Houston Seismic

AGC changes seismic amplitudes – but how? And why is it used and why is it NOT used? AGC, automatic gain control, has been used in seismic processing for years. People like AGC because it balances amplitudes along a trace. People don’t like AGC because it balances amplitudes along a trace. Hmmm. See below for examples from Matthew Rose, 2017, on medium.com and the SEG wiki page. I cropped the figures, added labels/arrows/boxes and the example gate length in red for illustrative purposes. The top figures (Rose) show a single trace before/after AGC. Time increases to the right. On the before trace, note strong amplitudes from 500-1000ms and weak amplitudes >1500ms. On the after trace, amplitudes are balanced along the entire 3000ms range. I’ve drawn a hypothetical gate length of 1000ms (red bracket; I don’t know what gate length was used in this example.). There are options determining how AGC can be run, but often something like the median amplitude within the gate is applied as a scalar to the value in the gate’s center. The process continues as the gate slides along from the top of the trace downward. The shorter the gate length, the more equalized the trace’s amplitudes become. The middle figures (Rose) show a gather before/after AGC (gate length unknown). Here you can see how AGC has balanced amplitudes everywhere. Notice that shallow near offset noise is reduced (red arrows) and deep signal reflections are boosted (yellow arrows). Here the agc has reduced the noise amplitudes and boosted the signal amplitudes. The bottom figures (seg wiki) show an unmigrated stack before/after AGC. Note how the stronger amplitudes (red boxes) and the weaker amplitudes (yellow boxes) become more equalized to each other after application. These examples are instructive. After AGC, signal/noise ratio might be increased – that’s the good news. But the bad news is that amplitudes start to look the same – thus it’s harder to derive amplitude-related geological information. Traditional AGC doesn’t care about surface conditions or regional geology; it only cares about balancing amplitudes up-and-down traces. Use at your discretion. https://lnkd.in/gA7kGHXv https://lnkd.in/gUbXJHfx Interested in a seismic training class? https://lnkd.in/gN3-RZa5 * I have almost 2 hours of online presentations introducing various aspects of seismic velocities and processing. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic

A vibroseis truck vs 2 raw eggs….what could go wrong? Check out the below images from a YouTube video that my friend Allan Chatenay notified me about. Allan is president of the seismic acquisition company Explor and they carried out this creative experiment. I screen-grabbed the images below from their video. The top left (1) shows the placement of 2 raw eggs in a sand-lined pit; one egg was placed upright and the other egg was placed horizontal. The top right (2) shows the location after the original sod was replaced on top of the eggs. The middle left (3) shows the vibroseis truck (and its specs) heading to the eggs. The middle right (4) describes the action. The bottom left (5) shows the egg retrieval. The bottom right (6) proves the eggs were raw and not hard boiled. Amazing – the vibroseis truck did NOT break the eggs! Pretty fun experiment. https://lnkd.in/gyGkAm4A * Interested in a seismic training class? https://lnkd.in/gN3-RZa5 * I have almost 2 hours of online presentations introducing various aspects of seismic velocities and processing. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic

Seismic data is often shown as a colored wiggle trace. But what’s the relationship between that colored wiggle trace, samples, segy values and the pretty images on my workstation? See below. The top/left image (1) shows a red curve wiggle trace, a representation of one seismic trace. The wiggle oscillates back-and-forth, similar to a coil inside a geophone going up/down relative to a magnet. The middle of the trace is zero amplitude and amplitude values increase positively/negatively to the right/left as the wiggle goes back-and-forth. Image 2 shows the wiggle trace as digitized (sampled), represented by the black dashes. The black dashes might be from digitizing every 1ms or 2ms or 4ms or some other value. So sample 1 might be amplitude +11; sample 2 might be amplitude +126; sample 3 might be amplitude +31 and so on. We can retain more of the high frequency wiggles with a smaller sample interval (i.e. 1ms samples retain higher frequencies compared to 4ms samples). Image 3 shows the individual samples without the wiggle and the top/right image (4) shows the individual amplitude numbers that are delivered in segy format. Digitization of the raw data occurs in the field. Seismic processing done by your friendly processor starts with these raw numbers. Then processing changes and rearranges all these raw numbers into finished, deliverable products. After seismic processing, the segy deliverables contain new values, like image 5 at bottom/left. These traces could be on a time trace or a depth trace (depth samples are typically sampled in feet or meters). The individual samples for one trace are shown at 6. Color fill for one trace is shown in 7. Then several traces along a section, with interpretation, is shown in 8 (Bianco, Agile Scientific for Figures 6-8; page since removed). Again, the smaller the sampling interval, the finer the vertical detail potentially retained. Interested in a seismic training class? https://lnkd.in/gN3-RZa5 * I have almost 2 hours of online presentations introducing various aspects of seismic velocities and processing. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic

Here’s an instructive example showing the importance of anisotropy for pre-stack depth migration (PSDM). See the images below (from Lal and others, 2015). The 3 gathers are from 3 PSDMs, each migration run with different applications of anisotropy. Depth increases downward and offset increases to the right for each gather. I’ve added the labels, arrows and circles for clarity. Anisotropy happens in material where the velocity changes based on the direction of wave propagation (far offsets are often faster than near offsets, notably in areas of horizontal layering). See the 1st gather. This gather was migrated without anisotropy terms. The blue circle shows where the far offsets are trending up, perhaps an indication of anisotropy. In addition, this gather does not tie known well tops (according to Lal’s paper). See the 2nd gather. This gather is from a migration including the delta anisotropy term. Lal defines delta as “The ratio that relates the vertical velocity and the imaging velocity (i.e. the velocity that apparently focuses the seismic data for the shorter offsets).” Lal goes on to state that: “An accurate vertical velocity in combination with correct deltas will result in depth images that tie with the available well tops.” The red arrows show where some events have moved shallower due to the application of delta, to better tie known well tops. You can see that delta and the shift is depth-variant as shallower events require less vertical shift. Also note that the events in the blue box remain non-flat as before. See the 3rd gather. This gather is from a migration including both the delta and epsilon anisotropy terms. Lal defines Epsilon as: “The ratio that relates the vertical velocity and the horizontal velocity.” Epsilon will help flatten far offsets. In this gather, far offsets appear to be flatter (inside the green box) after the correct application of epsilon. Similar to delta, epsilon terms can also be depth-variant (noted by the change in far offset flattening with depth). Using the correct velocity, delta and epsilon terms can help PSDM output flat gathers that tie well tops, generating pleasing results. These are common procedures for land psdm. https://lnkd.in/gzMhG-E3 Interested in a seismic training class? https://lnkd.in/gN3-RZa5 * I have almost 2 hours of online seismic presentations. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic

Here it is! Announcing our 4 day industry class – Understanding Seismic Data: Time, Depth and Geology. I will be teaching the course alongside my colleagues David Kessler and John Byrd. It will held in Houston from 24-27 March 2025. The host company is GeoLogica, who provides world-class training for the modern energy industry. We’ve put a lot of effort into making this a great class and we welcome interested participants to attend. Please see the link below for further details and to register. https://lnkd.in/gN3-RZa5

Anisotropy can be a difficult concept – so let’s try to understand one way it is commonly seen. Seismic velocity can sometimes increase with increasing source/receiver offset. That seems odd, why would velocity change based on the distance between your source and receiver? See below.   The top figure shows a single formation with horizontal layering (for instance shale). A sample near offset (more vertical raypaths) and a sample far offset (more horizontal raypaths) are shown in blue. There can be a change in velocity between the near offsets to the far offsets. The reason is that the seismic energy will choose the faster travel path – whether it is in the matrix or in between the layering. Velocity can increase at far offsets because the seismic takes advantage of the horizontal layering. The near offset rays don’t have this choice as they’re going almost perpendicular to the layering. The red arrows illustrate how near offsets tend to cut straight through the layering while far offsets can speed through the faster horizontal route.   See the bottom figure (Treadgold and others, 2008). Migrated gathers like this image are common in areas of horizontal layering. The far offsets have a faster velocity than the near offsets, thus the far offsets trend up after a simple velocity correction. This effect is sometimes called a “hockey stick”; note that the red line in the figure looks a bit like a hockey stick turned on its side.   There are ways to correct for these anisotropy-related complexities during seismic processing; some are more rigorous than others (e.g. incorporating anisotropic migration terms vs. post-migration gather flattening). Anisotropy can be even more complicated when the layers are dipping and/or when vertical fracturing is present. https://lnkd.in/eNumQ9Gy * Interested in a seismic training class for your organization? Leave a comment or email ron.kerr@houstonseismic.com * I have almost 2 hours of online presentations introducing various aspects of seismic velocities and processing. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic

How is a loud concert like seismic data? There is a frequency difference inside the concert hall compared to outside. You hear all the sounds inside, from the bottom-end bass and drums up to the singer’s high notes. But step outside and you might only hear the low bass-thumping sounds penetrating through the building’s walls. Why is this? The simple answer is that higher frequencies are absorbed more easily, whether at the concert building’s walls or seismic penetrating underground. That leaves the lower frequencies more prevalent further from stage and similarly reflecting from deeper in the earth. The top picture (Vishnu R Nair) looks like it was taken at a fun show. The bottom figures (Chopra and Sharma) show a seismic section comparing shallower (1000-1500ms) vs deeper (1500-2000ms). Along with the seismic, Chopra and Sharma show the frequencies within the two time ranges (blue figures, top left). On the graphs, frequencies increase to the right and amount of frequencies increases to the top. You can see the shallower section has a stronger presence of high frequencies (red arrow). Meanwhile the deeper section has a weaker presence of high frequencies (black arrow). The red and purple vertical lines in the seismic are data from 2 wells and are separate from the seismic, not pertinent to this discussion. This is typical – a broader frequency spectrum (with low and high frequency) in the shallow and a narrower frequency spectrum (with reduced high frequency) in the deep. Just like stepping outside a concert building. https://lnkd.in/gnjMEfvN https://lnkd.in/gc_bCb4W * Interested in a seismic training class for your organization? Leave a comment or email ron.kerr@houstonseismic.com * I have almost 2 hours of online presentations introducing various aspects of seismic velocities and processing. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic

Here are some great examples showing basic seismic velocity concepts. The examples are from Wakeel Ur Rehman; see below. Assume no anisotropy. The top/left shows a synthetic representation of layered geology. There are 5 formations; seismic velocity increases with each deeper layer. You can see source/receiver pairings at the top, from near offset to far offset. Direct raypaths are shown, but in actuality they would bend across the boundaries (look up Snell’s Law). The top/right shows the 4 seismic reflection events as they would appear in a gather of individual source/receiver pairs. Source-to-receiver offsets increase to the right and 2-way time increases to the bottom. The 4 reflections are shown curving down at farther offsets because it takes more time for far offset reflections to be recorded than near offset reflections (increased travel distance). The bottom/left image is the top/right image repeated. The bottom/middle shows the velocity semblances for this gather. Velocity increases to the right and the vertical time scale is unchanged. The yellow dots show optimal stacking velocities for each reflector. Note how deeper events show faster velocities than shallower events. The bottom/right shows what happens to the gather’s events after applying the correct velocities (applying nmo or normal moveout). The events are now flat for all offsets. The amplitudes would be added horizontally across this gather (i.e. stacked), then placed at one geographic location. There are many such gathers in modern seismic surveys. https://lnkd.in/ggRZjxGV * Interested in a seismic training class for your organization? Leave a comment or email ron.kerr@houstonseismic.com * I have almost 2 hours of online presentations introducing various aspects of seismic velocities and processing. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic

Do you see horizontal striping on your timeslices or depthslices? This could be footprint noise. One of the final steps in 3D seismic processing is acquisition footprint reduction. The geometric layout of the recording equipment often leaves an imprint on the migrated stack volume and various attributes (e.g. amplitude extractions). I’ve even seen remnants of acquisition footprint on marine velocity depth slices. See below for an instructive example from Sahia & Soofi, 2006. I added the labels and circles for clarity.   The top images are before/after footprint reduction on timeslices through a 3D land volume in South Texas, extracted at 1020ms. There are 2 individual surveys merged: one on the left (Survey B) and one on the right (Survey A). See the bottom image. The survey on the left appears to show horizontal footprint noise, in alignment with its acquisition layout (left circle). The survey on the right (acquired at a different orientation) appears to have less acquisition footprint noise.   A common footprint reduction technique is to reduce it within individual timeslices. The lateral periodicity of the noise on the timeslices is identified and reduced. See the right image/circle showing a reduction in the footprint noise. Note that both surveys within the volume are altered with the denoise application, as even the right survey appears to have some features diminished.   Acquisition footprint noise tends to be more noticeable shallow compared to deep. You need to be careful not to be too aggressive in reducing the noise so that you don’t harm underlying signal (e.g. potential small fractures or faults). I’ve had some clients prefer not to run it or they request separate deliverables (before/after). https://lnkd.in/gS54TDZr * Interested in a seismic training class for your organization? Leave a comment or email ron.kerr@houstonseismic.com * I have almost 2 hours of online presentations introducing various aspects of seismic velocities and processing. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic

Why do a complicated depth migration when a simple time migration is easier? One reason is you might get an answer closer to the truth. Let’s look at an example. The top image shows the “real” geology. Velocities are generally increasing with depth of burial (blue is slower, green/red is faster). There are 2 shallow velocity anomalies: one slow and one fast. There is another reflector at depth with velocities constant below. An anticline underlies the slow anomaly. The geology is flat under the fast anomaly. Assume no anisotropy. The middle image shows the output of a time migration (I’m assuming the velocities are *just* right to maximize this principle). The anticline on the left now looks flat. The flat geology on the right now looks like an anticline. If you believe this structure then you’ve been fooled. How does this happen? It takes more time to travel through a slow anomaly and it takes less time to travel through a fast anomaly. An old truck can’t travel the distance as fast as an F1 racer. You get time sags under slow anomalies and time pull-ups under fast anomalies. If you rely on the middle image then you can make some serious interpretation errors. The bottom image shows the output of a proper depth migration. Depth = Vel x Time. Use the recorded times and input the correct velocities to output the proper depth. Depth migration with proper velocity analysis (and a skilled processor) can distinguish between the old truck and the F1 racer. Note that the velocities have to be pretty good – otherwise you’ll still get the wrong structural image. * Interested in a seismic training class for your organization? Leave a comment or email ron.kerr@houstonseismic.com * I have almost 2 hours of online presentations introducing various aspects of seismic velocities and processing. There’s a modest cost – https://lnkd.in/gw3eQcCB #houstonseismic