[Solid-earth]

Supporting Information for

[Mapping the basement of the Ebro Basin in Spain with seismicambient noise autocorrelations]

[Paula Romero and Martin Schimmel]

[Institute of Earth Sciences Jaume Almera - CSIC]

Contents of this file

Text S1 to S5Figures S1 to S5Table S1

Introduction

Along this supplementary document, we show additional data and results to furtherexpand our approach presented in the main paper to aid the understanding and tostrengthen our findings. Firstly, the formulation of the two autocorrelation methods isexposed. Autocorrelations of two stations for time lag up to 20 seconds are shown as todemonstrate how deeper discontinuities, such as the Moho, manifest in our data afteremploying the same processing workflow as used for the shallow reflections in the basin.We present also the map of the Paleozoic basement of the Ebro Basin in two-way-time(TWT) to illustrate our final measurements which we use for the time-to-depthconversion. Section plots for two stations are presented to expose the importance ofanalyzing also daily autocorrelograms to reduce ambiguities in the interpretation of thetotal autocorrelation stacks. Finally, we present a map with all cited stations and wells tofacilitate their quick location.

Text S1.

As discussed in the main body of this work, the choice of the appropriate autocorrelationtechnique has been crucial to obtain clear arrival times for P-wave reflections from theEbro basement. We tested two methodologies: Phase Cross-Correlation (PCC) introducedby Schimmel, (1999) and the Classical Cross-Correlation with geometric normalization(CCGN). Benefits and limitations of both approaches have been presented by Schimmel,(1999) and Schimmel et. al. (2011) for different scenarios.

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The CCGN works directly with the measured amplitude of the ambient noise wave field.For each time lag t, the sum of the amplitude products of the two time series S1 (t+τ) andS2 (τ) are normalized by the geometric mean energy within a time window T (equation 1).In spite of the normalizations, this makes the CCGN sensitive to amplitude variations, i.e., higher amplitude events such as earthquakes may bias the correlation functions. Toavoid amplitude bias, Bensen et al. (2007) proposed a method to balance the amplitudesin the time and frequency domain before computing the correlation. Most commonlyused are the 1-bit normalization and spectral whitening. These procedures, however, arenon-unique and deteriorate the waveforms which may lead to a less effective signalextraction.

(1)

where t is the time-lag and T is the correlation window.

The second approach, PCC, uses the instantaneous phases of the analytical signal of theseismic trace (equation 2) and provides, in full analogy to the CCGN, a coherence orsimilarity measurement as function of lag time t. The analytical signals are obtainedthrough a Hilbert transform and are uniquely split into instantaneous amplitude and phaseterms. PCC employs only the instantaneous phase terms of the two time series S1 (t+τ)and S2 (t), e i (t+τ)ϕ and e iψ(τ) , which makes this measure explicitly amplitude unbiased.PCC is therefore based on the number of coherent samples rather than the sum ofamplitude products.

(2)

where and are the instantaneous phases of S1 and S2 and t is the time lag. Theautocorrelation is obtained in full analogy to the classical correlation by using S1 = S2 or

= . The two terms of the summation in equation 2 guarantee that thecorrelation becomes positive or negative for correlated or anti-correlated signals. I.e.,when the signals are perfectly correlated then the first term in the summation becomes 2while the second term equals 0 for each sample. For anti-correlated signals, the firstterm equals 0 while the second term is 2. If there is no correlation, both terms are moresimilar and subtract to a small number, at least within the window T. The minus signbetween the 2 terms and the normalization by 2T make that PCC is bounded between -1and 1. As shown in Schimmel et al. (2011), PCC is explicitly amplitude unbiased and noambient noise pre-processing is required to balance noise amplitudes. Nevertheless, abandpass filter is recommended to reduce the frequency band to the band of interest andcan therefore improve the signal waveform coherence by attenuating noise from outsidethe signal frequency band. The frequency bandpass also guarantees that the time seriesare zero-mean which is important for the construction of analytic signals, i.e., the splitinto an instantaneous amplitude and phase.

2

Figure 2 in the main body of this paper shows an example of the two methodologiesapplied to data for a station located in the central part of the basin. We explain theobserved differences by the fact that the small amplitude basement reflections aredeteriorated by the 1-bit normalization and spectral whitening. PCC detects these signalsindependent of their amplitude and since, on average, they are more phase coherent thanthe others.

As outlined in the main body of the article, all autocorrelations are stacked using thetime-frequency domain phase weighted stack (tf-PWS). Similar to PCC, the PWS takesadvantage of the instantaneous phases of analytic signals. tf-PWS is based on a coherencemeasure which is obtained from the constructive or destructive summation of theenvelope normalized analytic signals. The coherence measure is amplitude unbiased sinceonly based on the instantaneous phases and it is used to weight linear stacks as functionof time and frequency. The weight ranges between 0 and 1 and down weights incoherentsignal components of the linear stack. The tf-PWS has been introduced by Schimmel andGallart (2007) and Schimmel et al. (2011). It can also be used with wavelets and a recentextension of the approach has been presented in Ventosa et al. (2017).

