Patent Description:
Several very sensitive scientific experiments and technological / industrial processes are limited in their ultimate performance by the background seismic noise.

Those experiments are for example the detection of gravitational waves, atomic interferometry or the characterization of intrinsic noise of sensors such as gyroscopes and accelerometers.

The seismic noise refers to the mechanical vibrations of the ground It is measured with seismometers, gyroscopes and strainmeters. The seismic background noise is described in detail in the open file report "<NPL> (open file report <NUM>-<NUM>). Considering the translational components, it can be measured in terms of the acceleration (m/s<NUM>) or velocity (m/s) of the particle motion at one position, and along the three cartesian coordinates x-y-z. In the case of rotational components, it is measured in terms of rotation (rad/s) of the particle motion at one position, again along the three cartesian coordinates x-y-z. To highlight its spectral content it is typically represented with its power spectral density. The latter refers to the spectral energy distribution normalized to the unit time, and is expressed for the acceleration in m<NUM>/s<NUM>/Hz , for the velocity in m<NUM>/s<NUM>/Hz (see <FIG> and <FIG> of Peterson) and for the rotation in rad<NUM>/s<NUM>/Hz.

The background seismic noise, called SN, concerns the low frequency part of the background seismic noise, typically comprised between <NUM> and <NUM>.

The SN at a place of interest on the ground is mainly determined by the ocean waves even far located with respect to the place. This relation, suggested more than <NUM> years ago, has been thoroughly characterized, for example by <NPL>). The goal of the author is to derive ocean wave parameters from seismic noise measurements, for example to reconstruct past waves climates based on seismic records (abstract).

The publication of <NPL>) discloses a numerical model, which produces the seismic noise spectra expected on ground as a function of satellite-based sea state observations and a detailed knowledge of the winds on the oceans, presence of icebergs and coasts generating reflections, combined with a numerical wave model implementing excitation theory.

The publication of <NPL>, discloses a protocol to determine the oceanic microseism source patterns, i.e. the ocean areas mainly responsible of the seismic noise observed at three far located stations on ground. This objective is achieved by comparing so-called "observed locations" and "modeled or predicted locations".

There is presently a need for the above cited scientific experiments to better take into account the perturbation due to background seismic noise SN, and the goal of the invention is to be able to make forecasts of seismic wave-induced SN to optimize the planning of sensitive experiments in periods of low SN, or to improve the experimental sensitivity thanks to adaptive algorithms for the identification of target signals in the anticipated SN.

In a first aspect, the present invention provides a seismic noise prediction method at a point of interest as defined in independent claim <NUM>.

In a second aspect, the present invention provides a seismic noise prediction method at a point of interest as defined in independent claim <NUM>.

Further advantageous aspects of the present invention are defined in the dependent claims.

Embodiments of the present invention, and further objectives of advantages thereof, are described in details below with reference to the attached figures, wherein :.

The basic idea of the prediction method according to the invention is to use a model which couples values of predetermined ocean parameter(s) taken at predetermined ocean place(s) with background seismic noise SN, taken at the place of interest. Seismic noise predictions are obtained by injecting ocean parameters forecast into the model.

The seismic noise prediction method <NUM> at a point of interest POI located on the ground according to the invention is described on <FIG>.

In a first step A ocean weather forecast is retrieved from one or a plurality of predetermined ocean places called POPI. The ocean weather forecast covers values of one or a plurality of predetermined ocean parameters POPa.

In a step B a coupling function CF is retrieved, this function coupling the predetermined ocean parameter(s) POPa taken at said predetermined ocean place(s) POPI with the background seismic noise SN, that we will call seismic noise for simplicity, taken at the place of interest POI. The coupling function is defined on at least a spectral window Δf of the seismic noise included in the range [<NUM>,<NUM>; <NUM>].

On a step C seismic noise predictions SNP are determined based on the ocean weather forecast injected in said coupling function.

For a given place of interest POI, the predetermined ocean parameters, the predetermined ocean places and the coupling function have been determined in a preliminary step that can be performed once for all or regularly updated.

An example of the implementation of the prediction method <NUM> according to the invention and of the preliminary step will be described below.

The chosen place of interest is the location of the LSBB (Laboratoire Souterrain à Bas Bruit) in Rustrel (France) illustrated in <FIG>.

