered by the currently planned missions.
The NASA InSight mission has helped to measure the deep interior of Mars using observations of seismic waves excited by marsquakes. Currently, installation of seismometers on the moon is foreseen. We review the case for seismic experiments on all major planetary bodies of the solar system. We discuss scientific goals in accordance with the Decadal survey for planetary science and astrobiology and the ESA Voyage 2050 program as well as technical challenges and potential mission concepts, to answer the question: Where could we do seismology on other planets and why should we do it?
Quantitative characterization of subsurface properties is critical for many environmental applications and serves as the basis to simulate and better understand dynamic subsurface processes. Geophysical imaging methods allow to image subsurface property distributions and monitor their spatio-temporal changes in a minimally invasive manner. While it is widely agreed upon that models integrating multiple independent data sources are more reliable, the number of approaches to do so is increasing rapidly and often overwhelming for researchers and, particularly, novices to the field.With this work, we aim to contribute to the development multimethod imaging through (1) an overview of, and didactic introduction to, existing inversion approaches for the integration of multiple geophysical data sets with other measurement types (e.g., hydrological observations), petrophysical models, and process simulations, (2) a state-of-the-art review on the use and potentials of these approaches in various environmental applications, and (3) a discussion on new frontiers and remaining challenges in the field.We hope that this chapter provides an entry point to recent developments in multimethod geophysical imaging, clarifies similarities, differences, and development potentials of existing approaches, and ultimately helps practitioners to choose the optimum one to integrate their data sets.
Seismic moment tensors are an important tool in geosciences on all spatial scales and for a broad range of applications. The basic underlying theory is established since decades. However, various factors influence the reliability of the inversion result, several of them are mutually dependent. Hence, a reliable retrieval of seismic moment tensors is still hampered in many cases, especially at regional event-receiver distances.To sample the entire wavefield due to a seismic source we need six components: three translational and three rotational ones. Up to now, only translational ground motion recordings were used for moment tensor retrieval, missing out valuable information. Using rotational in addition to the classical translational ground motions during waveform inversion for moment tensors mainly adds information on the vertical displacement gradient to the inversion problem. Furthermore, having available six instead of only three components per receiver location provides additional constraints on the sampling of the radiation pattern. As a result, the moment tensor components are resolved with higher precision and accuracy, even when the number of recording receivers is considerably reduced. Especially, components with a dependence to depth as well as the centroid depth can benefit significantly from additional rotational ground motion. Up to the time of writing this review only a few studies are published on the topic. Here, I summarise their findings and provide an overview over the possible capabilities of including rotational ground motion measurements to waveform inversion for seismic moment tensor retrieval.
In a variety of scientific applications we wish to characterize a physical system using measurements or observations. This often requires us to solve an inverse problem, which usually has non-unique solutions so uncertainty must be quantified in order to define the family of all possible solutions. Bayesian inference provides a powerful theoretical framework which defines the set of solutions to inverse problems, and variational inference is a method to solve Bayesian inference problems using optimization while still producing fully probabilistic solutions. This chapter provides an introduction to variational inference, and reviews its applications to a range of geophysical problems, including petrophysical inversion, travel time tomography and full-waveform inversion. We demonstrate that variational inference is an efficient and scalable method which can be deployed in many practical scenarios.
Geophysical inversion by iterative modeling involves fitting observations by adjusting model parameters. Both seismic and potential‐field model responses can be influenced by the adjustment of the parameters of the rock properties. The objective of this “cooperative inversion” is to obtain a model which is consistent with all available surface and borehole geophysical data. Although inversion of geophysical data is generally non‐unique and ambiguous, we can lessen the ambiguities by inverting all available surface and borehole data. This paper illustrates this concept with a case history in which surface seismic data, sonic logs, surface gravity data, and borehole gravity meter (BHGM) data are adequately modeled by using least‐squares inversion and a series of forward modeling steps.
Deep learning has emerged as an effective approach for seismic data processing in general, and for earthquake monitoring in particular. The ability of deep learning models to generalize beyond the training and validation data is important for comprehensive earthquake monitoring; this ability furthermore depends on the availability of a sufficiently large and complete training dataset. However, this requirement can prove challenging to meet due to significant effort and time for data collection and labeling. Data augmentation provides an efficient and effective approach for increasing the dimension of training samples and improving generalization to unseen samples. In this paper, we present augmentation methods appropriate for seismic waveforms and demonstrate their ability to reduce bias and increase performance. These augmentation methods can be applied to a wide range of deep learning applications designed for seismic data.
Earth scientists and exploration geophysicists aim to infer plausible reconstruction of subsurface medium parameters, as a way to discover Earth's internal structures. One celebrated approach for this task is Bayesian inference method, which integrates statistical information from the forward modeling function, observed data, and experts' prior knowledge into a posterior probability density function (PDF). Conventional prior knowledge is based on empirical observations of subsurface structures like the smoothness of the subsurface image. However, such hand-designed priors are too generic to describe detailed subsurface structures. In this work, we focus on a learning-based prior knowledge generator, and address the important caveats that arise in the context. Unlike hand-designed priors, we use existing subsurface velocity models to train a deep generative adversarial network (GAN) that generates artificial models from a low-dimensional latent space. We test the proposed deep generator prior by applying it to traveltime tomography and full waveform inversion. Benefits of the proposed deep generator priors include: (1) the generated models share a similar spatial distribution with the existing models; (2) the latent space is much smaller than the model space yielding a significant reduction in the computational complexity for the inversion. Despite these benefits, the generality of the training set has a strong influence on the robustness of the deep generator prior. We propose a quantitative criterion to assess the generality and help establish the adequacy of the deep generator. Like most deep learning applications, a diverse training set (here, containing different spatial velocity distributions) is necessary and essential to make the deep generator prior effective.
