Yep, and in some cases the pressure building from the magma itself and gases is sufficient to push the overlying rocks into a stress regime that's right for tensile failure, so it's not just exploiting existing fractures, but sometimes also making new fractures.

I was kind of lumping that in with water being stored in the plant (either as water or as a part of biomass), but you are definitely correct.

Water in plants consumed by organisms will be respired, excreted in waste, or ultimately also returned to the environment when the organism dies. There is always some amount of water locked up in the biosphere, but this water is not lost in a real sense.

While a plant is alive, it is taking up water. Some of that water is stored in the plant itself and the rest is returned to the atmosphere via transpiration. When a plant dies, whatever water that is stored within the plant itself is going to be (1) returned to the atmosphere directly via evaporation as the plant biomass breaks down, (2) consumed by an organism eating the plant biomass, or (3) buried and contribute to soil moisture (or some mixture thereof). None of this water is "lost", though it may be transferred to a different part of the hydrologic cycle.

Global networks of tide gauges for the "historical" sea level, which gets us back relatively accurately to at least the late 1800s. There are a variety of geologic records of sea level which we can use to build sea level curves going back well beyond historical periods.

> The same question has bothered me about climate measurements which in the past were not digital and inaccurate

If you want a deep dive on this, starting with something like the 'physical science basis' product of the latest IPCC report would be a good start. The short version is that we can place individual temperature records into context with a vast numbers of proxy data that allow us to reconstruct temperature (e.g., oxygen isotopes, clumped isotopes, compound specific isotopes, tree rings, etc.) and climate modeling that all tell us effectively the same thing.

> When a volcano erupts, does this affect the pressure building up in other volcanoes?

Generally, no. At a simple level, any given volcano represents an isolated system, i.e., surface vents connected to a magma chamber within the crust, e.g., this diagram, while for a specific volcanic system is a decent generic representation to consider. If sufficient eruptable magma and conditions suitable for eruption exist (in terms of both volume of liquid, ratio of crystals to liquid, amount of dissolved gases, etc) within the magma chamber of a given system to cause an eruption, this will have no influence on other volcanoes because there is no connection between the systems.

The caveat would be if you're considering separate (but nearby) volcanoes that represent different vents or components of the same system. An example might be something like the big island of Hawaii where Mauno Loa and Kilauea effectively represent different vents of a related system (e.g., this super simplified diagram). Here we can see that while the two volcanoes have their own magma chambers in the shallow crust, they are "linked" by a single magma reservoir in the deeper crust. In detail, it's long been noted that eruptions at the two tend to be anti-correlated, i.e., one erupts which reduces activity at the other and then they switch, which many have assumed is related to competition for magma supply from the deeper reservoir (e.g., Klein, 1982). Further, there are suggestions that the eruptive process of one of these volcanoes might temporarily inhibit activity of the neighboring volcanoes through changes in the stress state induced by the eruption (e.g., Gonnermann et al., 2012).

In short, the eruption of one volcano has no bearing on distant volcanoes as there is no connection between their magma sources and the other changes that result from an eruption (e.g., changes in stress state) have a very limited spatial range. In the specific case of volcanoes very close to each other and that may share some portions of a magma plumbing system, eruption through one vent may influence (and specifically decrease) the activity of adjacent vents, but if there is a large pulse of magma that enters into all of the vents, then this "suppression" may not occur.

> In my minimal research and remembering university classes on environmental issues, I believe that sea level rise is caused predominantly by the increase in temperature of the ocean, which is caused by multiple different factors. My question (well kind of 2 questions) is/are, do we know how much h the different factors cause the seawater to expand?

This is largely incorrect. For current rates, this is pretty easy to find e.g., this page from NASA. The total sea level rise rate is 3.4 mm/yr. Of that, 2 mm/yr (or ~60%) is from increasing ocean mass (i.e., addition of mass to the ocean from melting land-based glaciers and ice sheet) and 1.2 mm/yr (or ~40%) is from steric changes (i.e., changes in volume related to both temperature increases - thermosteric changes - and salinity decreases - halosteric changes).

