I checked the connections specified in the old Gulap Pin Map and found that they do not correspond to the current values. I mapped out the current connections (in this case, the letter refers to the labeled pin on the mil/spec while the number refers to the pin on the 37 pin DSub, labeled consecutively):

A-1, B-2, C-3, D-4, E-5, F-6, G-7, H-Unused, J-8, K-unused, L-9, M-10, N -11, P-12, S-13, T-Unused, U-14, V-15, W-16, X-17, Y-18, Z-Unused, a-Unused, b-19, c-20, UnlabeledPin-Unused.

There are 20 pins in use of 26 total, which is good because that means Jenne and I can use the ~70m long 24 wire cable to make a new Gurlap 1 cable.

The parts Jenne and I ordered arrived today, so we made a long cable for Guralp 1 using a 24 + 1 wire 70 meter long cable, a female 37-pin DSub, and a 26-pin milspec. The pin map is the same as the one I specified in my previous E-log. I soldered both the milspec attachment and the DSub attachment, and used a Multimeter to check the connectivity of the cables. 20 of 20 connections worked (beeped), so I plugged  the cable into the Gurlap 1 seismometer and the Guralp box.

The time series comparison for the two cables

Old cable:

New cable: (I had to move GUR 1, so it's still stabilizing in the X and Y time series)

 New

The current signal spectrum

The BLRMS on the seismic strip also look similar using the two cables - it's more visible on the wall, but I will include a StripTool picture:

New Cable BLRMS (similar to old cable BLRMS)

Den and I moved the Streckeisen, Guralp 2, and Trillium seismometers to the isolation box in order to measure the noise of the Streckeisen while we have the Trillium.

As we do not have legs for Trillium, I was advised to use shims to adjust the levels. However, they produce extra resonance at ~30 Hz + harmonics. Coherence is lost at these frequencies.

 Brian Lantz / Dan Clark are looking around their lab to see if they forgot to ship the feet with the T-240.  They had taken the feet off to put it in a pod. 

Den and I installed a module in the c1pem model which has a feedforward neural network to classify seismic disturbance (10 means quiet, 20 truck, 30 earthquake). There is a channel SEIS_CLASS which should specify the class of the seismic signal. The code works for signals sampled at 256 Hz, so an anti-aliasing filter must be installed in order to decimate from the 2048 model.

The models were compiling slowly, so Alex removed the archiving feature (gzip and tar were taking a lot of time).

Den and I also had trouble with a simple for loop in our model, so we talked to Alex who noted that the -O3 compiler unravels for loops in a buggy way. Thus, we have compiled c1pem using the -O compiler.

PS: the Trilium seismometer now has legs.

Alex also modified RCG script to generate -O in the Makefile for c1pem model:

controls@pianosa:/opt/rtcds/rtscore/release/src/epics/util 127$ svn diff
feCodeGen.pl 
Index: feCodeGen.pl
===================================================================
--- feCodeGen.pl (revision 2999)
+++ feCodeGen.pl (working copy)
@@ -3183,7 +3183,12 @@
print OUTM "\n";
}
print OUTM "ALL \+= user_mmap \$(TARGET_RTL)\n";
+# do not optimize c1pem
+if ($skeleton eq "c1pem") {
+print OUTM "EXTRA_CFLAGS += -O -w -I../../include\n";
+} else {
print OUTM "EXTRA_CFLAGS += -O3 -w -I../../include\n";
+}
print OUTM "EXTRA_CFLAGS += -I/opt/gm/include\n";
print OUTM "EXTRA_CFLAGS += -I/opt/mx/include\n";

 Using Guralp, STS-2 and Trillium I compared Gur and STS-2 self-noise assuming that Trillium noise is not worse then STS-2 noise.

Interesting that STS-2 (or Trillium if its noise is worse) noise is not too much better then Guralp noise.

STS-2 - end of X arm

GUR 2 - isolation box

TRILLIUM - 1Y3 (DC power supply uses 1Y3 AC power, please do not close the door completely)

GUR 1 - end of Y arm

Now we have several "triangular seismic antennas". Different configurations can be chosen to compare the results.

