{"id":1335,"date":"2026-11-07T13:21:38","date_gmt":"2026-11-07T21:21:38","guid":{"rendered":"https:\/\/salemdata.net\/johnpress\/?p=1335"},"modified":"2026-11-07T19:35:22","modified_gmt":"2026-11-08T03:35:22","slug":"confirmed-milk-jug-transducer-captures-train-infrasounds","status":"publish","type":"post","link":"https:\/\/salemdata.net\/johnpress\/?p=1335","title":{"rendered":"Confirmed: Milk Jug Transducer Captures Train Infrasounds"},"content":{"rendered":"
<p>Today is a milestone for I have proven that a $35 Sensirion <a title="https:\/\/www.digikey.com\/en\/products\/detail\/sensirion-ag\/SDP810-125PA\/6605488?s=N4IgTCBcDaIMoBEAKAOAjABgLRrAViQEMQBdAXyA" href="https:\/\/www.digikey.com\/en\/products\/detail\/sensirion-ag\/SDP810-125PA\/6605488?s=N4IgTCBcDaIMoBEAKAOAjABgLRrAViQEMQBdAXyA" data-from-md="">SDP810-125PA<\/a> differential pressure transducer, connected to an ordinary empty milk jug, detected low-frequency pressure fluctuations coinciding with a freight train passing approximately 1,300 feet away.This physical setup of using back pressure on a transducer is based on the article by Marcello <em>et al.<\/em>\u00a0“Implementation, Characterization, and Evaluation of an Inexpensive Low-Power Low-Noise Infrasound Sensor Based on a Micromachined Differential Pressure Transducer and a Mechanical Filter” (2012). (See note below.)<\/p>\n<img class=" wp-image-1338" src="https:\/\/salemdata.net\/johnpress\/wp-content\/uploads\/2026\/11\/DSC_5698-scaled.webp" alt="" width="646" height="545" \/> Circuit breadboard in a nest of wires with milk jug\n<p>A freight train came by at 3:30 AM, Saturday morning, November 7, 2026.\u00a0 At that time I was logging audio (44,000 Hz) with a microphone outdoors and indoors two transducers (air pressure) at 100 readings\/second each, and one geophone vertical seismic motion at 330 readings\/second.\u00a0 Here are the analyses.<\/p>\n<h2>Audio recording<\/h2>\n<p>The gray area is a quiet period, the tan area is during the train event.\u00a0 These two areas were arbitrarily defined by me to have two “envelopes”: one of quiet and the other during the train event.<\/p>\n<img class=" wp-image-1339" src="https:\/\/salemdata.net\/johnpress\/wp-content\/uploads\/2026\/11\/05_audio_rms.png" alt="" width="540" height="166" \/> Audio 11\/7\/26\n<p>Sensor Recording<\/p>\n<p>Here are the three sensors.\u00a0 T2 is the “control” transducer which has both port open.\u00a0 T1 is the transducer that has one port connected to a leading milk jug to provide back pressure.\u00a0 The gray and tan time lines previously described are overlayed here.<\/p>\n<p><img class=" wp-image-1341 aligncenter" src="https:\/\/salemdata.net\/johnpress\/wp-content\/uploads\/2026\/11\/01_signal_envelopes.png" alt="" width="430" height="297" \/><\/p>\n<h2>Magnitude-squared<\/h2>\n<p>This graph compares the readings from the two pressure sensors (T1 and T2) with the geophone (G1), which measures ground vibrations.\u00a0\u00a0The higher the line, the more closely the two instruments’ readings are related at that frequency. A value of 1 indicates a very strong relationship; a value of 0 indicates no consistent relationship.<\/p>\n<p>The blue line represents the milk-jug sensor (T1), while the red line represents the control sensor (T2), which has both ports open.\u00a0Notice how the blue line rises at certain frequencies while the red line remains relatively low. This indicates that the milk-jug sensor detected patterns corresponding to the ground vibrations that the control sensor did not.<\/p>\n<img class=" wp-image-1340" src="https:\/\/salemdata.net\/johnpress\/wp-content\/uploads\/2026\/11\/04_coherence.png" alt="" width="507" height="338" \/> Transducers and geophone values\n<h2>Spectrogram<\/h2>\n<p>This spectrogram is for the train event only and for the T1 milk jug transducer and the geophone (“G1”).<\/p>\n<p><img class=" wp-image-1342 aligncenter" src="https:\/\/salemdata.net\/johnpress\/wp-content\/uploads\/2026\/11\/03_train_spectrograms.png" alt="" width="387" height="238" \/><\/p>\n<h2>Frequency Analysis<\/h2>\n<p>This is the most important chart: it shows frequencies that were captured both during the quiet event and the train event.\u00a0 You can see, for example, in the T1 comparison just above 15 Hz there is a noticeable peak when compared to the control.\u00a0 T2 shows that nothing was measured by the T2 transducer during the train event when compared with the quiet event.