Passive seismic imaging Brad Artman 650 723 6007 brad@sep.stanford.edu brad@sep.stanford.edu

The conjecture Reminds me of imaging Proof of the matter Synthetics Real data Extra Modes Business case The road ahead brad@sep.stanford.edu

Ambient Noise r1 r2 r1 r1 r1 r2 t lag brad@sep.stanford.edu 1968 Jon Claerbout, Geophysics: “The reflection seismogram from a surface source and a surface receiver is one side of the autocorrelation of the seismogram from a source at depth and the same receiver.” This idea leads to the conjecture that we could create a seismic section by correlation of traces at the surface that record arrivals from a buried source. Realizing that the correlation process removes complicated source functions that may be distributed at any time through the record we hypothesize that we can record for however long our equipment allows to collect sufficient energy. Orange on the left cartoon is energy: plane waves and rays. It is recorded by all the receivers on the surface. But receiver 2 also records a bounce from the free surface to a subsurface reflector. We don’t know when this happens, but by correlating the traces, we move the energy to zero time, and remove the complicated wave-form that was propagating through the earth. By correlating r1 w/ all others, we build hyperbolas exactly like conventional reflection seismic data. t lag brad@sep.stanford.edu

Raw modeled passive data over a two reflector model Raw modeled passive data over a two reflector model. Choosing the trace under the red line as the “shot”, we cross-correlate it with all the others to build the shot-panel on the right. Using all the traces as a “shot”, we generate n shot-gathers from n traces. While traces expand to n-squared, the time axis is drastically shortened. The shot-gathers so generated can be migrated just like conventional reflection seismic. Faint hyperbola at 0.1 seconds is an artifact of my poor modeling. It is the correlation of the two events with themselves. There will be an internal multiple at the same time of opposite polarity in the real world that will cancel that one out. I did not model the effects of multiples. If it’s not strong enough in real life to do so, the greater the velocity or dip contrast of the two reflectors in the shot-panel, the less well the hyperbolas will correlate. brad@sep.stanford.edu

The conjecture Reminds me of imaging Proof of the matter Synthetics Real data Extra Modes Business case The road ahead brad@sep.stanford.edu

Shot-Profile Migration I (x)=Σ U (w,x,s) D (w,x,s) ω z Realization: If D = U , correlation requirement of the passive seismic conjecture is fulfilled in the migration. Let the wave equation handle the unknown source. Brad’s conjecture: The correlation in the imaging condition of SP migration may satisfy the correlation needed for building the shot-gathers, and thus obviating that first step. Also, the wave-equation can propagate long weird source functions just as well as short impulsive ones. So let it go, and avoid mistakes in the correlation process. Since all geophones record the up-coming (U) and down-going (D) wave-fields, I’ll use the raw passive data for both U and D. Down side = no reduction of time axis from the correlation process, and needs independent method to find a velocity model. Up side= many fewer FFT’s and faster migrating 1 shot w/ n traces rather than n shots with n traces. brad@sep.stanford.edu

The conjecture Reminds me of imaging Proof of the matter Synthetics Real data Business case The road ahead brad@sep.stanford.edu

Flow model R U D T T U D T T j j+1 j j j j j+1 j+1 j+1 j+1 brad@sep.stanford.edu

Shot-profile datuming analogy R = U D R = R e R = U D e = U e (D e ) 1 * +i Kz Dz +i Kz(U) Dz + i Kz(D) Dz +i Kz(U) Dz -i Kz(D) Dz brad@sep.stanford.edu

Delft datuming brad@sep.stanford.edu

The conjecture Reminds me of imaging Proof of the matter Synthetics Real data Extra Modes Business case The road ahead brad@sep.stanford.edu

Finite difference modeled data w/ free surface should provide even more accurate insight. Modeled by Deyan Dragonov of Delft: 281 random sources were modeled and summed together. This model is better than the other one. brad@sep.stanford.edu

3 reference velocities w/ SSF migration of raw data (Up=Down) wave-fields in standard shot-profile migration. Raw data looks like TV static brad@sep.stanford.edu

Different runs yield slightly different illuminations sometimes if the magnitude of the sources varies a lot. Only one reference velocity was used: notice poor focus of syncline. brad@sep.stanford.edu

Different runs yield slightly different illuminations sometimes if the magnitude of the sources varies a lot. Only one reference velocity was used: notice poor focus of syncline. brad@sep.stanford.edu

brad@sep.stanford.edu

brad@sep.stanford.edu

The conjecture Reminds me of imaging Proof of the matter Synthetics Real data Extra Modes Business case The road ahead brad@sep.stanford.edu

Artman GeoServices brad@sep.stanford.edu Moss Landing, CA. 72 phones. 2x2meter array of 8x9 phones @ .25m spacing. NOT ENOUGH CHANNELS. Hoping to get water table- But it was at 3.5 meters: aperture of my lens not big enough. Going back soon with more phones. Also buried pipe 1 meter down. Hollow void target, 6” diameter, .5m long (probably less: ends not capped). brad@sep.stanford.edu

Spectrogram brad@sep.stanford.edu Noise spectrogram during the day – Cultural noise and wind brad@sep.stanford.edu

Active data brad@sep.stanford.edu Active data for comparison. Pipe anomaly present. Overmigration of hyperbola due to air cavity. Water table very clear. brad@sep.stanford.edu

Enlarged data set brad@sep.stanford.edu Simply cross-correlating traces w/ this few channels yields no results. If I insert 4 zero-traces between all the active phones, wave-front healing during the migration should interpolate the energy to give a finer-scale result. Cultural noise (power grid) is predominately below120Hz. With beach sand @ 180 – 300 m/s and 2 m offset, I have to low-cut that out anyway to have waves that don’t see my whole array as one station. brad@sep.stanford.edu

5min Night brad@sep.stanford.edu First look at the pipe. Anomaly at correct location. Not too convincing, should have buried an angled pipe to avoid any doubt. Only 5 min. of data used. Surprisingly short to get results. Bandpassed, not deconvolved. Before the migration pushes energy across all of the empty traces, there is lots of noise at the very shallow depths. brad@sep.stanford.edu

5min Night Deconvolved data brad@sep.stanford.edu

10min Day Day time, band-passed brad@sep.stanford.edu

Crustal scale progress brad@sep.stanford.edu

The conjecture Reminds me of imaging Proof of the matter Synthetics Real data Extra Modes Business case The road ahead brad@sep.stanford.edu

Forward Scattering brad@sep.stanford.edu

Converted transmission imaging V (x, z) and V (x, z) p s * Forward-scattered P to S mode conversion brad@sep.stanford.edu

Possibilities Scattering Mode Source Prop. Dir. Source Velocity Receiver Velocity Receiver Comp. FS P-P - P SV BS P-P + BS P-S S BS S-P BS S-S SH brad@sep.stanford.edu

Business Case It’s free and convenient Provide information to trigger active survey Low frequency images including deep crustal information brad@sep.stanford.edu

Novel Aspects of LoFS Hydrophone w/ absolute pressure Tide and wave-height monitoring at rig Drill/operations noise (SWD) 3-component geophones vector imaging condition anyone? brad@sep.stanford.edu