Surface Geology of Rocky Worlds

By the end of this unit, you should be able to describe the dominant physical interactions without collapsing all mechanisms into a single cause; translate an observable into a measurable quantity with units; design a synthetic comparison that varies one quantity at a time; identify at least two independent failure modes of an inference; and communicate why a simple classroom model is not an operational planetary prediction.

3.1 Impact craters as geological evidence

Central concept. Impact excavation, ejecta emplacement and modification create morphology that depends on size, target strength, gravity and atmosphere. Crater populations may indicate exposure history when production and degradation effects are modeled. In this part of surface geology of rocky worlds, the important skill is to connect a plausible physical mechanism to a particular, imperfect record. The phrase explains the evidence should never mean merely that a sketch resembles a photograph. A mechanism must be compatible with constraints on size, time, composition and energetics. It should predict something that another observation could disprove. The expected strength of the inference depends on the quality of the instrument and the uniqueness of the prediction, not on the visual appeal of a diagram.

What would actually be measured? Count craters above two diameter thresholds across synthetic equal-area terrain polygons and calculate densities. This is a constructed teaching example; no row should be mistaken for a spacecraft measurement. Define the measurable quantities before choosing a fitting equation, and preserve calibration units, coordinate frame and acquisition conditions. For a remote world, resolution may change over the scene and the hidden part of a terrain or atmosphere may have fundamentally different characteristics. Report not only a central estimate but also which conditions remained unobserved and which parameters were held fixed for convenience.

Failure mode and competing account. Secondary craters, image resolution, buried structures and nonstationary impact flux distort direct age interpretation. The strongest competing explanation is not an arbitrary alternative chosen to look weak. It should be physically plausible under the same observing circumstances. Ask which observable, spatial relationship or laboratory property differs between the explanations. If both models predict nearly the same signal at the available resolution, the honest result is non-identifiability. A mission proposal can still be useful in this situation by explicitly targeting that degeneracy. Do not increase certainty simply because repeated measurements use the same processing assumptions.

Worked quantitative case — Normalized crater density. Consider the fictional parameter value 15 craters. Under the stated classroom approximation, Observed count normalized from 14,000 km²; threshold must be documented. Substituting the input gives 1,071.43 per million km². This number is neither a retrieved physical parameter nor a validated forecast. Its role is to expose scale and sensitivity. First confirm that the units on both sides agree; next estimate how the result changes if the input moves by ten percent while all other conditions are fixed. Finally ask whether the model has an appropriate domain for extrapolation. A numerical answer without those checks can be exact arithmetic attached to an incorrect physical story.

Check your understanding. (1) Which assumption most strongly controls the numerical estimate for this lesson? (2) Describe how to distinguish the chosen physical process from its most plausible confounder using a new measurement. (3) Which claim would remain unjustified even if the calculation were arithmetically perfect? (4) Rewrite the result as a testable statement with units and explicit uncertainty. (5) State the advantage and the limitation of transferring an Earth-based analogy to this setting.

3.2 Lava flows and planetary volcanism

Central concept. Flow fronts, shields, fissures and volcanic plains record melt supply, rheology, eruption rate and surface slope. Similar appearances can arise from very different materials or extrusion environments. In this part of surface geology of rocky worlds, the important skill is to connect a plausible physical mechanism to a particular, imperfect record. The phrase explains the evidence should never mean merely that a sketch resembles a photograph. A mechanism must be compatible with constraints on size, time, composition and energetics. It should predict something that another observation could disprove. The expected strength of the inference depends on the quality of the instrument and the uniqueness of the prediction, not on the visual appeal of a diagram.

What would actually be measured? Measure mapped flow lengths and slopes in two synthetic settings and discuss what a simple viscous-flow approximation leaves out. This is a constructed teaching example; no row should be mistaken for a spacecraft measurement. Define the measurable quantities before choosing a fitting equation, and preserve calibration units, coordinate frame and acquisition conditions. For a remote world, resolution may change over the scene and the hidden part of a terrain or atmosphere may have fundamentally different characteristics. Report not only a central estimate but also which conditions remained unobserved and which parameters were held fixed for convenience.