The predetermined ocean places POPI are the location of six buoys also illustrated on <FIG>: <NUM> Marseille le Planier; <NUM> Nice; <NUM> Golf du lion; <NUM> Cap Ferret; <NUM> Gascogne Ouest Arcachon; <NUM> Bretagne. Three are located in the Mediterranean sea and three are located in the Atlantic ocean.

In this example the predetermined ocean parameters POPa have been selected among a plurality of ocean parameters OPa for which forecasts are available. Typically the ocean parameters OPa are chosen among: wave height (maximum and/or significant), wave period T, wave direction dir, wave spread, wind force, wind direction, air temperature, air pressure, water pressure. The availability of the forecast for the above listed ocean parameters depends on the forecast model and the place in the ocean.

Weather forecast models are presently available that can predict the wave features up to <NUM> days. Two of these models are:.

These models predict at different sites offshore and on the coasts the wave features like height, period and direction.

The predetermined ocean parameters POPa in this example are :
POPa : wave height WH and wave period T for the <NUM> places,
wave direction dir for ocean places <NUM> and <NUM>.

In the example the predictions of the ocean parameters POPa are obtained by interpolating at POPI the <NUM> days WAM forecasts on a <NUM> mesh.

In <FIG> is shown an example of the forecast for the wave height and in <FIG> the forecast for the wave direction for the western Europe Atlantic region and the western Mediterranean sea (WAM with a <NUM> mesh).

The ocean weather forecasts cover the entire world or more specific areas, and are available as data-files with all the predicted parameters on the adopted longitude/latitude mesh. An example of WAM prediction (<NUM> mesh) for the whole world generated on the <NUM>/<NUM>/<NUM> for the <NUM>/<NUM>/<NUM> at <NUM>:<NUM> is given on table I.

The table contains the following columns:.

and distinguishes between the "swell", gravity waves on the water surface originated by distant weather systems and with a relatively long wavelength, and the "wind sea wave", surface waves generated by the local wind blowing on the water basin on a distance called "fetch". Generally the swell carries more energy, and we adopt its parameters for the present example of forecast: we used the variable « SWE_H », « MEANSDIR » and « MEANSFR ».

<FIG> compares, at the location of buoy <NUM>, wave height predicted over <NUM> days from <NUM>/<NUM>/<NUM> to <NUM>/<NUM>/<NUM> in grey (curve <NUM>) (obtained by interpolation of available forecasts at the location of the buoy <NUM>) and wave height as real data recorded daily at the buoy <NUM> (in black, curve <NUM>) from <NUM>/<NUM>/<NUM> to <NUM>/<NUM>/<NUM>. A good overall agreement can be observed over the period of the prediction.

Two specific points, A December <NUM>th <NUM> at 12h00 and B January <NUM>th <NUM> at 6h00, that will be used later for seismic noise predictions, are highlighted with a dotted vertical line.

Using the forecast of the above listed POPa wave parameters (height and period T at the <NUM> buoys, and direction dir for buoys <NUM> and <NUM> ) and a predetermined coupling function FC (detailed later), predictions on the seismic noise spectrum are made at LSBB location on a spectral window Δf of [<NUM> ; <NUM>].

<FIG> show the forecast of the seismic noise respectively for the points A and B (dotted curves FA and FB) at LSBB, representing the spectral power density of the seismic noise along the z axis PSDz (z corresponding to the vertical axis) as a function of the frequency of seismic noise (in Hz) on the spectral window Δf of [<NUM> ; <NUM>].

The seismic noise spectra measured a posteriori at LSBB at the same points A and B are also plotted in solid curves MA and MB. Curves MA and MB are determined from the measurement of acceleration data along the vertical direction taken with a Streckeisen STS-<NUM> Broadband Sensor. Present and past data are available via the RESIF website (http://seismology. One spectrum is produced with a separation of <NUM> minutes, and taking each the seismic data for one hour window and producing an FFT. A binning procedure is applied to each spectrum, in order to reduce its information to an array of <NUM> frequency bins between <NUM> and <NUM>, <NUM> per decade and in exponential progression using a growth factor equal to <MAT>.

Curve NHNM means "new high-noise model" and curve NLNM means "new low-noise model", as defined in the <NPL>. They correspond to an average low and average high background noise power registered in a seismic network on earth.

The comparison of FA vs MA shows an excellent agreement over the frequency decade [<NUM>;<NUM>] and a good agreement over an enlarged window [<NUM>;<NUM>]; the agreement is higher when the ocean weather forecast is consistent with the ocean weather effectively observed, which typically means for times close to t0. Point A characterizes a high seismic noise (see PSDz scale and corresponding high wave height).