I fixed up the seismic.stp file for the StripTool display:

1. All BLRMS channels now have a y-axis range of 3 decades. So they all are displaying the same relative changes.
2. So the 0.01-0.1 Hz band which is all over the place is real, sort of. Masha says that it is due to the seismometer signal being dominated by noise below 0.1 Hz. She is going to fix this somehow.
3. I changed the samping time from 1 sec. to 10 sec. to make the traces less fuzzy.
4. We (Masha / Liz) should harmonize the colors of this file with what's on the summary pages.

Den and I measured the differential motion of the x and y arms using Guralp 1 at the end of the y arm, Guralp 2 at the beamsplitter, and the Streckeisen at the end of the x arm.

I calibrated the Streckeisen to the Guralp by calculating the relative gain of the seismometer signals at the microseism. The Guralp 1-y amplitude was 1.0237 times Guralp 2-y and Guralp 2-x was 38.54 times STS-x. The Guralp calibration (to go from counts to meters) I used was 0.61/1000/800/80/(2*pi*f) m/count.

The differential motion should keep decreasing at low frequencies because the ground will move together at such large wavelengths. It goes up because the seismometer noise begins to dominate at low frequencies (below about 0.5 Hz). Another possible error source could be that the seismometers are not perfectly aligned along the arm.

I estimated the transfer function of the seismic stacks using a rough model I made based on the LIGO document LIGO T000058 -00. I used a Q of 3.3 for the viton springs, and resonant frequencies of 2.3, 7.5, 15, and 22 Hz (measured in that document for the horizontal motion). I multiplied the simple mass-spring transfer function four times for each layer of metal/spring, with the respective resonant frequency for each. The pendulum suspending the test masses has a resonant frequency of 0.74 and a Q of 3, according to the same document.

When I multiply the net transfer function (pendulum included, the green line above) by the differential motion of the x arm that I measured in eLog 7186, I find the differential motion of the test mass (NOTE: I converted the differential motion to displacement by multiplying by (1/2*pi*f)).

It agrees within an order of magnitude to the seismic wall from the displacement noise spectrum hanging above the control room computers.

Finally, I looked at how the geophone and accelerometer noise spectra looked compared to the ground differential motion (any STACIS sensor signal will also be multiplied by the stack/pendulum transfer function, so I'm comparing to the differential motion before it goes through the chamber). Below about 1 Hz, it is clear from the plot below that the STACIS could never be of any benefit, even with accelerometers rather than geophones as the feedback sensors.

 I made the plots a little nicer and added new sensor noises (from Brian Lantz's scripts and measurements). Click to enlarge.

The last plot shows that these other sensors' noises are lower than the differential ground motion below 1 Hz.  Though 3 seismometers per STACIS is impractical, this shows that such seismometers could be used as feedforward sensors and provide isolation against differential ground motion. At these noise levels, the noise of the high voltage amplifier circuit in the STACIS would probably be the limiting factor.

Den and I decided to try to classify seismic signals in the frequency domain rather than the time domain. We looked at amplitude spectral density plots of all of the data in our set, and noted that there were noticeable differences in the frequency domain for midnight quiet, trucks, and earthquakes.

For example, here is the time series of quiet, midnight seismic noise as compared to the seismic noise at the peak of an earthquake - the earthquake signal is noticeably higher in the 1 - 3 Hz region. Likewise, for the truck signal, there are noticeable bumps that arise at 10 and 30 Hz during the peak of the truck's motion due to the resonant frequency of the truck bouncing on its wheels.

We investigated this potential means of classification further by considering the linear separability of the power of our signals in various frequency bands. Below is a plot of the power of a normalized signal in the 0.1 - 3.0 Hz region vs. the power of the normalized signal in the 3.0 - 30.0 Hz region - calculated by means of fft and separation of the discrete resulting frequencies (in short, an ideal filter).

There is rather clear linear separability of the normalized signals in this case, as two lines could potentially be drawn to separate trucks from quiet and earthquake in this case (with a few misclassified points due to quiet - since the lab isn't actually empty and quiet in the middle of the night, and man-made seismic disturbances to occur). The reason we have to normalize our signals lies in the fact that the data set had different gains for various seismometers at different times. Normalization not only allows us to use our data set for training effectively, but it also assures that the online classification, if the online signals are also normalized, will allow for variable seismometer gains in the future and still be able to classify signals.

I looked at the linear separability of our training set using various combinations of frequency bands, and deduced that the current separation in the BLRMS preforms best (coincidentally, since the BLRMS separations are just decades), which meant that we could use the current BLRMS system we have for online classification of seismic noise.