> And/or do we know how much the different factors cause the sealevel to rise?

This is described on those linked NASA pages as well. For the total sea level rise, this is something that is now measured directly from satellite altimetry, i.e., we measure the surface height of the ocean over time and find average changes in height. In terms of attributing the components, we can estimate changes in mass from satellite gravity measurements and we can estimate changes in temperature and salinity (and in turn estimate their contribution to steric changes) through measurements from "floats".

It's also worth noting that the above are effectively current rates. If we look at longer term averages over the last 100+ years (e.g., Frederikse et al., 2020), we find that the long term average is ~1.5 mm/yr (i.e., the current rate represents an acceleration). In terms of long-term contributions, changes in ocean mass again dominate with the steric (whether talking about thermosteric or halosteric) components being more variable in both time and space (i.e., at a global average level, their relative contribution varies through time, but also at a given time, their relative contribution are not consistent spatially).

In short, whether we're considering current rates or average rates over the last 100 years, changes in ocean mass dominate the signal of sea level rise. Steric changes are definitely important, but it's incorrect to say they are the largest component.

As highlighted in most the papers I linked to (1) in comparing it to natural flux you have to consider not just the total mass but also the composition, i.e., for the natural flux of meteorites only about 5% are metal rich whereas most are silicates and (2) within the metal meteorite comparison to satellite comparison, we're talking primarily iron/nickel (for metallic meteorites) vs aluminum compounds (for satellites). The concentration and chemistry both matter for potential effects.

The variables at play are (1) the mass of material added, (2) the level of the atmosphere to which the material is added, (3) the specific chemistry of the material added, and (4) the potential effects (e.g., change in albedo, etc) of those materials as a function of time and concentration. The type and magnitude of effect will scale with the mass and whatever the particular material does, but points 2 and 3 are also important as they control the residence time (i.e., the duration). We could consider something like sulfate aerosols that are injected into the atmosphere during things like large impacts or large volcanic eruptions. Residence time for these depend a lot on the level of the atmosphere the particles are in, e.g., Junium et al., 2022 consider residence times for sulfate related to the Chicxulub impact and highlight that particles injected into the troposphere might last a few days to weeks, whereas those in the stratosphere would linger for months to years. The specific chemistry also matters though, so behavior of one type of particle is not representative for all, i.e., if the particle in question readily reacts with something, the residence time might change. All of this is to highlight the uncertainty, i.e., without dedicated experiments we don't know exactly what the effect will be and it's not necessarily safe to just assume that it will be negligible.

It's a good question, but one that does not seem like it's answered yet (though it is theoretically addressable with global climate models, etc). There are a variety of papers in the last few years highlighting that both emissions from increasingly frequent rocket launches and material (like aluminum and other metals) added to the atmosphere via satellite deorbiting could have substantial impacts on a variety of things, but almost all of these are really calls for more attention and research as opposed to answers to the question itself (e.g., Ross & Toohey, 2019, Hobbs et al., 2020, Boley & Byers, 2021, Schulz & Glassmeier, 2021, Adilov et al., 2022, Ross & Jones, 2022, Shutler et al., 2022, Lawrence et al., 2022). There is at least one paper directly trying to answer this with modelling for the emissions from increasingly frequent rocket launches (e.g., Maloney et al., 2022), but I at least could not find a paper actually demonstrating what the impact of addition of significant amounts of metal to the upper atmosphere would be (beyond the generalizations in the previously linked papers that suggest it would likely do something). The closest is really the Hobbs et al., 2020, but sadly this is an abstract for a conference presentation and I couldn't find a follow up (might still be in the works, lag time between stuff presented at conferences and eventual publication can definitely be several years). It does seem like there is a fair bit of interest in this within pockets of the scientific community (as illustrated by all the "we should pay attention to this" papers cited above), so I wouldn't be surprised if there are studies in the works on this, but at least for me it's far enough outside my area that I don't know that for sure (maybe others more in this space can provide some details).