Failure mode and competing account. A smooth bright plain is not necessarily a lava flow; imaging artifacts, sedimentation or ice processes can imitate texture. The strongest competing explanation is not an arbitrary alternative chosen to look weak. It should be physically plausible under the same observing circumstances. Ask which observable, spatial relationship or laboratory property differs between the explanations. If both models predict nearly the same signal at the available resolution, the honest result is non-identifiability. A mission proposal can still be useful in this situation by explicitly targeting that degeneracy. Do not increase certainty simply because repeated measurements use the same processing assumptions.

Worked quantitative case — Crater footprint fraction. Consider the fictional parameter value 2.5 km diameter. Under the stated classroom approximation, Simple circular footprint in a 3000 km² area; ignores overlap. Substituting the input gives 0.163625 percent area. This number is neither a retrieved physical parameter nor a validated forecast. Its role is to expose scale and sensitivity. First confirm that the units on both sides agree; next estimate how the result changes if the input moves by ten percent while all other conditions are fixed. Finally ask whether the model has an appropriate domain for extrapolation. A numerical answer without those checks can be exact arithmetic attached to an incorrect physical story.

Check your understanding. (1) Which assumption most strongly controls the numerical estimate for this lesson? (2) Describe how to distinguish the chosen physical process from its most plausible confounder using a new measurement. (3) Which claim would remain unjustified even if the calculation were arithmetically perfect? (4) Rewrite the result as a testable statement with units and explicit uncertainty. (5) State the advantage and the limitation of transferring an Earth-based analogy to this setting.

3.3 Tectonics and global stress

Central concept. Fault scarps, rifts, folds and fractures record stress, material strength, lithospheric thickness and regional history. Contraction, tidal flexure, load adjustment and thermal expansion can yield different structural patterns. In this part of surface geology of rocky worlds, the important skill is to connect a plausible physical mechanism to a particular, imperfect record. The phrase explains the evidence should never mean merely that a sketch resembles a photograph. A mechanism must be compatible with constraints on size, time, composition and energetics. It should predict something that another observation could disprove. The expected strength of the inference depends on the quality of the instrument and the uniqueness of the prediction, not on the visual appeal of a diagram.

What would actually be measured? Classify synthetic lineaments by orientation and crosscutting to propose more than one stress history. This is a constructed teaching example; no row should be mistaken for a spacecraft measurement. Define the measurable quantities before choosing a fitting equation, and preserve calibration units, coordinate frame and acquisition conditions. For a remote world, resolution may change over the scene and the hidden part of a terrain or atmosphere may have fundamentally different characteristics. Report not only a central estimate but also which conditions remained unobserved and which parameters were held fixed for convenience.

Failure mode and competing account. A lineament visible in a single shadowed image may be an illumination artifact or a depositional boundary. The strongest competing explanation is not an arbitrary alternative chosen to look weak. It should be physically plausible under the same observing circumstances. Ask which observable, spatial relationship or laboratory property differs between the explanations. If both models predict nearly the same signal at the available resolution, the honest result is non-identifiability. A mission proposal can still be useful in this situation by explicitly targeting that degeneracy. Do not increase certainty simply because repeated measurements use the same processing assumptions.

Worked quantitative case — Local mapped slope. Consider the fictional parameter value 160 m relief. Under the stated classroom approximation, arctan(vertical rise / horizontal 2500 m run). Substituting the input gives 3.66194 degrees. This number is neither a retrieved physical parameter nor a validated forecast. Its role is to expose scale and sensitivity. First confirm that the units on both sides agree; next estimate how the result changes if the input moves by ten percent while all other conditions are fixed. Finally ask whether the model has an appropriate domain for extrapolation. A numerical answer without those checks can be exact arithmetic attached to an incorrect physical story.

Check your understanding. (1) Which assumption most strongly controls the numerical estimate for this lesson? (2) Describe how to distinguish the chosen physical process from its most plausible confounder using a new measurement. (3) Which claim would remain unjustified even if the calculation were arithmetically perfect? (4) Rewrite the result as a testable statement with units and explicit uncertainty. (5) State the advantage and the limitation of transferring an Earth-based analogy to this setting.

3.4 Volatiles, water and sedimentary records

Central concept. Channels, delta-like deposits, layered rocks and hydrated-mineral signatures inform past interaction of rock with water or other fluids. Liquid transport need not indicate a warm global climate, and a mineral can form by multiple routes. In this part of surface geology of rocky worlds, the important skill is to connect a plausible physical mechanism to a particular, imperfect record. The phrase explains the evidence should never mean merely that a sketch resembles a photograph. A mechanism must be compatible with constraints on size, time, composition and energetics. It should predict something that another observation could disprove. The expected strength of the inference depends on the quality of the instrument and the uniqueness of the prediction, not on the visual appeal of a diagram.