\u00a0 And the geophone shows it detected an increase of frequencies during the train event.<\/p>\n<img class=" wp-image-1343" src="https:\/\/salemdata.net\/johnpress\/wp-content\/uploads\/2026\/11\/02_welch_psd.png" alt="" width="617" height="474" \/> Frequency Analysis\n<h2 role="listitem">Conclusion<\/h2>\n<p>During the freight train event, there were low frequencies detected by T1 when compared with a quiet period just minutes before.\u00a0 The back-pressured transducer detected pressure differences during a train event.\u00a0 Hence, addition of a back-pressure to the transducer allows it to detect low frequencies.<\/p>\n<p>While the Marcillo back-pressure is carefully devised and tested, mine was just a simple: does it work rough test against a train event.\u00a0 I know from previous tests that low level seismic frequencies are present when freight trains come by, these results demonstrate that low level audio frequencies, ie. through the air as compared with seismic “through the ground”, occur during train events.\u00a0 I will now refine the physical back-pressure mechanism and test.<\/p>\n<h6>Note:<\/h6>\n<div id="refs" class="references csl-bib-body hanging-indent" role="list">\n<div id="ref-ImplementationCharacterizationandEvaluationofanInexpensiveLowPowerLowNoiseInfrasoundSensorBasedonaMicromachinedDifferentialPressureTransducerandaMechanicalFilter" class="csl-entry" role="listitem">Marcillo, Omar, Jeffrey B. Johnson, and Darren Hart. 2012.<br \/>\n“Implementation, Characterization, and Evaluation of an<br \/>\nInexpensive Low-Power Low-Noise Infrasound Sensor Based on a<br \/>\nMicromachined Differential Pressure Transducer and a Mechanical<br \/>\nFilter.” <em>Journal of Atmospheric and Oceanic Technology<\/em><br \/>\n(Boston MA, USA) 29 (9): 1275-84. <a href="https:\/\/doi.org\/10.1175\/JTECH-D-11-00101.1">https:\/\/doi.org\/10.1175\/JTECH-D-11-00101.1<\/a>.<\/div>\n<\/div>\n<p>Here is a summary:<\/p>\n<pre>Analysis script: analyze_train_20261107.py\r\nRun: 2026-11-07 11:24:55 PST\r\nInput log: geotrans_20261107_Sat_030000.log\r\nAudio: saturday_train.zip\r\nTrain: 03:29:20\u201303:36:50 PST; Background 03:19:00\u201303:25:00 PST\r\n\r\nT1\r\n background detrended RMS: 0.024355207; top PSD peaks: [0.2, 0.65, 0.9, 24.9, 28.6, 23.75, 15.1, 24.65]\r\n train detrended RMS: 0.04075246; top PSD peaks: [0.2, 15.9, 15.75, 16.2, 16.05, 20.05, 19.8, 19.6]\r\n Band (0.1, 1): train 0.000342685, bg 0.000360437, ratio -0.22 dB\r\n Band (1, 5): train 2.04647e-05, bg 1.11463e-05, ratio 2.64 dB\r\n Band (5, 20): train 0.000537886, bg 3.02347e-05, ratio 12.50 dB\r\n Band (20, 40): train 0.000293156, bg 3.93389e-05, ratio 8.72 dB\r\n\r\nT2\r\n background detrended RMS: 0.0068583718; top PSD peaks: [4.8, 8.6, 0.5, 5.35, 1.2, 9.8, 7.65, 0.65]\r\n train detrended RMS: 0.006869717; top PSD peaks: [13.35, 0.65, 12.5, 6.2, 0.4, 14.9, 9.15, 10.0]\r\n Band (0.1, 1): train 1.34688e-06, bg 1.28021e-06, ratio 0.22 dB\r\n Band (1, 5): train 5.04324e-06, bg 5.05579e-06, ratio -0.01 dB\r\n Band (5, 20): train 1.77835e-05, bg 1.78006e-05, ratio -0.00 dB\r\n Band (20, 40): train 1.64998e-05, bg 1.61447e-05, ratio 0.09 dB\r\n\r\nG1\r\n background detrended RMS: 2.2681042e-05; top PSD peaks: [28.6, 25.75, 26.65, 25.45, 34.2, 24.9, 25.05, 25.25]\r\n train detrended RMS: 8.6053226e-05; top PSD peaks: [15.95, 16.05, 27.7, 15.7, 16.3, 13.9, 33.75, 26.85]\r\n Band (0.1, 1): train 5.14341e-11, bg 4.02122e-13, ratio 21.07 dB\r\n Band (1, 5): train 4.28721e-11, bg 9.20576e-13, ratio 16.68 dB\r\n Band (5, 20): train 1.56285e-09, bg 6.73316e-11, ratio 13.66 dB\r\n Band (20, 40): train 2.09853e-09, bg 2.00237e-10, ratio 10.20 dB\r\n\r\nCoherence background\r\n T1 vs G1: top frequencies [(16.85, 0.348), (17.55, 0.301), (16.8, 0.295), (15.1, 0.281), (15.75, 0.239), (17.15, 0.222), (11.1, 0.206), (16.9, 0.202), (17.45, 0.198), (14.3, 0.193)]\r\n T2 vs G1: top frequencies [(10.45, 0.259), (19.2, 0.2), (4.9, 0.174), (9.55, 0.141), (11.35, 0.138), (2.05, 0.132), (19.15, 0.129), (14.6, 0.128), (17.45, 0.127), (1.65, 0.127)]\r\n\r\nCoherence train\r\n T1 vs G1: top frequencies [(15.9, 0.842), (15.7, 0.826), (15.95, 0.81), (15.8, 0.799), (15.75, 0.794), (15.85, 0.791), (15.65, 0.76), (15.6, 0.696), (16.05, 0.682), (16.0, 0.656)]\r\n T2 vs G1: top frequencies [(0.95, 0.187), (17.5, 0.158), (17.55, 0.152), (0.9, 0.124), (14.35, 0.123), (2.5, 0.114), (0.85, 0.103), (18.9, 0.102), (9.3, 0.098), (4.55, 0.095)]\r\n\r\nGenerated by analyze_train_20261107.py | 2026-11-07 11:24:55 PST\r\n\r\n<\/pre>\n<\/div>