Yep, it's even got a name, Maui Nui and included Kaho‘olawe as well (and some bits that are no longer islands). The general concept has been around for a while (e.g., Stearns & Macdonald, 1942) and there's been a variety of efforts to "reconstruct" what this island would have looked like (e.g., Price & Fisk, 2004). In detail, the Price and Fisk reconstruction suggests that Maui Nui at its maximum extent was actually larger than the "Big" Island is today.

First we need to consider the Hawaiian Islands in their full context, i.e., the Hawaii-Emperor Seamount Chain, which are all generated from the same hotspot. To make sure we're all on the same page, yes, the general idea is that the hotspot is semi-fixed with respect to the moving plates (reality is a bit more ugly, e.g., this FAQ - but for our purposes we can say the hotspot is effectively fixed). As such there is hotspot volcanism in the location above the hotspot for a time - which if the hotspot is erupting through an oceanic section of a plate and magma production is sufficient, will tend to produce an island - until this spot on the plate is advected away sufficiently by plate motion to generate a new eruptive center, eventually forming a new island (again, reality is a bit more complicated in terms of how connection between a soon-to-be-dead and new eruptive centers are severed and established, again, covered in one of our FAQs). For kind of schematic representation, consider this image from the National Park Service.

For the Hawaii-Emperor chain, if we look along this full hotspot trend, we'll see a general pattern of increasing size and elevation going from the oldest end (i.e., the bit that is actively being subducted at the Kuril Trench) to the youngest end (i.e., the Hawaiian islands). The reasons for this progressive increase in size are three fold:

1. Subsidence. This is probably the largest effect, but the relative contribution between it and the next driver are a bit hard to partition out. In short, in addition to the volcanism, there are two things that are happening in the section of oceanic lithosphere directly above the plume, it's getting hotter AND it's physically being pushed up by the warmer, more buoyant section of the mantle that is the plume itself. For the first, generally warmer lithosphere is less dense and through isostasy tends to have higher average elevation (this, for example, is why depth of portions of the oceanic basins are largely tied to lithospheric age since for areas not influenced by a hotspot, age is a proxy for temperature as sections lose heat as they move away from mid-ocean ridges). For the second, the plume in the mantle generates a "swell", i.e., a dome like uplift centered on the plume, which would fall under the umbrella of dynamic topography. As an oceanic island is advected away from the plume, it will subside (i.e., sink) both through thermal relaxation (i.e., it and the surrounding lithosphere will get colder and more dense) and from moving off the swell under the plume. This means you generally expect oceanic islands to decrease in elevation and thus reduce in size at the surface (until they completely sink, becoming seamounts, guyots, etc.). Recent work would suggest of these two forces, subsidence related to moving off the swell is the dominant one (e.g., Huppert et al., 2020).
2. Magma supply. The productivity of a plume, both in terms of melt generated but also the amount of melt that successfully erupts and contributes to the volume of oceanic island in question, is not necessarily constant. In the case of the Hawaii-Emperor chain, considering its longterm magma supply rate (e.g., Figure 2 from Poland et al., 2014) suggests that broadly the time in which the modern Hawaiian chain was forming (e.g., the last ~5 million years) represents a period of heightened productivity. Specifically for the big island, we can see that some estimates (e.g., the Vidal and Bonneville one) suggest that the modern productivity is significantly higher than anytime in the past.
3. Erosion. While not a dominant factor generally (at least compared to either subsidence or supply), while an oceanic hotspot island is above the plume and actively erupting frequently, the topographic expression of the plume reflects a balance between material added via volcanism and material removed by erosion (along with the isostatic and dynamic effects discussed in point 1). Once the volcanic system is shut off, the edifice will be degraded by erosion with effectively no processes to balance it out. Erosion will come in the form of river and hillslope processes, wave action, and mass wasting. The last can be significant for oceanic hotspot islands as they are prone to large "mega-landslides" (e.g., Holcomb & Searle, 1991, Oehler et al., 2008, etc). The Hawaiian islands are no exception and in fact a significant portion of O'ahu broke off ~1 million years ago and is visible in the bathymetry (i.e., the Nuʻuanu Slide). Broadly, once an island has been submerged through the combined action of subsidence and erosion, most of the erosional processes will stop (whereas the subsidence processes will continue).