What would actually be measured? Construct a sediment budget for a synthetic drainage basin and compare source-area erosion with deposit volume. This is a constructed teaching example; no row should be mistaken for a spacecraft measurement. Define the measurable quantities before choosing a fitting equation, and preserve calibration units, coordinate frame and acquisition conditions. For a remote world, resolution may change over the scene and the hidden part of a terrain or atmosphere may have fundamentally different characteristics. Report not only a central estimate but also which conditions remained unobserved and which parameters were held fixed for convenience.

Failure mode and competing account. Scale, gravity, grain size and fluid density alter transport thresholds; Earth analogs cannot simply be transplanted. The strongest competing explanation is not an arbitrary alternative chosen to look weak. It should be physically plausible under the same observing circumstances. Ask which observable, spatial relationship or laboratory property differs between the explanations. If both models predict nearly the same signal at the available resolution, the honest result is non-identifiability. A mission proposal can still be useful in this situation by explicitly targeting that degeneracy. Do not increase certainty simply because repeated measurements use the same processing assumptions.

Worked quantitative case — Eroded sediment mass. Consider the fictional parameter value 1.9 million m³ of eroded sediment. Under the stated classroom approximation, Dry bulk density 2200 kg/m³; no trapping or deposition. Substituting the input gives 4.1800e+09 kg. This number is neither a retrieved physical parameter nor a validated forecast. Its role is to expose scale and sensitivity. First confirm that the units on both sides agree; next estimate how the result changes if the input moves by ten percent while all other conditions are fixed. Finally ask whether the model has an appropriate domain for extrapolation. A numerical answer without those checks can be exact arithmetic attached to an incorrect physical story.

Check your understanding. (1) Which assumption most strongly controls the numerical estimate for this lesson? (2) Describe how to distinguish the chosen physical process from its most plausible confounder using a new measurement. (3) Which claim would remain unjustified even if the calculation were arithmetically perfect? (4) Rewrite the result as a testable statement with units and explicit uncertainty. (5) State the advantage and the limitation of transferring an Earth-based analogy to this setting.

3.5 Planetary mapping and surface spectroscopy

Central concept. Images provide albedo and morphology, while reflected-light and thermal spectra constrain composition under radiative-transfer assumptions. Geologic unit boundaries combine these observations with an interpretive classification. In this part of surface geology of rocky worlds, the important skill is to connect a plausible physical mechanism to a particular, imperfect record. The phrase explains the evidence should never mean merely that a sketch resembles a photograph. A mechanism must be compatible with constraints on size, time, composition and energetics. It should predict something that another observation could disprove. The expected strength of the inference depends on the quality of the instrument and the uniqueness of the prediction, not on the visual appeal of a diagram.

What would actually be measured? Map five invented spectral endmembers on a synthetic raster with varying illumination and evaluate classification confusion. This is a constructed teaching example; no row should be mistaken for a spacecraft measurement. Define the measurable quantities before choosing a fitting equation, and preserve calibration units, coordinate frame and acquisition conditions. For a remote world, resolution may change over the scene and the hidden part of a terrain or atmosphere may have fundamentally different characteristics. Report not only a central estimate but also which conditions remained unobserved and which parameters were held fixed for convenience.

Failure mode and competing account. Space weathering, coatings, grain size, dust and mixtures mean a spectral feature is not a direct mineral census. The strongest competing explanation is not an arbitrary alternative chosen to look weak. It should be physically plausible under the same observing circumstances. Ask which observable, spatial relationship or laboratory property differs between the explanations. If both models predict nearly the same signal at the available resolution, the honest result is non-identifiability. A mission proposal can still be useful in this situation by explicitly targeting that degeneracy. Do not increase certainty simply because repeated measurements use the same processing assumptions.

Worked quantitative case — Continuum-normalized band depth. Consider the fictional parameter value 0.54 band reflectance. Under the stated classroom approximation, Continuum reflectance fixed at 0.85; values are synthetic. Substituting the input gives 0.364706 fraction. This number is neither a retrieved physical parameter nor a validated forecast. Its role is to expose scale and sensitivity. First confirm that the units on both sides agree; next estimate how the result changes if the input moves by ten percent while all other conditions are fixed. Finally ask whether the model has an appropriate domain for extrapolation. A numerical answer without those checks can be exact arithmetic attached to an incorrect physical story.

