Toward Improved Infrasound Events Location Michael O’Brien 1, Doug Drob 2 and Roger Bowman 1 1 – Science Applications International Corporation 2 – Naval Research Laboratory Presented at the Infrasound Technology Workshop Tokyo, Japan November 13-16, 2007 Approved for public release; distribution unlimited DISCLAIMER “The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either express or implied, of the U.S. Army Space and Missile Defense Command or the U.S. Government.”

2 Objectives  Improve infrasound event location accuracy/ability  Improve prediction of infrasound signal properties – arrival time, back azimuth, slowness, amplitude, etc.  Improve horizontal wind model (HWM) MLT (mesosphere / lower thermosphere) region ~50 to 150 km altitude A lot of wind data since HWM-93 + growing database of infrasound data New HWMs / G2Ss (G2S = HWM + Numerical Weather Prediction): –HWM-07 / G2S-07: wind data only –HWM-08 / G2S-08: wind data + infrasound data  Assess infrasound event location capability Quantitative statistical comparisons of predictions and observations As many reliable ground-truth infrasound data as we can get

3 Motivation (1)  Mechanics of infrasound location Take a model of atmospheric properties: –HWM-93/MSISE-00: climatological model –G2S-00: climatology + Numerical Weather Prediction (epochal) Run it through a numerical propagator to predict infrasound observables: –Ray tracing: 1-D (Tau-P), 2-D (NRL RAMPE), 3-D (HARPA) Range dependent, range independent –Full-wave methods Combine it with infrasound observations to infer source location and time Observation Error Location Error Propagator Error Model Error ++=

4 Motivation (2)  What do the errors all add up to??? Easier to look at from perspective of predicting infrasound observables Side steps dealing with mechanics of locating sources Travel-time residuals:O(5-10%) Back-azimuth residuals:O(5°)  Location errors:O(5-10% of source-receiver range) Observation Prediction Error Location Error Propagator Error Model Error = ++

5 Large Source of Model Errors: Wind  Horizontal wind estimates from NRL-G2S: Below50 km:Constrained by NWP  epoch specific, pretty accurate Above50 km:Constrained by HWM-93  climatological averages, significant inaccuracies + = HWM-93 climatological average Numerical Weather Prediction epochal NRL-G2S hybrid Climatological Model – not epoch specific (inaccurate in MLT) No NWP above 50 km NWP below 50 km – epoch specific Much less reliable above 50 km Reliable below 50 km – epoch specific

6 Deficiencies in HWM-93  A preponderance of wind measurements collected since HWM-93, identifies serious errors in HWM-93 above 50 km where there is no NWP to provide superior constraint HWM-93 data distribution HWM-07 data distribution

7 Process for HWM Refinement  Statistical infrasound evaluation of wind model a key element of this study Prepare Atmospheric Data Get Infrasound Data – Waveforms – Ground Truth Analyze Signals (Measure Observables) Evaluate Models Predict Observables Predict Locations Generate/Refine Atmospheric Models feature predictions Evaluations New models signal features Subjects of this talk

8 New HWMs: Improved Parameterization   Complete basis of vector spherical harmonics, Fourier modulated Increased resolution   5 km cubic splines Also increased resolution

9 HWM-93 vs. Preliminary HWM-07   Better representation of vertical variations Zonal Wind [m/s] Zonal Wind [m/s] Meridional Wind [m/s] Meridional Wind [m/s] —— HWM-93 —— HWM-07 —— WINDI data

10 HWM-93 vs. Preliminary HWM-07 (2)   Better representation of temporal variations   Fewer artifacts —— HWM-93 —— HWM-07 —— WINDI data Ringing in HWM-93 eliminated in HWM-07 Local Solar Time [h] Meridional Wind [m/s] –40 S, 105 km 10–20 S, 93 km 10–20 S, 93 km Latitude WINDI wind dataHWM-07HWM-93 Local Solar Time [h] Zonal Wind [m/s] December, 130 km

11 HWM-93 vs. Preliminary HWM-07: Infrasound   Increased winds and lower turning heights   shorter travel times and additional travel paths G2S-07 Two thermospheric arrivals predicted G2S-00 One thermospheric arrival predicted

12 Ground-Truth Infrasound Data: Requirements   Infrasound observables are data against which to assess model predictions Travel time, back azimuth (amplitude, slowness duration) Source-receiver range far enough to sample MLT or upper stratosphere Source time and location must be known independently of infrasound, well enough to provide reliable constraint on the observable predictions: – –Travel time: a few % – –back azimuth: a few degrees Fractional Travel Time Back Azimuth DataModelSource Parameters Residual Uncertainties

13 Ground-Truth Infrasound Data: New Events   Over 100 events added since last year: Accidental explosions (munitions factories / depots, gas pipes, etc.) – location constraint sometimes very good – time constraint usually poor Controlled sources – Mine blasts: time good location ok to good Armory Explosion, Maputo,Mozambique Munitions factory Explosion, Novaky, Slovakia Gas Pipe Explosion, St. Petyersburg Russia Mine Blasts Powder River Basin, WY Mine Blasts, Siberia Russia

14 Ground-Truth Infrasound Data: Deleted Events   All earthquakes Don’t really know where/when the infrasound was generated   Rocket launches / re-entries Don’t currently know trajectories Don’t know where/when along trajectory infrasound is generated   Historical nuclear tests Source locations and times generally adequate but… Receiver locations and time registrations poor   Others Rejected / admitted on a case-by-case basis