The agreement FB vs MB shows an excellent overlap on the frequency window [<NUM>; <NUM>], and a good one over the frequency widow [<NUM>;<NUM>]. The agreement is lower for frequency below <NUM>; point B characterizes a lower seismic noise.

It can be seen that the result and interest of the method according to the invention is to make accurate seismic noise predictions at a given point of interest, those predictions being deduced from ocean weather forecast.

The prediction method according to the invention can then be repeated in continuous, by converting the wave forecasts every time as a new prediction is available (typically every few hours). In this way the invention provides a regularly updated forecasting tool for seismic noise at the specific location.

So the invention allows to know in advance the expected seismic noise background in a given location, typically with several days of advance (and with an uncertainty increasing with the forecast time depth), thanks to:.

The anticipated knowledge of the background seismicity of a given place has direct scientific and technological advantages:.

Neither research experiments nor industrial activities schedule nowadays their run time in function of the foreseen background seismic activity, which to date is not forecast. The normal approach is to repeat the experimental/industrial activity, lowering the impact of the background seismic noise via statistical averaging where possible, or selecting a posteriori the best realizations otherwise.

The determination of the coupling function is performed in a preliminary step A0. A first phase consists in the collection of the data required to characterize the underlying dependence between the ocean weather (the "cause") and the seismic noise (the "effect") at the place of interest.

The expected delay between cause and effect is linked to the speed of propagation of seismic waves, of the order of a few km/s, and to the distance between the source of the seismic event and its measurement; in the following typical distances between the monitoring of the ocean weather and the site of interest of <NUM>-<NUM> is considered, i.e. a delay ranging from a fraction of a minute to a few minutes. Considering that the data of the ocean weather are typically available at an hourly rate (at best every half hour), this means the cause-effect propagation can be considered instantaneous. If the data of the ocean weather are available with a time resolution comparable or even better than the propagation time of seismic waves to the point of interest the specific contributions of separated sea regions could be untangled.

Two data-sets are required:
A first date-set is ocean weather data, and a sub step A01 of step A0 is retrieving ocean weather data OWD recorded at various places in the ocean OPI, and during a period of time PT, said ocean weather data comprising values of a plurality of ocean parameters OPa including the predetermined ocean parameters POPa.

In the example, OWD consists in information provided by <NUM> buoys placed in different the sea/oceanic water basin and available for example via the National Data Buoy Center of the NOAA, or via the Data Buoy Cooperation Panel from the World Meteorological Organization and Intergovernmental Oceanographic Commission of UNESCO. The buoys are both on the coasts and offshore, and each buoy has an identification number.

The <NUM> buoys are: Anglet (<NUM>) - Atlantic Ocean • Saint-Jean-de-Luz (<NUM>) - Atlantic Ocean • Cap Ferret (<NUM>) - Atlantic Ocean • Oleron large (<NUM>) - Atlantic Ocean • Ile d'Yeu Nord (<NUM>) - Atlantic Ocean • Belle-Ile (<NUM>) - Atlantic Ocean • Plateau du Four (<NUM>) - Atlantic Ocean • Les Pierres Noires (<NUM>) - Atlantic Ocean • Gascogne Ouest Arcachon (<NUM>) - Atlantic Ocean • Bretagne (<NUM>) - Atlantic Ocean • K1 Atlantique (<NUM>) - Atlantic Ocean • Brehat - Iles-de-Brehat (<NUM>) - English Channel • Cherbourg (exterieur) (<NUM>) - English Channel • Paluel (<NUM>) - English Channel • Manche Greenwich (<NUM>) - English Channel • Channel Lightship (<NUM>) - English Channel • Lomond AWS (<NUM>) - North Sea • Magnus AWS (<NUM>) - Norwegian Sea • Marseille (Le Planier) (<NUM>) - Mediterranean sea • Nice (<NUM>) - Mediterranean sea • Golfe du Lion (<NUM>) - Mediterranean sea • Monaco (<NUM>) - Mediterranean sea • Porquerolles (<NUM>) - Mediterranean sea • Espiguette (<NUM>) - Mediterranean sea • Sete (<NUM>) - Mediterranean sea • Leucate (<NUM>) - Mediterranean sea • Banyuls (<NUM>) - Mediterranean sea.