Taken together, even without the Hawaii-Emperor specific bit of a general trend in increasing eruption rates, with just the patterns in subsidence and erosion, you would broadly expect that the most recent main eruptive center within an oceanic hotspot track to usually be the largest. Adding in the trend toward greater eruptive volumes through time that we see in the Hawaii-Emperor chain further reinforces this pattern. However, we always have to consider that we're looking at snapshots of a dynamic system. For example, the big island is the youngest subaerial part of the Hawaii-Emperor system (and also the largest), but it's not the youngest part of the system as a whole. A new seamount (and likely eventually a new island) has been forming for the last ~400,000 years to the southeast of the big island, i.e., Kamaʻehuakanaloa. The subsequent evolution of the system, e.g., when will Kamaʻehuakanaloa eclipse the big island in terms of size, is hard to predict since projecting out the eruption rates and accounting for things like mega landslides are challenging.

Seismic tomography, which exploits changes in seismic wave speed as a function of temperature and other properties to "image" the interior structure of the lithosphere and mantle, can help fill in some details of the geometry of plate boundaries with depth, but I'm not aware of an application where it's been used to reveal the location of a plate boundary we didn't already know about through other means.

Earthquake distributions get you most of the way there, e.g., compare the locations of earthquake with that of plate boundaries. More recently, definition and identification of individual plates (along with understanding directions of motion) have been aided by a variety of geodetic data, chiefly GPS (e.g., this map showing velocity vectors as determined by individual permanent GPS stations). With these type of data, you can relatively quickly begin to identify areas that are "torsionally rigid", i.e., their motion can be described by a single rotation (plate motions, becuase they're occurring on a sphere are best described as rotations which we can define with an Euler Pole and an angular rotation rate and direction about that pole) with deformation (as signified by earthquakes) localized along their edges and with limited internal deformation, i.e., plates.

In detail though, if you look at either the distribution of earthquakes or the GPS velocity vectors, you'll see that some plate boundaries appear much more messy than others. While some boundaries (like the majority of mid-ocean ridges) are relatively distinct and effectively are represented by a single fault, many others tend to be better thought of as relatively wide zones (and this is exactly how people who study these processes describe them, i.e., plate boundary zones). Good examples of areas better described as plate boundary zones are regions like the Himalaya or much of the western United States. In these cases, we tend to pick a single large mapped structure (e.g., in portions of the the western US, the San Andreas fault) to define as the formal plate boundary, but deformation related to the plate boundary extends well beyond this single structure.

It's not "masturbatory" to explain the terminology used by the domain scientists who are relevant for a question (of which I am one, i.e., a professional geologist with a Ph.D. who studies natural hazards, and specifically earthquakes, as part of their research). If you choose not to believe me in terms of the pervasiveness of these terms and their usage in this context, how about the USGS?

More broadly, there are myriad examples where the specific use of terminology within a branch of science is different than common usage. In this case, the distinction drawn between these two words in the context of the scientific community of interest is useful in terms of describing what we can and cannot do (and very specifically why the community of scientists who study these make the distinction that you are complaining about). Ultimately, the point of this subreddit is for people with specific expertise to communicate that knowledge to interested parties. If you're not interested in learning, you're welcome to not read or comment on future posts in this subreddit.

To the main point, the distinction between forecast and prediction as drawn in my original comment is common within natural hazards risk assessment, e.g., this chapter or this discussion for laypeople with specific application for how we use these terms in the context of earthquakes.

Speaking briefly as a moderator of this subreddit, this comment is rude and unhelpful (and incorrect in context). Please consider our guidelines regarding civility before commenting in the future.