Check your understanding. (1) Which assumption most strongly controls the numerical estimate for this lesson? (2) Describe how to distinguish the chosen physical process from its most plausible confounder using a new measurement. (3) Which claim would remain unjustified even if the calculation were arithmetically perfect? (4) Rewrite the result as a testable statement with units and explicit uncertainty. (5) State the advantage and the limitation of transferring an Earth-based analogy to this setting.

Research question. What can landforms and materials reveal about resurfacing, deformation and water? Use the fabricated evidence packet generated for this unit, not a downloaded mission observation, to test the issue. The packet provides five numerical inputs corresponding to five distinct lesson calculations. Prepare an analysis log before computing results: specify which data are invented, which constants are adopted from reference physics, which quantities are exact in the model, and which are uncertain in an actual measurement. Do not call synthetic demonstration values planetary facts.

Workflow A — observations and provenance. Build a small metadata table with columns for source, timestamp or hypothetical epoch, spatial footprint, units, calibration state, quality flag and an explicit 'synthetic' indicator. Check that every row belongs to the same stated physical or geometrical reference when the comparison requires that. If different observations cover different regions or epochs, say what assumption permits their comparison. A useful practice is to write down the interpretation you expect before opening a calculated output; doing so can reveal a tendency to favor a result after seeing it.

Workflow B — model construction. Start with one conservation, geometry or rate relationship from the unit. Derive the form in symbols, then set parameter values. Write an extreme-case check: an input tending to zero, a very long orbital or cooling time, or a hypothetical measurement at the edge of the instrument's range. Not every extreme has physical meaning, but the exercise can reveal sign errors or a model's restricted domain. Compare at least one dimensionless ratio where possible. Record any calibration or geological assumptions that cannot be derived from the formula itself.

Workflow C — alternatives and sensitivity. Construct a second explanation able to reproduce at least one headline feature of the mock dataset. Change a single uncertain parameter by plus or minus ten percent and tabulate the output. Then alter the model structure itself: for example, replace uniform material properties with a layered assumption, permit a nonstationary flux, or introduce a time-varying forcing condition. When the inferred history changes more from model structure than from stated measurement error, the report must say that the quantity is weakly identified. A precise fitted value is not automatically a precise scientific claim.

Workflow D — cross-unit integration. Connect this unit to two other themes in the book. For example, relate a dynamical interpretation to geological chronology, a spectral inference to atmospheric chemistry, or an internal energy source to an icy-surface observation. Each connection should contain one shared variable and one new source of uncertainty. This prevents a detailed but isolated calculation from being treated as a full planetary system model. If a comparison relies on terrestrial experiments, identify the gravitational, thermal or compositional regime where the analogy ceases to be straightforward.

Workflow E — reporting and evaluation. Submit a one-page methods statement, a table of computed values, one plot with labeled axes, an uncertainty inventory, and a claim supported by the data packet. Include an explicit alternative interpretation and a feasible next observation. The teacher should assess whether units, underlying equations, provenance and falsifiability are correct; artistic polish alone is not evidence of scientific rigor. Use language such as consistent with, constrained by and not distinguished by these data when warranted. Do not advertise an invented training result as mission validation.

The scientific difficulty of surface geology of rocky worlds is not simply locating one more fact. It is learning what the available observations exclude. A strong synthesis distinguishes a process being physically possible from that process being historically demonstrated. It also distinguishes a local interpretation from a global planetary pattern and a present state from the history that formed it. Return to the unit's guiding question and state three claims at different strengths: one direct observation, one explanation that can be tested, and one conjecture for which available information is insufficient. For each, name the likely effect of improved instrumental precision, better geographic coverage and stronger physical modeling. These improvements address different errors and should not be treated as interchangeable. Explore whether the most valuable next mission measurement is an imaging survey, an in-situ measurement, a laboratory calibration or repeated monitoring.

Capstone prompt. You have resources for one additional investigation only. Present the competing hypotheses in a table and estimate which candidate observation would most clearly separate their predictions per unit cost and risk. Your justification must acknowledge a scenario in which your chosen observation gives an ambiguous result. A responsible conclusion is an explicit account of what remains unknown, not an invented certainty.