For an overview of the whole network see Fig. . The oceanic component of the RHUM-RUM experiment consisted of 57 broadband ocean bottom seismometers deployed over an area of 2000 km × 2000 km from September 2012 to November 2013. The OBS clustered relatively densely around the island of La Réunion, out to distances of 400–500 km, including the vicinity of Mauritius (Fig. ). This relative dense coverage was extended eastward to the Central Indian Ridge, in order to investigate hypothesized asthenospheric flow from hotspot to ridge . The seismicity in the reliably active South Sandwich subduction zone generates body-wave paths which sample the mantle beneath La Réunion at greater depths. Sampling with opposite azimuth is provided by earthquakes in the subduction zones of the south west Pacific, especially since the OBS network is augmented by RHUM-RUM land stations on Madagascar, and on the Îles Éparses in the Mozambique Channel. A linear, less dense arrangement of OBS followed the strike of the Central Indian and Southwest Indian ridges to the east and south, at 800–1200 km distance from the hotspot. Waves originating from earthquakes in the Alpine-Himalayan orogens and recorded at these stations again sample deeper levels of the mantle beneath La Réunion, but are also used to study the mid-ocean ridges themselves. A dense sub-array of 8 OBS, referred to as the “SWIR Array”, was deployed around an active seamount on the Southwest Indian Ridge in order to investigate the structure and seismicity of this ultra-slow spreading ridge. The sub-array had a footprint of about 70 km × 50 km and was located in segment 8 of the ridge, following the nomenclature of .

The OBS were deployed in October 2012 by the French research vessel Marion Dufresne and were recovered in October/November 2013 by the German research vessel Meteor. The instruments spent the intervening 13 months recording on the seafloor.

At each deployment site, the seafloor was surveyed with R/V Marion Dufresne's multi-beam bathymeter and sediment echo sounder before dropping the OBS over board in a location deemed most suitable. The ship left immediately after deployment so that only deployment (and recovery) coordinates are known; no attempt was made to acoustically triangulate the landing positions of the OBS, with the notable exception of the 8 OBS in the densified SWIR Array. In general, OBS recovery positions were found to differ from their deployment positions by no more than a few hundred meters.

Here we give a brief overview of the hardware deployed (see Table ) and the recording settings used, especially as they relate to the performance assessment of Sect.  (see Table  for an overview).

Instrument responses specify the transfer functions of seismometers and hydrophones (three seismogram channels and one hydrophone channel per station). The RESIF (RÉseau SIsmologique & géodésique Français) data centre serves this information in the format of StationXML or dataless SEED files.

To our knowledge, detailed meta-data information for DEPAS OBS has not been published elsewhere. Therefore, we added a detailed discussion of the instrument responses as an appendix to this paper (Sect. ). Figures  and  show the total responses of instruments and data loggers for hydrophones and seismometers. Figure  shows instrument-corrected waveforms. For all seismometer types, instrument correction results in the same P-waveform.

Three out of 48 DEPAS stations (RR02, RR04, RR15) delivered neither seismometer nor hydrophone data because their data loggers failed (reason unclear). The seismometers in nine DEPAS stations (RR23, RR24, RR32, RR35, RR42, RR49, RR51, RR54, RR57) featured a redesigned sensor/casing package with broader band CMG-OBS40T sensors (120 s), which had previously not been deployed in the deep sea. The levelling mechanisms failed (remained stuck) in all nine stations, for reasons that are still under investigation. Automatic, prolonged attempts to level the sensors drained their batteries prematurely so that the functioning hydrophones also ran out of power 8–9 months into the experiment. DEPAS seismometers RR27 and RR45 recorded, but at high noise levels (reason under investigation). The hydrophones of these stations worked normally. The seismometer in one of the four Geomar stations failed (RR39), and noise levels at Geomar station RR33 are unusually high, although this might not be due to the sensor. The hydrophones in RR33 and RR39 measured, but at a high noise level.

The 9 INSU stations (RR28, RR29, RR31, RR34, RR36, RR38, RR40, RR50, RR52) were affected by a bug in the data logger software that activated the level-sensing circuitry every 3620 s (roughly every hour). Each such event caused a “glitch” in the seismograms of roughly 1200 s duration, i.e. a characteristic, complex pulse shape, that is very similar but not identical across events. Pulse amplitudes are between 500–800 counts, corresponding to 1.5 µm ground displacement after instrument correction and filtering between 20 and 500 s period. This artefact is rarely visible on horizontal components where noise levels are much higher in this period band, but it exceeds noise amplitudes on the vertical channels by 15 dB. Figure  shows that the glitch amplitude is comparable to body wave arrivals of intermediate-size, teleseismic earthquakes, here a M6.6 earthquake at 71∘ distance. Efforts are under way to suppress this artefact by matched filtering.