> I notice the recent turkey / Syria quakes occurred on the day of the full moon. Since tidal stresses peak at new and full moons, this seems like an interesting coincidence. Is there any correlation with quake timing and moon phase.

While slightly under the cutoff for the particular analysis in this paper, Hough, 2018 succinctly sums up the extent to which lunar phase has anything to do with earthquakes. As discussed in this paper (with citations to relevant papers), there are a variety of suggestions that there may be real correlations between lunar phase and some details of earthquake statistics in certain magnitude ranges or settings. Importantly though, and especially in the context forecasting, these tend to be global correlations, e.g., for certain earthquakes and certain systems, there might be a slightly higher probability of earthquakes occurring in relation to tidal stresses, but this tells you nothing about specific risk on any specific fault or location so it has pretty minimal utility for actual, meaningful prediction or even contribution to forecasting.

> I’ve previously also seen a study indicating more larger earthquakes occur during a certain phase of a 30 year cycle of earth’s interday rotation time variation. The prediction was more earthquakes would occur in the 5 years following the peak of the variation cycle. The peak was in 2017. Has there been any validation of an increase in large magnitude quakes during the past 5 years?

Without any real detail to go on there, I'm going to guess you're thinking of this paper by Bendick & Bilham, 2017 which was published in 2017, not suggesting a peak in 2017? There has been a follow up in the sense of later papers like Bendick & Mencin, 2020 finding additional support for "synchronization" in global earthquake catalogs. The crucial bit (and this is also discussed in Hough, 2018 more directly) is that generally papers like this are fundamentally misinterpreted by the media and lay audience. Both the Bendick & Bilham and later Bendick & Mencin are pretty explicit about how these observations have very limited utility for earthquake prediction, e.g., from Bendick & Bilham, "Global seismic synchronization has no utility for the precise prediction (in a strict sense) of specific damaging earthquakes" or Bendick & Mencin, "The most notable shortcoming of this outcome is that the empirical synchronization approach provides no useful constraints on the location of events in a developing cluster; they occur globally"

So in the end for both of these types of potential correlations (and any real underlying causation), the extent to which these provide anything actionable is unclear. I.e., does saying that the risk of an earthquake for all places, globally, already at a moderate to high risk for earthquakes are slightly higher on full moons help anything? Is everyone in a seismically active area across the entire globe going to do something different around every full moon as a result based on something like this? Studies like these are useful in the sense of working out all of the myriad controls on aspects of the seismic cycle, but their real world applications in the sense of forecasting are pretty limited.

In the simplest sense, you're guaranteed to get a pattern, one that we already know, i.e., seismic hazard is the highest around plate margins. Beyond that, sure, there's been a lot of interest in considering whether various machine learning or AI approaches might have value in forecasting. For example, there's been interest in using such approaches to perform "nowcasting", e.g., Rundle et al., 2022, which is basically trying to leverage ML techniques to figure out where in the seismic cycle we might be for particular areas (and thus improve the temporal resolution of our forecasts, i.e., trying to narrow down how far into the future we might expect a large earthquake on a given system).

Ultimately though, for anyone who's even dabbled with ML approaches (and specifically with supervised learning type approaches which are largely what's relevant for an attempt to forecast something), you'll recognize that the outcomes of these are typically only as good as the training data you can provide and this is where we hit a pretty big stumbling block. We are considering processes that, in many cases, have temporal scales of 100s to 1000s of years at minimum, but may also have significant variations occurring over 100,000s to 1,000,000s of year timescales. In terms of relatively robust and complete datasets from global seismology records, we have maybe 50 years of data. The paleosesimology or archaeoseismology records are important for forecasting, but also very spotty so we are missing huge amounts of detail, such that trying to include them in a training dataset is pretty problematic. Beyond that, there are significant problems generally from the expectation that a method (which is agnostic to the mechanics of a system) will be able to fully extrapolate behaviors based on a super limited training data set.