Thus, I built a neural network which performed classification with the following parameters:

- One hidden layer of 20 neurons

- Gradient descent backpropagation with learning parameter mu = 0.175

- Sigmoidal activation functions for each neuron (computationally achieved by a parametrized hyperbola rather than an actual hyper-tangent in order to save on computation time). 

- 5 inputs - the normalized fft^2 of the signal (since the root of a signal doesn't add linearly to 1) in the following frequency regions: 0.1 - 0.3, 0.3 - 1.0, 1.0 - 3.0, 3.0 - 10.0 and 10.0 - 30.0 Hz. Since this division was done through the (frequency, fft value) return in Matlab, the signal was essentially filtered ideally into these frequency bands.

- 3 output neurons representing an output vector, with desired output vectors of [1, 0, 0] for earthquake, [0, 1, 0] for truck, and [0, 0, 1] for quiet.

- 1,600,000 training epochs (batch backpropagation on all of the data)

Below is the best learning curve for this network, representing the total amount of inputs misclassified out of 224. The best result achieved was 30 misclassified signals out of 224. Obviously this is not ideal, but our data is not totally linearly separable. This could, however, be reduced with further iterations, but given the close to 0 slope of the learning curve between iteration number 1,000,000 and number 1,500,000, this could take a very long time.

Thus, I trained the network, generated the weight vectors and optimal activation function parameters, and was ready to implement a feed-forward neural network (with no online training). My next e-log (Part 2) will be about this system and will be posted shortly.

As promised in previous e-log, this log is all about the current online seismic noise classification system.

While we had the BLRMS system already in place (which I helped make), Den realized that we would need better filters for the BLRMS channels, as we wanted a strong cut-off, but we also wanted a short step-response so that we could quickly classify seismic signals. Likewise, having a step response which oscillates is also undesirable as this could lead to false classifications of post-truck signal as trucks as a filter adjusts and then dips back down. Thus, after experimenting with many different filters, Den chose to use a combination of

chebyl("LowPass", 1, 1, 0.03)*chebyl("LowPass", 1, 1, 0.03)

as our low-pass filter. The step response and bode plot are below.

 The next step was to write C code that would implement the feedforward neural network with my newly generated weights.

Next, I had to implement the code in the c1pem model, and normalize the inputs. Below is an overview of the model, and a close up of the C block section.

The above close-up includes the process of normalization (dividing by the square of the incoming signal), feeding through the neural network, and classifying.

Each seismometer channel set (GUR1X, GUR1Y, GUR1Z, GUR2X, GUR2Y, GUR2Z, STS1X, STS1Y, STS1Z) now has channels (and corresponding DQ channels) of the following form:

SEIS_CLASS : The class of seismic noise 1.0 means Earthquake, 0.5 means Quiet, and 0.0 means Truck. (There are only these 3 digital values).

SEIS_CLASS_EQ, SEIS_CLASS_TRUCK, SEIS_CLASS_QUIET: These channels represent the confidence of the neural network's classification. The class of the current signal will have an output of 1, where the other two channels will have an output between 0 and 1 representing the ratio of the neural network's output in that class neuron to the output in the classification vector neuron. To simply - suppose the neural network classified an earthquake. Ideally, the neural network output neurons would have the value [1, 0, 0], and SEIS_CLASS would equal 1.0 for earthquake. However, the output neurons probably read something along the lines of [0.9, 0.3, 0.5] - SEIS_CLASS is still 1.0, but SEIS_CLASS_EQ would be 1.0, and SEIS_CLASS_TRUCK would be 0.5 / 0.9 and SEIS_CLASS_QUIET would be 0.3 / 0.9. The lower the other two signals are, the better - this means that we are more confident in our classification.

The MEDM screen for this system (in the RMS system) has the following form for all seismometer channels (this one is GUR1X):

These are the screens I edited earlier in the summer, with modifications. The bottom filter banks represent the norm of the seismometer signal, which we use to normalize the inputs to the neural network.

Here a close-up of the most important part:

The orange meter on the right points to the current signal type. Here it reads truck - this is ok because it's the middle of the day, and there are a lot of trucks around. The left side represents our confidence in the signal - the signal is classified as a truck, so the "Truck" bar is saturated. The quiet signal bar is very low, which is good since it means that the neural network thinks that it's definitely not quiet. The earthquake bar has some magnitude, since earthquake signals and trucks have some degree of linear non-separability.