Today is a milestone for I have proven that a $35 Sensirion SDP810-125PA<\/a> differential pressure transducer, connected to an ordinary empty milk jug, detected low-frequency pressure fluctuations coinciding with a freight train passing approximately 1,300 feet away.This physical setup of using back pressure on a transducer is based on the article by Marcello et al.<\/em>\u00a0“Implementation, Characterization, and Evaluation of an Inexpensive Low-Power Low-Noise Infrasound Sensor Based on a Micromachined Differential Pressure Transducer and a Mechanical Filter” (2012). (See note below.)<\/p>\n

\"\"
Circuit breadboard in a nest of wires with milk jug<\/figcaption><\/figure>\n

A freight train came by at 3:30 AM, Saturday morning, November 7, 2026.\u00a0 At that time I was logging audio (44,000 Hz) with a microphone outdoors and indoors two transducers (air pressure) at 100 readings\/second each, and one geophone vertical seismic motion at 330 readings\/second.\u00a0 Here are the analyses.<\/p>\n

Audio recording<\/h2>\n

The gray area is a quiet period, the tan area is during the train event.\u00a0 These two areas were arbitrarily defined by me to have two “envelopes”: one of quiet and the other during the train event.<\/p>\n

\"\"
Audio 11\/7\/26<\/figcaption><\/figure>\n

Sensor Recording<\/p>\n

Here are the three sensors.\u00a0 T2 is the “control” transducer which has both port open.\u00a0 T1 is the transducer that has one port connected to a leading milk jug to provide back pressure.\u00a0 The gray and tan time lines previously described are overlayed here.<\/p>\n