Text S2.

Ambient noise autocorrelations have been used recently for mapping the Moho indifferent geological contexts. In this work we used a high frequency band (about 3-12Hz) to increase the resolution of the autocorrelograms and to extract the shallowsubsurface reflection response. However, as prove of concept we also calculated theautocorrelation for two stations (CE03 and CE04) in the lower frequency band of 2-4 Hzto retrieve reflections from deeper discontinuities.

Station CE03 is located in sub-Pyrenees zone (see Figure S3) affected by the south trustfault of the mountain range where the thickness of the Moho is between 38 and 40 km(Chevrot et.al. 2014). We found 5 clear P-wave reflections, however the geologicalmeaning in not very clear for the first 2. Choukroune et. al. (1990), found similar signalsin their active seismic studies which they attributed to the reminiscent of a Permian basindiscontinuity (reflection 1) and the traces of a Variscan thrust system (reflection 2).Reflection 3 is the top of the lower crust recognized in several studies. The Iberian crustis known to be stratified what explains the reflection number 4. Finally, the Moho isidentified at 13.2 s TWT corresponding to a depth of 40 km using a constant velocity of 6km/s.

Station CE04, is located inside the Cenozoic Ebro Basin in a less deformed zone. Onlytwo reflections are clearly identified: the top of the lower crust at 6.2 s TWT and theMoho at 11,7 s TWT corresponding to a depth of 35 km. The ripples after the lower crustreflection are attributed of the stratified Iberian crust.

3

Figure S1. Autocorrelation functions for stations CE03 (Sub-Pyrenees) andCE04 (Central Ebro Basin) for the frequency band of 2-4 Hz. Red arrows markidentified reflections explained in the text.

Text S3.

We processed data of 42 seismic stations but for the sake of simplicity weshow in this paper and this supplementary material section data from 7selected stations. The position of these stations is shown in Figure S2. Themap further shows the wells that have been cited in the text.

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Figure S2. Geological map of Ebro Basin with the location of seismic stationsused in our examples and wells cited in the text.

Text S4.

Figure 9 shows the Palezoic basement of the Ebro Basin as function of depth.The time-to-depth conversion was made with a velocity model built from welldata and information from active seismic profiles (Lanaja, 1987, Pedreira,2005, Jurado,1990, Juliá et.al., 1998). Figure S3, shows the map of the EbroBasin as function of our TWT measurements. The comparison between bothmaps shows that the time-to-depth conversion kept the same generalbasement structure and has not introduced artefacts.

The exact time was picked in a two steps procedure: firstly manually and thenrefined in a procedure to find the minimum amplitude in a small time windowto increase precision. Due to the complexity of the basin and to reach moreaccurate results, we divided the study area into three regions (1, 2 and 3)with one average velocity model for each region which then was used for thetime-to-depth conversion. Table S1 lists the results for each station with thepicked arrival time and the corresponding depth.

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Figure S3. TWT measurements for the P-wave reflections of the Paleozoicbasement of the Ebro Basin. The numbers (1, 2, 3) mark the regions withdifferent velocity model for the time-to-depth conversion. Well data has notbeen used for the surface construction and are shown only for reference.

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Table S1. Results for each station with confident P-wave reflectionidentification.

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Text S5.

For some stations, the picking of the P-wave arrival time is not straight forward. Theprofiles from Figure 9 highlight the variability of the encountered signals which hamperstheir discrimination at some stations while for other the reflections are clear and unique.Nevertheless, the interpretation and identification of P-wave reflections is often easierdone when analyzing all the daily autocorrelation functions together in a section plot suchas Figures 2 and 3. Figures S4 and S5 show two other examples.

Station SC24 (Figure S4) is the first station of profile A-A'. The identification of the P-wave basement reflection is easier in the section plot than in the single total data stack.The loss of reflectivity right after the P-wave reflection is an indicator of the Paleozoicbasement reflection.

Station PE21 (Figure S5) is the 7th station of the profile A-A’ (Figure 9). The identifiedbasin reflection is not evident from the profile, nevertheless, the reflection is easilyrecognized at 0.9 s TWT. Its identification in the profile is also hampered by theamplitude balance to increase the overall visibility of the autocorrelograms.

Figure S4. Daily autocorrelation functions of station SC24 with proposedinterpretation based on well-log data from neighboring wells.

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Figure S5. Daily autocorrelation functions of station PE21 with proposedinterpretation.

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