How has this been performing? Firstly, all of the seismometer channels have the same classification readout, which is good. Last night, all of the classes were "quiet", with an "earthquake" which occurred when Den jumped around GUR1 to simulate an EQ. This morning it was on "truck" as expected. The filters are still not fine enough to detect individual trucks, but I will continue to monitor the performance over the coming days.

If anyone has ideas on how better to represent this information, please let me know. This was the first thing that came into my head that would work with my MEDM monitor options, and I'm open to suggestions!

I added a widget to the C1PEM_OVERVIEW MEDM screen. The screen shows the nine seismometer channels (GUR1, GUR2, and STS1 X, Y, and Z), the current signal class in dark red, and the overall confidence in the classification, as Rana suggested. The confidence indication thresholds range from 0.1 to 0.9, in intervals of 0.1. Basically, if a signal class is completely dark red, and the other two classes show only white, or, better yet, nothing at all, this means that we have a clear classification. If, however, the other regions have some yellow, or even red indicators, this means that we are not very confident in our signal classification.

This is a screenshot of the widget. The nine seismometer channels are classifying the signal as quiet, which is good both because it's the middle of the night, and because the nine seismometer signals somehow agree (I'd use the word correspond with one another, but that implies a strong level of coherence..). The confidence is high, seeing as there's little indication in the truck and earthquake regions (none whatsoever in the truck, meaning that the signal, given our classification method, could not possibly be a truck, and some in the earthquake region (below 0.1 of the quiet signal classification strength, however), possibly due to low seismic disturbance).

I've put EM 172 microphones inside Steve's isolation box to measure their noise. I've attached mics to each other and aligned them using the tape.

At low frequencies (below 1 Hz) the noise is limited by ADC as there is a 10 Hz high-pass filter inside mic readout box.

ADC noise is measured by splitting the signal from 1 mic into 2 ADC channels.

Since the classification finally works (or seems to work..), I wrote triangulation scripts in Python which triangulate the signals, and a plotting script in Matlab which generates a heat map of seismic noise source locations. I switched the ADC Streckeisen and Trillium connections in order to better triangulate with the current channels, and will return them either tomorrow, or when I come back from Livingston so that we can have weekday data as well.

There was a 5.6 Earthquake that occurred near Tofino, Canada about 30 minutes ago. It showed up rather strongly on the BLRMS.

The neural network classification system also picked up on it, but oscillated from Earthquake (1.0) to Quiet (0.5) perhaps due to the filters we currently have installed. Here is a shot of the GUR1X classification channel at the time of the EQ:

Temperature sensor for vacuum. How many : 2 or 3 ?  $350 each

Glass encapsulated thermistor #55007  with Ceramabond 835-m glued onto spade connector and hooked up to controller DP25-TH-A with analoge output.

This zero to 10Vdc can go to ADC

It seems as though there is something funny going on around ~1.5 Hz, starting a little over an hour ago.

We see it in the BLRMS channels, the raw seismometer time series, as well as in various suspensions and LSC control signals.  It's also pretty easy to see on the camera views of all the spots (MC, arms, transmissions....AS is a little harder to tell since it's flashing, but it's there too).

The plots I'm attaching are only for ~10min after the jump happened, but there has been no change in the BLRMS since it started.  Usually, we'd see an earthquake in all the channels, and even big ones ring down after a little while.  This is concentrated at a pretty narrow frequency (some of Den's plots for later have this peak), and it's not ringing down, so it's not clear what is going on.

Here is a whole pile of plots.  Recall that the T-240 is plugged into the "STS_3" channels, and we don't have BLRMS for it, so you can look at the time series, but not any frequency specific stuff.

Atm1,  I'm not sure about the seismic data.   Baja earthquake magnitude 3.0 at  yesterday morning.Seismometers do not see them !

Atm2,  No posted seismic activity.  Someone is jump walking in the lab? Why are there time delays between the suspensions?

The MC1 accelerometer cube (3 accelerometers arranged in x,y,z) is under the PSL table, as I found it at the beginning of the summer.

The MC2 accelerometer cube is on the table where I worked on the STACIS, right when you walk into the lab from the main entrance. Their cables are dangling near the end of the mode cleaner, so the accelerometers are ready to be placed there if wanted.