15 Ground-Truth Infrasound Data: Event Set   211 events, 1011 arrivals (and counting) Infrasound Event Data Set Chemical Explosion (33) Mine Explosion(130) Rocket Motor Test (5) Bolide (25) Volcano (32) Landslide (1) (211) —— 1 signal —— 2 signals —— 3-4 signals —— 5-8 signals —— >8 signals

16 Ground-Truth Infrasound Data: Quality   Improvement of ground-truth source parameter specifications has substantially reduced the related uncertainties in predictions Travel Time Back Az. < 1% 1° < 5% 5° Total Prediction Uncertainties Related to Source Parameters Most are small enough to constrain models Many source-related uncertainties are very small

17 Statistics For Assessing Wind Models  Build statistics from observations and predictions Residuals Quantification Measures of Fit Comparison Model-Model Test Statistics Uncertainties on  Tk and  Ak

18 Testing the Machinery   Compare G2S-00 with HWM-93/MSISE-00 HWM-07/G2S-07 not ready for statistical evaluation G2S-00 = HWM-93/MSISE-00 + NWP should be obvious winner   101 events Events for which we had G2S-00 models in hand   First arrivals only Take prediction that best matches first arrival time – –Rigid but objective – –Avoids vagaries of matching multiple arrivals to multiple predictions   HARPA 3-D ray tracer Eigenray mode No topography There are issues – discussed later

19 G2S-00 vs. HWM-93/MSISE-00: Preliminaries   Instructive to group results by maximum predicted turning height < 55 km – tropospheric/stratospheric (TS) arrivals > 100 km – mesospheric / lower thermospheric (MLT) arrivals Troposphere / Stratosphere (TS) Mesosphere / Lower Thermosphere (MLT) Predicted Turning Heights for First Arrivals HWM-07 aimed at improving these

20 G2S-00 vs. HWM-93/MSISE-00: Residuals Troposphere / Stratosphere Mesosphere / Lower Thermosphere median = median = median = 0.14 median = 0.11 Significant late bias in MLT predictions Travel TimeBack Azimuth G2S residuals clearly smaller Differences between HWM/MSISE and G2S less obvious

21 G2S-00 vs. HWM-93/MSISE-00: Comparison  F statistics:  What to anticipate from HWM-07 / G2S-07? Improved MLT predictions More predicted MLT arrivals Data Type Maximum Predicted Turning Height < 65 km> 65 kmAll Travel times only1.84 (99.7%)1.20 (82.9%)1.40 (98.9%) Back azimuths only1.13 (67.5%)2.11 (99.9%)1.66 (100%) Travel time and back azimuths1.34 (95.7%)1.84 (100%)1.70 (100%) (two-sided confidence) F > 1  G2S-00 better predictor than HWM- 93/MSISE-00 High confidences  It’s not likely an acccident

22 Issues Generating Predictions  Ray chaos complicates prediction Terrain slopes scatter reflected rays at the resolution of GLOBE (~2 km) 30,000 rays; one azimuth; take-off angle from 0-60°; NRL-RAMPE 2-D ray tracer Rays landing within 1 km of array center Terrain with No Slopes Buncefield Oil Depot Explosion – I26DE Linearly Interpolated Slopes Buncefield Oil Depot Explosion – I26DE many more paths excited if terrain slope changes continuously

23 Issues Generating Predictions (2)  Predicted arrivals made up of contributions with wide range of ray parameters Stepwise Terrain (No slope ) Stepwise Varying Slope Continuously Varying Slope Travel Time [s] Take-Off Angle [ ° ] Discrete Eigenmodes: Arrivals predicted from small ranges of take-off angle Easy to find with iterative scheme Rays Predicted to Land Within 1km of Array Center (Buncefield Oil Depot Explosion – IS26 (30000 rays from 0-60°) Distributed Eigenmodes: Arrivals comprised of enery from wide ranges of take-off angle Note small fraction of rays that land within 1 km of receiver In many cases, the neighboring rays (1/500 th degree different take-off angle) do not! 248 of rays 219 of rays 230 of rays —— observed arrival

24 Issues Generating Predictions (3)  Ray chaos influences choice of method for generating predictions Sound wavelengths are less than 2 km at 0.2 Hz and above!  The chaotic effect is not an accident of over sampling the topography  There may not be well-behaved eigenmodes in the ray-parameter space  Iterative schemes may fail to find eigenrays  Exhaustive search of ray parameters may be necessary –Impractical in 3-D for large dataset  Future predictions in 2-D with NRL-RAMPE ray tracer Fast ½ minute topography Many modifications already made for input/output flexibility needed for the large ray-tracing task at hand Modern code (Fortran 90) is easier to modify and maintain

25 Summary  Significant progress made on all fronts Preliminary HWM-07 well underway –Shows improved wind representation and some improved predictions NRL-RAMPE code refined for batch application to large dataset Ground-truth infrasound dataset expanded and refined for statistical assessment Statistical comparison between HWM-93/MSISE-00 and G2S-00 verifies mechanics  Work ahead Further refinements to preliminary HWM-07 to match wind data Infusion of infrasound observations into HWM-08 Continued collection and refinement of ground-truth infrasound dataset –more IS better! Statistical evaluation of HWM-07/G2S-07, HWM-08/G2S-08