The time interval TP is from February <NUM> to February <NUM>. Scientific agencies like the NOAA freely provides the ocean weather data over the last <NUM> hours, and can provide historical databases. Data from buoys are typically available on an hourly rate.

The ocean weather data comprise parameters related to waves: wave height (significant and/or maximum, in meter); wave period (T in seconds, average and/or maximal); wave direction (dir in degrees); wave spread (in degrees). Ocean weather data can also comprise the water temperature (tempH20), wind force (in knots; <NUM> knot is equal to <NUM>,<NUM>/h), wind direction (in degrees), air temperature (in degrees), air pressure (in Pascals). Not all the quantities are available for all buoys and at all time.

Ocean weather data may also be collected from satellite survey.

A second data set is seismic noise at the place of interest and a sub step A02 of step A0 is retrieving seismic noise data SND recorded at the place of interest POI during the same period of time PT. Thus to run the preliminary step seismographic data must be available at the site of interest.

For the example of the LSBB several seismometers and accelerometers are present on site, and the present and past data are freely available via the RESIF website. Time intervals characterized by geological seismic events like earthquakes and volcano eruptions are removed from the seismic data stream, because then the acceleration signal is mainly due by causes other than ocean waves. To identify these time intervals, we use the list of earthquakes events registered worldwide (for example on the NOAA website) and we monitor the total power of the seismic spectra, looking for sudden changes.

Once the two set of data have been retrieved, the determination of the predetermined ocean places POPI, of the predetermined ocean parameters POPa and of the coupling function CF, is performed in a sub step A03, from a relationship between said ocean weather data and said seismic noise data.

According to a first embodiment substep A03 comprises :.

Then, CF is determined by solving a multivariable regression protocol, based on said correlations.

In this first embodiment a selection among a plurality of ocean parameters OPa and a selection among a plurality of ocean places are performed. The result of this selection are the so called predetermined ocean parameters POPa and predetermined ocean places POPI.

The selection can be operated by comparing evolution over time of seismic noise at POI and a chosen ocean parameter OPa, for each available ocean place. An example is given in <FIG> and <FIG> with the ocean parameter wave height (in meter).

On the left side A, in grey is plotted the power at <NUM> taken from the hourly noise spectra at LSBB over the <NUM> months period May-August <NUM> (vetical axis on the left side) and with black dots is plotted the waves' height (vertical axis on the right side) measured at <NUM> different buoys : <FIG> buoys <NUM>, <NUM>, <NUM>; <FIG> buoys <NUM>, <NUM>, <NUM>.

To highlight the correlation, the two previous quantities are plotted as x and y coordinates on the right side B. For the <NUM> and <NUM> buoys the color code shows the wave direction. A linear correlation is evident to the eye for the Mediterranean buoys <NUM>, <NUM> and <NUM>, less for the Atlantic ones.

In a first approach the selection is realized by calculating a correlation factor such as the Pearson Correlation. This first approach of the first embodiment is applied in the example.

The Pearson correlation is the correlation ρXi,Yj between the time series for each quantity Xi (ocean parameter) measured at the buoys of interest and the seismic power Yj in the j-th frequency bin:
<MAT> with "cov" covariance and σ standard deviation.

<FIG> and <FIG> show in grey code the Pearson correlation between the seismic noise at LSBB and different ocean weather parameters measured for <NUM> buoys: the <NUM> used later (selected) for the prediction (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) and a <NUM>th, the <NUM> "Magnus AWS" buoy, located in the Norwegian Sea at the GPS coordinates <NUM> N <NUM> E. The correlation is determined in spectral bins from <NUM> to <NUM>.

<FIG> shows the positive correlation (factor between <NUM> and +<NUM>), and <FIG> shows the negative one (between <NUM> and -<NUM>). Note that for each buoy the correlation is calculated for all the ocean parameters available. These parameters may be different from one buoy to another, and not all the buoy ocean parameters may be available in the chosen forecast model. For example water temperature (tempH20) is not a parameter available in the forecasts accessible to us. Therefore and independently of its level of correlation, this parameter cannot be retained for the implementation of the method. The closer the factor is to one in absolute value the higher is its value as a prediction parameter.

It can be seen on <FIG> and <FIG> that the best correlation is obtained for the <NUM> buoys in the Mediterranean sea and for the ocean parameters: wave height, wav height max, wave period T, or Tmax, or Taverage, wave direction dir (for some buoys), wind force (which is related to wave height). A lower correlation exists also for the <NUM> Atlantic ocean buoys, and even buoy <NUM>, located in the Norwegian sea, shows a certain degree of correlation.