All accelerometers are also plugged into their ADC channels.

I recently realized that I may have over-trained my classification neural network and used too many parameters, so that my weight vectors are too fine-tuned to my particular data set and do not generalize well. I lowered the number of hidden neurons in the network to 15, and the number of epochs to 25000, and regularized based on the deltas (the gradient). Here is the most recent learning curve:

The old weights and code are saved in the c1pem directory in the file "classify_seismic_20neurons.c", while the current 15 neuron network is saved as "classify_seismic.c". I'll monitor the performance of this current network throughout the day, and decide which one we should keep.

Shasky day yesterday postpones venting. We had about 11 shakes larger than mag 4.0 Mag5.5 was the largest at  13:58 Sunday, Aug 26 at  the Salton Sea area.

Atm3,  ITMX and ETMX  did not come back to it's position

 The vacuum envelope must be sealed with light doors on o-rings to insure a bug free IFO.  This was a violation!

I've measured seismic and acoustic noise on BS and AS tables. It seems that horizontal motion of BS table is ~1.5-2 times more then AS table in the frequency range 5-50 Hz.

Edit by Den: this was POI table, not BS!

I've suspended microphones around the lab

 This seismic measurement is for BS and AS tables.

 This was in response to my suggestion to move the REFL beam path to the table containing the BS/PRM Oplevs. From this seismic data it is clear that the BS table is no worse than the AS table, so we should plan to make the layout change during the next vent.

I've installed Guralp readout box back and it turned out that it does not work with voltage provided from the rack (+13.76 0 -14.94).  +/-12 voltage regulators inside the box convert it to -0.9 0 -12. I've connected the box to +/-15 DC voltage supply to measure seismic motion at the ETMY table. Readout box works fine with +/- 15.

Seismic noise on the ETMY table measured to be a few times higher then on the floor in horizontal direction in the frequency range 50 - 200 Hz. Attached are compared spectrums of X, Y and Z motions.

 I'm not sure what the problem is here.  Den and I looked at it for a few minutes, before I went back to helping with putting doors on.  The Sorensons are not supplying the rack power for 1X1.  There are some flat cables which go from the fuses on the side of the rack up to the cable tray, and go elsewhere.  Den is going to continue looking into this, but I think it's a moderately high priority, since lots of things should be getting served by that same power.

Changed the list of channels to be written to frames from having the IN1 suffix to OUT. Now we can load the calibration of the channel into the filter module and the DQ channel will be calibrated.

We should do this wherever possible so that our channels will have real calibrations associated with them.
SEIS_GUR1_X_OUT 256
SEIS_GUR1_Y_OUT 256
SEIS_GUR1_Z_OUT 256
SEIS_GUR2_X_OUT 256
SEIS_GUR2_Y_OUT 256
SEIS_GUR2_Z_OUT 256
SEIS_STS_1_X_OUT 256
SEIS_STS_1_Y_OUT 256
SEIS_STS_1_Z_OUT 256
SEIS_STS_2_X_OUT 256
SEIS_STS_2_Y_OUT 256
SEIS_STS_2_Z_OUT 256
SEIS_STS_3_X_OUT 256
SEIS_STS_3_Y_OUT 256
SEIS_STS_3_Z_OUT 256
MIC_1_OUT 2048
MIC_2_OUT 2048
MIC_3_OUT 2048
MIC_4_OUT 2048
MIC_5_OUT 2048
MIC_6_OUT 2048
ACC_MC1_X_OUT 2048
ACC_MC1_Y_OUT 2048
ACC_MC1_Z_OUT 2048
ACC_MC2_X_OUT 2048
ACC_MC2_Y_OUT 2048
ACC_MC2_Z_OUT 2048
XARM_DIFFERENTIAL_MOTION_IN1 256
XARM_DIFFERENTIAL_MOTION_OUT 256
YARM_DIFFERENTIAL_MOTION_IN1 256
YARM_DIFFERENTIAL_MOTION_OUT 256

Next we should up the rate at which the model runs up to 16 kHz so that we can record the microphones at 16 kHz. FM radio has information up to 20 kHz. AM radio goes up to ~8 kHz. We should be at least as modern as AM radio. How do we make the change? How do we make sure the FOTON file stays OK?