At the end of the day, sure, you could pump global seismicity into a variety of ML or AI techniques (and people have), but it's problematic to have expectations of reasonable performance of these approaches when you're only able to train such methods with fractions of a percent of the data necessary to adequately characterize the system beyond very specific use cases (like those highlighted above).

For the sake of argument, lets sidestep that we can't effectively induce earthquakes in a controlled sense (i.e., we can't do something that we know for sure that will generate an earthquake of a target magnitude) or that wholesale changing the style of strain release of a given fault zone from something like 1 Mw 8.0 every 100 years to 1 Mw 5.0 every day (which is effectively what you would need to release the same radiated energy of a Mw 8.0 in Mw 5.0 events spread out over 100 years) is impossible.

Let's instead entertain the idea that there is some mechanism to start this process, i.e., we begin chipping away at the stored elastic strain sufficient to generate a Mw 8.0 with a carefully targeted Mw 5.0 event that we some how arrest the rupture of to keep it at a Mw 5.0. What did we accomplish? Well, we released a miniscule fraction of the total radiated energy we need, but we also have now changed the stress state on other parts of the target fault and neighboring faults (and in this, we need to remember that virtually no large fault is a single fault, but a network of faults, i.e., a fault system) through Coulomb stress transfer. So when we move to our next "patch" to try to rupture, the stored strain (and proximity to failure, etc.) will no longer be the same, not to mention we've now loaded adjoining faults, etc. The point being, you can't just have patches of fault fail in a vacuum, each one will impact the state of the system and not always in the direction you want, i.e., an earthquake on one patch can increase the strain on another patch, etc.

Yeah, so, this is completely antithetical to everything I just laid out. I.e., you're effectively asking for a prediction after I just spent a significant amount of time trying to explain why these are not possible. PSHA maps for a given region are going to be the best bet for effectively background risk. As new events occur, these of course are updated as we consider whether a large event has increased or decreased risk in a certain place (e.g., through loading or unloading of related faults through Coulomb stress transfer, etc.) and as we learn more about an area (i.e., expanded paleoseismology records, faults are discovered through mapping, etc). Similarly, there will be specific short duration forecasts related to individual large earthquakes, i.e., aftershock forecasts. Beyond that, even within the area of the world I specifically focus on (and for which I understand the local geology and earthquake hazards reasonably well), there is no meaningful way for me, or anyone else, to make statements like what you're asking for. Anyone who does is either irresponsible or trying to sell you something.

As a relevant aside, for anyone musing on the potential benefits of true earthquake prediction in the sense outlined in my earlier answer (and sidestepping all of the reasons why we don't generally think it's possible), I would highly recommend this opinion piece by Dave Petley (a geologist who works on quantifying natural hazards). The general thesis is that basically, unless predictions (as defined above) are 100% accurate (which they never could be, even in the rosiest view of our future capabilities), they are unlikely to improve outcomes anymore than forecasts (as defined above) and would likely actually have significant negative outcomes potentially making "predictions" worse than "forecasts", i.e., the risks associated with either false negatives or false positives are very large both in an economic and human life sense.

Good question! So the duration of the ground motion in a specific place is not usually directly related to what we would call the "source time function" (STF), i.e., a description of how long the earthquake rupture on the fault took to occur.

Let's first consider the STF, this is typically considered in terms of moment rate per time (e.g., Figure 2 in Vallee, 2013) where the total seismic moment released during an earthquake (which directly relates to the mangitude, i.e., the moment magnitude) is effectively the area under a STF curve. From figure in the linked paper we can see that the same magnitude earthquake can have different patterns of moment release (i.e., Figure 2 a-c are all the same magnitude events and thus released the same total moment, but with either ruptures that occurred more slowly or quickly so moment rate varies between them). There are a variety of details of an earthquake where the STF is important, but as we'll see, duration of ground shaking at a location is not usually one of them.