\"\"<\/p>\n

Magnitude-squared<\/h2>\n

This graph compares the readings from the two pressure sensors (T1 and T2) with the geophone (G1), which measures ground vibrations.\u00a0\u00a0The higher the line, the more closely the two instruments’ readings are related at that frequency. A value of 1 indicates a very strong relationship; a value of 0 indicates no consistent relationship.<\/p>\n

The blue line represents the milk-jug sensor (T1), while the red line represents the control sensor (T2), which has both ports open.\u00a0Notice how the blue line rises at certain frequencies while the red line remains relatively low. This indicates that the milk-jug sensor detected patterns corresponding to the ground vibrations that the control sensor did not.<\/p>\n

\"\"
Transducers and geophone values<\/figcaption><\/figure>\n

Spectrogram<\/h2>\n

This spectrogram is for the train event only and for the T1 milk jug transducer and the geophone (“G1”).<\/p>\n

\"\"<\/p>\n

Frequency Analysis<\/h2>\n

This is the most important chart: it shows frequencies that were captured both during the quiet event and the train event.\u00a0 You can see, for example, in the T1 comparison just above 15 Hz there is a noticeable peak when compared to the control.\u00a0 T2 shows that nothing was measured by the T2 transducer during the train event when compared with the quiet event.\u00a0 And the geophone shows it detected an increase of frequencies during the train event.<\/p>\n

\"\"
Frequency Analysis<\/figcaption><\/figure>\n

Conclusion<\/h2>\n

During the freight train event, there were low frequencies detected by T1 when compared with a quiet period just minutes before.\u00a0 The back-pressured transducer detected pressure differences during a train event.\u00a0 Hence, addition of a back-pressure to the transducer allows it to detect low frequencies.<\/p>\n

While the Marcillo back-pressure is carefully devised and tested, mine was just a simple: does it work rough test against a train event.\u00a0 I know from previous tests that low level seismic frequencies are present when freight trains come by, these results demonstrate that low level audio frequencies, ie. through the air as compared with seismic “through the ground”, occur during train events.\u00a0 I will now refine the physical back-pressure mechanism and test.<\/p>\n

Note:<\/h6>\n
\n
Marcillo, Omar, Jeffrey B. Johnson, and Darren Hart. 2012.
\n“Implementation, Characterization, and Evaluation of an
\nInexpensive Low-Power Low-Noise Infrasound Sensor Based on a
\nMicromachined Differential Pressure Transducer and a Mechanical
\nFilter.” Journal of Atmospheric and Oceanic Technology<\/em>
\n(Boston MA, USA) 29 (9): 1275-84. https:\/\/doi.org\/10.1175\/JTECH-D-11-00101.1<\/a>.<\/div>\n<\/div>\n