I have made some changes to the daily summary file to compensate. New files is /users/public_html/40m-summary/share/c1_summary_page.ini.

We observed one or two ants climbing over PMC optics without booties and safety glasses.

The floor was mopped with strong Bayer Home Pest Control solution in the Vertex area.

Do not work inside the 40m lab if you are sensitive to chemicals!

I spotted around 4 within 30 minutes working at the PSL table even after the deathly spray. They seem to be running down from the cables on the oscilloscope rack to the table and the optics.

Guralp readout box received +13.7 /0/ -15V instead +15V because of the broken fuse. Power provided by the source is normal.

Edit by Den: I've found a similar fuse on one of the tables and borrowed it. Guralp is not working again.

 I've added calibration gains to Guralp (to um/sec) and EM172 (to Pa) channels.

We can run PEM at 16 kHz. I think Foton file stores both sos-representation and filter commands which are independent of the sampling frequency, so it should be possible to change model sampling frequency quickly.

In fact, we can save data at 64 kHz from iop models. I've done this once with MC_F channel. However, I did not test EM172 noise at frequencies > 1 kHz.

 I've meant Guralp is NOW working again 

Accelerometers were installed on the ETMY table and nearby ground to measure amplification of the seismic noise due to the table. During this experiment ground and table motions were measured simultaneously.

 I've added xml file with measurement settings and data to 40m svn at directory 40m_seismic/etmy.

 I've measured ETMX table motion compared to ground motion using accelerometers. Data and settings in the xml file are at the svn directory 40m_seismic/etmx.

 High frequency (>60 Hz) resonances that are present at the ETMX motion spectrum seem to be understandable. Amplification ETMX/GROUND of a factor of 2 at 1 Hz is interesting. I've monitored ACC DQ channels for a few hours and noticed that usually spectrum looks like in the previous elog. But every ~40 min ETMX motion is much higher then ground motion at low frequencies (<5 Hz). I wonder if this a reaction of a table to outside disturbances or accelerometer issue.

 This could come from AA board, its range is +/- 2.5 V, RMS of the ETMX table motion is a few times higher then ground motion, so ETMX accelerometer signal was corrupted.

As this small AA range has already caused problems before, I decided to increase it. I've looked through the board scheme and found that all its differential line receives and output amplifiers have absolute maximum range of 40V. We used KEPKO power supply for this board with a voltage range up to 6 V. So I've replaced it with a BK PRECISION power supply and set it to +/- 15 V. Now AA board range is 7.5 V.

I'll leave accelerometers near ETMX table. It's interesting to measure table motion in the morning when trucks drive by.

 That low frequency effect was due to AA board, now it is gone.

All accelerometers are now at the table behind 1X4, cables are near readout box.

 The floor is mopped again with strong PEST CONTROL SOLUTION in water in the Vertex area.

Do not plane to work in the IFO-room till noon if you are sensitive to chemicals!

 We now stop using bench DC power supplies for microphone preamp and PEM AA board. DC power is wired from 1x5 rack suppliers. I've installed a beam to mount fuse houses in the 1x7 as we did not have one.

High particle count confirmed with #2 counter

Microphone preamp box had a low-pass filter at 2kHz, Ayaka changed it to 20 kHz by replacing 100pF capacitor with a 10pF.

We've measured frequency response of the box. Signal from the microphone was split into two. One path went to the box, while another was amplified by the gain 20 (and bandpass filter 1Hz - 300kHz) and sent to spectrum analyzer. Coherence and frequency response were measured using box output and amplified input. Low-pass filter in the box does not limit our sensitivity.

Acoustic noise significantly decreases at frequencies higher then 2kHz. So we need to modify the circuit by adding whitening filter.

I've plugged in PMC length channel into PEM board CH15 through and amplifier (gain=200) that is AC coupled to avoid ~2.5 DC V coming from PMC servo.  I measured coherence with microphone that was located ~30 cm higher. Measurements show contribution of acoustic noise to PMC length in the frequency range 20-50 Hz. In this range PMC length / MC length coherence is ~0.5.

Acoustic noise couples to PMC length in a non-stationary way. 5 minutes after the first measurement I already see much higher contribution. This was already discussed here. I've made C1:X02-MADC3_TP_CH15 a DQ channel at 64kHz. This a fast PMC length channel.

Next step will be to use several microphones located around PMC for acoustic noise cancellation.