Field studies at terrestrial analogue sites represent an important contribution to the science of ocean worlds. The value of the science and technology investigations conducted at field analogue sites depends on the relevance of the analogue environment to the target ocean world. We accept that there are no perfect analogues for many of the unique environments represented by ocean worlds but suggest that a one-to-one matching of environmental characteristics and conditions is not crucial to the success or impact of the work. Instead, we must instead determine which processes and parameters are required to map directly to the target ocean world environment with high fidelity to address the science question or engineering challenge. Where there are discrepancies between the model and target environment, we must fully understand how those limitations impact the applicability of the study, and mitigate these where possible using alternative approaches. Here we present a two-step approach to 1) identify the most crucial processes and parameters associated with a given science question and 2) assess the fidelity of these processes and parameters at a proposed field site to those expected for the target ocean world. We demonstrate this approach in a test case evaluating three types of ocean world analogue environments with respect to a science question. Our proposed framework will not only enhance the scientific rigor of field research but also provide access to a broader range of field sites relevant to ocean worlds processes, enabling a greater diversity of ocean and geological science researchers.

Europa’s compositional evolution is not well constrained. Observations only provide approximations of the current interior structure of Europa. However, dynamic models [Hussmann & Spohn 2004] resolve the magnitude of interior heating produced by tidal interaction over time. We couple the heat production to thermodynamic and chemical equilibrium models Perple_X [Connolly 2005], Rcrust [Mayne+ 2016] and CHIM-XPT [Reed 1998] to compute compositional changes of the interior and ocean. Assuming that Europa’s interior is not molten now, a Fe core could have accommodated up to 24 wt % S during accretion, assuming chondritic accretion material. However, a metal-silicate segregated magma ocean was needed to allow such high S content in the core. More likely, accretion proceeded with low impact rates that allowed heat dissipation. Based on this and experimental metal-silicate partition behavior, Europa’s core contains ~1 wt % S. Two mantle melting events were calculated corresponding to putative events in Europa’s thermal-orbital evolution: a first event that melted up to 30 vol % of the volatile-rich silicate shell, at pressures of 2.5 – 1.2 GPa ≥4 Ga ago, and a possible melting event ~1.3 Ga ago resulting from increased dissipation as the mantle’s rigidity increased [Hussmann & Spohn 2004]. Melt intrusive to extrusive ratios (I/E) for Europa are unknown, but eruption to the ocean-rock interface would have been hindered by high stress needed to cause fracture propagation and melt migration at depth [Byrne+ 2018]. Assuming I/E = 10, <7 wt % melt would have erupted (Fig 1). Even if lava erupted during the first event, limited heat transfer from, and dehydration of, the mantle may not have prevented the second event from occurring. Considering Europa’s volcanism enables us to predict the minerals likely to have influenced the ocean’s composition and the mineralogy of concurrent water-rock activity. Erupted lava reacting with the ocean results in water-to-rock ratio dependent proportions of sulfides, saponite, chlorite and carbonates. We will describe implications for the ocean’s composition and habitability. A part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Copyright 2018. All rights reserved.

Distributed sensor networks empower real-time in-situ computing and seismic imaging without transmitting the raw data to the remote data center. This attribute is valuable for planet exploration that has stringent bandwidth. The previously distributed ambient noise imaging algorithm computes results using data from near neighbors, while the information from distant neighbors is not utilized, hence the image quality is compromised. To overcome this problem, this work proposes an innovative common-receiver-based decentralized ambient noise imaging algorithm. In this algorithm, the basic imaging algorithm is still Eikonal tomography, but the distant neighbors are also used to computing the seismic image so that the quality of the output image can be preserved. An in-situ computing and clustering algorithm is created to optimize the data transition and computation while meeting the bandwidth constrains. The experiments were performed on both synthetic data from Enceladus and real data from the USArray archives. The new algorithm generates higher-resolution images under the same bandwidth constraints, comparing to previous algorithms, and the quality of the output image is satisfactorily preserved. The communication cost reduction over the raw data collection is in several orders of magnitude (e.g., 1: 1600). It meets the desired bandwidth constraint in planetary exploration applications.

Titan’s surface icy shell is likely composed of water ice and methane clathrate [1, 2]. Methane clathrate may play a role in Titan’s methane cycle [3–5] affect Titan’s thermal profile [6] , and may affect the habitability of Titan’s ocean. Although the bulk properties of clathrates are similar to those of pure water ice, the thermal conductivity of methane clathrate is about 20% the value for pure water ice [7, 8]. The lower thermal conductivity acts to insulate Titan’s icy shell, changing the thermal profile of Titan. As seismic wave speeds [9, 10] and attenuation [11] are dependent on temperature, any changes to the thermal profile will result in changes to seismic waveforms recorded by seismic instrumentation. Here, we compare the seismic waveforms of model with a 100 km thick pure water ice shell, versus a model with a 10 km clathrate lid over 90 km of pure water ice. Our results have implications for the upcoming Dragonfly mission, which will carry seismic instrumentation as part of its payload [12]. Methods: We use PlanetProfile [13] to create interior structures models of a pure water ice shell and a model with a pure water ice shell with a 10 km clathrate lid. The interior structure models are used as inputs with AxiSEM [14] and Instaseis ([15] to generate seismic waveforms. We interpret the results to quantify the differences in seismic velocities, arrival times of seismic phases, and amplitudes of seismic waveforms at the surface of Titan. Results: The interior structure models show a clathrate lid will reduce the conductive lid thickness by ~ 2/3 compared to the pure water ice shell model. As a result, the clathrate lid model reaches higher temperatures at shallower depths (Figure 1a). The temperature profile affects the seismic velocity (Figure 1b), and the seismic quality factor (Q, Figure 1c) profiles. A clathrate lid creates a steeper negative gradient in seismic velocities and Q. The greatest difference in seismic velocities occurs at the base of the clathrate lid (Figure 2). Because of the change in seismic velocities, the arrival times and observable distances of seismic phases will be different between the two models. Using TauP [16], we calculate the differences for several seismic phases. We find that the change in seismic velocity profile results in a difference of a few seconds at most in arrival times. The range of observable distances will also vary by a few degrees. The small changes might be noticeable on waveforms, but would require high signal to noise ratios, and precise determinations of location and depth of the event. The changes in seismic velocities and Q will also impact the observed ground motion. Using AxiSEM and InstaSEIS, we create a database of seismic waveforms spaced 1 degree in epicentral distance. We compare the same event magnitude and distance between source and seismometer for the two models. For each waveform we calculate the root mean square (RMS) using ground acceleration.