The differential pressure gauge in INSU station RR31 had high artefacts roughly every 9000 s. Seismic signals are visible in between, but may be difficult to use. For station RR38, gaps in the data had to be fixed. Although this was carefully done, it is possible that artefacts were introduced.

The internal clocks of the data recorders are affected by drifts on the order of one second per year. Over 13 months of autonomous recording, drift of this magnitude is non-negligible for certain applications, such as body-wave tomography. Prior to deployment, each recorder clock was synchronized to GPS, and upon recovery it was compared to GPS time again, yielding the clock drift or “skew”. Assuming that the skew accumulated linearly over the deployment period, the clock error can be corrected for any moment in time. Previous studies show that linearity is a good first order approximation for the clocks used in the DEPAS instruments. For the LCPO2000 instruments used in the INSU pool, found that drift rates can vary over the course of days. We assume that this effect is cancelling out for longer deployments, therefore RHUM-RUM data at the RESIF data centre are linearly corrected for skew, where available.

Unfortunately a significant number of DEPAS clocks stopped before recovery, so that the skew could not be measured (entries “NA” in Table ). Clock shutdown was not anticipated even if batteries became weak. At a critical voltage level of 6.0 V (down from 13.0 V), the recorder was programmed to switch off seismometer and hydrophone, allowing its low-consuming clock to continue for several months. The Lithium batteries for long-term deployments have a faster current drop than the alkali batteries for normal deployments, which caused a problem for multiple stations. Superimposed on a gradual voltage decline, the log files show brief, steep voltage drops associated with levelling events every 21 days. Towards the end of the recording period, this led to uncontrolled shutdown of some recorders and clocks, presumably when a drop below critical voltage occurred too suddenly.

Using cross-correlation of ambient noise, presented a method to determine the relative clock error between two seismometers a posteriori, which successfully applied to OBS data. Likewise, succeeded in estimating clock drift for the SWIR sub-array of the RHUM-RUM network (RR42-RR48, inter-station distances of 30–40 km). His results suggest that indeed clock errors accumulated linearly over the installation period. For the remainder of the RHUM-RUM network, inter-station distances were unfortunately found to be too large (> 150 km) to apply this ambient noise method, especially given the high self-noise level of the DEPAS OBS packages.

In an attempt to estimate the clock drift of these 11, otherwise well-functioning OBS a posteriori, we did a dry run of several recorders in the DEPAS lab with batteries and seismometers attached for over a month. Afterwards, we compared the value of the internal clock with GPS time. These experiments reproduced the sign of the clock error (clocks generally ran too slow) but probably not their values, at least not to an accuracy that would be useful in practice. The likely reason is that we did not simulate the low water temperatures on the seafloor. The experiment is described in detail in Appendix .

Noise levels can be characterized by Probabilistic Power Spectral Density distributions (PPSDs, ) for each of the four sensor components. We obtain PPSDs by computing power spectra on hour-long broadband time series, and by stacking the hourly results over the recording period. Figure  shows PPSDs for DEPAS station RR26 (depth 4259 m) and for INSU station RR28 (depth 4540 m), which were deployed at 150 km distance between each other.

We created a poster of PPSDs for all 57 stations and all 4 channels, which is published as a Supplement to this article and shows that the relative noise differences of Fig.  are characteristic for INSU versus DEPAS stations more generally.

We expect two sources for temporal noise variations: (1) varying wave heights due to storm activity, which affects mostly the microseismic noise band. (2) Water current-induced tilt, which creates long period noise.

Figure  shows the temporal evolution of noise levels between October 2012 and October 2013 at DEPAS station RR01 near La Réunion (depth 4298 m), between 2 and 60 s). In the secondary microseismic noise band (2–10 s period), peak noise intervals coincide with cyclone passages during southern summer (blue frames). Cyclones are tropical storms, the Indian Ocean equivalent of hurricanes and typhoons. Their correlation to microseismic noise is most pronounced on the BHZ component. In fact, were able to track the path of a cyclone across the RHUM-RUM network using recordings of secondary microseismic noise only.

By contrast, peak noise episodes in the 20–60 s band show no clear correlation with cyclone passages. Rather, the highest levels occur during southern winter (March to September), out of cyclone season. Seasonal variations in deep-sea currents might explain tilt noise at these lower frequencies. The HYCOM-based global ocean circulation model (GLBa0.08/expt_90.9) does predict more episodes of strong currents at RR01 during southern autumn, (Fig.  bottom), but its absolute velocity values would appear low for effectively shaking an OBS. However, global ocean circulation models for this region have very poor resolution in the bottom layer, so that true bottom currents may be different. A recent measurement of current profiles at 23∘ S, 48∘ E suggests that bottom velocities generally do not exceed a few cm per second in the region (L. Ponsoni, personal communication, 2015). Unfortunately, the nearest RHUM-RUM station, (RR23) failed to deliver seismograms for comparison.