If we shift our attention to the duration of ground motion, we can consider a range of empirical equations that have developed to try to estimate duration of shaking, specifically Table 1 from Yaghmaei-Sabegh et al., 2014, we can see that total moment (in the form of moment magnitude) appears in all of these equations, but none of them directly consider anything about the STF or speed of the rupture in a formal sense. Instead, you'll see that in addition to the magnitude, there are few other general earthquake properties (e.g., depth of the hypocenter), but then a lot of things specific to the "site" you're considering, both in the sense of things in relation to the specific earthquake (e.g., distance from the rupture) but also more generally (e.g., soil type, etc.). This reflects that broadly, while there are obvious controls from the available seismic energy (which will be dictated primarily by the total moment, i.e., magnitude, and the sites distance from the source), there are also a lot of site effects which can impact duration of shaking (and other important details, like peak ground acceleration, dominant period of the shaking, etc). In detail, the type of rocks and their geometry can play a large role in the specifics of shaking in a particular place. E.g., seismic waves in sedimentary basins tend to "reverberate" and thus the duration of shaking can be significantly longer than outside the basin and as they reverberate, they can have both constructive and destructive interference with each other and in many cases can amplify shaking at particular frequencies (which is very important to understand if you're trying to engineer a building to survive an earthquake).

> Can the static tension of tectonic plates be quantified?

So, the way we as geologists would discuss this would be in terms of measuring the magnitude and direction of stress(es) within the crust. There are a variety of ways we can directly measure stress, e.g., borehole breakouts, overcoring, etc., which we can then use to produce maps of stress like the World Stress Map. Ultimately though, while maps of stress are useful for some aspects of assessing earthquake hazard, we cannot directly apply these to "predicting" earthquake hazards as this would require knowing much more about the stress history (as opposed to short term measurements), how stress changes with depth, the amount of accumulated strain on individual faults, the strength of individual faults, along with a whole host of other properties. Stress maps and estimates are one part of what we can do to assess hazards though.

> how are predictions about future quakes are made?

Here we want to be explicit about what we can and can't do and moreover what is implied by specific terms when used by professionals. Geologists, seismologists, and others who work on natural hazards often draw an important distinction between forecasts and predictions. This may seem pedantic, but these two terms imply very different things when being used by people like myself who works on natural hazards. Forecasts are hopefully partially intuitive from weather forecasting and we can use this to explore the implications of these two terms in this context. A weather forecast would be something like, "There is a 80% chance of rain today in this region", whereas a weather prediction would be "There will be exactly 1 cm of rain, falling at a rate of 1 cm / hr, starting at exactly 4 pm at this precise location." I.e., for something to be a prediction implies certainty in time, location, and magnitude. Generally, we can forecast the weather, but we cannot predict it and the same is true for earthquakes. The reason we cannot predict earthquakes is much the same reason we cannot predict weather, i.e., incomplete data characterizing a non-linear dynamic (i.e., chaotic) system. The utility of the two are also the same, i.e., even though we can't predict the weather in a perfect sense, the forecast helps us plan (i.e., if you saw the forecast from above, you'd probably bring a rain coat or umbrella with you, etc.). If you want to read even more about why we can't predict earthquakes in the true sense of the word, this FAQ goes into more detail.

For earthquakes, where do the forecasts from? Mixtures of basic mapping of fault locations and geometries, theoretical understanding of earthquake mechanics from both observations and modeling, a variety of geodetic measurements and measurements of stress (like from the first part), and records of earthquake histories from paleoseismology, historical seismology, and/or archaeoseismology. From all of these, we build assessments of how often particular faults have earthquakes, what the variability in style/size of those earthquakes are, time since the last event, and other various details we can glean from the geologic record. In the end, we end up with things largely similar to our weather forecast example, i.e., a probabilistic seismic hazard assessment, like the various ones for the US. These focus on different regions and consider different lengths of time (going back to the weather forecast analogy, largely equivalent to the difference between a daily forecast and the 10 day forecast, etc.). If you look at many of these, you'll see they are presented in a somewhat similar way to weather forecasts, i.e., the probability that a particular area will experience significant shaking in the relevant time frame covered by the map. Just like the weather forecast, while not a prediction, it provides a tool for us to assess risk and make preparations. I.e., much in the same way a forecast of sunny skies vs a chance of rain might determine your choice of clothes for the day, living in an area with a 20% chance of experiencing significant shaking in the next 10 years has very different implications than living in an area with a 1% chance of experiencing significant shaking in the next 10 years and you (and governments, etc) would/could/should respond accordingly.