Here is a summary:<\/p>\n

Analysis script: analyze_train_20261107.py\r\nRun: 2026-11-07 11:24:55 PST\r\nInput log: geotrans_20261107_Sat_030000.log\r\nAudio: saturday_train.zip\r\nTrain: 03:29:20\u201303:36:50 PST; Background 03:19:00\u201303:25:00 PST\r\n\r\nT1\r\n background detrended RMS: 0.024355207; top PSD peaks: [0.2, 0.65, 0.9, 24.9, 28.6, 23.75, 15.1, 24.65]\r\n train detrended RMS: 0.04075246; top PSD peaks: [0.2, 15.9, 15.75, 16.2, 16.05, 20.05, 19.8, 19.6]\r\n Band (0.1, 1): train 0.000342685, bg 0.000360437, ratio -0.22 dB\r\n Band (1, 5): train 2.04647e-05, bg 1.11463e-05, ratio 2.64 dB\r\n Band (5, 20): train 0.000537886, bg 3.02347e-05, ratio 12.50 dB\r\n Band (20, 40): train 0.000293156, bg 3.93389e-05, ratio 8.72 dB\r\n\r\nT2\r\n background detrended RMS: 0.0068583718; top PSD peaks: [4.8, 8.6, 0.5, 5.35, 1.2, 9.8, 7.65, 0.65]\r\n train detrended RMS: 0.006869717; top PSD peaks: [13.35, 0.65, 12.5, 6.2, 0.4, 14.9, 9.15, 10.0]\r\n Band (0.1, 1): train 1.34688e-06, bg 1.28021e-06, ratio 0.22 dB\r\n Band (1, 5): train 5.04324e-06, bg 5.05579e-06, ratio -0.01 dB\r\n Band (5, 20): train 1.77835e-05, bg 1.78006e-05, ratio -0.00 dB\r\n Band (20, 40): train 1.64998e-05, bg 1.61447e-05, ratio 0.09 dB\r\n\r\nG1\r\n background detrended RMS: 2.2681042e-05; top PSD peaks: [28.6, 25.75, 26.65, 25.45, 34.2, 24.9, 25.05, 25.25]\r\n train detrended RMS: 8.6053226e-05; top PSD peaks: [15.95, 16.05, 27.7, 15.7, 16.3, 13.9, 33.75, 26.85]\r\n Band (0.1, 1): train 5.14341e-11, bg 4.02122e-13, ratio 21.07 dB\r\n Band (1, 5): train 4.28721e-11, bg 9.20576e-13, ratio 16.68 dB\r\n Band (5, 20): train 1.56285e-09, bg 6.73316e-11, ratio 13.66 dB\r\n Band (20, 40): train 2.09853e-09, bg 2.00237e-10, ratio 10.20 dB\r\n\r\nCoherence background\r\n T1 vs G1: top frequencies [(16.85, 0.348), (17.55, 0.301), (16.8, 0.295), (15.1, 0.281), (15.75, 0.239), (17.15, 0.222), (11.1, 0.206), (16.9, 0.202), (17.45, 0.198), (14.3, 0.193)]\r\n T2 vs G1: top frequencies [(10.45, 0.259), (19.2, 0.2), (4.9, 0.174), (9.55, 0.141), (11.35, 0.138), (2.05, 0.132), (19.15, 0.129), (14.6, 0.128), (17.45, 0.127), (1.65, 0.127)]\r\n\r\nCoherence train\r\n T1 vs G1: top frequencies [(15.9, 0.842), (15.7, 0.826), (15.95, 0.81), (15.8, 0.799), (15.75, 0.794), (15.85, 0.791), (15.65, 0.76), (15.6, 0.696), (16.05, 0.682), (16.0, 0.656)]\r\n T2 vs G1: top frequencies [(0.95, 0.187), (17.5, 0.158), (17.55, 0.152), (0.9, 0.124), (14.35, 0.123), (2.5, 0.114), (0.85, 0.103), (18.9, 0.102), (9.3, 0.098), (4.55, 0.095)]\r\n\r\nGenerated by analyze_train_20261107.py | 2026-11-07 11:24:55 PST\r\n\r\n<\/pre>\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"
<p>Today is a milestone for I have proven that a $35 Sensirion <a title="https:\/\/www.digikey.com\/en\/products\/detail\/sensirion-ag\/SDP810-125PA\/6605488?s=N4IgTCBcDaIMoBEAKAOAjABgLRrAViQEMQBdAXyA" href="https:\/\/www.digikey.com\/en\/products\/detail\/sensirion-ag\/SDP810-125PA\/6605488?s=N4IgTCBcDaIMoBEAKAOAjABgLRrAViQEMQBdAXyA" data-from-md="">SDP810-125PA<\/a> differential pressure transducer, connected to an ordinary empty milk jug, detected low-frequency pressure fluctuations coinciding with a freight train passing approximately 1,300 feet away.This physical setup of using back pressure on a transducer is based on the article by […]<\/p>\n","protected":false},"author":2,"featured_media":1270,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_gfmr_meta_descriptions":[],"_gfmr_multilingual_taxonomy_terms":[],"footnotes":""},"categories":[144,148],"tags":[149],"class_list":["post-1335","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sound","category-transducers","tag-transducers"],"_links":{"self":[{"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=\/wp\/v2\/posts\/1335","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1335"}],"version-history":[{"count":28,"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=\/wp\/v2\/posts\/1335\/revisions"}],"predecessor-version":[{"id":1369,"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=\/wp\/v2\/posts\/1335\/revisions\/1369"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=\/wp\/v2\/media\/1270"}],"wp:attachment":[{"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1335"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1335"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salemdata.net\/johnpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1335"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}