It can also be seen that the best correlation is concentrated on the spectral window [<NUM>,<NUM> - <NUM>].

Preferentially the predetermined ocean place(s) are situated in the water basins nearby the point of interest.

Each specific site requires a preliminary selection to determine which restricted set of buoys should be considered to later realize the forecast. For this choice the correlation matrix previously calculated can be used (see <FIG> and <FIG>), to select the buoys presenting the highest degree of correlation with the seismic noise at LSBB.

So based on a visual observation of the correlation matrix, or by considering a set of thresholds, the predetermined parameters PDPa and the predetermined ocean places POPI are selected. In the example the <NUM> buoys <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM> are selected, with ocean parameter wave height and wave period.

The inter-dependence between the ocean weather variables and the seismic noise at the place of interest is obviously more complex than a linear one: it is easy to see that waves with the same height but different direction or spread will have a different effect in terms of seismic energy transmitted at the place of interest.

We describe here the simplest model based on multiple linear regression. For simplicity we consider now a specific frequency bin, and the same procedure is then repeated on all the frequency bins. The vertical component of the seismic power spectral density Yi measured in the bin of interest is repeated for i = <NUM>,. n successive observations (in our case it consists in a one hour integrated measurement repeated every half an hour), and it is considered as a variable that depends linearly on n sea weather variables:
<MAT>.

The linear regression algorithm determines the best set of βj parameters that minimizes the RMS difference between the predicted seismic signal and the measured one over a large set of historic data; this operation constitutes the "training" of the algorithm. The set of βj parameters can then be used to transform sea weather forecasts into seismic noise forecasts, constituting the coupling function FC.

The entire process can be applied to the other components of the seismic motion, in order to observe polarization features of the wave-induced background seismic noise and of its prediction.

In a second approach the selection of the most significant combinations of ocean weather variables to model the seismic noise at the place of interest, constituting independent variables, are determined by PCA (Principal Component Analysis).

More generally several mathematical tools can be exploited to model this multi-variable regression problem.

In a second embodiment there may be no ocean place and parameters selection, or a lesser selection. In this approach a plurality of ocean places are considered (for example the <NUM> previously considered, or even more), a plurality of ocean parameters are considered (it may be all the parameters for which a forecast is available, or a set among those parameters). Those ocean places and set of parameters constitute the input of a machine or deep learning algorithm, and the background seismic noise constitutes its output. The obtained trained algorithm constitutes the coupling function implemented in sub step A03.

The algorithm may be regularly improved as new data become available for training.

Several machine learning algorithms have been successfully implemented to quantify the dependence between sea weather data and seismic noise power in specific spectral bins.

Claim 1:
Seismic noise prediction method at a point of interest, POI, comprising:
A : Retrieving ocean weather forecast from one or a plurality of predetermined ocean place(s), POPI, said ocean weather forecast covering values of one or a plurality of predetermined ocean parameter(s), POPa,
B : Retrieving a coupling function, CF, coupling said predetermined ocean parameter(s), POPa, taken at said predetermined ocean place(s), POPI, with a seismic noise, SN, taken at the place of interest, POI, said coupling function being defined on at least a spectral window, Δf, of the seismic noise included in the range [<NUM>,<NUM>; <NUM>],
C : Determining seismic noise predictions, SNP, based on said ocean weather forecast injected in said coupling function,
said method comprising a preliminary step A0 comprising the sub-steps of:
A01 : Retrieving ocean weather data, OWD, recorded at various places in the ocean, OPI, and during a period of time PT, said ocean weather data comprising values of a plurality of ocean parameters, OPa, including said predetermined ocean parameter(s),
A02 : Retrieving seismic noise data, SND, recorded at the place of interest, POI, during the same period of time PT,
A03 : Determining said predetermined ocean place(s), POPI, said predetermined ocean parameter(s), POPa, and said coupling function, CF, from a relationship between said ocean weather data and said seismic noise data,
wherein the sub-step A03 comprises:
- identifying ocean parameter(s) or combinations of ocean parameter(s) leading to a correlation with seismic noise at the place of interest, said identified ocean parameter(s) corresponding to said predetermined ocean parameter(s), and identifying the ocean place(s) where the correlation is relevant, said identified ocean places corresponding to said predetermined ocean place(s),
- solving a multivariable regression protocol in order to determine said coupling function, CF, based on said correlations.