The interaction of seismic waves and the surface of the Earth can produce measurable pressure waves in the atmosphere (e.g., Donn & Posmentier, 1964), but generally nothing that's going to be damaging. This of course depends on what the fluid in contact with the solid earth surface is though as a tsunami effectively represents a displacement wave from surface deformation (from actual vertical change in the ocean bottom as opposed to seismic waves specifically), but here, it's not the pressure wave that's dangerous per se, but the resulting inundation when this approaches shore.

The first thing to cover is that both sides moved, the appearance that one side moved is just a perspective / reference frame thing. The easiest way to consider this is through the concept of elastic rebound, basically the idea that the upper part of the Earth's crust behaves like an elastic material. There's a diagram on the wiki page, but others are better, like this one. Referring to that, the underlying idea of elastic rebound is that if you start from an undeformed state (i.e. line A-B-C-D-E-F-G), as the two sides of a fault move, areas in the "far field" (e.g., spots A' and G') record the full motion, but as you approach the "locked" fault, there is increasingly less interseismic (i.e., between earthquake) deformation until you get to the fault (point D) where there is no deformation. This is equivalent to flexing an elastic beam that you hold parallel to yourself and you pull one side toward you and one side away from you, the center of the beam will not move. Eventually, the stored elastic strain overcomes the friction of the fault and the elastic deformation is "recovered" and points near the fault move to "catch up" with the far field deformation, by varying degrees depending on their proximity to the fault (e.g., point B' doesn't move too much, point C' moves more, and point D bifurcates into points O and P).

As to what would happen to a person on one side or the other or straddling the rupture, for sure you'd fall down. Beyond that, and barring that nothing fell on you, you didn't fall into a fissure that opened up along something like a mole track, you didn't end up sinking into a liquefaction feature, or were damaged by the eruption of something like a sand boil, I'm not sure you'd necessarily be injured. I'm not aware of any indication that seismic waves have ever induced air pressure waves to the point where they'd be physically damaging to a person for example.

Yes, slow slip events, or alternatively episodic tremor and slip (ETS), and a variety of other "aseismic" processes represent long-duration versions of strain release that occur on a variety of subduction zones (Cascadia included) either completely independent of traditional seismic events or in concert (e.g., afterslip) with them. Of relevance though, they are explicitly not earthquakes in the typical definition (i.e., they are aseismic) and as the focus of the question is "do small magnitude earthquakes impact the probability of large magnitude earthquakes?", slow-slip / tremor discussions gets a bit into the weeds (so me leaving them out was a conscious choice).

If we consider equivalent magnitudes, most observed slow-slip or ETS events are still kind of in the ball park of "small events" , i.e., mid 5s to 6s, but some do release equivalent magnitudes of strain as a Mw 8+ if you "sum up" the total moment of the event over the days, weeks, months, etc. (e.g., Schwartz & Rokosky, 2007). Perhaps more importantly, the extent to which patches of subduction zones which experience these various aseismic type of slow/quiet/silent slip (1) restrict which patches fail seismically, (2) influence the balance between seismic slip vs aseismic afterslip in the patches that do fail at least in part seismically, or (3) themselves can rupture seismically given the right conditions are all very active areas of research, largely without clear answers, or at least answers that are easily generalized to all subduction zones (e.g., Rolandone et al., 2018, Mallick et al., 2021, Zhao et al., 2022, etc.). Thus, while it is reasonable to consider that slow slip and similar aseismic processes influence the style of seismic strain release, how they do so (both mechanistically but also in terms of actual event temporal and spatial statistics) is a large open question.